Care Outcomes for Chiropractic Outpatient Veterans (COCOV): a qualitative study with veteran stakeholders from a pilot trial of multimodal chiropractic care

Background Low back pain (LBP) is common among military veterans seeking treatment in Department of Veterans Affairs (VA) healthcare facilities. As chiropractic services within VA expand, well-designed pragmatic trials and implementation studies are needed to assess clinical effectiveness and program uptake. This study evaluated veteran stakeholder perceptions of the feasibility and acceptability of care delivery and research processes in a pilot trial of multimodal chiropractic care for chronic LBP. Methods The qualitative study was completed within a mixed-method, single-arm, pragmatic, pilot clinical trial of chiropractic care for LBP conducted in VA chiropractic clinics. Study coordinators completed semi-structured, in person or telephone interviews with veterans near the end of the 10-week trial. Interviews were audiorecorded and transcribed verbatim. Qualitative content analysis using a directed approach explored salient themes related to trial implementation and delivery of chiropractic services. Results Of 40 participants, 24 completed interviews (60% response; 67% male gender; mean age 51.7 years). Overall, participants considered the trial protocol and procedures feasible and reported that the chiropractic care and recruitment methods were acceptable. Findings were organized into 4 domains, 10 themes, and 21 subthemes. Chiropractic service delivery domain encompassed 3 themes/8 subthemes: scheduling process (limited clinic hours, scheduling future appointments, attendance barriers); treatment frequency (treatment sufficient for LBP complaint, more/less frequent treatments); and chiropractic clinic considerations (hire more chiropractors, including female chiropractors; chiropractic clinic environment; patient-centered treatment visits). Outcome measures domain comprised 3 themes/4 subthemes: questionnaire burden (low burden vs. time-consuming or repetitive); relevance (items relevant for LBP study); and timing and individualization of measures (questionnaire timing relative to symptoms, personalized approach to outcomes measures). The online data collection domain included 2 themes/4 subthemes: user concerns (little difficulty vs. form challenges, required computer skills); and technology issues (computer/internet access, junk mail). Clinical trial planning domain included 2 themes/5 subthemes: participant recruitment (altruistic service by veterans, awareness of chiropractic availability, financial compensation); and communication methods (preferences, potential barriers). Conclusions This qualitative study highlighted veteran stakeholders’ perceptions of VA-based chiropractic services and offered important suggestions for conducting a full-scale, veteran-focused, randomized trial of multimodal chiropractic care for chronic LBP in this clinical setting. Trial registration ClinicalTrials.govNCT03254719

Adaptive Robust Control for Uncertain Systems via Data-Driven Learning

Although solving the robust control problem with offline manner has been studied, it is not easy to solve it using the online method, especially for uncertain systems. In this paper, a novel approach based on an online data-driven learning is suggested to address the robust control problem for uncertain systems. To this end, the robust control problem of uncertain systems is first transformed into an optimal problem of the nominal systems via selecting an appropriate value function that denotes the uncertainties, regulation, and control. Then, a data-driven learning framework is constructed, where Kronecker’s products and vectorization operations are used to reformulate the derived algebraic Riccati equation (ARE). To obtain the solution of this ARE, an adaptive learning law is designed; this helps to retain the convergence of the estimated solutions. The closed-loop system stability and convergence have been proved. Finally, simulations are given to illustrate the effectiveness of the method.

Comparing Online Webcam- and Laboratory-Based Eye-Tracking for the Assessment of Infants’ Audio-Visual Synchrony Perception

Online data collection with infants raises special opportunities and challenges for developmental research. One of the most prevalent methods in infancy research is eye-tracking, which has been widely applied in laboratory settings to assess cognitive development. Technological advances now allow conducting eye-tracking online with various populations, including infants. However, the accuracy and reliability of online infant eye-tracking remain to be comprehensively evaluated. No research to date has directly compared webcam-based and in-lab eye-tracking data from infants, similarly to data from adults. The present study provides a direct comparison of in-lab and webcam-based eye-tracking data from infants who completed an identical looking time paradigm in two different settings (in the laboratory or online at home). We assessed 4-6-month-old infants (n = 38) in an eye-tracking task that measured the detection of audio-visual asynchrony. Webcam-based and in-lab eye-tracking data were compared on eye-tracking and video data quality, infants’ viewing behavior, and experimental effects. Results revealed no differences between the in-lab and online setting in the frequency of technical issues and participant attrition rates. Video data quality was comparable between settings in terms of completeness and brightness, despite lower frame rate and resolution online. Eye-tracking data quality was higher in the laboratory than online, except in case of relative sample loss. Gaze data quantity recorded by eye-tracking was significantly lower than by video in both settings. In valid trials, eye-tracking and video data captured infants’ viewing behavior uniformly, irrespective of setting. Despite the common challenges of infant eye-tracking across experimental settings, our results point toward the necessity to further improve the precision of online eye-tracking with infants. Taken together, online eye-tracking is a promising tool to assess infants’ gaze behavior but requires careful data quality control. The demographic composition of both samples differed from the generic population on caregiver education: our samples comprised caregivers with higher-than-average education levels, challenging the notion that online studies will per se reach more diverse populations.

An aerosol vertical data assimilation system (NAQPMS-PDAF v1.0): development and application

Aerosol vertical stratification information is important for global climate and planetary boundary layer (PBL) stability, and no single method can obtain spatiotemporally continuous vertical profiles. This paper develops an online data assimilation (DA) framework for the Eulerian atmospheric chemistry-transport model (CTM) Nested Air Quality Prediction Model System (NAQPMS) with the Parallel Data Assimilation Framework (PDAF) as the NAQPMS-PDAF for the first time. Online coupling occurs via a memory-based approach with two-level parallelization, and the arrangement of state vectors during the filter is specifically designed. Scaling tests provide evidence that the NAQPMS-PDAF can make efficient use of parallel computational resources for up to 2.5 k processors with weak scaling efficiency up to 0.7. One-month-long aerosol extinction coefficient profiles measured by the ground-based lidar and the concurrent hourly surface PM2.5 are solely and simultaneously assimilated to investigate the performance and application of the DA system. The hourly analysis and subsequent one-hour simulation are validated through lidar and surface PM2.5 measurements assimilated and not assimilated. The results show that lidar DA can significantly improve the underestimation of aerosol loading, especially at a height of approximately 400 m in the free-running (FR) experiment, with the BIAS changing from −0.20 (−0.14) 1/km to −0.02 (−0.01) 1/km and correlation coefficients increasing from 0.33 (0.28) to 0.91 (0.53) averaged over sites with measurements assimilated (not assimilated). Compared with the FR experiment, simultaneously assimilating PM2.5 and lidar can have a more consistent pattern of aerosol vertical profiles with a combination of surface PM2.5 and lidar, independent extinction coefficients from the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP), and aerosol optical depth (AOD) from the Aerosol Robotic Network (AERONET). Lidar DA has a larger temporal impact than that in PM2.5 DA but has deficiencies in subsequent quantification on the surface PM2.5. The proposed NAQPMS-PDAF has great potential for further research on the impact of aerosol vertical distribution.

The feasibility of a strategy for the remote recruitment, consenting and assessment of recent referrals: a protocol for phase 1 of the On-Line Parent Training for the Initial Management of ADHD referrals (OPTIMA)

Background In the UK, children with high levels of hyperactivity, impulsivity and inattention referred to clinical services with possible attention-deficit/hyperactivity disorder (ADHD) often wait a long time for specialist diagnostic assessment. Parent training (PT) has the potential to support parents during this difficult period, especially regarding the management of challenging and disruptive behaviours that often accompany ADHD. However, traditional face-to-face PT is costly and difficult to organise in a timely way. We have created a low-cost, easily accessible PT programme delivered via a phone app, Structured E-Parenting Support (STEPS), to address this problem. The overall OPTIMA programme will evaluate the efficacy and cost-effectiveness of STEPS as a way of helping parents manage their children behaviour while on the waitlist. To ensure the timely and efficient evaluation of STEPS in OPTIMA, we have worked with children’s health services to implement a remote strategy for recruitment, screening and assessment of recently referred families. Part of this strategy is incorporated into routine clinical practice and part is OPTIMA specific. Here, we present the protocol for Phase 1 of OPTIMA—a study of the feasibility of this remote strategy, as a basis for a large-scale STEPS randomised controlled trial (RCT). Methods This is a single arm observational feasibility study. Participants will be parents of up to 100 children aged 5-11 years with high levels of hyperactivity/impulsivity, inattention and challenging behaviour who are waiting for assessment in one of five UK child and adolescent mental health or behavioural services. Recruitment, consenting and data collection will occur remotely. The primary outcome will be the rate at which the families, who meet inclusion criteria, agree in principle to take part in a full STEPS RCT. Secondary outcomes include acceptability of remote consenting and online data collection procedures; the feasibility of collecting teacher data remotely within the required timeframe, and technical difficulties with completing online questionnaires. All parents in the study will receive access to STEPS. Discussion Establishing the feasibility of our remote recruitment, consenting and assessment strategy is a pre-requisite for the full trial of OPTIMA. It can also provide a model for future trials conducted remotely.

What Prevents You to Plug in to Online Surveys?

In today's world, which has been administered by computers and artificial intelligence in many areas, online data gathering has become an inevitable way of collecting data. Many researchers have preferred online surveying, considering the advantages of this method over the classical ones. Hence, the factors that may affect the response rate of online surveying have become a prominent research topic. In line with the popularity of this issue, the purpose of this chapter was to clarify the concept of online surveys; give information about their types, advantages, and usage; and investigate the factors that affect the participants' response behaviors. Besides the discussions on the theoretical framework of online surveying, an online survey aiming to determine the factors affecting the participation in online surveying was administered to a group of people to investigate the response behaviors thoroughly. The findings revealed that rs might affect ants' response behaviors to online surveys in various ways radically.

Deep Learning and Machine Learning Techniques for Analyzing Travelers' Online Reviews

Social media platforms play a tremendous role in the tourism and hospitality industry. Social media platforms are increasingly becoming a source of information. The complexity and increasing size of tourists' online data make it difficult to extract meaningful insights using traditional models. Therefore, this scoping and comprehensive review aimed to analyze machine learning and deep learning models applied to model tourism data. The study revealed that deep learning and machine learning models are used for forecasting and predicting tourism demand using data from search query data, Google trends, and social media platforms. Also, the study revealed that data-driven models can assist managers and policymakers in mapping and segmenting tourism hotspots and attractions and predicting revenue that is likely to be generated, exploring targeting marketing, segmenting tourists based on their spending patterns, lifestyle, and age group. However, hybrid deep learning models such as inceptionV3, MobilenetsV3, and YOLOv4 are not yet explored in the tourism and hospitality industry.

Determinants of Online Purchase Intention of Apparel Products in Lebanon

This study investigates factors (perceived usefulness, perceived ease of use, privacy concerns, trust, innovativeness, and perceived benefits) affecting Lebanese consumers’ attitude toward online purchasing and their intention to purchase online. Data collected from 416 online shoppers were analyzed using regression analysis. All the factors affect attitude towards online purchasing except trust and privacy concerns which were not supported. In addition, results show that attitude toward online purchasing affect online purchase intention. This study is the first to empirically examine factors influencing online purchase intention towards apparel products in Lebanon. The study provides insight from an overlooked emerging country, Lebanon.

A New Framework for Politics, Law, and Government in the Digital Era

In the 21st century, the digital world has taken over law and politics. Political war is on online platforms, and various decisions are made based on the digital data. Another problem is to provide security of online data. Most of the world is unsatisfied with the government and policymakers. A lack of satisfaction among the people leads towards civil war or it can cause the fall of an entire selected government, or it can collapse the law systems of the world. To cope with this problem, the authors propose the judge-based political system (JBPS). The new political system can deal with the advancement of technology and cybersecurity. Judge-based politics will help to control this type of threat and provide satisfaction in the upcoming era of democracy.