Background: Founders hold a powerful position that can shape the future and direction of their new ventures, especially in the early stages of venture development. Prior work experiences are known to be influential for future growth, but the relationship between founders' prior work experiences and their growth intentions is poorly understood.  Purpose: The study aims to contribute to the existing literature by furthering the awareness and understanding of how founders are using their prior work experiences when shaping and evaluating their growth intentions. The study answers the research question:  How do founders' prior work experiences influence growth intentions in the early stages of venture development?  Method: Our method is based on qualitative research and adapts an explanatory purpose to capture the process between prior work experiences and growth intentions and elaborate the understanding of how growth intentions are created in the early stage of the ventures' development. Through a case study with semi-structured interviews, we have interviewed nine new venture founders in the tech sector, which have been the ground for our empirics. The analysis of data was conducted in three steps, namely empirical analysis, retroduction, and corroboration.  Conclusion: Our study advances the explanation of how founders' prior work experiences shape how they manage their ventures' internal environments, which impact their growth intentions. By conducting a conceptual model, we explained how building social working environment, structuring people and practices, and implementing routines manifest as mechanisms in the process between prior experiences and growth.

As electronic computer technology advances, files and data are kept in computers and exchanged through networks. The computer is a physically closed system for users, making it harder for others to steal data via direct touch. Computer networks, on the other hand, can be used by hackers to gain access to user accounts and steal sensitive data. The academics are concentrating their efforts on preventing network attacks and assuring data security. The Intrusion Detection System (IDS) relies on network traffic and host logs to detect and protect against network threats. They all, however, necessitate a lot of data analysis and quick reaction tactics, which puts a lot of pressure on network managers. The advancement of AI allows computers to take over difficult and time-consuming data processing activities, resulting in more intelligent network attack protection techniques and timely alerts of suspected network attacks. The SCVIC-APT-2021 dataset which is specific to the APT attacks is generated to serve as a benchmark for APT detection. A Virtual Private Network (VPN) connects two network domains to form the basic network environment for creating the dataset. Kali Linux is used as a hacker to launch multiple rounds of APT attacks and compromise two network domains from the external network. The generated dataset contains six APT stages, each of which includes different attack techniques. Following that, a knowledge-based machine learning model is proposed to detect APT attacks on the developed SCVIC-APT-2021 dataset. The macro average F1-score increases by 11.01% and reach up to 81.92% when compared to the supervised baseline model. NSL-KDD and UNSW-NB15 are then utilized as benchmarks to verify the performance of the proposed model. The weighted average F1-score on both datasets can reach 76.42% and 79.20%, respectively. Since some network attacks leave host-based information such as system logs on the network devices, the detection scheme that integrates network-based features and host-based features are used to boost the network attack detection capabilities of IDS. The raw data of CSE-CIC-IDS2018 is utilized to create the SCIVC-CIDS-2021 dataset which includes both network-based features and host-based features. To ensure precise classification results, the SCVIC-CIDS-2021 is labelled with the attacking techniques. Due to the high dimensionalities of the features in the produced dataset, Autoencoder (AE) and Gated Recurrent Unit (GRU) are employed to reduce the dimensionality of network-based and host-based features, respectively. Finally, classification of the data points is performed using knowledge-based PKI and PKI Difference (PKID) models. Among these, the PKID model performs better with a macro average F1-score of 96.60%, which is 7.62% higher than the results only utilizing network-based features.

This research focuses on the development of a deterministic-probabilistic framework for agricultural land use and management, specifically for both annual crops, such as wheat, barley and maize, and permanent crops, such as vineyards. The goal is to predict crop greening and peak crop development progressively through the growing season, based on accumulating information as the crop develops and matures, and to provide an accompanying uncertainty statement (credible interval) with each prediction. The integrated area underneath the phenology curve can be associated, although not explicitly in our example, with per-area crop yield. The prediction model relies on remotely sensed data, including science data products from the Landsat and MODIS (Moderate Resolution Imaging Spectroradiometer) spaceborne instruments, field data from agro-meteorological stations, and statistical data from prior years. The development of the deterministic-probabilistic model focuses on northeastern Italy, a region of small agricultural plots set in a diverse physical landscape, which is typical of many areas of old-world and developing-nation agriculture. The estimation process uses the phenological cycle of the MODIS Enhanced Vegetation Index (EVI), extracted from the satellite imagery at 500 m spatial resolution. Landsat data, at 30-m spatial resolution, are fused with MODIS data, to provide fine-scale information better suited to small-field agriculture. By applying a piecewise logistic function to model the time trajectory of EVI values, crop development and peak greenness are estimated and characterized based on the main phenological stages determined from the remote imagery trained with ground station observations. The deterministic-probabilistic model is later validated with observations from reference testing stations and statistical crop and yield data obtained independently by administrative districts such as regional and national organizations. A temporal filter of the main phenological stages, here called a crop calendar, plays a critical role. A Bayesian approach to integrate stochastically the parameters related to a certain area provides a way to include the different datasets at the different dimensions and scales and to assess the probability to obtain a vegetation index within a given uncertainty. The model becomes, therefore, a typical generalized linear model problem, deterministically described by a piecewise logistic function, with the parameters describing the peak phenological curve estimated probabilistically, with their own uncertainty. / 2026-01-31T00:00:00Z

Bovine respiratory disease (BRD) is a significant health problem for cattle producers in terms of economic cost and animal welfare. In the United States (US), it is one of the leading causes of sickness and death in beef calves prior to weaning. Although much research has been conducted to develop vaccines for prevention and antibiotics for treatment, the morbidity and mortality of BRD in beef calves prior to weaning has not improved over the years. The identification of risk factors associated with BRD is an area of focus which might ultimately allow producers to minimize morbidity and mortality from BRD. Little research has been performed to understand factors contributing to the risk of BRD in beef calves prior to weaning. BRD affects the beef cattle industry through losses due to mortality, prevention cost, treatment cost, or morbidity effect on productivity. Currently, the economic losses due to BRD for beef calves prior to weaning is not available. Price paid for feeder cattle is a major factor influencing the income of producers. The effect of BRD is a complicated problem since the parameters associated with the cost of BRD in beef cow-calf production are variable and interrelated. To better understand the economic effect of BRD in beef calves prior to weaning, concepts of uncertainty, variability, stochasticity, nonlinearity, and feedback might be involved during the process of assessing risk. The objectives of this dissertation are the following: 1) to test if calf sex, birth weight, and age of dam are associated with BRD of beef calves prior to weaning in different age periods; 2) to identify factors affecting the national market price of beef feeder cattle in the US and how the prices change over time; 3) to investigate the prevention and treatment cost of BRD in beef calves prior to weaning; 4) to estimate the economic cost of BRD in US beef calves prior to weaning; and 5) to understand the effect of BRD occurrence or absence on the national net income of the US beef cow-calf industry.

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