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1. Recent reports on insect decline have highlighted the need for long-term data on insect communities towards identifying their trends and drivers.
2. With the launch of many new insect monitoring schemes to investigate insect communities over large spatial and temporal scales, Malaise traps have become one of the most important tools due to the broad spectrum of species collected and reduced capture bias through passive sampling of insects day and night. However, Malaise traps can vary in size, shape, and colour, and it is unknown how these differences affect biomass, species richness, and composition of trap catch, making it difficult to compare results between studies.
3. We compared five Malaise trap types (three variations of the Townes and two variations of the Bartak Malaise trap) to determine their effects on biomass and species richness as identified by metabarcoding.
4. Insect biomass varied by 20%–55%, not strictly following trap size but varying with trap type. Total species richness was 20%–38% higher in the three Townes trap models compared to the Bartak traps. Bartak traps captured lower richness of highly mobile taxa but increased richness of ground-dwelling taxa. The white roofed Townes trap captured a higher richness of pollinators.
5. We find that biomass, total richness, and taxa group specific richness are all sensitive to Malaise trap type. Trap type should be carefully considered and aligned to match monitoring and research questions. Additionally, our estimates of trap type effects can be used to adjust results to facilitate comparisons across studies.
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.
Driven by decreasing PV and energy storage prices, increasing electricity costs and policy supports from Thai government (self-consumption era), rooftop PV and energy storage systems are going to be deployed in the country rapidly that may disrupt existing business models structure of Thai distribution utilities due to revenue erosion and lost earnings opportunities. The retail rates that directly affect ratepayers (non-solar customers) are expected to increase. This paper focuses on a framework for evaluating impacts of PV with and without energy storage systems on Thai distribution utilities and ratepayers by using cost-benefit analysis (CBA). Prior to calculation of cost/benefit components, changes in energy sales need to be addressed. Government policies for the support of PV generation will also help in accelerating the rooftop PV installation. Benefit components include avoided costs due to transmission losses and deferring distribution capacity with appropriate PV penetration level, while cost components consist of losses in revenue, program costs, integration costs and unrecovered fixed costs. It is necessary for Thailand to compare total costs and total benefits of rooftop PV and energy storage systems in order to adopt policy supports and mitigation approaches, such as business model innovation and regulatory reform, effectively.
A local non-restrictive ramp metering strategy PRO is introduced. It is based on the stochasticity of capacity. The ramp metering algorithm shows innovative features:
• upstream time shifted measurements for anticipation
• measurements are actuated every second
• up to three vehicles per green are allowed
Details of the theory of this strategy are described in the first part. At freeway B27 three ramp meters with the PRO algorithm were installed. In the second part, based on extensive detailed traffic and accident data the effects on traffic flow and safety are described. The impact is positive regarding vehicle speed, queue duration and length as well as capacity and traffic safety. The improvements of speeds, travel times and capacities are statistically significant. The ramp metering systems are highly cost effective.
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.
Numerous research methods have been developed to detect anomalies in the areas of security and risk analysis. In healthcare, there are numerous use cases where anomaly detection is relevant. For example, early detection of sepsis is one such use case. Early treatment of sepsis is cost effective and reduces the number of hospital days of patients in the ICU. There is no single procedure that is sufficient for sepsis diagnosis, and combinations of approaches are needed. Detecting anomalies in patient time series data could help speed the development of some decisions. However, our algorithm must be viewed as complementary to other approaches based on laboratory values and physician judgments. The focus of this work is to develop a hybrid method for detecting anomalies that occur, for example, in multidimensional medical signals, sensor signals, or other time series in business and nature. The novelty of our approach lies in the extension and combination of existing approaches: Statistics, Self Organizing Maps and Linear Discriminant Analysis in a unique and unprecedented way with the goal of identifying different types of anomalies in real-time measurement data and defining the point where the anomaly occurs. The proposed algorithm not only has the full potential to detect anomalies, but also to find real points where an anomaly starts.
With less than 6% of total global water resources but one fifth of the global population, China is facing serious challenges for its water resources management, particularly in rural areas due to the long-standing urban-rural dualistic structure and the economic-centralized developmental policies. This paper addresses the key water crises in rural China including potable water supply, wastewater treatment and disposal, water for agricultural purposes, and environmental concerns, and then analyzes the administrative system on water resources from the perspective of characteristics of the current administrative system and regulations; finally, synthetic approaches to solve water problems in rural China are proposed with regard to institutional reform, regulation revision, economic instruments, technology innovation and capacity-building. These recommendations provide valuable insights to water managers in rural China so that they can identify the most appropriate pathways for optimizing their water resources, reducing the total wastewater discharge and improving their water-related ecosystem.
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.
Concerns over climate change, air pollution, and oil supply have stimulated the market for battery electric vehicles (BEVs). The environmental impacts of BEVs are typically evaluated through a standardized life-cycle assessment (LCA) methodology. Here, the LCA literature was surveyed with the objective to sketch the major trends and challenges in the impact assessment of BEVs. It was found that BEVs tend to be more energy efficient and less polluting than conventional cars. BEVs decrease exposure to air pollution as their impacts largely result from vehicle production and electricity generation outside of urban areas. The carbon footprint of BEVs, being highly sensitive to the carbon intensity of the electricity mix, may decrease in the nearby future through a shift to renewable energies and technology improvements in general. A minority of LCAs covers impact categories other than carbon footprint, revealing a mixed picture. Up to date little attention is paid so far in LCA to the efficiency advantage of BEVs in urban traffic, the gap between on-road and certified energy consumption, the local exposure to air pollutants and noise and the aging of emissions control technologies in conventional cars. Improvements of BEV components, directed charging, second-life reuse of vehicle batteries, as well as vehicle-to-home and vehicle-to-grid applications will significantly reduce the environmental impacts of BEVs in the future.
Purification of mRNA with oligo(dT)-functionalized magnetic particles involves a series of magnetic separations for buffer exchange and washing. Magnetic particles interact and agglomerate with each other when a magnetic field is applied, which can result in a decreased total surface area and thus a decreased yield of mRNA. In addition, agglomeration may also be caused by mRNA loading on the magnetic particles. Therefore, it is of interest how the individual steps of magnetic separation and subsequent redispersion in the buffers used affect the particle size distribution. The lysis/binding buffer is the most important buffer for the separation of mRNA from the multicomponent suspension of cell lysate. Therefore, monodisperse magnetic particles loaded with mRNA were dispersed in the lysis/binding buffer and in the reference system deionized water, and the particle size distributions were measured. A concentration-dependent agglomeration tendency was observed in deionized water. In contrast, no significant agglomeration was detected in the lysis/binding buffer. With regard to magnetic particle recycling, the influence of different storage and drying processes on particle size distribution was investigated. Agglomeration occurred in all process alternatives. For de-agglomeration, ultrasonic treatment was examined. It represents a suitable method for reproducible restoration of the original particle size distribution.
Unternehmen verlassen sich bei der Entwicklung von Software und Lösungen häufig auf das Know-How externer Dienstleister. Moderne Arbeits- und Kollaborationsformen verändern gleichzeitig die Entwicklung von Produkten und Dienstleistungen. Wie beeinflussen diese Trends die Zusammenarbeit und Kooperation zwischen Unternehmen und ihren externen agilen Dienstleistern? Ziel dieser wissenschaftlichen Arbeit ist es herauszufinden, welche Schritte unternehmen müssen, um agiles Arbeiten und die Zusammenarbeit mit externen Dienstleistern umzusetzen. Daher wurde anhand einer Fallstudie inklusive einer qualitativen Befragung herausgefunden und aufgezeigt, welche Maßnahmen und Handlungen Unternehmen ergreifen müssen, um das Ziel einer effektiven Umsetzung einer agilen Zusammenarbeit und Kooperation zu erreichen. Drei Kernthemen wurden identifiziert, auf deren Grundlage die Forschungsfragen zu den Maßnahmen beantwortet werden: Erstens, welche Möglichkeiten Unternehmen haben, ein internes agiles Setup zu implementieren, um mit agilen Dienstleistern auf Augenhöhe zusammenzuarbeiten. Zweitens, welche Vertragsvarianten die agile Zusammenarbeit unterstützen und verbessern können und drittens, welche agilen Techniken und Methoden in der agilen Zusammenarbeit eingesetzt werden sollten. Die Ergebnisse der Fallstudien bestätigen die Annahme, dass die drei identifizierten Kernthemen für eine effektive Zusammenarbeit im agilen Umfeld essenziell sind. Während einerseits nachgewiesen wurde, dass sich die Vertragsanforderungen hinsichtlich ihrer Flexibilität und Anpassungsfähigkeit veränderten, wurde andererseits auch nachgewiesen, dass das interne Setup agile Treiber, Techniken und Methoden erfordert, um eine effektive Zusammenarbeit mit agilen Dienstleistern zu ermöglichen. Dieser Artikel gibt einen Überblick über die wichtigsten Inhalte innerhalb der drei genannten Kernthemen und gibt Unternehmen zudem Hinweise, wie sie eine Basis für eine effektive Zusammenarbeit schaffen können.
Containerization is one of the most important topics for modern data centers and web developers. Since the number of containers on one- and multi-node systems is growing, knowledge about the energy consumption behavior of single web-service containers is essential in order to save energy and, of course, money. In this article, we are going to show how the energy consumption behavior of single containerized web services/web apps changes while creating replicas of the service in order to scale and balance the web service.
This paper analyzed the characteristic of the tourism destination ecosystem from perspective of entropy in Dunhuang City. Given these circumstances, an evaluation index system that considers the potential of sustainable development was formed based on dissipative structure and entropy change for the tourism destination ecosystem. The sustainable development potential evaluation model for tourism destination ecosystem was built up based on information entropy. Then, we analyzed each indicator impact for the sustainable development potential and proposed some measures for the tourism destination ecosystem. The conclusions include: (a) the requirements of Dunhuang tourism destination ecosystem on the natural ecosystem continuously grew between 2000 and 2012; (b) The sustainable development potential of the Dunhuang tourism destination ecosystem was on an oscillation upward trend during the study period, which is dependent on government attention, and pollution problems were improved.
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.
One key for successful and fluent human-robot-collaboration in disassembly processes is equipping the robot system with higher autonomy and intelligence. In this paper, we present an informed software agent that controls the robot behavior to form an intelligent robot assistant for disassembly purposes. While the disassembly process first depends on the product structure, we inform the agent using a generic approach through product models. The product model is then transformed to a directed graph and used to build, share and define a coarse disassembly plan. To refine the workflow, we formulate "the problem of loosening a connection and the distribution of the work" as a search problem. The created detailed plan consists of a sequence of actions that are used to call, parametrize and execute robot programs for the fulfillment of the assistance. The aim of this research is to equip robot systems with knowledge and skills to allow them to be autonomous in the performance of their assistance to finally improve the ergonomics of disassembly workstations.
Global vernetzte Supply Chains (SC) führen bei den Unternehmen zu geringeren Kosten, aber zugleich erhöhen diese auch die Abhängigkeit ggü. den Lieferanten und die Störanfälligkeit der SCs wird erhöht. In den vergangenen Jahren haben zudem die Unsicherheiten für die SCs stark zugenommen. Treiber waren hier u.a. der Brexit, Handelskonflikte oder auch die Corona-Pandemie. In diesem Zusammenhang steht verstärkt die Entwicklung neuer SC-Strategien im Fokus der Unternehmen. Dabei wird stark auf die Verbesserung der Resilienz der SCs geachtet, um dadurch u.a. die Risiken für die SCs zu reduzieren. Die Arbeit beschäftigt sich mit den Auswirkungen steigender Unsicherheiten auf das Design sowie die Resilienz von SCs und hat das Ziel zu ermitteln, ob es Änderungen in der SC bedarf, um auf die Auswirkungen steigender Unsicherheiten zu reagieren und wie die Resilienz zukünftig sichergestellt werden kann (Trade-off zwischen Resilienz und Kosteneffizienz). Im Rahmen der Untersuchung erfolgte eine qualitative Forschung in Form von Experteninterviews, da so u.a. spezifische Meinungen oder auch Begründungen und Einstellungen von bestimmten Personen zu den vorliegenden Thematiken ermittelt werden können. Die Ergebnisse zeigen, dass die Kosten weiterhin meist der entscheidende Aspekt sind und es mehr Transparenz, Flexibilität sowie ein besseres Risikomanagement nötig ist. Des Weiteren bedarf es zukünftig u.a. einer größeren Berücksichtigung von Unsicherheiten, eine Erhöhung von Sicherheitsbeständen sowie eine Verringerung der Komplexität der SCs und u.U. mehr Local Sourcing. Es empfiehlt sich eine weitere Untersuchung hinsichtlich der Kosten, die durch Resilienz-Instrumente sowie durch fehlende Resilienz entstehen können, durchzuführen.
Der Automobilhandel befindet sich in einer momentanen Phase des Umbruchs. Der Trend zum Kauferlebnis online, verstärkt durch die Pandemie, und die durch die Dieselproblematik entfachte Diskussion zum Emissionsausstoß und Fahrverboten für Verbrennungsmotoren im Fahrzeugbereich, treibt den Wandel zu elektrischen Antriebstechnologien an. Durch diese Umstellung ergeben sich neue Möglichkeiten für das Fahrzeug im Bereich der Digitalisierung. Die branchenübergreifende Digitalisierung findet sich auch in den neuen Vertriebssystemen der Automobilhersteller wieder. Hierbei ist zudem eine Veränderung der Absatzkanäle zu beobachten. Der Kunde weicht beim Autokauf davon ab, exklusiv zu dem Händler seines Vertrauens zu gehen und sich hier durch den gesamten Verkaufsprozess hinweg betreuen zu lassen. Vielmehr verschieben sich einzelne Teilbereiche in das Internet. So müssen etablierte Prozesse neugestaltet werden, um weiterhin im Markt und im Wettbewerb bestehen zu können.
Das Ziel dieser Arbeit ist die Beantwortung zweier Forschungsfragen. Zum einen soll der Status-Quo der Digitalisierung von Vertriebsinstrumenten im Autohaus untersucht werden. Konkret geht es dabei um die Frage, in welcher Art und Weise die Digitalisierung den Vertrieb beeinflusst und welche Effekte sich daraus ergeben. Zum anderen stellt sich die Frage wie die Digitalisierung in Form des Internets, als Absatzkanal für die Automobilindustrie geeignet ist, und ob derart komplexe Produkte wie ein Neufahrzeug über diesen Kanal abgesetzt werden können. Weiterhin soll betrachtet werden, welche Veränderungen in den Vertriebsstrukturen das beim stationären Autohändler verursacht.
Die Arbeit befasst sich ausschließlich mit der Digitalisierung bei Vertragshändlern mit Volumenzielen im Neuwagen-Bereich. Damit sind die Luxus- oder Premiummarken ausgeschlossen. Außerdem wird im Autohaus in zwei Kundenkategorien unterschieden. Es gibt die Großabnehmer und die Einzelkunden. Die Großabnehmer sind Unternehmen, die durch eine vordefinierte Anzahl an Mindestabnahmen einen besonderen Status und Konditionen genießen. Die Einzelkunden sind private Abnehmer oder gewerbliche Kunden, die die Mindestabnahme nicht erreichen. Die Arbeit beschränkt sich ausschließlich auf die Analyse und Auswirkungen der Veränderungen im Segment der Einzelkunden.
Deep brain stimulation (DBS) is an established therapy for movement disorders such as in Parkinson's disease (PD) and essential tremor (ET). Adjusting the stimulation parameters, however, is a labour-intensive process and often requires several patient visits. Physicians prefer objective tools to improve (or maintain) the performance in DBS. Wearable motion sensors (WMS) are able to detect some manifestations of pathological signs, such as tremor in PD. However, the interpretation of sensor data is often highly technical and methods to visualise tremor data of patients undergoing DBS in a clinical setting are lacking. This work aims to visualise the dynamics of tremor responses to DBS parameter changes with WMS while patients performing clinical hand movements. To this end, we attended DBS programming sessions of two patients with the aim to visualise certain aspects of the clinical examination. PD tremor and ET were effectively quantified by acceleration amplitude and frequency. Tremor dynamics were analysed and visualised based on setpoints, movement transitions and stability aspects. These methods have not yet been employed and examples demonstrate how tremor dynamics can be visualised with simple analysis techniques. We therefore provide a base for future research work on visualisation tools in order to assist clinicians who frequently encounter patients for DBS therapy. This could lead to benefits in terms of enhanced evaluation of treatment efficacy in the future.
Abstract: This paper is about detecting the difference between fully-random and semi-random shuffleing data sets, with the use of unsupervised learning algorithms. Because of the limits of the k-means algorithm alone, a recurrent autoencoder is used for feature extraction to improve the results of k-means. In the next step the autoencoder alone is used for clustering.
Introduction: In the last years, machine learning has been used more and more in different areas and it is also appropriate for for pattern recognition in data. Random data is characterized through the missing of defined patterns. Permutations without repetitions have the highest amount of entropy for a sequence of its length, which is similar to random data according to Andrei Kolmogorov, who states that random data have the highest amount of information and can’t be compressed. Therefore, this paper analyses the difference between random permutations and good shuffled permutations, which have some remaining patterns left. This is done via a recurrent autoencoder.
The services sector is also called “tertiary sector” and has become increasingly important in the last few decades. The process of this structural change occurrence is characterized by a significant increase in employment in the services sector. On the other hand, the former economic importance in traditional areas, such as agriculture and forestry, as well as manufacturing, is declining. In this article the research field of the service sector is shown beginning from the 70s up to the present. The goal of the article is to demonstrate the necessity of service engineering research.