The Potential of AutoML for Demand Forecasting

In demand forecasting, which can depend on various internal and external factors, machine learning (ML) methods can capture complex patterns and enable precise forecasts. Accurate forecasts facilitate targeted, demand-oriented planning and control of production and underline the importance of this task. The implementation of ML-algorithms requires knowledge of the specific domain as well as knowledge of data science and involves an elaborate set up process. This often makes the application of ML to potential industrial problems economically unattractive. The major skills shortage in the field of data science further exacerbates this. Automation and better accessibility of ML methods is therefore a key prerequisite for widespread use. This is where the principle of automated ML (AutoML) comes in, automating large parts of a ML pipeline and thus leading to a reduction in human labour input. Therefore, the aim of the publication is to investigate the extent to which AutoML solutions can generate added value for demand planning in the context of production planning and control. For this purpose, publicly available datasets deriving from Walmart as well as an anonymised manufacturing company are used for short-term and long-term forecasting. The AutoML tools from Microsoft, Dataiku and Google conduct these forecasts. Statistical models serve as benchmarks. The results show that the forecasting quality varies depending on the software, the input data and their demand patterns. Overall, the prepared models from Microsoft show the most accurate results in average and the potential of AutoML becomes particularly clear in the short-term forecast. This paper enriches the research field through its broad application, giving valuable insights into the use of AutoML tools for demand planning. The resulting understanding of limitations and benefits of AutoML tools for the case studies presented fosters their suitable application in practice

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.Peer reviewe

Review existing strategies to improve circularity, sustainability and resilience of wind turbine blades – A comparison of research and industrial initiatives in Europe

To become the first climate-neutral continent by 2050, the EU is committing to ambitious targets such as reducing greenhouse gas emissions by at least 55% by 2030 (compared to 1990) and increasing the share of renewable energies in the EU energy mix to 45% by 2030. To meet these targets, the wind industry has to grow significantly while ensuring enough resources across the supply chains to scale the wind energy market and by striving to reduce its already shallow environmental impact. As material extraction and the production of wind turbines cause the most emissions, costs and risks on the scarcity of resources, the implementation of a circular economy could support the wind industry in meeting the EU target and improving its sustainability and resilience. However, the circular economy is a complex concept, and it can be challenging to translate it into precise action points, objectives and measurements. To clarify how a circular economy can support the wind industry, this paper takes the example of wind turbine blades, and it establishes a structured overview of research results and industrial initiatives aiming at implementing circularity for improving sustainability and resilience. The overview is used to investigate if objectives and clear actions are stated and how those differ between research and industry. By identifying gaps, future research and industry initiatives can be directed towards closing the bigger picture. The results show that many initiatives are ongoing, but only some circular strategies are comprehensively investigated, and clear objectives and measurements often remain to be included. The industry and research progressed the furthest on recycling. Future research and industry activities should further follow the path of closing the loop but need to also concentrate on reducing material use, extending the lifetime of blades and enabling a second lifecycle of blades

Do We Really Know The Benefit Of Machine Learning In Production Planning And Control? A Systematic Review Of Industry Case Studies.

The field of machine learning (ML) is of specific interest for production companies as it displays a perspective to handle the increased complexity within their production planning and control (PPC) processes in an economic and ecologic effective as well as efficient way. Several studies investigate applications of ML to different use cases. However, the research field lacks in research on industry case studies. A broad understanding from a practical perspective and in this context, an evaluation from a data mining and business standpoint is key for gaining trust in ML solutions. Therefore, this paper gives a comprehensive overview of evaluation dimensions and outlines the current state of research in ML-PPC by conducting a systematic research overview. First, the present work provides key dimensions of business and data mining objectives as evaluation metric. Business objectives are clustered into economic, ecological and social objectives and data mining objectives are grouped into prediction accuracy, model’s explainability, model’s runtime, and model’s energy use. Secondly, the systematic literature review identifies 45 industry case studies in MLPPC from 2010-2020. The work shows that the scientific publications only rarely reflect in detail on a wide range of evaluation metrics. Instead, researchers mainly focus on prediction accuracy and seldom investigate the effect of their results to a business context. Positively, some papers reflect on further aspects and can inspire future research. This resulting transparency supports decision makers of companies in their prioritization process when setting up a future ML-roadmap. In addition, the research gaps identified herein invite researchers to join the research field

Nerve Ultrasound as Helpful Tool in Polyneuropathies

Background: Polyneuropathies (PNP) are a broad field of diseases affecting millions of people. While the symptoms presented are mostly similar, underlying causes are abundant. Thus, early identification of treatable causes is often difficult. Besides clinical data and basic laboratory findings, nerve conduction studies are crucial for etiological classification, yet limited. Besides Magnetic Resonance Imaging (MRI), high-resolution nerve ultrasound (HRUS) has become a noninvasive, fast, economic and available tool to help distinguish different types of nerve alterations in neuropathies. Methods: We aim to describe typical ultrasound findings in PNP and patterns of morphological changes in hereditary, immune-mediated, diabetic, metabolic and neurodegenerative PNP. Literature research was performed in PubMed using the terms ‘nerve ultrasound’, neuromuscular ultrasound, high-resolution nerve ultrasound, peripheral nerves, nerve enlargement, demyelinating, hereditary, polyneuropathies, hypertrophy’. Results: Plenty of studies over the past 20 years investigated the value of nerve ultrasound in different neuropathies. Next to nerve enlargement, patterns of nerve enlargement, echointensity, vascularization and elastography have been evaluated for diagnostic terms. Furthermore, different scores have been developed to distinguish different etiologies of PNP. Conclusions: Where morphological alterations of the nerves reflect underlying pathologies, early nerve ultrasound might enable a timely start of available treatment and also facilitate follow up of therapy success

Nerve Ultrasound as Helpful Tool in Polyneuropathies

Background: Polyneuropathies (PNP) are a broad field of diseases affecting millions of people. While the symptoms presented are mostly similar, underlying causes are abundant. Thus, early identification of treatable causes is often difficult. Besides clinical data and basic laboratory findings, nerve conduction studies are crucial for etiological classification, yet limited. Besides Magnetic Resonance Imaging (MRI), high-resolution nerve ultrasound (HRUS) has become a noninvasive, fast, economic and available tool to help distinguish different types of nerve alterations in neuropathies. Methods: We aim to describe typical ultrasound findings in PNP and patterns of morphological changes in hereditary, immune-mediated, diabetic, metabolic and neurodegenerative PNP. Literature research was performed in PubMed using the terms ‘nerve ultrasound’, neuromuscular ultrasound, high-resolution nerve ultrasound, peripheral nerves, nerve enlargement, demyelinating, hereditary, polyneuropathies, hypertrophy’. Results: Plenty of studies over the past 20 years investigated the value of nerve ultrasound in different neuropathies. Next to nerve enlargement, patterns of nerve enlargement, echointensity, vascularization and elastography have been evaluated for diagnostic terms. Furthermore, different scores have been developed to distinguish different etiologies of PNP. Conclusions: Where morphological alterations of the nerves reflect underlying pathologies, early nerve ultrasound might enable a timely start of available treatment and also facilitate follow up of therapy success

The tomato genome sequence provides insights into fleshy fruit evolution

Tomato (Solanum lycopersicum) is a major crop plant and a model system for fruit development. Solanum is one of the largest angiosperm genera1 and includes annual and perennial plants from diverse habitats. Here we present a high-quality genome sequence of domesticated tomato, a draft sequence of its closest wild relative, Solanum pimpinellifolium2, and compare them to each other and to the potato genome (Solanum tuberosum). The two tomato genomes show only 0.6% nucleotide divergence and signs of recent admixture, but show more than 8% divergence from potato, with nine large and several smaller inversions. In contrast to Arabidopsis, but similar to soybean, tomato and potato small RNAs map predominantly to gene-rich chromosomal regions, including gene promoters. The Solanum lineage has experienced two consecutive genome triplications: one that is ancient and shared with rosids, and a more recent one. These triplications set the stage for the neofunctionalization of genes controlling fruit characteristics, such as colour and fleshiness

Clinical manifestations of intermediate allele carriers in Huntington disease

Objective: There is controversy about the clinical consequences of intermediate alleles (IAs) in Huntington disease (HD). The main objective of this study was to establish the clinical manifestations of IA carriers for a prospective, international, European HD registry. Methods: We assessed a cohort of participants at risk with <36 CAG repeats of the huntingtin (HTT) gene. Outcome measures were the Unified Huntington's Disease Rating Scale (UHDRS) motor, cognitive, and behavior domains, Total Functional Capacity (TFC), and quality of life (Short Form-36 [SF-36]). This cohort was subdivided into IA carriers (27-35 CAG) and controls (<27 CAG) and younger vs older participants. IA carriers and controls were compared for sociodemographic, environmental, and outcome measures. We used regression analysis to estimate the association of age and CAG repeats on the UHDRS scores. Results: Of 12,190 participants, 657 (5.38%) with <36 CAG repeats were identified: 76 IA carriers (11.56%) and 581 controls (88.44%). After correcting for multiple comparisons, at baseline, we found no significant differences between IA carriers and controls for total UHDRS motor, SF-36, behavioral, cognitive, or TFC scores. However, older participants with IAs had higher chorea scores compared to controls (p 0.001). Linear regression analysis showed that aging was the most contributing factor to increased UHDRS motor scores (p 0.002). On the other hand, 1-year follow-up data analysis showed IA carriers had greater cognitive decline compared to controls (p 0.002). Conclusions: Although aging worsened the UHDRS scores independently of the genetic status, IAs might confer a late-onset abnormal motor and cognitive phenotype. These results might have important implications for genetic counseling. ClinicalTrials.gov identifier: NCT01590589