Back Support Research Articles

Development and psychometric properties of the Functioning and Recovery Scale: a new measure to assess psychosocial functioning after a suicide attempt.

Few measures have been developed to assess the efficacy of community-based suicide prevention and recovery services. The current study aimed to develop a scale to provide accurate assessment and monitoring of functional recovery for people following a recent suicide attempt at The Way Back Support Service in Australia. The study was conducted in multiple iterative stages: (1) literature review to identify existing scales; (2) structured informant interview with people with lived experience of a suicide attempt; (3) expert survey of researchers, clinicians, and people with lived experience on relevance and acceptability of candidate items; and (4) quantitative survey of the pilot scale to assess psychometric properties. An 11-item scale assessing recovery in people who have recently attempted suicide was demonstrated to be a unidimensional measure with sound psychometric properties (α=0.94). The scale was highly acceptable to researchers, clinicians, and people with lived experience. A short-form 6-item scale was also developed. The Functioning and Recovery Scale is likely to be useful for evaluating suicide prevention programs. No existing scale captures the broad construct of psychosocial functioning with sound psychometric rigor and the involvement of people with lived experience of suicide attempt.

Non-Contact Evaluation of Pigs' Body Temperature Incorporating Environmental Factors.

Internal body temperature is the gold standard for the fever of pigs, however non-contact infrared imaging technology (IRT) can only measure the skin temperature of regions of interest (ROI). Therefore, using IRT to detect the internal body temperature should be based on a correlation model between the ROI temperature and the internal temperature. When heat exchange between the ROI and the surroundings makes the ROI temperature more correlated with the environment, merely depending on the ROI to predict the internal temperature is unreliable. To ensure a high prediction accuracy, this paper investigated the influence of air temperature and humidity on ROI temperature, then built a prediction model incorporating them. The animal test includes 18 swine. IRT was employed to collect the temperatures of the backside, eye, vulva, and ear root ROIs; meanwhile, the air temperature and humidity were recorded. Body temperature prediction models incorporating environmental factors and the ROI temperature were constructed based on Back Propagate Neural Net (BPNN), Random Forest (RF), and Support Vector Regression (SVR). All three models yielded better results regarding the maximum error, minimum error, and mean square error (MSE) when the environmental factors were considered. When environmental factors were incorporated, SVR produced the best outcome, with the maximum error at 0.478 °C, the minimum error at 0.124 °C, and the MSE at 0.159 °C. The result demonstrated the accuracy and applicability of SVR as a prediction model of pigs′ internal body temperature.

Short‐Term Load Forecasting Based on Gaussian Process Regression with Density Peak Clustering and Information Sharing Antlion Optimizer

Short‐term load forecasting is essential for reliable and economical operation of the power system. Gaussian process regression (GPR), as a novel machine learning prediction algorithm, has become one of the commonly used algorithms in the field of short‐term load prediction because it can fully consider the nonlinear and uncertain characteristics of short‐term load sequence. However, due to the unreasonable selection of training set and the inaccurate acquisition of hyper parameters, the prediction accuracy of GPR decreases. In order to improve the accuracy of power system load forecasting, a GPR prediction model based on density peak clustering (DPC) and information sharing antlion optimizer (ISALO) is proposed. Firstly, the DPC is used to find similar days from the historical load data to construct a more reasonable training set. Then, the ISALO, an improved ALO by introducing information sharing mechanism, is used to optimize the hyper parameters of the GPR. Experiments show that the DPC‐ISALO‐GPR model has a 3.33 and 1.22% reduction in mean absolute percentage error compared to Back Propagation Neural Network (BP) and support vector machines, which is suitable for engineering practical applications. © 2020 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.

Correlation analysis of aeroengine operation monitoring using deep learning

To analyze the reliability of aeroengine more accurately, based on the analysis of operation reliability and complex reliability, the deep learning method is adopted to deal with the nonlinear and time-varying problems between the state parameters and operation reliability of aeroengine, and the condition monitoring method and deep learning of aeroengine are discussed. The results show that, based on the deep learning integrated network, the remaining useful life of aeroengine is predicted, and the key parameters of aeroengine are fitted and predicted by the back propagation (BP) algorithm. The artificial neural network method is used to predict the aeroengine parameters. For the collected aeroengine monitoring parameters, the greedy layer by layer training algorithm is used to mine the relationship between the parameters, extract the evaluation features, and evaluate the performance degradation, which verify the statistical significance and robustness of the conclusions. The proposed algorithm is more accurate and robust than the results of back BP neural network and support vector machine. It can prevent the over-fitting of small samples in aeroengine condition monitoring and further improve its nonlinear processing and generalization ability.

Heavy metals in submicronic particulate matter (PM1) from a Chinese metropolitan city predicted by machine learning models

The aim of this study was to establish a method for predicting heavy metal concentrations in PM1 (aerosol particles with an aerodynamic diameter ≤ 1.0 μm) based on back propagation artificial neural network (BP-ANN) and support vector machine (SVM) methods. The annual average PM1 concentration was 26.31 μg/m3 (range: 7.00–73.40 μg/m3). The concentrations of most metals were higher in winter and lower in autumn and summer. Mn and Ni had the highest noncarcinogenic risk, and Cr the highest carcinogenic risk. The hazard index was below safe limit, and the integrated carcinogenic risk was less than precautionary value. There were no obvious differences in the simulation performances of BP-ANN and SVM models. However, in both models many elements had better simulation effects when input variables were atmospheric pollutants (SO2, NO2, CO, O3 and PM2.5) rather than PM1 and meteorological factors (temperature, relative humidity, atmospheric pressure and wind speed). Models performed better for Pb, Tl and Zn, as evidenced by training R and test R values consistently >0.85, whereas their performances for Ti and V were relatively poor. Predicted results by the fully trained models showed atmospheric heavy metal pollution was heavier in December and January and lighter in August and July of 2019. For the period covering the COVID-19 outbreak in China, from January to March 2020, most of the predicted element concentrations were lower than in 2018 and 2019, and the concentrations of nearly all metals were lowest during the nationwide implementation of countermeasures taken against the pandemic.

The Risks of Being Otologist, an Ergonomic and Occupational Hazard Review.

To review occupational ergonomic risks for the Otologist and Neurotologist. MEDLINE, OVID, PubMed, and Google Scholar. A search was conducted to identify all studies in the English language that involve ergonomic-related risks for surgeons. Occupational hazards, particularly musculoskeletal disorders (MSDs), are common in the surgical community in general and among Otolaryngologists in particular. Very few studies have been conducted assessing MSDs specific to Otologists and Neurotologists. However, extrapolating from other surgical professions with similar ergonomic postures in the operation room and office, one can infer that cervical and lumbar pain are related to prolonged static sitting and neck flexion when working with a microscope and begins early in training. Early institution of correct ergonomic training is feasible and may be effective. Improved ergonomic habits include upright sitting, avoidance of neck flexion, initiating short breaks, and the use of chairs with arm and back support. Future technologies incorporated into otologic surgery should have improved ergonomic design. Otologists and Neurotologists are exposed to MSDs directly related to their work demands. Incorporating healthy ergonomics into surgical training as well adopting correct posture and the use equipment designed for back support may help mitigate the long-terms risks of MSD.

Effects of Positioning on Cough Peak Flow and Muscular Electromyographic Activation in Duchenne Muscular Dystrophy.

Advanced stages of Duchenne muscular dystrophy (DMD) result in muscle weakness and the inability to generate an effective cough. Several factors influence the effectiveness of cough in patients with DMD. The aim of this study was to assess whether differences in positioning affect cough peak flow (CPF) and muscular electromyographic activation in subjects with DMD compared with paired healthy subjects. Optoelectronic plethysmography and surface electromyography were used to assess chest wall volumes, chest wall inspiratory capacity, CPF, breathing pattern, and electromyographic activity of sternocleidomastoid, scalene, rectus abdominis, and external oblique muscles during inspiratory and expiratory cough phases in the supine position, supine position with headrest raised at 45°, and sitting with back support at 80° in 12 subjects with DMD and 12 healthy subjects. Subjects with DMD had lower CPF (P < .01) in comparison to control subjects in all positions; the DMD group also exhibited lower CPF (P = .045) in the supine position versus 80°. Moreover, the relative volume contributions of the rib cage and abdominal compartments to tidal volume modified significantly with posture. The electromyographic activity during inspiratory and expiratory cough phases was lower in subjects with DMD compared to healthy subjects for all evaluated muscles (P < .05), but no significant differences were observed with posture change. In subjects with DMD, posture influenced CPF and the relative contribution of the rib cage and abdominal compartments to tidal volume. However, muscular electromyographic activation was not influenced by posture in subjects with DMD and healthy subjects.

A Study on Fatigue Reduction of Driver by Changing Back Support Position During Long Time Driving

Our main study theme is to reduce fatigue of driver during long time driving. In order to reduce fatigue of drivers during long time driving, we focused on a method of using back support mechanisms to change the posture of the driver. We predicted that fatigue level of driver would change in accordance with the change of back support position. Furthermore, we evaluated the muscle activity change when the support position changed. By examining the correlation between the evaluated data, we expected that the driver’s optimum support position would be found out. As a result of conducting driving and subjective assessment, it was found that there was a difference in fatigue reduction effect on the change of the driver’s back support position. When the driver’s fatigue level was low, muscle activity was low.

Outsourcing and Organizational Performance: A Comparative Analysis of Nigeria Bottling Company Plant and Camela Vegetable Oil Company, Owerri, Imo State, Nigeria

Achievement of organizations’ already set out goals and the level of attainment of those goals measure how successful the organizations are. They seek numerous strategies to help them in this quest. One of such strategies is outsourcing. Outsourcing is a management strategy where an organization delegates her major non-core functions to a specialized and efficient service provider enabling her to save both time and money. This research took a holistic look at outsourcing as a strategy and its effect on organizational performance using Nigeria Bottling Company Plant and Camela Vegetable Oil Company both situated in Owerri, Imo State as focal points. Core competency theory was used as theoretical framework. The survey research design was used, utilizing questionnaire and interviews as instruments of primary data collection. Library research was source of secondary data used for the analytical discussion. It was observed that outsourcing back office, primary and support activities have significant positive effect on organizational performance. The research therefore, recommends outsourcing as a strategy for improved performance in organizations. Key words: Outsourcing, Organizational performance, Back office activities, Primary activities, Support activities, Core competency theory DOI: 10.7176/RHSS/10-12-09 Publication date: June 30th 2020

“Inclusive business” in agriculture: Evidence from the evolution of agricultural value chains

Abstract Sustained interest by the business community in commercial agriculture in the global South has been welcomed for its potential to bring capital into long neglected rural areas, but has also raised concerns over implications for customary land rights and the terms of integration of local land and labor into global supply chains. In global development policy and discourse, the concept of “inclusive business” has become central in efforts to resolve these tensions, with the idea that integrating smallholders and other disadvantaged actors into partnerships with agribusiness firms can generate benefits for national economies, private investors, and local livelihoods. Scholarly treatment of the topic has tended to be polarized into win/lose narratives, or points to the contingency and social differentiation of localized experiences. This review paper takes a different approach, exploring published evidence on the structural factors shaping agricultural value chains and their implications for social inclusion. We develop a typology of seven agricultural value chains, and use this to select a sample of crops in specific world regions for an analysis of how structural factors in value chain relations - from crop features, to market dynamics and policy drivers – affect social inclusion (and exclusion). Such an approach allows us to ask whether inclusive agribusiness is a realistic goal given the broader structuring of agribusiness and the global economic system. Our study finds that while the characteristics of specific crops and supply chains exert a strong influence on opportunities and constraints to inclusion, the overall trend is towards more exclusive agribusiness as governments scale back support to smallholders, more stringent standards raise barriers to entry, and firms streamline operations to enhance competitiveness. This raises questions about the feasibility of this goal under the current political economic system. Findings point to the need to re-consider the policy choices behind these trends, and how we deploy the fiscal, legislative, and gate-keeper functions of the state to shape agrarian trajectories.

Wheelchair backs that support the spinal curves: Assessing postural and functional changes

Objective: To compare outcomes using a wheelchair back designed to support the natural seated spinal curves versus an upholstered back that promotes posterior pelvic tilt and thoracolumbar kyphosis. Design: Cross-over intervention. Setting: Two free-standing spinal cord injury (SCI) model system hospitals. Participants: Fifty adults with motor complete SCI C6-T4, between the ages of 18–60 years who use a manual wheelchair for mobility were recruited from a convenience sample. Intervention: Each participant’s wheelchair back support was removed and replaced by an upholstered back and a solid back in randomized order. Postural and functional outcomes, pain, and satisfaction were evaluated using each back. Outcome measures: Seated postural measurements included pelvic angle, spinal angle of kyphosis and linear measurement of spine. Functional outcomes included vertical forward reach, one stroke push, timed forward wheeling, ramp ascent and descent. Numerical pain rating and a satisfaction survey provided input pertaining to both backs. Results: The solid back demonstrated significance in seated postural measurements. Participants using the solid back trended to higher scores in functional outcome measures including vertical forward reach, one stroke push and timed ramp ascent. Participants reported increased satisfaction with comfort and stability with the solid back. Conclusions: This pilot study demonstrated that a wheelchair back, which supports the seated spinal curves improves upright posture, functional reach, and wheelchair propulsion skills. Further research is necessary to demonstrate statistical findings as well as to assess back height and lateral support.

Influence of back support shape in wheelchairs offering pelvic support on asymmetrical sitting posture and pressure points during reaching tasks in stroke patients

Many poststroke hemiplegic patients have an asymmetrical wheelchair-sitting posture. This study aimed to investigate the impact of different back support shapes on asymmetrical sitting posture and pressure points among poststroke hemiplegic patients during an activities of daily living–related reaching task. This study included 23 poststroke hemiplegic patients who performed tasks that involved the movement of objects using the unaffected upper limb to the affected side while sitting in a conventional wheelchair (C-WC) with a flat back support or a wheelchair providing pelvic and thoracic support (P-WC). Body alignment angles from video images and pressure distribution on supporting surfaces were measured using a two-dimensional motion analysis software (Dartfish) and a pressure mapping system (FSA). Regarding movement performance, although postural asymmetry increased in both wheelchair types, the degree of postural variation was smaller with P-WC use than C-WC use (p < 0.05), with partly reduced postural asymmetry. With P-WC, one-sided ischial asymmetrical pressure was significantly less after the movement (p < 0.05). In conclusion, P-WC’s back support shape contributed to a decrease in postural asymmetry for pelvic girdle support both at rest and during movement. This highlights the importance of a wheelchair back support shape and may help to increase the quality of activities of daily living movement in poststroke hemiplegic patients in wheelchairs.

Biomechanical evaluation of a new passive back support exoskeleton

The number one cause of disability in the world is low-back pain, with mechanical loading as one of the major risk factors. To reduce mechanical loading, exoskeletons have been introduced in the workplace. Substantial reductions in back muscle activity were found when using the exoskeleton during static bending and manual materials handling. However, most exoskeletons only have one joint at hip level, resulting in loss of range of motion and shifting of the exoskeleton relative to the body. To address these issues, a new exoskeleton design has been developed and tested.The present study investigated the effect of the SPEXOR passive exoskeleton on compression forces, moments, muscle activity and kinematics during static bending at six hand heights and during lifting of a box of 10 kg from around ankle height using three techniques: Free, Squat and Stoop.For static bending, the exoskeleton reduced the compression force by 13–21% depending on bending angle. Another effect of the exoskeleton was that participants substantially reduced lumbar flexion. While lifting, the exoskeleton reduced the peak compression force, on average, by 14%. Lifting technique did not modify the effect of the exoskeleton such that the reduction in compression force was similar.In conclusion, substantial reductions in compression forces were found as a result of the support generated by the exoskeleton and changes in behavior when wearing the exoskeleton. For static bending, lumbar flexion was reduced with the exoskeleton, indicating reduced passive tissue strain. In addition, the reduced peak compression force could reduce the risk of compression induced tissue failure during lifting.

A Review Over The Ergonomic Assessment Of The Women In Bead Works For A Healthy And Productive Workstyle

Most of the families of a village Mangali in Hisar block of Haryana are involved in production of wooden beads for decades. The production process is a joint effort made by both male and female members of the family. However the works done by male members start from procurement of wood to making of beads till their marketing whereas women work to string the beads for whole days and give them finishing looks for selling in not more than 50-100 rupees per day that too only if work is available in abundance. Even the male members only get a limited selling price as all the benefits are secured by the middlemen. The women also face physiological hazards and musculo-skeletal discomforts for doing all the works sitting on floor in a bending posture. The plight of the women is so high that even after the long day seasonal field works, they are sitting over floors in a bent posture which is causing them discomforts over the body for next many days. The activities performed by the women which are related to beads are numerous in nature like jewellery, crafts, etc, and hence the postures. Squatting and cross legged are the major postures that are observed among the women in forward-bent condition and which cause them further musculo-skeletal disorders. A workstation having the facilities regarding back support, eye-level works, wrist support, storage and light requirements is suggested. The multipurpose workstation can help the women to healthy work not for the bead works only but also for other housing chores to speed the activities and enhance the productivity.

Application of machine learning into organic Rankine cycle for prediction and optimization of thermal and exergy efficiency

Organic Rankine cycle (ORC) is a promising technology to recovery and utilization of low grade thermal energy. In recent years, there are few researches on ORC performance prediction based on Machine Learning, mainly due to a lack of reasonable methodology and case demonstration. This paper presented a comprehensive method to achieve a reasonable application of Machine Learning into ORC research for prediction and optimization of ORC’s parameter and performance. Firstly, a cycle database was established by thermodynamic modeling, including four ORC configurations and seven working fluids. Then, for Machine Learning, the Back Propagation Neural Network (BPNN) and Support Vector Regression (SVR) prediction models for ORC were built by predicting error analysis with part of the database which can determine the best parameters of BPNN and SVR. Finally, taking RORC as example, cycle parameter analysis and multi-objective optimization of ORC were conducted based on the thermodynamic model and prediction model to maximize the thermal and exergy efficiency simultaneously. By the prediction and optimization results, it can be deserved that the accurate and fast prediction of the thermal efficiency and exergy efficiency of ORC with multi-parameter, multi-configuration and multi-working fluid was realized, and the optimization results based on the prediction model as the proxy model were also greatly close to the traditional optimization results based on the thermodynamic model. It should be noted that the comprehensive performance of prediction and optimization will be better with more data input. In conclusion, considering accuracy, calculation time, economic cost and safety, the ORC prediction and optimization method proposed in this paper is a promising technology combining Machine Learning and energy utilization, which could provide a new perspective for research in this field.

Effect of machining parameters on surface roughness for compacted graphite cast iron by analyzing covariance function of Gaussian process regression

This study employs Gaussian process regression (GPR) with square exponential covariance function to predict surface roughness of Compacted Graphite Cast Iron (CGI). In addition, a comparative study is conducted on prediction performances for GPR with and without cross-validation, back propagation neural network (BPNN) and support vector machine (SVM) for milling experiment of CGI. Experimental results indicate that prediction performances of GPR without cross-validation and GPR with cross-validation (GPRCV) are similar, and both superior to BPNN and SVM. The effect of machining parameters on surface roughness characterized via length-scale hyperparameters of covariance function is excavated according to prediction principle of GPR. The result shows that cutting speed and feed speed significantly affect surface roughness, depth of cut produces little impact on surface roughness within the given parameter intervals. The analysis of distance between test and training sets and 3D merged surface of surface roughness have verified that the effect is reasonable.

Testing the Effectiveness and Safety of a New Prototype of a Passive Industrial Exoskeleton for Back Support

Testing the Effectiveness and Safety of a New Prototype of a Passive Industrial Exoskeleton for Back Support

Backdrivable and Fully-Portable Pneumatic Back Support Exoskeleton for Lifting Assistance

To reduce the possibility of lower back pain (LBP), which is the most frequent injury in manual labor, several back support exoskeletons have been developed and implemented for lifting motion assistance. Although pneumatic power transmission is attractive due to its inherent compliance and backdrivability, the portability of the pneumatic system is highly limited due to the bulky air compressors that provide compressed air to the system. Therefore, we aimed to develop a fully-portable pneumatic back support exoskeleton by integrating all pneumatic components in the system. The compressed air consumption and generation of pneumatic system were modeled to meet design requirements. The developed exoskeleton was completely stand-alone and provides 80 Nm of maximum extension torque for 6 liftings per minute (6 l/m). The upper limit of the resistance torque was estimated to be about 2 Nm, which implies high backdrivability. Finally, lifting experiments were performed and surface electromyography (sEMG) was measured to validate the physical assistance of the developed exoskeleton system for ten subjects. Compared to the case with no exoskeleton, the back muscle activation was significantly reduced with the assistances.

Selecting the appropriate input variables in a regression approach to estimate actively generated muscle moments around L5/S1 for exoskeleton control

Back support exoskeletons are designed to prevent work-related low-back pain by reducing mechanical loading. For actuated exoskeletons, support based on moments actively produced by the trunk muscles appears a viable approach. The moment can be estimated by a biomechanical model. However, one of the main challenges here is the feasibility of recording the required input variables (kinematics, EMG data, ground reaction forces) to run the model. The aim of this study was to evaluate how accurate different selections of input variables can estimate actively generated moments around L5/S1. Different multivariate regression analyses were performed using a dataset consisting of spinal load, body kinematics and trunk muscle activation levels during different lifting conditions with and without an exoskeleton. The accuracy of the resulting models depended on the number and type of input variables and the regression model order. The current study suggests that third-order polynomial regression of EMG signals of one or two bilateral back muscle pairs together with exoskeleton trunk and hip angle suffices to accurately estimate the actively generated muscle moment around L5/S1, and thereby design a proper control system for back support exoskeletons.

Quality detection of pomegranate fruit infected with fungal disease

ABSTRACT The presence of hidden fungal disease inside pomegranate fruit has reduced the price in the trade of the pomegranate. Alternaria spp. is a widespread fungal disease threatening pomegranate quality. The present study aimed to examine the efficiency of the Electronic nose (E-nose) system as a fast, nondestructive, and low-cost method in diagnosis the amount of Alternaria fungi of the pomegranate. Sixty samples were classified to 0, 25, 50, 75, and 100% amount of Alternaria spp. Linear Discriminant Analysis (LDA), Back Propagation Neural Network (BPNN) and Support Vector Machine (SVM) methods were applied and compared as linear and non-linear analysis methods for detection. The results showed that the LDA method successfully detected healthy and infected samples with 100% accuracy, only by using two Metal Oxide Semiconductor (MOS) sensors. As a prediction method, BPNN showed higher accuracy of 100% in the detection of 0, 25, 50, 75, and 100% infected pomegranates. The results indicated that the E-nose technique is a reliable instrument to detect the quality of the pomegranate with high precision.