This paper takes the value chain and the five elements of profit model as the main analytical thread, focusing on Mixue Bingcheng, a representative enterprise in the new-style tea beverage sector, to systematically reveal its dual-wheel driving logic of "low price, scale, and supply chain integration" and "content dissemination and community ritualization." Upstream, the company reduces unit costs through raw material and production capacity organization; midstream, it enhances turnover efficiency via warehousing and distribution networks alongside information systems; downstream, it captures traffic through high-density stores, online mini-programs/delivery platforms, and private domain linkages, while depositing brand assets through symbolic dissemination such as theme songs and the "Snow King" IP, ultimately translating value chain advantages into stable profit sources and growth resilience. Meanwhile, the study points out that although intensive store opening and the elimination of regional protection facilitate scale expansion and brand spillover, attention must be paid to structural risks arising from single-store traffic diversion and difficulties in upward price positioning. The conclusions of this paper provide the industry with an operational pathway from "value chain to profit model" and propose policy and management recommendations regarding cost governance, channel pacing, community norms, and brand upgrading.

With the impressive rise in e-commerce, the implementation of artificial intelligence (AI) and Internet of Things (IoT) in automation of the warehouse became faster. Mobile robots may be widespread in warehouses; nevertheless, their effectiveness proves to be unsatisfactory as they cannot navigate and coordinate effectively, namely breakdown between the implement system and Enterprise Resource Planning, Warehouse Management System lead to low performance. To reach the integrative optimization, this paper implement the in depth case study approach, and the analysis of Amazon logistics model was taken. Using a review of technical literature, industry reports, and publicly available performance data, this paper analyzes the application of the artificial intelligence technologies, including AI bot collaboration and path optimization, at Amazon in a manner that is synergistic. The review reveals that this holistic strategy has enabled innovation in major operation data such as throughput, inventory precision, and scalability. Precisely, the combination of AI and IoT with ERP/WMS systems contributes greatly to the responsiveness of the systems, as well as the effectiveness of decision making which results in a tangible change in the inventory accuracy (more than 95 percent), picking efficiency (by up to 40 percent), and space utilization (by 20 percent-35 percent). The research points out that to become optimized logistics in the Industry 4.0 age, it is important to have technological integration and not isolated automation.

The continuously increasing number of engineering projects and the gradually accelerating delivery speed have brought continuous pressure on the warehousing of building waterproofing material companies. As a leader in the industry, Oriental Yuhong's overall warehouse capacity can basically meet business needs, but the space distribution inside the warehouse, storage location management, and even organization methods are uneven and inefficient as a whole., resulting in "locally crowded, aisle piled up, and some storage locations even idle for a long period of time" in some storage links, resulting in an increase in time and operating costs The reason is to analyze the challenges from three levels: spatial structure + operational methods + data management. After summarizing the results of relevant research on warehouse space utilization, layout optimization, and building materials warehousing, this paper focuses on analyzing the space utilization problems of Oriental Yuhong's warehouse of building waterproofing materials from the perspective of "time cost + monetary cost". It tries to find out the reason from three levels: spatial structure, operation mode and data management, and find a method that can improve the warehouse space utilization without significantly expanding the storage capacity. These methods will provide a feasible improvement solution for the similar building materials company.

Against the backdrop of China's rapid e-commerce development, platforms and logistics companies are enhancing customer experience by densifying warehousing and transportation networks and launching multi-tiered services such as same-day and next-day delivery. However, this has led to a significant increase in warehousing, transportation, and last-mile delivery costs. This report, based on a systematic review of supply chain cost-response theory and network design models, and combined with macroeconomic statistics, industry reports, and platform practices such as JD.com, analyses the main contradictions faced by transportation network design under conditions of multiple time-sensitive products coexisting. On the one hand, increased logistics density and service levels are beneficial to supporting e-commerce development and regional economic upgrading; on the other hand, high-density networks and high-time-sensitivity services bring continuous investment and operational pressure. The study points out that e-commerce platforms need to coordinate facility layout, transportation modes, and service levels at the overall network level, and identify the "appropriate service level range" through scenario comparison to achieve a relatively reasonable balance between service improvement and cost control.

This study uses samples of Chinese A-Shares non-financial listed companies from 2007 - 2019, combined with the phased implementation of the margin trading system as a quasi-natural experiment, to study whether short-selling mechanism is affected by the rule of law and how the rule of law moderates the impact of short-selling mechanism on the earnings management of non-financial listed companies of Chinese A-share. Short selling curbs both accrual-based and real earnings management (beta=-0.012 and-0.015), and it restrains real earnings management more. The rule of law has a positive moderating effect on this governance effect, the suppression of earnings fraud is significantly higher in high rule of law (DA: -0.018,REM:-0.020) compared to low rule of law (DA: -0.006,REM:-0.007) places. Heterogeneity analysis shows that the moderating effect is more pronounced on non-state owned firms and regulated industries. From the mechanism tests, this study can see that short selling restrains earnings management by improving the information transparency and reinforcing financing constraints. The analyst coverage and institutional ownership will boost the effect. This study proposes judicial coordination, different treatment for short sale targets, and to build a 'law-short selling' risk early warning mechanism.

With the upgrading of consumption and the increasing uncertainty of the external environment, e-commerce platforms urgently need to build a supply chain system that is both efficient and resilient. For Chinese consumers, their needs have changed from simply "being able to buy" to "buying well and buying fast". "Same-day delivery" or even "one-hour delivery" is no longer an extra value-added service, but has become a key factor in platform competition. Based on JD.com, a representative Chinese e-commerce company that has built its own logistics system, this paper studies the evolution path of its e-commerce supply chain under the background of same-day delivery service and the effects it brings. This paper focuses on the integration path of the three paradigms of "lean, agile and resilient". Under the macro background of deepening reforms and strengthening the resilience of industrial and supply chains, exploring the applicability of this e-commerce supply chain evolution in China has important practical significance.

Cervical cancer, a highly preventable disease primarily caused by HPV, remains a critical global health issue. In Canada, its incidence rate grew fastest among all cancers from 2015 to 2023, highlighting a pressing public health challenge. To inform future planning, this study forecasts national cervical cancer mortality by comparing traditional and machine learning time-series models. The author analyzed annual age-standardized mortality data (1950-2022) from the Public Health Agency of Canada, evaluating the Exponential Smoothing (ETS), ARIMA, Neural Network Autoregression (NNAR), and eXtreme Gradient Boosting (XGBoost) models using rolling-window cross-validation. Results showed that traditional models outperformed their machine learning counterparts in predictive accuracy. The ETS and ARIMA models achieved lower Root Mean Square Errors (RMSEs of 0.45 and 0.44, respectively) compared to NNAR (0.65) and XGBoost (1.39), indicating that simpler methods better captured the linear historical trend. Despite its lower overall accuracy, XGBoost provided a crucial epidemiological insight: it identified a strong 16-18 year lag in mortality rates, which accounted for over 83% of the model's feature importance, suggesting deep-seated cohort effects or the long-term impact of prevention programs. These forecasts offer actionable data for Canadian health authorities to optimize screening and vaccination strategies. Moreover, the demonstrated lag between intervention and outcome provides a vital evidence-based framework for other countries, supporting the global goal of reducing cervical cancer mortality through timely, data-driven policy.

With the development of digital and intelligent technologies, artificial intelligence has evolved from a mere support tool into a critical mechanism for making decisions in management accounting. With the Haier Smart Home Co., Ltd. and Ping An Insurance (Group) Company of China, Ltd. as the main case studies, this paper analyzes data preprocessing, intelligent analysis, and result feedback through a comparative approach. The study shows that artificial intelligence does not replace traditional management accounting. However, it gives two distinct paths: value creation and efficiency-risk control. With the participation of artificial intelligence, traditional accounting becomes more organizational and more objective. Haier's value-creation-oriented path figures out that the role of artificial intelligence is focusing more on process. Artificial intelligence provides real-time feedback for Haier's management accounting. This transforms the function of management accounting, bringing it from the background to the forefront. By contrast, Ping An pays more attention to the efficiency and risk-oriented path. It builds a shared financial services framework and incorporates machine learning to strengthen detection and alert the risk. In this model, artificial intelligence plays a crucial role. Enterprises can make expertise assist in establishing process controls and compliance constraints. This effectively transforms risk governance into financial process capabilities. Overall, with the development of artificial intelligence, this paper seeks to strengthen the understanding about support mechanisms in management accounting. It tries to provide theoretical insights for enterprises promoting intelligent financial transformation.

With the rapid upgrading of consumption structures in China, the new tea beverage industry has experienced sustained growth over the past decade. According to industry reports, the market size of China's new-style tea beverage sector exceeded RMB 290 billion in 2023, driven by urban lifestyle changes and young consumer demand. As a representative premium brand originating from the Guangdong–Hong Kong–Macao Greater Bay Area (GBA), HEYTEA has established strong brand influence through product innovation, digital marketing, and experiential retail strategies. This paper focuses on HEYTEA as a case study to analyze its market competition strategy within the GBA. On the basis of industry trends available in the public domain and industry literature, this study applies SWOT analysis and 4P concepts of marketing to scrutinize internally HEYTEA's strengths and weaknesses, as well as its threats and opportunities in the external environment. The proposed study will also attempt to unlock HEYTEA's secret to success and highlight the marketing challenges confronted by this company in a market that appears to be becoming saturating by the day.

This study develops and evaluates a hybrid ARIMA–GARCH model with Student-t innovations to simulate the U.S. short-term interest rate path and price the interest rate caplet. As a key monetary policy tool and a fundamental benchmark in financial markets, the behaviour of short-term interest rates is characterised by volatility clustering and non-Gaussian innovations, features that constant-volatility lognormal models inadequately capture. Utilising the U.S. 3-Month Treasury Bill rate as a proxy for the short rate, this study estimates the model parameters and simulates 10,000 Monte Carlo paths over a three-month horizon. A caplet is then valued under a short-rate framework with payoffNτmax(rT-K,0), and pathwise discounting, and the resulting price is compared with a Black–76 benchmark. The empirical results indicate strong volatility persistence and heavy-tailed innovations, supporting the need for time-varying volatility and non-Gaussian errors. In pricing, the ARIMA–GARCH–t Monte Carlo approach yields a significantly higher caplet value than the Black–76 benchmark, underscoring the sensitivity of derivative values to volatility and distributional assumptions. Overall, the proposed framework provides a more realistic representation of short-rate risk and a more defensible basis for caplet valuation, with practical value for hedging and stress testing in interest-rate markets.