Intrahospital transports are often required for diagnostic or therapeutic reasons. Depending on the hospital layout, transportation between nursing wards and service units is either provided by ambulances or by trained personnel who accompany patients on foot. In many large German hospitals, the patient transport service is poorly managed and lacks work-flow coordination. This contributes to higher hospital costs (e.g., because a patient is not delivered to the operating room on time) and to patient inconvenience (e.g., because of long waiting times). We designed a computer-based planning system, Opti-TRANS^©, that supports all phases of the transportation flow, including travel booking, dispatching transport requests, and monitoring and reporting trips in real time. The methodology, which we developed to solve the underlying optimization problem—a dynamic dial-a-ride problem with hospital-specific constraints, draws on fast heuristic methods to ensure the efficient and timely provision of transports. We illustrate the strong impact of Opti-TRANS on the daily performance of the patient transportation service of a large German hospital. The major benefits of Opti-TRANS include streamlined transportation processes and work flow, significant savings, and improved patient satisfaction. Moreover, it has contributed to increased awareness among hospital staff of the importance of implementing efficient logistics practices.

The selection of weight initialization in an artificial neural network is one of the key aspects and affects the learning speed, convergence rate and correctness of classification by an artificial neural network. In this paper, we investigate the effects of weight initialization in an artificial neural network. Nguyen-Widrow weight initialization, random initialization, and Xavier initialization method are paired with five different activation functions. This paper deals with a feedforward neural network, consisting of an input layer, a hidden layer, and an output layer. The paired combination of weight initialization methods with activation functions are examined and tested and compared based on their best achieved loss rate in training. This work aims to better understand how weight initialization methods in neural networks, in combination with activation functions, affect the learning speed in comparison after a fixed number of training epochs.

This study explores the application of retrieval-augmented generation (RAG) to improve the accuracy and reliability of large language models (LLMs) in the context of financial report analysis. The focus is on enabling private investors to make informed decisions by enhancing the question-and-answering capabilities regarding the half-yearly or quarterly financial reports of banks. The study adopts a Design Science Research (DSR) methodology to develop and evaluate an RAG system tailored for this use case. The study conducts a series of experiments to explore models in which different RAG components are used. The aim is to enhance context relevance, answer faithfulness, and answer relevance. The results indicate that model one (OpenAI ADA and OpenAI GPT-4) achieved the highest performance, showing robust accuracy and relevance in response. Model three (MiniLM Embedder and OpenAI GPT-4) scored significantly lower, indicating the importance of high-quality components. The evaluation also revealed that well-structured reports result in better RAG performance than less coherent reports. Qualitative questions received higher scores than the quantitative ones, demonstrating the RAG’s proficiency in handling descriptive data. In conclusion, a tailored RAG can aid investors in providing accurate and contextually relevant information from financial reports, thereby enhancing decision making.

Startups have high failure rates due to their inability to attain a sufficient product/market fit, i.e., delivering a solution that best matches the user needs in the market. Requirement engineering is the activity that could help startup teams identify the value proposition that provides high value to the users and continuously innovate it. The objective of the study is to analyse the state of art of the requirement engineering research in the context of startups, as available in the literature. The analysis of the research area highlights the research trends to achieve two things i.e., (a) predict how much support the startups can get from the literature for enhancing their success rates and (b) identify the research gaps to motivate researchers to conduct future research that could be adoptable in startup contexts. Systematic mapping is conducted on studies extracted from the four bibliographic databases (IEEExplore, ACM, Springerlink and ScienceDirect) and studies extracted by using a forward snowballing approach. Individual studies are coded to yield the classification scheme. Formulated schemes and those already available in literature, were populated with information extracted from the abstracts of the studies. The research is mostly focused on generic requirement engineering and product validation activities. The research is conducted mostly as evaluations (empirical studies) with the outcome of providing theory to the research community. Major underlying motivation of the research is to attain the product/market fit. However, research studies focusing on requirement documentation, prioritization and elicitation are losing focus from 2017, 2018 and 2019, respectively. The literature lacks the studies that reports research solutions which are validated in laboratory settings or in real contexts, experience reports, opinion papers and philosophical papers. The positive side of the finding is that the number of requirement engineering research studies in a startup context have increased in the past five years. At this instant, unfortunately the literature has limited ability to support startups by providing solutions (for instance, research solutions, evidence to support decision makings, best practices, experiences etc.) that are adoptable in their real context. Uniform focus of the researchers across all sub-activities of requirement engineering is required with effort distributed across different research types that supports startups, not only by providing validated solutions but experience reports, opinions, new conceptual frameworks and empirical evidence that can aid their decision making.