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Concerning human and environmental health, safe alternatives to synthetic pesticides are urgently needed. Many of the currently used synthetic pesticides are not authorized for application in organic agriculture. In addition, the developed resistances of various pests against classical pesticides necessitate the urgent demand for efficient and safe products with novel modes of action. Botanical pesticides are assumed to be effective against various crop pests, and they are easily biodegradable and available in high quantities and at a reasonable cost. Many of them may act by diverse yet unexplored mechanisms of action. It is therefore surprising that only few plant species have been developed for commercial usage as biopesticides. This article reviews the status of botanical pesticides, especially in Europe and Mediterranean countries, deepening their active principles and mechanisms of action. Moreover, some constraints and challenges in the development of novel biopesticides are highlighted.
Background: Stratified care has the potential to be efficient in addressing the physical and psychosocial components of low back pain (LBP) and optimise treatment outcomes essential in low-income countries. This study aimed to investigate the perceptions of physiotherapists and patients in Nigeria towards stratified care for the treatment of LBP, exploring barriers and enablers to implementation.
Methods: A qualitative design with semistructured individual telephone interviews for physiotherapists and patients with LBP comprising research evidence and information on stratified care was adopted. Preceding the interviews, patients completed the Subgroups for Targeted Treatment tool. The interviews were recorded, transcribed and analysed following grounded theory methodology.
Results: Twelve physiotherapists and 13 patients with LBP participated in the study (11 female, mean age 42.8 (SD 11.47) years). Seven key categories emerged: recognising the need for change, acceptance of innovation, resistance to change, adapting practice, patient’s learning journey, trusting the therapist and needing conviction. Physiotherapists perceived stratified care to be a familiar approach based on their background training. The prevalent treatment tradition and the patient expectations were seen as major barriers to implementation of stratified care by the physiotherapists. Patients see themselves as more informed than therapists realise, yet they need conviction through communication and education to cooperate with their therapist using this approach. Viable facilitators were also identified as patients’ trust in the physiotherapist and adaptations in terms of training and modification of the approach to enhance its use.
Conclusion: Key barriers identified are the patients’ treatment expectations and physiotherapists’ adherence to the tradition of practice. Physiotherapists might facilitate implementation of the stratified care by communication, hierarchical implementation and utilisation of patients’ trust. Possibilities to develop a consensus on key strategies to overcome barriers and on utilisation of facilitators should be tested in future research.
Background: In recent years, the volume of medical knowledge and health data has increased rapidly. For example, the increased availability of electronic health records (EHRs) provides accurate, up-to-date, and complete information about patients at the point of care and enables medical staff to have quick access to patient records for more coordinated and efficient care. With this increase in knowledge, the complexity of accurate, evidence-based medicine tends to grow all the time. Health care workers must deal with an increasing amount of data and documentation. Meanwhile, relevant patient data are frequently overshadowed by a layer of less relevant data, causing medical staff to often miss important values or abnormal trends and their importance to the progression of the patient’s case.
Objective: The goal of this work is to analyze the current laboratory results for patients in the intensive care unit (ICU) and classify which of these lab values could be abnormal the next time the test is done. Detecting near-future abnormalities can be useful to support clinicians in their decision-making process in the ICU by drawing their attention to the important values and focus on future lab testing, saving them both time and money. Additionally, it will give doctors more time to spend with patients, rather than skimming through a long list of lab values.
Methods: We used Structured Query Language to extract 25 lab values for mechanically ventilated patients in the ICU from the MIMIC-III and eICU data sets. Additionally, we applied time-windowed sampling and holding, and a support vector machine to fill in the missing values in the sparse time series, as well as the Tukey range to detect and delete anomalies. Then, we used the data to train 4 deep learning models for time series classification, as well as a gradient boosting–based algorithm and compared their performance on both data sets.
Results: The models tested in this work (deep neural networks and gradient boosting), combined with the preprocessing pipeline, achieved an accuracy of at least 80% on the multilabel classification task. Moreover, the model based on the multiple convolutional neural network outperformed the other algorithms on both data sets, with the accuracy exceeding 89%.
Conclusions: In this work, we show that using machine learning and deep neural networks to predict near-future abnormalities in lab values can achieve satisfactory results. Our system was trained, validated, and tested on 2 well-known data sets to ensure that our system bridged the reality gap as much as possible. Finally, the model can be used in combination with our preprocessing pipeline on real-life EHRs to improve patients’ diagnosis and treatment.
In this paper, the mechanical damage behavior is investigated based on the characteristic roughness on the surface and the orientation of superficial structures. The main goal is to explore the surface roughness on mechanically loaded copper conductors as a lifetime indicator. For this purpose, copper conductors are mechanically stressed in accordance with EN 50,396 and then examined metallographically and microscopically. The microstructure examination shows that the roughness is caused by material extrusion and cracks due to work hardening in the surface area. Using confocal microscopy, it is shown for the first time that significant formation of surface roughness takes place over the service life of copper conductors. The roughness increases monotonically, but not linearly with number of cycles, due to internal microstructural processes and can be divided into three sections. First inspections of the conductor surface over lifetime show a correlation between the intensity of structures orientated 45° to the loading direction and the roughness. This phenomenon, already known from microscopic slip lines, is thus also evident in macroscopic roughness formation and is well founded by the research theory on material extrusion along dislocation lines. In summary, a lifetime determination is possible based on its developing roughness which enables the utilization as a sensor element.
In 2019 at IBM, it was found that there is a strong dependence on a few large banks in bank sales, and the growth targets of the sales division cannot be achieved due to the existing business with these same customers. To counteract this dependency, an NCA-specific sales team for the banking industry was established to support small and medium-sized banks with personal commitment and expertise and to develop them into long-term business partners of IBM. This research focuses on the development of a performance measurement system for NCA-Sales teams. It postulates the hypothesis that more effective and better-suited performance measurement systems can be developed for NCA-Sales of information technology towards financial institutions. Authors use the methodology of expert interviews and Mayrings qualitative content analysis to gain insights into the relevant factors that need to be considered when evaluating the performance of such sales teams. The paper identifies stakeholders, challenges, and goals that should be integrated into a performance measurement system as well as KPIs to measure them. The results are being consolidated into a conceptual sketch for an NCA-sales optimized PMS. The paper distinguishes itself from other research through an approach that gives detailed guidance for the practical implementation of its findings. The research was conducted with professionals in the IT sector; however, all of them were working for the same company, and the data was collected in the short span of one week as it was part of a research. The outcome can be used for further studies on how to effectively measure performance in NCA-Sales teams.
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.
In the single-processor scheduling problem with time restrictions there is one main processor and B resources that are used to execute the jobs. A perfect schedule has no idle times or gaps on the main processor and the makespan is therefore equal to the sum of the processing times. In general, more resources result in smaller makespans, and as it is in practical applications often more economic not to mobilize resources that will be unnecessary and expensive, we investigate in this paper the problem to find the smallest number B of resources that make a perfect schedule possible. We show that the decision version of this problem is NP-complete, derive new structural properties of perfect schedules, and we describe a Mixed Integer Linear Programming (MIP) formulation to solve the problem. A large number of computational tests show that (for our randomly chosen problem instances) only B=3 or B=4 resources are sufficient for a perfect schedule.
In this paper two simple synthetic aperture radar (SAR) methods are applied on data from a 24 GHz FMCW radar implemented on a linear drive for educational purposes. The data of near and far range measurements are evaluated using two different SAR signal processing algorithms featuring 2D-FFT and frequency back projection (FBP) method (Moreira et al., 2013). A comparison of these two algorithms is performed concerning runtime, image pixel size, azimuth and range resolution. The far range measurements are executed in a range of 60 to 135 m by monitoring cars in a parking lot. The near range measurement from 0 to 5 m are realised in a measuring chamber equipped with absorber foam and nearly ideal targets like corner reflectors. The comparison of 2D-FFT and FBP algorithm shows that both deliver good and similar results for the far range measurements but the runtime of the FBP algorithm is up to 150 times longer as the 2D-FFT runtime. In the near range measurements the FBP algorithm displays a very good azimuth resolution and targets which are very close to each other can be separated easily. In contrast to that the 2D-FFT algorithm has a lower azimuth resolution in the near range, thus targets which are very close to each other, merge together and cannot be separated.
While the contribution of renewable energy technologies to the energy system is increasing, so is its level of complexity. In addition to new types of consumer systems, the future system will be characterized by volatile generation plants that will require storage technologies. Furthermore, a solid interconnected system that enables the transit of electrical energy can reduce the need for generation and storage systems. Therefore, appropriate methods are needed to analyze energy production and consumption interactions within different system constellations. Energy system models can help to understand and build these future energy systems. However, although various energy models already exist, none of them can cover all issues related to integrating renewable energy systems. The existing research gap is also reflected in the fact that current models cannot model the entire energy system for very high shares of renewable energies with high temporal resolution (15 min or 1-h steps) and high spatial resolution. Additionally, the low availability of open-source energy models leads to a lack of transparency about exactly how they work. To close this gap, the sector-coupled energy model (UCB-SEnMod) was developed. Its unique features are the modular structure, high flexibility, and applicability, enabling it to model any system constellation and can be easily extended with new functions due to its software design. Due to the software architecture, it is possible to map individual buildings or companies and regions, or even countries. In addition, we plan to make the energy model UCB-SEnMod available as an open-source framework to enable users to understand the functionality and configuration options more easily. This paper presents the methodology of the UCB-SEnMod model. The main components of the model are described in detail, i.e., the energy generation systems, the consumption components in the electricity, heat, and transport sectors, and the possibilities of load balancing.
The following paper aims to find out consumers' expectations and attitudes towards the innovation "Metaverse". It will also be explored which role the Meta Group plays in mass adaption and how the company influences consumers' possible use and opinion on the project. These results are connected to the fashion industry, further exploring new types of products and a possible distribution channel. Therefore, this study is useful to developers of Metaverses and AR/VR products, the Meta Group, and fashion companies. The main results of this research are: Meta and the Metaverse are seen as critical, the required technology has not yet reached mainstream use, but interest is present. Digital fashion had participants divided, some not willing to spend any money and some already having spent over 100€, although the Metaverse's influence on future purchases is little. The Metaverse could serve as a new distribution channel for clothing products. To conduct this research Google Forms was used. The research is classified as survey-based. The biggest limitation is the nonexistence of the Metaverse as envisioned by Meta, making it hard for participants to answer some of the questions asked.
This paper presents a feasibility study for the production of recycled glycol modified polyethylene terephthalate (PETG) material for additive manufacturing. Past studies showed a variety of results for the recycling of 3D-printing material, therefore the precise effect on the material properties is not completely clear. For this work, PETG waste of the same grade was recycled once and further processed into 3D printing filament. The study compares three blend ratios between purchased plastic pellets and recycled pellets to determine the degradation effect of one recycling cycle and possible blend ratios to counter these effects. Furthermore, the results include a commercially available filament. The comparison uses the filament diameter, the dimensional accuracy of the printed test specimen and mechanical properties as quality criteria. The study shows that the recycled material has a minor decrease concerning the tensile strength and Young’s modulus.
Objective: In this article, the methods used to simplify the business modelling and founding of new companies are presented and critically reflected. Furthermore, it is discussed to what extent a specific method is advantageous, disadvantageous, applicable, not applicable, or even contradictory.
Methodology: The theoretical analysis is underpinned by a qualitative interview study asking company founders about applying the methods mentioned above. The work is based on scientific papers and books and is complemented by the data originating from a specially designed study.
Findings: The results conclude that business model founding instruments provide strategic guidelines favouring entrepreneurs, yet they turn out to be minor in its real-life significance as numerous factors rooted in different fields of expertise play in.
Value Added: The added value of this paper is in the elaboration of efficiency bringing and risk-minimizing components of the methods, respectively. Accordingly, managers and entrepreneurs of all industries are intended to be equipped with sufficient information content that eases the decision for or against one of the methods as realistic expectations considering the application are likely to emerge.
Recommendations: The limitations of this study are rooted in the chosen qualitative research since every interviewee is a subject to their individual perception.
Purpose: In this article, the canvas used to simplify business modeling of a platform and its visual depiction are put into the entrepreneurial context, and critically reflected accordingly. Furthermore, it is discussed to what extent the canvas is advantageous, disadvantageous, applicable, not applicable, or even contradictory.
Methodology: The analysis is based on theoretical research. Additionally, qualitative interviews with business founders were conducted.
Results: The results conclude that the canvas employed to ease the business model sharpening process supplies founders with essential aspects to cover, yet they are part of a large set of factors that play in.
Conclusion: The limitations of this study are rooted in the chosen research design based on the conceptual review.
This research paper discusses how RFID technology could improve current deposit bottle logistic processes in food retailing and which obstacles impede successful implementations. Research Methodology include desk research: Library, EBSCOhost, wiso.net, Google Scholar, Scientific Journals, Statista, SpringerLink. Implementation of RFID is potentially beneficial, but same obstacles remain outlook. To validate the conclusion further studying and practical proof of concept are necessary. Contributions: supply chain management, return logistics, food retail, beverage industry
The objective of this study is to allow a better understanding of the role of industry 4.0 technologies, especially filament extrusion technology in the reduction of costs, environmental impact, energy consumption, and the possibility to expand the range of printable materials. The study focuses on the desktop Filament Extruders available in the market now, where these machines are assessed and future possible modifications for these apparatuses are presented. The research leading to the publication of this study consists of a review of the existing literature, in addition, information from different extruders manufacturers’ websites has been used. The study has demonstrated that the extrusion of material at home is still not an exact science, and the process ends up costing the user large sums of money over time. However, there are still limitations to the use of this technology such as the lack of standardized extrusion settings, the necessity of pre-drying the pellets, and the complexity of the extruder cleaning process after each use.
The concept of Circular Economy (CE) is becoming increasingly important in the pursuit of more sustainable societies. CE strategies are being applied in the sustainable management of a plethora of areas, such as energy, water, food and eco-industrial parks. The present paper focuses on the question of how CE principles can support the sustainable management of water in the agricultural sector around the world, considering different legislative environments, water resources management guidelines, environmental stressors, and CE practices. Considering these practices and circumstances, seven countries were compared: Brazil, Germany, Japan, Mexico, Morocco, Portugal, and Taiwan. Together, CE experts in the seven countries developed a set of 44 criteria to assess each of these areas. Broader establishment and respect of water resources legislation was found to be strongly correlated with lower agricultural water use. While the application of CE practices was found to not be correlated with lower consumption, this is still novel in most countries. Based on the studied countries, it can be concluded that a global CE agenda has not been reached for water resources. Further application and variety of practices is required to better represent the impact of CE on a national scale, but local success stories could support the wider application of CE in agriculture. The findings and the framework of the study can be applied to other countries in directing CE strategies for more sustainable water use in agriculture. Increasing CE implementation, motivated by legislation and better management can help ensure water security throughout nations.
This paper is structured into two parts, which are closely related: first, the analysis of the parlamentary and governmental measures against the covid-19 pandemic; and second, the future regulatory framework about freedom of movement and other rights in the European area, according to the new European pact on migration and asylum.
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 purpose of this article is to evaluate optimal expected utility risk measures (OEU) in a risk-constrained portfolio optimization context where the expected portfolio return is maximized. We compare the portfolio optimization with OEU constraint to a portfolio selection model using value at risk as constraint. The former is a coherent risk measure for utility functions with constant relative risk aversion and allows individual specifications to the investor’s risk attitude and time preference. In a case study with three indices, we investigate how these theoretical differences influence the performance of the portfolio selection strategies. A copula approach with univariate ARMA-GARCH models is used in a rolling forecast to simulate monthly future returns and calculate the derived measures for the optimization. The results of this study illustrate that both optimization strategies perform considerably better than an equally weighted portfolio and a buy and hold portfolio. Moreover, our results illustrate that portfolio optimization with OEU constraint experiences individualized effects, e.g., less risk-averse investors lose more portfolio value in the financial crises but outperform their more risk-averse counterparts in bull markets.
Covid-19 outbreak had a huge impact on the economy worldwide as businesses had to close or cease their activities due to the lockdown regulations. The “luckiest” firms were able to operate but under restricted conditions. In order to avoid what certain authors called “bankruptcy epidemic” European countries took economic and fiscal measures to help companies compensate their financial losses. In addition to Government Grants, emergency legislations have been adopted with the aim to adapt insolvency and restructuring procedures to the sanitary situation and specific rules relating to company Law have also been implemented. This paper deals with the measures taken by the state of Luxembourg and gives a brief overview of the legal amendments.