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Background: High numbers of consumable medical materials (eg, sterile needles and swabs) are used during the daily routine of intensive care units (ICUs) worldwide. Although medical consumables largely contribute to total ICU hospital expenditure, many hospitals do not track the individual use of materials. Current tracking solutions meeting the specific requirements of the medical environment, like barcodes or radio frequency identification, require specialized material preparation and high infrastructure investment. This impedes the accurate prediction of consumption, leads to high storage maintenance costs caused by large inventories, and hinders scientific work due to inaccurate documentation. Thus, new cost-effective and contactless methods for object detection are urgently needed.
Objective: The goal of this work was to develop and evaluate a contactless visual recognition system for tracking medical consumable materials in ICUs using a deep learning approach on a distributed client-server architecture.
Methods: We developed Consumabot, a novel client-server optical recognition system for medical consumables, based on the convolutional neural network model MobileNet implemented in Tensorflow. The software was designed to run on single-board computer platforms as a detection unit. The system was trained to recognize 20 different materials in the ICU, while 100 sample images of each consumable material were provided. We assessed the top-1 recognition rates in the context of different real-world ICU settings: materials presented to the system without visual obstruction, 50% covered materials, and scenarios of multiple items. We further performed an analysis of variance with repeated measures to quantify the effect of adverse real-world circumstances.
Results: Consumabot reached a >99% reliability of recognition after about 60 steps of training and 150 steps of validation. A desirable low cross entropy of <0.03 was reached for the training set after about 100 iteration steps and after 170 steps for the validation set. The system showed a high top-1 mean recognition accuracy in a real-world scenario of 0.85 (SD 0.11) for objects presented to the system without visual obstruction. Recognition accuracy was lower, but still acceptable, in scenarios where the objects were 50% covered (P<.001; mean recognition accuracy 0.71; SD 0.13) or multiple objects of the target group were present (P=.01; mean recognition accuracy 0.78; SD 0.11), compared to a nonobstructed view. The approach met the criteria of absence of explicit labeling (eg, barcodes, radio frequency labeling) while maintaining a high standard for quality and hygiene with minimal consumption of resources (eg, cost, time, training, and computational power).
Conclusions: Using a convolutional neural network architecture, Consumabot consistently achieved good results in the classification of consumables and thus is a feasible way to recognize and register medical consumables directly to a hospital’s electronic health record. The system shows limitations when the materials are partially covered, therefore identifying characteristics of the consumables are not presented to the system. Further development of the assessment in different medical circumstances is needed.
The aim of this work was to develop and evaluate the reinforcement learning algorithm VentAI, which is able to suggest a dynamically optimized mechanical ventilation regime for critically-ill patients. We built, validated and tested its performance on 11,943 events of volume-controlled mechanical ventilation derived from 61,532 distinct ICU admissions and tested it on an independent, secondary dataset (200,859 ICU stays; 25,086 mechanical ventilation events). A patient “data fingerprint” of 44 features was extracted as multidimensional time series in 4-hour time steps. We used a Markov decision process, including a reward system and a Q-learning approach, to find the optimized settings for positive end-expiratory pressure (PEEP), fraction of inspired oxygen (FiO2) and ideal body weight-adjusted tidal volume (Vt). The observed outcome was in-hospital or 90-day mortality. VentAI reached a significantly increased estimated performance return of 83.3 (primary dataset) and 84.1 (secondary dataset) compared to physicians’ standard clinical care (51.1). The number of recommended action changes per mechanically ventilated patient constantly exceeded those of the clinicians. VentAI chose 202.9% more frequently ventilation regimes with lower Vt (5–7.5 mL/kg), but 50.8% less for regimes with higher Vt (7.5–10 mL/kg). VentAI recommended 29.3% more frequently PEEP levels of 5–7 cm H2O and 53.6% more frequently PEEP levels of 7–9 cmH2O. VentAI avoided high (>55%) FiO2 values (59.8% decrease), while preferring the range of 50–55% (140.3% increase). In conclusion, VentAI provides reproducible high performance by dynamically choosing an optimized, individualized ventilation strategy and thus might be of benefit for critically ill patients.
Background: Deficiency in musculoskeletal imaging (MI) education will pose a great challenge to physiotherapists in clinical decision making in this era of first-contact physiotherapy practices in many developed and developing countries. This study evaluated the nature and the level of MI training received by physiotherapists who graduate from Nigerian universities.
Methods: An online version of the previously validated Physiotherapist Musculoskeletal Imaging Profiling Questionnaire (PMIPQ) was administered to all eligible physiotherapists identified through the database of the Medical Rehabilitation Therapist Board of Nigeria. Data were obtained on demographics, nature, and level of training on MI procedures using the PMIPQ. Logistic regression, Friedman’s analysis of variance (ANOVA) and Kruskal-Wallis tests were used for the statistical analysis of collected data.
Results: The results (n = 400) showed that only 10.0% of the respondents had a stand-alone entry-level course in MI, 92.8% did not have any MI placement during their clinical internship, and 67.3% had never attended a MI workshop. There was a significant difference in the level of training received across MI procedures [χ2 (15) = 1285.899; p = 0.001]. However, there was no significant difference in the level of MI training across institutions of entry-level programme (p = 0.36). The study participants with transitional Doctor of Physiotherapy education were better trained in MI than their counterparts with a bachelor’s degree only (p = 0.047).
Conclusions: Most physiotherapy programmes in Nigeria did not include a specific MI module; imaging instructions were mainly provided through clinical science courses. The overall self-reported level of MI training among the respondents was deficient. It is recommended that stand-alone MI education should be introduced in the early part of the entry-level physiotherapy curriculum.
This paper describes the project “Visual Knowledge Communication”, a joint project that started recently. The partners are psychologists and computer scientists from four universities of the German state Rhineland-Palatinate. The starting point for the project was the fact that visualizations have attracted considerable interest in psychology as well as computer science within the last years. However, psychologists and computer scientists pursued their investigations independently from each other in the past. This project has as its main goal the support and fostering of cooperation between psychologists and computer scientists in several visualization research projects.
The paper sketches the overall project. It then discusses in more detail the authors' subproject which deals with a peer review process for animations developed by students. The basic ideas, the main goals, and the project plan are described.
This paper is a work-in-progress report. Therefore, it does not contain any results.
The study traces the development of compulsory vaccination in Germany against the background of political discussion and legislative activities, focusing on the area of tension between state health protection and the right to medical self-determination in the context of constitutional balancing. It is based on the assumption that the right to medical self-determination traditionally dominates state decisions in a democratic constitutional state and that the scope for decision-making is constantly being further contoured in the face of current challenges.
Study design: Systematic review with meta-analysis and meta-regression.
Background and objectives: We systematically reviewed and delineated the existing evidence on sustainability effects of motor control exercises on pain intensity and disability in chronic low back pain patients when compared with an inactive or passive control group or with other exercises. Secondary aims were to reveal whether moderating factors like the time after intervention completion, the study quality, and the training characteristics affect the potential sustainability effects.
Methods: Relevant scientific databases (Medline, Web of Knowledge, Cochrane) were screened. Eligibility criteria for selecting studies: All RCTs und CTs on chronic (≥ 12/13 weeks) nonspecific low back pain, written in English or German and adopting a longitudinal core-specific/stabilizing sensorimotor control exercise intervention with at least one pain intensity and disability outcome assessment at a follow-up (sustainability) timepoint of ≥ 4 weeks after exercise intervention completion.
Results and conclusions: From the 3,415 studies that were initially retrieved, 10 (2 CTs & 8 RCTs) on N = 1081 patients were included in the review and analyses. Low to moderate quality evidence shows a sustainable positive effect of motor control exercise on pain (SMD = -.46, Z = 2.9, p < .001) and disability (SMD = -.44, Z = 2.5, p < .001) in low back pain patients when compared to any control. The subgroups’ effects are less conclusive and no clear direction of the sustainability effect at short versus mid versus long-term, of the type of the comparator, or of the dose of the training is given. Low quality studies overestimated the effect of motor control exercises.
Intervention in the form of core-specific stability exercises is evident to improve trunk stability. The purpose was to assess the effect of an additional 6 weeks sensorimotor or resistance training on maximum isokinetic trunk strength and response to sudden dynamic trunk loading (STL) in highly trained adolescent athletes. The study was conducted as a single-blind, 3-armed randomized controlled trial. Twenty-four adolescent athletes (14f/10 m, 16 ± 1 yrs.;178 ± 10 cm; 67 ± 11 kg; training sessions/week 15±5; training h/week 22±8) were randomized into resistance training (RT; n=7), sensorimotor training (SMT; n = 10), and control group (CG; n = 7). Athletes were instructed to perform standardized, center-based training for 6 weeks, two times per week, with a duration of 1 h each session. SMT consisted of four different core-specific sensorimotor exercises using instable surfaces. RT consisted of four trunk strength exercises using strength training machines, as well as an isokinetic dynamometer. All participants in the CG received an unspecific heart frequency controlled, ergometer-based endurance training (50 min at max. heart frequency of 130HF). For each athlete, each training session was documented in an individual training diary (e.g., level of SMT exercise; 1RM for strength exercise, pain). At baseline (M1) and after 6 weeks of intervention (M2), participants’ maximum strength in trunk rotation (ROM:63°) and flexion/extension (ROM:55°) was tested on an isokinetic dynamometer (concentric/eccentric 30°/s). STL was assessed in eccentric (30°/s) mode with additional dynamometer-induced perturbation as a marker of core stability. Peak torque [Nm] was calculated as the main outcome. The primary outcome measurements (trunk rotation/extension peak torque: con, ecc, STL) were statistically analyzed by means of the two-factor repeated measures analysis of variance (α = 0.05). Out of 12 possible sessions, athletes participated between 8 and 9 sessions (SMT: 9 ± 3; RT: 8 ± 3; CG: 8 ± 4). Regarding main outcomes of trunk performance, experimental groups showed no significant pre–post difference for maximum trunk strength testing as well as for perturbation compensation (p > 0.05). It is concluded, that future interventions should exceed 6 weeks duration with at least 2 sessions per week to induce enhanced trunk strength or compensatory response to sudden, high-intensity trunk loading in already highly trained adolescent athletes, regardless of training regime.
The increasing availability of off-the-shelf high-frequency components makes radar measurement become popular in mainstream industrial applications. We present a cooperative FM radar for strongly reflective environments, being devised for a range of up to approx. 120 m. The target is designed with an unambiguous signature method and satisfies coherence. A prototype is built with commercial semiconductor components that operates in the 24 GHz industrial, scientific and medical band. First experimental results taken in sewage pipes are presented, using the target prototype and a standard FMCW radio station. An overview on four data acquisition procedures is given.
Background: Core-specific sensorimotor exercises are proven to enhance neuromuscular activity of the trunk, improve athletic performance and prevent back pain. However, the dose-response relationship and, therefore, the dose required to improve trunk function is still under debate. The purpose of the present trial will be to compare four different intervention strategies of sensorimotor exercises that will result in improved trunk function.
Methods/design: A single-blind, four-armed, randomized controlled trial with a 3-week (home-based) intervention phase and two measurement days pre and post intervention (M1/M2) is designed. Experimental procedures on both measurement days will include evaluation of maximum isokinetic and isometric trunk strength (extension/flexion, rotation) including perturbations, as well as neuromuscular trunk activity while performing strength testing. The primary outcome is trunk strength (peak torque). Neuromuscular activity (amplitude, latencies as a response to perturbation) serves as secondary outcome.
The control group will perform a standardized exercise program of four sensorimotor exercises (three sets of 10 repetitions) in each of six training sessions (30 min duration) over 3 weeks. The intervention groups’ programs differ in the number of exercises, sets per exercise and, therefore, overall training amount (group I: six sessions, three exercises, two sets; group II: six sessions, two exercises, two sets; group III: six sessions, one exercise, three sets). The intervention programs of groups I, II and III include additional perturbations for all exercises to increase both the difficulty and the efficacy of the exercises performed. Statistical analysis will be performed after examining the underlying assumptions for parametric and non-parametric testing.
Discussion: The results of the study will be clinically relevant, not only for researchers but also for (sports) therapists, physicians, coaches, athletes and the general population who have the aim of improving trunk function.
Background: Improving movement control might be a promising treatment goal during chronic non-specific low back pain (CLBP) rehabilitation. The objective of the study is to evaluate the effect of a single bout of game-based real-time feedback intervention on trunk movement in patients with CLBP.
Methods: Thirteen CLBP patients (8female;41 ± 16 years;173 ± 10 cm;78 ± 22 kg) were included in this randomized cross-over pilot trial. During one laboratory session (2 h), participants performed three identical measurements on trunk movement all including: first, maximum angle of lateral flexion was assessed. Secondly, a target trunk lateral flexion (angle: 20°) was performed. Main outcome was maximum angle ([°]; MA). Secondary outcomes were deviation [°] from the target angle (angle reproduction; AR) and MA of the secondary movement planes (rotation; extension/flexion) during lateral flexion. The outcomes were assessed by an optical 3D-motion-capture-system (2-segment-trunk-model). The measurements were separated by 12-min of intervention and/or resting (randomly). The intervention involved a sensor-based trunk exergame (guiding an avatar through virtual worlds). After carryover effect-analysis, pre-to-post intervention data were pooled between the two sequences followed by analyses of variances (paired t-test).
Results: No significant change from pre to post intervention for MA or AR for any segment occurred for the main movement plane, lateral flexion (p > .05). The upper trunk segment showed a significant decrease of the MA for trunk extension/flexion from pre to post intervention ((4.4° ± 4.4° (95% CI 7.06–1.75)/3.5° ± 1.29° (95% CI 6.22–0.80); p = 0.02, d = 0.20).
Conclusions: A single bout of game-based real-time feedback intervention lead to changes in the secondary movement planes indicating reduced evasive motion during trunk movement.
Stabilization exercise (SE) is evident for the management of chronic non-specific low back pain (LBP). The optimal dose-response-relationship for the utmost treatment success is, thus, still unknown. The purpose is to systematically review the dose-response-relationship of stabilisation exercises on pain and disability in patients with chronic non-specific LBP. A systematic review with meta-regression was conducted (Pubmed, Web of Knowledge, Cochrane). Eligibility criteria were RCTs on patients with chronic non-specific LBP, written in English/German and adopting a longitudinal core-specific/stabilising/motor control exercise intervention with at least one outcome for pain intensity and/or disability. Meta-regressions (dependent variable = effect sizes (Cohens d) of the interventions (for pain and for disability), independent variable = training characteristics (duration, frequency, time per session)), and controlled for (low) study quality (PEDro) and (low) sample sizes (n) were conducted to reveal the optimal dose required for therapy success. From the 3,415 studies initially selected, 50 studies (n = 2,786 LBP patients) were included. N = 1,239 patients received SE. Training duration was 7.0 ± 3.3 weeks, training frequency was 3.1 ± 1.8 sessions per week with a mean training time of 44.6 ± 18.0 min per session. The meta-regressions’ mean effect size was d = 1.80 (pain) and d = 1.70 (disability). Total R2 was 0.445 and 0.17. Moderate quality evidence (R2 = 0.231) revealed that a training duration of 20 to 30 min elicited the largest effect (both in pain and disability, logarithmic association). Low quality evidence (R2 = 0.125) revealed that training 3 to 5 times per week led to the largest effect of SE in patients with chronic non-specific LBP (inverted U-shaped association). In patients with non-specific chronic LBP, stabilization exercise with a training frequency of 3 to 5 times per week (Grade C) and a training time of 20 to 30 min per session (Grade A) elicited the largest effect on pain and disability.
Introduction: Annually, 2 million sports-related injuries are reported in Germany of which athletes contribute to a large proportion. Multiple sport injury prevention programs designed to decrease acute and overuse injuries in athletes have been proven effective. Yet, the programs’ components, general or sports-specific, that led to these positive effects are uncertain. Despite not knowing about the superiority of sports-specific injury prevention programs, coaches and athletes alike prefer more specialized rather than generalized exercise programs. Therefore, this systematic review aimed to present the available evidence on how general and sports-specific prevention programs affect injury rates in athletes.
Methods: PubMed and Web of Science were electronically searched throughout April 2018. The inclusion criteria were publication dates Jan 2006–Dec 2017, athletes (11–45 years), exercise-based injury prevention programs and injury incidence. The methodological quality was assessed with the Cochrane Collaboration assessment tools.
Results: Of the initial 6619 findings, 15 studies met the inclusion criteria. In addition, 13 studies were added from reference lists and external sources making a total of 28 studies. Of which, one used sports-specific, seven general and 20 mixed prevention strategies. Twenty-four studies revealed reduced injury rates. Of the four ineffective programs, one was general and three mixed.
Conclusion: The general and mixed programs positively affect injury rates. Sports-specific programs are uninvestigated and despite wide discussion regarding the definition, no consensus was reached. Defining such terminology and investigating the true effectiveness of such IPPs is a potential avenue for future research.
Freedom of trade, occupation and profession in times of the Covid-19 pandemic in South Africa
(2022)
This paper evaluates the freedom of trade, occupation, and profession in South Africa from a Covid-19 pandemic context. It does that by focusing on the pertinent provisions and rights contained in the Constitution of the Republic of South Africa, 1996 (the Constitution) and relevant international and regional human rights instruments. It proceeds by discussing the interlinkage between (the freedom of trade, occupation, and profession and other pertinent fundamental) rights, limitation, enforcement, and interpretation of rights. This is followed by some final observations.
Purpose: The well-to-wheel (WTW) methodology is widely used for policy support in road transport. It can be seen as a simplified life cycle assessment (LCA) that focuses on the energy consumption and CO2 emissions only for the fuel being consumed, ignoring other stages of a vehicle’s life cycle. WTW results are therefore different from LCA results. In order to close this gap, the authors propose a hybrid WTW+LCA methodology useful to assess the greenhouse gas (GHG) profiles of road vehicles.
Methods: The proposed method (hybrid WTW+LCA) keeps the main hypotheses of the WTW methodology, but integrates them with LCA data restricted to the global warming potential (GWP) occurring during the manufacturing of the battery pack. WTW data are used for the GHG intensity of the EU electric mix, after a consistency check with the main life cycle impact (LCI) sources available in literature.
Results and discussion: A numerical example is provided, comparing GHG emissions due to the use of a battery electric vehicle (BEV) with emissions from an internal combustion engine vehicle. This comparison is done both according to the WTW approach (namely the JEC WTW version 4) and the proposed hybrid WTW+LCA method. The GHG savings due to the use of BEVs calculated with the WTW-4 range between 44 and 56 %, while according to the hybrid method the savings are lower (31–46 %). This difference is due to the GWP which arises as a result of the manufacturing of the battery pack for the electric vehicles.
Conclusions: The WTW methodology used in policy support to quantify energy content and GHG emissions of fuels and powertrains can produce results closer to the LCA methodology by adopting a hybrid WTW+LCA approach. While evaluating GHG savings due to the use of BEVs, it is important that this method considers the GWP due to the manufacturing of the battery pack.
Innovative biogas multi-stage biogas plant and novel analytical system: First project experiences
(2012)
The here presented applied research and development project is targeted to the development and application of new and improved techniques in plant design, performance analysis and process control. Hereto following the required steps are illustrated and the goals are outlined. The project covers the development of a previously patented anaerobic digestion process, adaption of flow cytometry as an analytical instrument and investigation of innovative ways of disposal of solid fermentation wastes. The preliminary experiences with a newly built research plant employing a novel anaerobic biogas digestion technique are discussed. In this paper the first outcomes concerning the construction and operation are discussed. A novel method of disposal of the fermentation wastes is also discussed and first results are shown.
In the last decades, there has been a widespread implementation of Green Infrastructures worldwide. Among these, green roofs appear to be particularly flexible sustainable drainage facilities. To predict their effectiveness for planning purposes, a tool is required that provides information as a function of local meteorological variables. Thus, a relatively simple daily scale, one-dimensional water balance approach has been proposed. The crucial evapotranspiration process, usually considered as a water balance dependent variable, is replaced here by empirical relationships providing an a-priori assessment of soil water losses through actual evapotranspiration. The modelling scheme, which under some simplification can be used without a calibration process, has been applied to experimental runoff data monitored at a green roof located near Bernkastel (Germany), between April 2005 and December 2006. Two different empirical relationships have been used to model actual evapotranspiration, considering a water availability limited and an energy limited scheme. Model errors quantification, ranging from 2% to 40% on the long-term scale and from 1% to 36% at the event scale, appear strongly related to the particularly considered relationship.
Since operational managers often monitor large numbers of wind turbines (WTs), they depend on a toolset to provide them with highly condensed information to identify and prioritize low performing WTs or schedule preventive maintenance measures. Power curves are a frequently used tool to assess the performance of WTs. The power curve health value (HV) used in this work is supposed to detect power curve anomalies since small deviations in the power curve are not easy to identify. It evaluates deviations in the linear region of power curves by performing a principal component analysis. To calculate the HV, the standard deviation in direction of the second principal component of a reference data set is compared to the standard deviation of a combined data set consisting of the reference data and data of the evaluated period. This article examines the applicability of this HV for different purposes as well as its sensitivities and provides a modified HV approach to make it more robust and suitable for heterogeneous data sets. The modified HV was tested based on ENGIE's open data wind farm and data of on- and offshore WTs from the WInD-Pool. It proved to detect anomalies in the linear region of the power curve in a reliable and sensitive manner and was also eligible to detect long term power curve degradation. Also, about 7 % of all corrective maintenance measures were preceded by high HVs with a median alarm horizon of three days. Overall, the HV proved to be a promising tool for various applications.
Species distribution models (SDMs) are key tools in biodiversity and conservation, but assessing their reliability in unsampled locations is difficult, especially where there are sampling biases. We present a spatially-explicit sensitivity analysis for SDMs – SDM profiling – which assesses the leverage that unsampled locations have on the overall model by exploring the interaction between the effect on the variable response curves and the prevalence of the affected environmental conditions. The method adds a ‘pseudo-presence’ and ‘pseudo-absence’ to unsampled locations, re-running the SDM for each, and measuring the difference between the probability surfaces of the original and new SDMs. When the standardised difference values are plotted against each other (a ‘profile plot’), each point's location can be summarized by four leverage measures, calculated as the distances to each corner. We explore several applications: visualization of model certainty; identification of optimal new sampling locations and redundant existing locations; and flagging potentially erroneous occurrence records.
Artificial light at night (ALAN) is a widespread alteration of the natural environment that can affect the functioning of ecosystems. ALAN can change the movement patterns of freshwater animals that move into the adjacent riparian and terrestrial ecosystems, but the implications for local riparian consumers that rely on these subsidies are still unexplored. We conducted a 2-year field experiment to quantify changes of freshwater-terrestrial linkages by installing streetlights in a previously light-naïve riparian area adjacent to an agricultural drainage ditch. We compared the abundance and community composition of emerging aquatic insects, flying insects, and ground-dwelling arthropods with an unlit control site. Comparisons were made within and between years using two-way generalized least squares (GLS) model and a BACI design (Before-After Control-Impact). Aquatic insect emergence, the proportion of flying insects that were aquatic in origin, and the total abundance of flying insects all increased in the ALAN-illuminated area. The abundance of several night-active ground-dwelling predators (Pachygnatha clercki, Trochosa sp., Opiliones) increased under ALAN and their activity was extended into the day. Conversely, the abundance of nocturnal ground beetles (Carabidae) decreased under ALAN. The changes in composition of riparian predator and scavenger communities suggest that the increase in aquatic-to-terrestrial subsidy flux may cascade through the riparian food web. The work is among the first studies to experimentally manipulate ALAN using a large-scale field experiment, and provides evidence that ALAN can affect processes that link adjacent ecosystems. Given the large number of streetlights that are installed along shorelines of freshwater bodies throughout the globe, the effects could be widespread and represent an underestimated source of impairment for both aquatic and riparian systems.
Online Learning algorithms and Indoor Positioning Systems are complex applications in the environment of cyber-physical systems. These distributed systems are created by networking intelligent machines and autonomous robots on the Internet of Things using embedded systems that enable the exchange of information at any time. This information is processed by Machine Learning algorithms to make decisions about current developments in production or to influence logistics processes for optimization purposes. In this article, we present and categorize the further development of the prototype of a novel Indoor Positioning System, which constantly adapts its knowledge to the conditions of its environment with the help of Online Learning. Here, we apply Online Learning algorithms in the field of sound-based indoor localization with low-cost hardware and demonstrate the improvement of the system over its predecessor and its adaptability for different applications in an experimental case study.