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The body tracking systems on the current market offer a wide range of options for tracking the movements of objects, people, or extremities. The precision of this technology is often limited and determines its field of application. This work aimed to identify relevant technical and environmental factors that influence the performance of body tracking in industrial environments. The influence of light intensity, range of motion, speed of movement and direction of hand movement was analyzed individually and in combination. The hand movement of a test person was recorded with an Azure Kinect at a distance of 1.3 m. The joints in the center of the hand showed the highest accuracy compared to other joints. The best results were achieved at a luminous intensity of 500 lx, and movements in the x-axis direction were more precise than in the other directions. The greatest inaccuracy was found in the z-axis direction. A larger range of motion resulted in higher inaccuracy, with the lowest data scatter at a 100 mm range of motion. No significant difference was found at hand velocity of 370 mm/s, 670 mm/s and 1140 mm/s. This study emphasizes the potential of RGB-D camera technology for gesture control of industrial robots in industrial environments to increase efficiency and ease of use.

In recent years, the use of indoor localization techniques has increased significantly in a large number of areas, including industry and healthcare, primarily for monitoring and tracking reasons. From the field of radio frequency technologies, an ultra-wideband (UWB) system offers comparatively high accuracy and is therefore suitable for use cases with high precision requirements in position determination, for example for localizing an employee when interacting with a machine tool on the shopfloor. Indoor positioning systems with radio signals are influenced by environmental obstacles. Although the influence of building structures like walls and furniture was already analysed in the literature before, the influence of metal machine tools was not yet evaluated concerning the accuracy of the position determination. Accordingly, the research question for this article is defined: To what extent is the positioning accuracy of the UWB system influenced by a metal machine tool?The accuracy was measured in a test setup, which consists of a total of four scenarios in a production environment. For this purpose, the visual contact between the transmitter and the receiver modules, including the influence of further interfering factors of a commercially available indoor positioning system, was improved step by step from scenario 1 to 4. A laser tracker was used as the reference measuring device. The data was analysed based on the type A evaluation of standard uncertainty according to the guide to the expression of uncertainty in measurement (GUM). It was possible to show an improvement in standard deviation from 87.64cm±32.27cm to 6.07cm±2.24cm with confidence level 95% and thus provides conclusions about the setup of an indoor positioning system on the shopfloor.

Changeover times are an important element when evaluating the Overall Equipment Effectiveness (OEE) of a production machine. The article presents a machine learning (ML) approach that is based on an external sensor setup to automatically detect changeovers in a shopfloor environment. The door statuses, coolant flow, power consumption, and operator indoor GPS data of a milling machine were used in the ML approach. As ML methods, Decision Trees, Support Vector Machines, (Balanced) Random Forest algorithms, and Neural Networks were chosen, and their performance was compared. The best results were achieved with the Random Forest ML model (97% F1 score, 99.72% AUC score). It was also carried out that model performance is optimal when only a binary classification of a changeover phase and a production phase is considered and less subphases of the changeover process are applied.

While new production areas (greenfields) have state-of-the-art technologies for implementing digitalization, existing production areas (brownfields) and devices must first be upgraded with technologies before digitalization can be implemented. The aim of this research work is to use a case study to identify the differences in the implementation of machine learning (ML) projects in brownfields and greenfields. For this purpose, an ML application for the detection of changeover times on milling machines is implemented and analyzed in the brownfield and greenfield scenarios as well as a combined scenario. Particular attention is paid to the selection of sensors and features. It was found that the abundant availability of features in the greenfield scenario poses pitfalls when creating ML projects if the underlying sensors cannot be checked for their suitability. For the changeover detector use case, the best model quality was achieved for the combined scenario, followed by the greenfield scenario.

The performance indicator, Overall Equipment Effectiveness (OEE), is one of the most important ones for production control, as it merges information of equipment usage, process yield, and product quality. The determination of the OEE is oftentimes not transparent in companies, due to the heterogeneous data sources and manual interference. Furthermore, there is a difference in present guidelines to calculate the OEE. Due to a big amount of sensor data in Cyber Physical Production Systems, Machine Learning methods can be used in order to detect several elements of the OEE by a trained model. Changeover time is one crucial aspect influencing the OEE, as it adds no value to the product. Furthermore, changeover processes are fulfilled manually and vary from worker to worker. They always have their own procedure to conduct a changeover of a machine for a new product or production lot. Hence, the changeover time as well as the process itself vary. Thus, a new Machine Learning based concept for identification and characterization of machine set-up actions is presented. Here, the issue to be dealt with is the necessity of human and machine interaction to fulfill the entire machine set-up process. Because of this, the paper shows the use case in a real production scenario of a small to medium size company (SME), the derived data set, promising Machine Learning algorithms, as well as the results of the implemented Machine Learning model to classify machine set-up actions.

In the context of environmental protection, the construction industry plays a key role with significant CO2 emissions from mineral-based construction materials. Recycling these materials is crucial, but the presence of hazardous substances, i.e., in older building materials, complicates this effort. To be able to legally introduce substances into a circular economy, reliable predictions within minimal possible time are necessary. This work introduces a machine learning approach for detecting trace quantities (≥0.06 wt%) of minerals, exemplified by siderite in calcium carbonate mixtures. The model, trained on 1680 X-ray powder diffraction datasets, provides dependable and fast predictions, eliminating the need for specialized expertise. While limitations exist in transferability to other mineral traces, the approach offers automation without expertise and a potential for real-world applications with minimal prediction time.

The cerebral thrombin system is activated in the early stage after intracerebral hemorrhage (ICH). Expression of thrombin leads to concentration dependent secondary neuronal damage and detrimental neurological outcome. In this study we aimed to investigate the impact of thrombin concentration and activity in the cerebrospinal fluid (CSF) of patients with ICH on clinical outcome. Patients presenting with space-occupying lobar supratentorial hemorrhage requiring extra-ventricular drainage (EVD) were included in our study. The CSF levels of thrombin, its precursor prothrombin and the Thrombin-Antithrombin complex (TAT) were measured using enzyme linked immune sorbent assays (ELISA). The oxidative stress marker Superoxide dismutase (SOD) was assessed in CSF. Initial clot size and intraventricular hemorrhage (IVH) volume was calculated based on by computerized tomography (CT) upon admission to our hospital. Demographic data, clinical status at admission and neurological outcome were assessed using the modified Rankin Scale (mRS) at 6-weeks and 6-month after ICH. Twenty-two consecutive patients (9 females, 11 males) with supratentorial hemorrhage were included in this study. CSF concentrations of prothrombin (p < 0.005), thrombin (p = 0.005) and TAT (p = 0.046) were statistical significantly different in patients with ICH compared to non-hemorrhagic CSF samples. CSF concentrations of thrombin 24h after ICH correlated with the mRS index after 6 weeks (r2 = 0.73; < 0.005) and 6 months (r2 = 0.63; < 0.005) after discharge from hospital. Thrombin activity, measured via TAT as surrogate parameter of coagulation, likewise correlated with the mRS at 6 weeks (r2 = 0.54; < 0.01) and 6 months (r2 = 0.66; < 0.04). High thrombin concentrations coincide with higher SOD levels 24h after ICH (p = 0.01). In this study we found that initial thrombin concentration and activity in CSF of ICH patients did not correlate with ICH and IVH volume but are associated with a poorer functional neurological outcome. These findings support mounting evidence of the role of thrombin as a contributor to secondary injury formation after ICH.

Climate adaptation and learning support organizations in coping with the current and projected impacts of climate change by identifying challenges as opportunities, ensuring business continuity and increasing their economic efficiency. In addition to material resources, climate adaptation requires knowledge, technical know-how and the ability to learn. Our article examines the relationship between climate adaptation and organizational learning, as the consideration of climate adaptation in the long term and with regard to organizational learning or reorientation is still very little represented in research. Therefore, a quantitative study is conducted in order to determine whether companies already have climate-related structures conducive to learning, whether they take responsibility for the learning object (climate change), and which elements limit the learning process. The survey of 288 companies and craft businesses in a German industrial region shows that intangible resources such as a sense of responsibility, a positive attitude among managers and shared values have a significant influence on how companies deal with climate change. Managers are key players in setting corporate goals, developing strategies and monitoring functional processes. The study shows that the number of climate-related measures taken is increasing due to higher resource capacities. As we draw conclusions about the changing learning requirements, conditions and media in the face of climate change, the results can provide relevant suggestions for researchers and practitioners to understand climate adaptation as a valuable and strategic challenge and to improve the resilience of the organization itself.

Background The main objective of every new development in total hip arthroplasty (THA) is the longest possible survival of the implant. Periprosthetic stress shielding is a scientifically proven phenomenon which leads to inadvertent bone loss. So far, many studies have analysed whether implanting different hip stem prostheses result in significant preservation of bone stock. The aim of this preclinical study was to investigate design-depended differences of the stress shielding effect after implantation of a selection of short-stem THA-prostheses that are currently available. Methods Based on computerised tomography (CT), a finite elements (FE) model was generated and a virtual THA was performed with different stem designs of the implant. Stems were chosen by osteotomy level at the femoral neck (collum, partial collum, trochanter sparing, trochanter harming). Analyses were performed with previously validated FE models to identify changes in the strain energy density (SED). Results In the trochanteric region, only the collum-type stem demonstrated a biomechanical behaviour similar to the native femur. In contrast, no difference in biomechanical behaviour was found between partial collum, trochanter harming and trochanter sparing models. All of the short stem-prostheses showed lower stress-shielding than a standard stem. Conclusion Based on the results of this study, we cannot confirm that the design of current short stem THA-implants leads to a different stress shielding effect with regard to the level of osteotomy. Somehow unexpected, we found a bone stock protection in metadiaphyseal bone by simulating a more distal approach for osteotomy. Further clinical and biomechanical research including long-term results is needed to understand the influence of short-stem THA on bone remodelling and to find the optimal stem-design for a reduction of the stress shielding effect.

Climate adaptation and learning support organizations in dealing with the current and projected consequences of climate change by recognizing challenges as opportunities, ensuring business continuity and increasing their economic efficiency. Beside material resources, climate adaptation requires knowledge, technical expertise, and learning capacity. Our contribution examines the interrelationship between climate adaptation and organizational learning, as the consideration of climate adaptation in the long term and with respect to organizational learning or realignment is still represented very little in research. Thus, we conduct a regional study to analyze to what extent the companies already have climate-related structures conducive to learning, to what extent they take responsibility in terms of the learning object (climate change), and which elements prove to be limiting here. The survey of 288 companies and handicraft companies illustrate that intangible resources such as a sense of responsibility, executives' positive attitude and common values have a significant influence on the way companies deal with climate change. Executives characterize key actors for organizational goal setting, strategic development, and functional process monitoring. The study shows that the number of climate-related measures taken increases due to higher resource capacities. As we draw conclusions about changing learning requirements, conditions, and mediums in the face of climate change, findings can provide relevant inspiration for scholars or practitioners to perceive climate adaptation as a valuable and strategic challenge to enhance the organization's resilience per se.