PurposeThis study investigates how family ownership and involvement in management and directorship influenced audit fees in Chinese family firms during the COVID-19 pandemic, leveraging China's unique cultural and institutional context.Design/methodology/approachUsing data from the China Stock Market and Accounting Research Database, the study analysed 18,889 firm-year observations from family-owned listed firms over the 2015-2022 period, employing two-way fixed-effect regressions, robustness tests and controls for endogeneity. Pre- and post-COVID-19 periods (2015-2018 versus 2019-2022) were compared to assess the impact of family governance on audit fees during the pandemic.FindingsThe results reveal a significant negative relationship between family ownership and audit fees during COVID-19, compared to the insignificant negative pre-crisis association. Family involvement in management consistently reduced audit fees across the whole period, while directorship showed no significant effect. The findings indicate that Confucian-inspired familial alignment mitigated Type I agency problems, reducing audit risk without significantly increasing Type II agency conflicts.Practical implicationsFamily firms can leverage trust-based management to strengthen resilience and reduce external oversight costs, while auditors can optimise procedures for Chinese family firms, reducing costs associated with lower perceived risk. Policymakers could consider tailoring regulations to acknowledge culturally specific governance strengths.Originality/valueThis study contributes to the audit fee literature by emphasising the moderating influence of Confucian values and China's cultural and institutional environment. By demonstrating reduced audit fees in Chinese family firms during a global crisis, the study challenges Western-centric agency theory predictions.
Mixed methods research is often used in software engineering, but researchers outside of the social or human sciences often lack experience when using these designs. This paper provides guiding principles and advice on how to design mixed method research, and to encourage the intentional, rigorous, and innovative application of mixed methods in software engineering. It also presents key properties of core mixed method research designs. Through a number of fictitious but recognizable software engineering research scenarios, we showcase how to choose suitable mixed method designs and consider the inevitable trade-offs any design choice leads to. We describe several antipatterns that illustrate what to avoid in mixed method research, and when mixed method research should be considered over other approaches.
Hexagonal boron nitride (hBN), has shown great promise as a single-photon source (SPS). However, progress has been largely confined to early-stage research, focused on identifying various SPSs more than developing specific emitters to work as building blocks of quantum devices. To fully leverage hBN's potential in quantum technologies, there is a need for a comprehensive strategic plan emphasizing its role as a reliable single-photon generator for quantum processes. The novelty of this work is to highlight the need for a clear roadmap to bridge foundational research on the quantum properties of hBN to the development of quantum devices based on this material. While the vision of constructing hBN-based quantum devices holds significant promise, achieving this requires overcoming significant technical challenges.
Personalized Federated Learning (PFL) is widely employed in the Internet of Things (IoT) to handle high-volume, non-iid client data while ensuring data privacy. However, heterogeneous edge devices owned by clients may impose varying degrees of resource constraints, causing computation and communication bottlenecks for PFL. Federated Dropout has emerged as a popular strategy to address this challenge, wherein only a subset of the global model, i.e. a sub-model, is trained on a client's device, thereby reducing computation and communication overheads. Nevertheless, the dropout-based model-pruning strategy may introduce bias, particularly towards non-iid local data. When biased sub-models absorb highly divergent parameters from other clients, performance degradation becomes inevitable. In response, we propose federated learning with stochastic parameter update (FedSPU). Unlike dropout that tailors local models to small-size sub-models, FedSPU maintains the full model architecture on each device but randomly freezes a certain percentage of neurons in the local model during training while updating the remaining neurons. This approach ensures that a portion of the local model remains personalized, thereby enhancing the model's robustness against biased parameters from other clients. Experimental results demonstrate that FedSPU outperforms federated dropout by 4.45% on average in terms of accuracy. Furthermore, an introduced early stopping scheme leads to a significant reduction of the training time in FedSPU by 25%~71% while maintaining high accuracy.
The huge consumption of onion produces bulk waste (500,000 tons/annum) which leads to environmental issues. This study focuses on the development of biosorbents from the waste onion. The porosity was generated by using HNO3, H3PO4, KOH and NaOH as chemical activators in the 400–800 °C temperature range. Nitrogen adsorption-desorption isotherms were used to explore the effect of activators and temperature on the pore texture and structural parameters of materials which find a way to explore the adsorption study. The carbon samples activated at 600 °C and 800 °C show type IV isotherm with H3 hysteresis. Moreover, the samples activated at 400 °C depict type II isotherm with no hysteresis indicating the absence of porous network at lower temperature. The BET surface area of materials was found in the range of 0.01–635 m2/g. SEM analysis confirmed the porous and rough surface of materials with the development of channels and cavities. The existence of functional groups and their chemical state necessary for the adsorption of contaminants was verified by XPS analysis. A series of samples were then tested for the removal of arsenic. Among all the adsorbent materials onion derived caron activated with HNO3 (ACH6), H3PO4 (ACH3/6), KOH (ACK6) and NaOH (ACNa6) at 600 °C recorded better removal efficiency.
