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Zusammenfassung Im Beitrag wird die Weiterbildungsarbeit im Rahmen der betrieblichen Sozialberatung untersucht. Als empirische Basis dient eine qualitative Querschnittstudie, in der 16 Experteninterviews mit Fachkräften der betrieblichen Sozialberatung in Deutschland geführt wurden. Die Studie erweitert den Stand der Forschung zur betrieblichen Weiterbildung, indem sie Einblick in die Heterogenität der Veranstaltungsarten und Veranstaltungsthemen sowie in das Handeln betrieblicher Sozialberatender bei der Entwicklung und Durchführung von Weiterbildungsangeboten gibt.

Fluvial floods endure as one of the most catastrophic weather-induced disasters worldwide, leading to numerous fatalities each year and significantly impacting socio-economic development and the environment. Hence, the research and development of new methods and algorithms focused on improving fluvial flood prediction and devising robust flood management strategies are essential. This study explores and assesses the potential application of 1D-Convolution Neural Networks (1D-CNN) for spatial prediction of fluvial flood in the Quang Nam province, a high-frequency tropical cyclone area in central Vietnam. To this end, a geospatial database with 4156 fluvial flood locations and 12 flood indicators was considered. The ADAM algorithm and the MSE loss function were used to train the 1D-CNN model, whereas popular performance metrics, such as Accuracy (Acc), Kappa, and AUC, were used to measure the performance. The results indicated remarkable performance by the 1D-CNN model, achieving high prediction accuracy with metrics such as Acc = 90.7%, Kappa = 0.814, and AUC = 0.963. Notably, the proposed 1D-CNN model outperformed benchmark models, including DeepNN, SVM, and LR. This achievement underscores the promise and innovation brought by 1D-CNN in the realm of susceptibility mapping for fluvial floods.

Solid polymer electrolytes are promising candidates for implementation in next-generation all-solid-state batteries (ASSBs) which could replace conventional batteries used today. However, the materialization of ASSBs on a mass scale is restricted by the low ionic conductivity and high interfacial resistance of solid electrolytes. In this work, succinonitrile (SN) with lithium (trifluoromethylsulphonyl)imide (LiTFSI) and Al-doped Li7La3Zr2O12 (Al-LLZO) nanoparticles were used to improve the ionic conductivity of a polyethylene oxide-based composite electrolyte. The Al-LLZO nanoparticles were synthesized by a facile synthesis process at low temperatures, which contributed to an enhancement in the ionic conductivity. A solid polymer electrolyte with 7.5 wt% of Al-LLZO and 15 wt% of SN achieved a high ionic conductivity of 4.17 × 10−4 Scm−1 at room temperature and a large value of 0.451 for the lithium-ion transport number at 60 °C. By adding 10 wt% SN and 10 wt% of Al-LLZO in the LiFePO4 cathode, the cell could operate at 25 °C with a specific capacity of 130 mAh g−1 and 89% capacity retention after 200 cycles at current density of 20 mA g−1. This study therefore proposes a solution to improve the ionic conductivity of solid polymer electrolytes in all-solid-state batteries.

Purpose: A systematic literature review has neglected for years in both national and international vocational educational and training (VET) policy research. Recently, scholarly interest in and the need for such a review has increased rapidly. This review introduces the application of the systematic literature review method, with a focus on research work completed in European VET policy. Approach: To investigate the value and applicability of the systematic literature review method in European VET policy research, we conducted a pilot study following the guidelines and procedures presented by Gessler and Siemer. Findings: First, the process of conducting a literature review and its major methodological steps are described, followed by a descriptive analysis of the sample and characteristics of the studies reviewed. Second, initial insights into the research methodology and the topics that emerged during its application are presented. Altogether, we documented a first attempt to systematize research on European VET policy, including lessons learned from conducting a systematic literature review. Conclusion: The review revealed that although research on international European VET policy research has increased in recent years, hardly any systematization of the current research has been proposed. Instead, most research has been limited to identifying specific country-related factors. By comparison, we propose a systematic approach to reviewing research on European VET policy, being well aware of the strengths and limitations of the proposed method and the results. Thus, this systematic review presents a substantial starting point and research agenda for further studies on this topic.

All-solid-state batteries have gained significant attention as promising candidates to replace liquid electrolytes in lithium-ion batteries for high safety, energy storage performance, and stability under elevated temperature conditions. However, the low ionic conductivity and unsuitability of lithium metal in solid polymer electrolytes is a critical problem. To resolve this, we used a cubic garnet oxide electrolyte (Li7La3Zr2O12 – LLZO) and ionic liquid in combination with a polymer electrolyte to produce a composite electrolyte membrane. By applying a solid polymer electrolyte on symmetric stainless steel, the composite electrolyte membrane shows high ionic conductivity at elevated temperatures. The effect of LLZO in suppressing lithium dendrite growth within the composite electrolyte was confirmed through symmetric lithium stripping/plating tests under various current densities showing small polarization voltages. The full cell with lithium iron phosphate as the cathode active material achieved a highest specific capacity of 137.4 mAh g−1 and a high capacity retention of 98.47% after 100 cycles at a current density of 50 mA g−1 and a temperature of 60°C. Moreover, the specific discharge capacities were 137 and 100.8 mAh g−1 at current densities of 100 and 200 mA g−1, respectively. This research highlights the capability of solid polymer electrolytes to suppress the evolution of lithium dendrites and enhance the performance of all-solid-state batteries.

With various information sources (e.g., from IoT sensors to social media), it is difficult to provide users with trustworthy services in ambient environment. The aim of this paper is i) to design trust ontology for representing semantics of the ambient services and ii ) to compute trust measures among users by using a personalized trust ontology. In particular, given a large amount of data collected from ambient sensors, efficient trust computation and reasoning are required for the stability and reliability. Thereby, we propose trust ontology-based framework for deriving personalized ontologies for individual users according to their preference, perspective, and purpose. To evaluate the proposed model, we have figured out a method how the degree of trust is estimated based on the trust ontology. Furthermore, we have proved that the proposed method is reliable with a case study on a social media (Twitter) for a particular domain (restaurant).

Since trust among entities can change according to various conditions, it is necessary for ambient services to determine when and how the trust has to be updated. Therefore, our contribution in this paper is to present: 1) a new definition of trust that can be extended to various domains; 2) a novel method based on social events and patterns to trigger trust refreshment in ambient services; and 3) a web application framework (called SocioScope) for collecting and analyzing data from multiple data sources. Finally, the case study suggests that this proposal could be applied to trust-aware ambient and recommendation systems.

This work introduces a novel approach to improve the precision of distance estimation in localization systems by using existing LoRaWAN and RSSI-based techniques. Despite the benefits of range and power efficiency, these systems exhibit limited accuracy in practical situations. To address the limitation, this study provides an innovative technique that greatly improves the precision of distance estimations, particularly in urban environments. The fundamental basis of this approach lies in the use of a dynamic path loss model. An additional element is to accommodate the varied and dynamic conditions of signal transmission in metropolitan areas. A better Kalman filter is also used in the study. This is important because it reduces the effects of multipath fading and environmental noise that often make RSSI-based localization in LoRaWAN networks less accurate. The study further examines the influence of the environmental exponent, also known as the path loss exponent, on the RSSI results and the precision of the distance measurements. This methodology achieves the average error under 1 meters for indoor environments and under 7 meters for outdoor environments. Finally, the Cumulative Density Function (CDF) shows 90 % of the distance estimation algorithm error for indoor environment is lower than 1.08 meters while for outdoor environment is lower than 7.55 meters. Based on these improvements, the introduced methodology not only enhances and improves existing approaches but also optimizes the precision and dependability of urban localization technologies, with substantial implications for a variety of practical applications.

The prediction of patient attendance in emergency departments (ED) is crucial for effective healthcare planning and resource allocation. This paper proposes an early warning system that can detect emerging trends in ED attendance, offering timely alerts for proactive operational planning. Over 13 years of historical ED attendance data (from January 2010 till December 2022) with 1,700,887 data points were used to develop and validate: (1) a Seasonal Autoregressive Integrated Moving Average with eXogenous factors (SARIMAX) forecasting model; (2) an Exponentially Weighted Moving Average (EWMA) surge prediction model, and (3) a trend persistence prediction model. Drift detection was achieved with the EWMA control chart, and the slopes of a kernel-regressed ED attendance curve were used to train various machine learning (ML) models to predict trend persistence. The EWMA control chart effectively detected significant COVID-19 events in Singapore. The surge prediction model generated preemptive signals on changes in the trends of ED attendance over the COVID-19 pandemic period from January 2020 until December 2022. The persistence of novel trends was further estimated using the trend persistence model, with a mean absolute error of 7.54 (95% CI: 6.77–8.79) days. This study advanced emergency healthcare management by introducing a proactive surge detection framework, which is vital for bolstering the preparedness and agility of emergency departments amid unforeseen health crises.

This paper presents an innovative development process of Artificial Neural Network (ANN) to predict four properties of Graphene Oxide (GO) modified asphalt, including penetration, softening point, ductility, and viscosity. To this goal, a GO-modified asphalt database is carefully constructed and divided into 4 subsets, using input variables related to GO characteristics, mixing procedure, aging type, and properties of the initial asphalt before being modified. The model training and selection process is then conducted with random sampling techniques via Monte Carlo simulation to ensure the models’ reliability and generalizability. The results show that the selected ANN models have high performance and accuracy, with a coefficient of determination (R2) = 0.994, 0.996, 0.999, and 0.983, for penetration, softening point, ductility, and viscosity dataset, respectively. In addition, sensitivity analysis is used to evaluate the influence of input variables on the 4 properties. The findings, in good agreement with experimental results, reveal that 2 input variables, namely aging type and corresponding properties of the initial asphalt, have the most influence on the predictability of ANN models. Overall, with verified sensitivity analysis and high prediction accuracy, the proposed models could be used by material engineers to avoid costly and time-consuming experiments.

BackgroundThe COVID-19 pandemic has had a major impact on health systems globally. The sufficiency of hospitals' bed resource is a cornerstone for access to care which can significantly impact the public health outcomes.ObjectiveWe describe the development of a dynamic simulation framework to support agile resource planning during the COVID-19 pandemic in Singapore.Materials and MethodsThe study data were derived from the Singapore General Hospital and public domain sources over the period from 1 January 2020 till 31 May 2020 covering the period when the initial outbreak and surge of COVID-19 cases in Singapore happened. The simulation models and its variants take into consideration the dynamic evolution of the pandemic and the rapidly evolving policies and processes in Singapore.ResultsThe models were calibrated against historical data for the Singapore COVID-19 situation. Several variants of the resource planning model were rapidly developed to adapt to the fast-changing COVID-19 situation in Singapore.ConclusionThe agility in adaptable models and robust collaborative management structure enabled the quick deployment of human and capital resources to sustain the high level of health services delivery during the COVID-19 surge.

Abstract Background Patients with chronic kidney disease (CKD) are at high risk of end-stage kidney disease (ESKD). The Kidney Failure Risk Equation (KFRE), which predicts ESKD risk among patients with CKD, has not been validated in primary care clinics in Southeast Asia (SEA). Therefore, we aimed to (1) evaluate the performance of existing KFRE equations, (2) recalibrate KFRE for better predictive precision, and (3) identify optimally feasible KFRE thresholds for nephrologist referral and dialysis planning in SEA. Methods All patients with CKD visiting nine primary care clinics from 2010 to 2013 in Singapore were included and applied 4-variable KFRE equations incorporating age, sex, estimated glomerular filtration rate (eGFR), and albumin-to-creatinine ratio (ACR). ESKD onset within two and five years were acquired via linkage to the Singapore Renal Registry. A weighted Brier score (the squared difference between observed vs predicted ESKD risks), bias (the median difference between observed vs predicted ESKD risks) and precision (the interquartile range of the bias) were used to select the best-calibrated KFRE equation. Results The recalibrated KFRE (named Recalibrated Pooled KFRE SEA) performed better than existing and other recalibrated KFRE equations in terms of having a smaller Brier score (square root: 2.8% vs. 4.0–9.3% at 5 years; 2.0% vs. 6.1–9.1% at 2 years), less bias (2.5% vs. 3.3–5.2% at 5 years; 1.8% vs. 3.2–3.6% at 2 years), and improved precision (0.5% vs. 1.7–5.2% at 5 years; 0.5% vs. 3.8–4.2% at 2 years). Area under ROC curve for the Recalibrated Pooled KFRE SEA equations were 0.94 (95% confidence interval [CI]: 0.93 to 0.95) at 5 years and 0.96 (95% CI: 0.95 to 0.97) at 2 years. The optimally feasible KFRE thresholds were > 10–16% for 5-year nephrologist referral and > 45% for 2-year dialysis planning. Using the Recalibrated Pooled KFRE SEA, an estimated 82 and 89% ESKD events were included among 10% of subjects at highest estimated risk of ESKD at 5-year and 2-year, respectively. Conclusions The Recalibrated Pooled KFRE SEA performs better than existing KFREs and warrants implementation in primary care settings in SEA.

Intradural lumbar disc herniation is a rare complication of disc disease. The mechanism by which a herniated disc tears the dura matter remains unknown. The preoperative diagnosis of an intradural lumbar disc herniation is still difficult. We report our experience dealing with a case of intradural lumbar disc herniation at level L3/4 in a 34-year-old man. Based on current experience, we found that attachment of the ventral dura to the posterior longitudinal ligament can be a favorable factor indicating intradural lumbar disc herniation. One should pay attention to those with cauda equina syndrome, as it can thereby promptly suggest a preoperative diagnosis of intradural lumbar disc herniation. Surgeons need to avoid omitting intraoperative lesions by palpating the dura mater during surgery for suspected tumor cases.

Stone Mastic Asphalt (SMA) is a tough, stable, rut-resistant mixture that takes advantage of the stone-to-stone contact to provide strength and durability for the material. Besides, the warm mix asphalt (WMA) technology allows reducing emissions and energy consumption by reducing the production temperature by 30–50 °C, compared to conventional hot mix asphalt technology (HMA). The dynamic modulus |E*| has been acknowledged as a vital material property in the mechanistic-empirical design and analysis and further reflects the strains and displacements of such layered pavement structures. The objective of this study is twofold, aiming at favoring the potential use of SMA with WMA technique. To this aim, first, laboratory tests were conducted to compare the performance of SMA and HMA through the dynamic modulus. Second, an advanced hybrid artificial intelligence technique to accurately predict the dynamic modulus of asphalt mixtures was developed. This hybrid model (ANN-TLBO) was based on an Artificial Neural Network (ANN) algorithm and Teaching Learning Based Optimization (TLBO) technique. A database containing the as-obtained experimental tests (96 data) was used for the development and assessment of the ANN-TLBO model. The experimental results showed that SMA mixtures exhibited higher values of the dynamic modulus |E*| than HMA, and the WMA technology increased the dynamic modulus values compared with the hot technology. Furthermore, the proposed hybrid algorithm could successfully predict the dynamic modulus with remarkable values of R2 of 0.989 and 0.985 for the training and testing datasets, respectively. Lastly, the effects of temperature and frequency on the dynamic modulus were evaluated and discussed.

The International Roughness Index (IRI) is the one of the most important roughness indexes to quantify road surface roughness. In this paper, we propose a new hybrid approach between adaptive network based fuzzy inference system (ANFIS) and various meta-heuristic optimizations such as the genetic algorithm (GA), particle swarm optimization (PSO), and the firefly algorithm (FA) to develop several hybrid models namely GA based ANGIS (GANFIS), PSO based ANFIS (PSOANFIS), FA based ANFIS (FAANFIS), respectively, for the prediction of the IRI. A benchmark model named artificial neural networks (ANN) was also used to compare with those hybrid models. To do this, a total of 2811 samples in the case study of the north of Vietnam (Northwest region, Northeast region, and the Red River Delta Area) within the scope of management of the DRM-I Department were used to validate the models in terms of various criteria like coefficient of determination (R) and the root mean square error (RMSE). Experimental results affirmed the potentiality and effectiveness of the proposed prediction models whereas the PSOANFIS (RMSE = 0.145 and R = 0.888) is better than the other models named GANFIS (RMSE = 0.155 and R = 0.872), FAANFIS (RMSE = 0.170 and R = 0.849), and ANN (RMSE = 0.186 and R = 0.804). The results of this study are helpful for accurate prediction of the IRI for evaluation of quality of road surface roughness.

The main objective of this study is to develop and compare hybrid Artificial Intelligence (AI) approaches, namely Adaptive Network-based Fuzzy Inference System (ANFIS) optimized by Genetic Algorithm (GAANFIS) and Particle Swarm Optimization (PSOANFIS) and Support Vector Machine (SVM) for predicting the Marshall Stability (MS) of Stone Matrix Asphalt (SMA) materials. Other important properties of the SMA, namely Marshall Flow (MF) and Marshall Quotient (MQ) were also predicted using the best model found. With that goal, the SMA samples were fabricated in a local laboratory and used to generate datasets for the modeling. The considered input parameters were coarse and fine aggregates, bitumen content and cellulose. The predicted targets were Marshall Parameters such as MS, MF and MQ. Models performance assessment was evaluated thanks to criteria such as Root Mean Squared Error (RMSE), Mean Absolute Error (MAE) and correlation coefficient (R). A Monte Carlo approach with 1000 simulations was used to deduce the statistical results to assess the performance of the three proposed AI models. The results showed that the SVM is the best predictor regarding the converged statistical criteria and probability density functions of RMSE, MAE and R. The results of this study represent a contribution towards the selection of a suitable AI approach to quickly and accurately determine the Marshall Parameters of SMA mixtures.