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A rational and efficient business management and operation model is a prerequisite for the smooth operation of an organization. This study aims to build an intensive and efficient business management model with the characteristics of the “oil company” model. Compared with the traditional business management model, the “oil company” model has significant advantages in terms of organizational structure, communication efficiency, work style and work efficiency, etc. Due to the compression of management levels and the shortening of business chains, the communication efficiency and work efficiency are higher, and the composition of personnel and work style are more flexible. Therefore, based on the theory of the “oil company” and on the idea of compressing management levels and shortening management chains, business management departments should be integrated, streamlined and optimized, and business chains should be sorted out.

Today, corporate companies spend a lot of time on and attach importance to job interviews in recruitment. In job interviews, they select or eliminate many of the candidates using interview techniques. This poses a serious problem, especially for candidates who have not had job interview experience, even though they meet sufficient requirements. Because these candidates do not have previous interview experience, they experience more difficulties in this process. In this regard, the study is about a system created in a virtual environment that allows a job interview experience through a user interface. The system subject of the study is a model consisting of a user interface unit, server, interview information database, language processing unit, behavior analysis unit, virtual interview unit, reporting unit, and social media integration unit. This proposed model has been detailed modularly through the web-based system.

The Resource Management System (RMS) is a comprehensive solution designed to optimize resource allocation, enhance project efficiency, and streamline customer interactions within a dynamic business environment. The system encompasses four key components: projects, resources, customers, and business units. It offers a range of functionalities including create, update, retrieve, and delete operations to facilitate seamless operations and data management. The RMS is built using Spring Boot (Java) for the backend, providing a robust and scalable foundation. The front end is developed using React, ensuring a modern and user-friendly interface. Data is managed in a MySQL database, offering reliability and data integrity. Communication between frontend and backend is achieved through RESTful APIs. Resource Management system typically manage and allocate non-human resources and as well human resources throughout an organization, However, if we choose to pursue human resources management, we will recruit, hire, train, manage employees and staff which is also a variation of resources. It provides comprehensive solution designed to optimize resource allocation, enhance project efficiency, and streamline customer interactions within a dynamic business environment.

PurposeThis paper outlines a contemporary conceptual framework for the embedding of experiential learning into a business consultancy module. Experiential learning is a fundamental teaching approach that allows students to apply theory into a working business context.Design/methodology/approachAs a conceptual and not an empirical paper, the methodological approach was to draw upon the literature reviewed and to build a framework to support student learning through a business consultancy module.FindingsExploration of the literature suggests that there are four elements critical to student learning in experiential learning environments: action, reflection, social and context. A framework has been developed utilising these elements with the interaction between the factors being key to developing learning.Research limitations/implicationsSo far, the framework is conceptual, and further research is needed to explore its use when staff members are developing these types of modules and to understand the interaction of the factors over the course of the student learning experience.Originality/valueThe originality comes from the intersection and interaction between the core factors in experiential learning, which enables this framework to move thinking beyond more static models and hence work in a more fluid student learning environment.

The establishment of Business English (abbreviated as BE hereafter) as an independent discipline for undergraduate education appeals to the higher professionalism of BE teachers’ knowledge structure. This paper introduces the results of a study that, by in-depth semi-structured interviews, investigated the knowledge structure of 18 teachers across three different BE course modules. Data are analyzed based on grounded theory. Eleven knowledge types required of a qualified BE teacher across three different structures (i.e., three BE course modules) are identified. The construction of BE teachers’ knowledge structures differs in the three BE course modules. The findings are a meaningful supplement to the theoretical analysis of the “should-be” description of BE teachers’ knowledge structure and can improve the understanding of the constituents of BE teachers’ knowledge structures at a micro-level, which should, in turn, provide references for different module teachers’ development.

This book illustrates how to become a successful trader step by step. The process begins just like someone is starting a brand new business, that is, with a business plan. The business plan outlines the business “modules” and how all the modules would be used together to generate revenue and keep expenses to a defined cost. For trading, it is exactly the same way. A trading plan is created instead of a business plan. The trading plan shows how one will trade. It shows the fundamental system, the technical system, money management, assumptions, and so forth, which will be used for trading. The lessons learned in the book are the basic foundations for becoming a good trader. It is up to the readers to follow the lessons, do the research, do the backtesting, and to think.

PurposeThis study shows various pathways manufacturers can take when embarking on digital servitization (DS) journeys. It builds on the DS and modularity literature to map the strategic trajectories of product–service–software (PSSw) configurations.Design/methodology/approachThe study is exploratory and based on the inductive theory building method. The empirical data were gathered through a workshop with focus groups of 15 servitization manufacturers (with 22 respondents), an on-site workshop (in-depth case study), semi-structured interviews, observations and document study of archival data.FindingsThe DS trajectories are idiosyncratic and dependent on design architectures of PSSw modules, balancing choices between standardization and innovation. The adoption of software systems depends on the maturity of the industry-specific digital ecosystem. Decomposition and integration of PSSw modules facilitate DS transition through business model modularity. Seven testable propositions are presented.Research limitations/implicationsWith the small sample size from different industries and one in-depth case study, generalizing the findings was not possible.Practical implicationsThe mapping exercise is powerful when top management from different functional departments can participate together to share their expertise and achieve consensus. It logs the “states” that the manufacturer undergoes over time.Originality/valueThe Digital Servitization Cube serves as a conceptual framework for manufacturers to systematically map and categorize their current and future PSSw strategies. It bridges the cross-disciplinary theoretical discussion in DS.

Community ecology was traditionally an integrative science devoted to studying interactions between species and their abiotic environments in order to predict species' geographic distributions and abundances. Yet for philosophical and methodological reasons, it has become divided into two enterprises: one devoted to local experimentation on species interactions to predict community dynamics; the other devoted to statistical analyses of abiotic and biotic information to describe geographic distribution. Our goal here is to instigate thinking about ways to reconnect the two enterprises and thereby return to a tradition to do integrative science. We focus specifically on the community ecology of predators and prey, which is ripe for integration. This is because there is active, simultaneous interest in experimentally resolving the nature and strength of predator–prey interactions as well as explaining patterns across landscapes and seascapes. We begin by describing a conceptual theory rooted in classical analyses of non-spatial food web modules used to predict species interactions. We show how such modules can be extended to consideration of spatial context using the concept of habitat domain. Habitat domain describes the spatial extent of habitat space that predators and prey use while foraging, which differs from home range, the spatial extent used by an animal to meet all of its daily needs. This conceptual theory can be used to predict how different spatial relations of predators and prey could lead to different emergent multiple predator–prey interactions such as whether predator consumptive or non-consumptive effects should dominate, and whether intraguild predation, predator interference or predator complementarity are expected. We then review the literature on studies of large predator–prey interactions that make conclusions about the nature of multiple predator–prey interactions. This analysis reveals that while many studies provide sufficient information about predator or prey spatial locations, and thus meet necessary conditions of the habitat domain conceptual theory for drawing conclusions about the nature of the predator–prey interactions, several studies do not. We therefore elaborate how modern technology and statistical approaches for animal movement analysis could be used to test the conceptual theory, using experimental or quasi-experimental analyses at landscape scales.

With the development of Internet of manufacturing things, decentralized scheduling in flexible job shop is arousing great attention. To deal with the challenges confronted by personalized manufacturing, such as high level of flexibility, agility and robustness for dynamic response, we design a centralized-learning decentralized-execution (CLDE) multi-agent reinforcement learning scheduling structure based on Graph Convolutional Network (GCN), namely graph-based multi-agent system (GMAS), to solve the flexible job shop scheduling problem (FJSP). Firstly, according to the product processing network and job shop environment, the probabilistic model of directed acyclic graph for FJSP is constructed. It models the FJSP as the process of topology graph structure predicting, and the scheduling strategy is adjusted by predicting the connection probability among edges. Then, the multi-agent reinforcement learning system consisting of environment module, job agent module, and machine agent module is constructed. The job agents execute scheduling actions by interacting with environment and machine agents in a decentralized way. Meanwhile, the interaction between job agents is extracted as an abstract global action based on GCN. The experimental results demonstrate that GMAS outperforms its rivals on FJSP, especially in complicated situations. Our results thus shed light on a novel direction for FJSP in dynamic and complex scenarios.

Lettuce ( Lactuca sativa ), a widely cultivated leafy vegetable, is highly susceptible to bacterial and fungal infections that severely reduce yield and quality. Rapid and accurate disease identification is therefore essential for precision agriculture and sustainable crop management. This study proposes Efficient-FBM-FRMNet, a modular deep learning framework for automated lettuce disease detection. The model integrates EfficientNetB4 with dilated convolutions, a Feature Bottleneck Module (FBM) for redundancy reduction, a Reasoning Engine for higher-order semantic inference, and a Feature Refinement Module (FRM) for enhanced generalization. The framework was trained and validated on a publicly available dataset of 2,813 lettuce leaf images (bacterial, fungal, and healthy classes) using stratified 5-fold cross-validation. The proposed Efficient-FBM-FRMNet achieved an overall accuracy of 97.5%, outperforming baseline CNNs such as EfficientNetB4, ResNet50, and DenseNet121. It demonstrated superior precision (96.0%), recall (96.6%), and F1-score (97.0%), confirming its robustness and consistency across multiple folds. Statistical significance analysis (p < 0.05) verified that the performance gains were not due to random variation. The integration of FBM, Reasoning Engine, and FRM enhances discriminative feature learning, interpretability, and stability while reducing computational cost (8.2 MB model size, 23 ms inference). These results demonstrate the model’s potential for real-world deployment in greenhouse monitoring, UAV-based surveillance, and mobile diagnostic systems, contributing to sustainable, AI-driven precision agriculture.