Tianjin University of Commerce

We explore the combined optimization of financial and operational decisions initiated by a buyer's cooperation with a supplier for customized production. A cost-sharing agreement provides a powerful incentive to enhance suppliers' delivery performance within an alliance relationship. We categorize the costs into production and effort costs to investigate the effects of two kinds of cost-sharing agreements. The demand for customized products presents a significant challenge for suppliers, particularly small and medium-sized enterprises facing financing and delivery issues. It contributes to the combined effect of purchase order financing and cooperative cost-sharing agreements. We consider the supplier's efficiency information asymmetry and obtain results regarding the signal game, describing the interactions among the supplier, buyer, and banks. The paper examines the contract parameter settings (price and cost-sharing fraction) and how various signaling mechanisms (signal type or signal quantity) influence delivery and profits (trade-off between separation costs and information rent). We show that strategically designing the signals can extend the feasible region for the least costly separating equilibrium. The findings highlight the varied roles of cost-sharing agreements in delivery incentives and supply chain coordination, offering valuable directions for managers to leverage cost-sharing agreements for improved strategy formulation in information signaling mechanisms.

Color-coded fringe projection profilometry (FPP) is one of the most important single-frame three-dimensional (3D) shape measurement techniques. Transverse chromatic aberration (TCA) in a color-coded FPP system yields imaging deviations, which will result in 3D shape measurement error. In this work, a TCA compensation method for color-coded FPP is proposed. The proposed method establishes the relationship among phase, pixel deviation, and imaging position for decreasing the 3D shape measurement error introduced by TCA. Compared to existing methods that use the relationship among depth, pixel deviation, and imaging position or polynomial fitting for each pixel, the proposed method offers the merits of precise and simple implementation, since it voids the cumulative error in-depth calculation and removes the time-consuming process of polynomial fitting for each pixel. For validation, a color-coded FPP system is established and calibrated. Two verification experiments, including a translation test and 3D shape measurement of a regular spherical object, are conducted with the calibrated system. Experimental results demonstrate that the proposed TCA compensation method is capable of effectively reducing the 3D shape measurement error caused by TCA.

Self-circulating casing treatment (SRCT) is usually employed to expand the flow range of compressors, while the influencing mechanisms of SRCT on compressors are usually discussed from local and steady perspectives. In this study, the role of SRCT for a compressor is investigated in the perspective of global and unsteady features. The dynamic-mode-decomposition (DMD) method is employed to analyze the global unsteady features of the compressor with and without SRCT. The temporal and spatial characteristics of the DMD modes are explored to elucidate their physical meanings. The evolutions of the mode structures and mode energies during the compressor throttling process are studied, to explore the unsteady mechanisms of the stability enhancement of the SRCT. The following results are obtained. Under the design condition, due to the unsteady and non-uniform suction of the SRCT, the flow structure of the tip leakage vortex (TLV) in the SRCT model is altered, the circumferential uniformity is enhanced, and the unsteady pulsation intensity is weakened. Under the near-stall condition, the SRCT hinders the propagation and interference of upstream and downstream disturbances through unsteady suction, preventing the fragmentation of the TLV and maintaining the stability of the blade leading edge and the entire flow field. This study helps to obtain a deeper comprehension of the unsteady mechanisms of SRCT and provides new ideas for flow stability optimization of compressors by unsteady flow structures regulation.

Passive localization using visible light sensing has been considered as a promising solution for indoor human detection. A major challenge is to avoid false target positioning in multi-target positioning scenarios. Besides, a reasonable probable target area model is also critical to the accuracy of positioning and counting targets. In this article, a novel passive localization scheme is proposed to locate multiple targets. This scheme introduces a rectangular probable target area model, which is used to calculate a rectangular area containing the potential location of the target to be located. Compared with existing models, it is more suitable for positioning scenarios under low-density deployment of sensing nodes. Furthermore, we present an improved successive cancellation (SC) algorithm to excluding false target localization. To determine the authenticity of targets by the SC algorithm, a Bayesian model is introduced to optimize the SC algorithm according to the multidimensional shad-owed link information of candidate targets. Numerous simulation results show that the proposed multi-target passive localization scheme can improve the problem of false target localization in multi-target localization scenarios. And it also can achieve outstanding performance in localization accuracy.

The method based on machine learning and laser-induced breakdown spectroscopy (LIBS) is effective for rapid characterization of waste organic polymers (WOP). However, the lack of mechanistic interpretability leads to raises concerns regarding its reliability in practical applications. This study systematically investigated the fundamental chemical correlations between WOP fuel properties and LIBS spectral features through feature selection and machine learning interpretability analysis. Thirteen radical-associated key peaks were selected and strategically categorized into two groups for model construction. Under optimal conditions, the prediction accuracy for carbon, hydrogen, oxygen content and lower heating value (LHV) reach 97.74%, 91.22%, 91.28% and 97.02%, respectively. Notably, models utilizing 10 selected key peaks demonstrated superior performance compared to those employing raw LIBS spectra or principal components, especially with the absolute difference reaching 14.57% for O content prediction. Interpretability analysis showed that C2 swan bands had highest effects impacts on carbon, oxygen content and LHV prediction, whereas H I line was essential for hydrogen content prediction. This mechanistic investigation provided theoretical validation for LIBS-based rapid characterization systems, facilitating their practical implementation in downstream energy recovery processes. The established methodology offers a scientific foundation for advancing sustainable waste management and promoting circular economy development through efficient resource utilization.

Colorectal cancer (CRC) ranks among the highest commonly diagnosed cancers globally. Meanwhile, with the acceleration of modern life rhythms, people's mental health is deteriorating. This article aims to explore the connection between mental stress and the genesis and development of CRC as well as discuss relevant medications. Through literature analysis, it is found that chronic stress has capacity of triggering not only the sympathetic nervous system (SNS) but also the hypothalamic-pituitary-adrenal (HPA) axis, giving rise to enhanced levels of cortisol and catecholamines, which promotes tumor and angiogenesis metastasis, resulting in the genesis and development of CRC through all kinds of mechanisms, including dysbiosis of intestinal flora, triggering of inflammatory responses, and stimulation of colorectal epithelial-mesenchymal transition. In addition, stress-related behavioral changes, such as dietary habits, may cause an increased risk of cancer. In terms of drug research, beta-blockers such as propranolol and antidepressants such as tricyclic antidepressants (TCAs) have shown certain anti-CRC potential. In conclusion, new approaches to CRC prevention and treatment have been developed with the help of a thorough comprehending of the link between mental stress and the disease as well as the enhancement of CRC patients' quality of life.

This research explores the best practices of luxury brands in utilizing social media to engage younger demographics, specifically millennials and Gen Z. As these groups gain more influence over the luxury sector, conventional marketing methodssuch as lavish fashion shows and print adsare proving less effective. Platforms like Facebook, Instagram, and TikTok have become essential for luxury brands aiming to reach these young consumers. The study begins by analyzing the advantages and disadvantages of various social media marketing strategies, including influencer partnerships, word-of-mouth (WOM) on social platforms, and premium pricing tactics. Influencer collaborations have been shown to significantly impact consumer perceptions and buying choices; however, there is a potential risk if an influencer's actions do not align with the brands values. Word-of-mouth on social media serves as a powerful mechanism for influencing purchasing intentions but requires careful management to protect the brand's image. Setting high prices can effectively foster brand loyalty and exclusivity; nonetheless, it is crucial to balance this with quality online service to maintain consumer confidence. In summary, this research enhances our understanding of how luxury brands can utilize social media effectively to attract younger audiences while offering actionable recommendations for improving brand equity, business outcomes, and sales performance.

Sludge-derived biochar (SDB) synthesized by the pyrolysis of sludge is gaining enormous interest as a sustainable solution to wastewater treatment and sludge disposal. Despite the proliferation of general biochar reviews, a focused synthesis on SDB-specific advances, particularly covering the recent surge in multifunctional wastewater treatment applications (2020–2025), receives little emphasis. In particular, a critical analysis of recent trends, application challenges, and future research directions for SDB is still limited. Unlike broader biochar reviews, this mini-review highlights the comparative advantages and limitations of SDB, identifies emerging integration strategies (e.g., bio-electrochemical systems, catalytic membranes), and outlines future research priorities toward enhancing the durability and environmental safety of SDB applications. Specifically, this review summarized the advances from 2020 to 2025, focusing exclusively on functional modifications, and practical applications of SDB across diverse wastewater treatment technologies involved in adsorption, catalytic oxidation, membrane integration, electrochemical processes and bio-treatment systems. Quantitative comparisons of adsorption capacities (e.g., >99% Cd2+ removal, >150 mg/g tetracycline adsorption) and catalytic degradation efficiencies are provided to illustrate recent improvements. The potential of SDB in evaluating traditional and emerging contaminant degradation among the Fenton-like, persulfate, and peracetic acid activation systems was emphasized. Integration with membrane technologies reduces fouling, while electrochemical applications, including microbial fuel cells, yield higher power densities. To improve the functionality of SDB-based systems in targeting contamination removal, modification strategies, i.e., thermal activation, heteroatom doping (N, S, P), and metal loading, played crucial roles. Emerging trends highlight hybrid systems and persistent free radicals for non-radical pathways. Despite progress, critical challenges persist in scalability, long-term stability, lifecycle assessments, and scale-up implementation. The targeted synthesis of this review offers valuable insights to guide the development and practical deployment of SDB in sustainable wastewater management.

The conventional Fenton process generates large amounts of Fenton sludge during wastewater treatment. Achieving effective utilization of Fenton sludge and reducing its production remain pivotal challenges. In this study, Fenton sludge biochar catalysts (Cat) were prepared using Fenton sludge via pyrolysis. In addition, chemical oxygen demand (COD) from secondary effluent was removed by Fenton sludge biochar catalysts activated with H2O2/Fe(VI). Specifically, the removal efficiency of COD could reach 46.2% in the Cat−2/H2O2/Fe(VI) system under weakly alkaline conditions. The mechanistic analysis confirmed that high-valent iron, •OH, O2•−, and ¹O2 all participate in the degradation process. Furthermore, a continuous-flow reactor was applied to treat secondary effluent, with COD decreasing from 65 mg/L to 36 mg/L. This study provides new insights into the resource utilization of Fenton sludge and the treatment of complex wastewater.

Highly (001)‐orientated diisopropylammonium bromide (DIPAB) films were prepared by spin‐coating method. The temperature and concentration of spin‐coating solution were changed to investigate their impact on the growth of films. As the concentration increases and the temperature rises, uniform and integrated films could be obtained. The temperature‐dependent variation of dielectric constant was studied, indicating a Curie temperature of 150 °C. The in situ domain structure evolution induced by temperature confirmed that this temperature corresponds to the ferroelectric to paraelectric phase transition. Additionally, electric field‐induced domain evolution was investigated after a DC bias of 15 kV/cm was applied. Coarse domains narrowed while new fine stripe‐like domains emerged, indicating electric‐field‐driven domain reconfiguration. It revealed that the applied field reduces switching energy barriers. This drives polarization, reorientation, and elastic strain relaxation via domain wall redistribution. These findings highlight the interplay between external stimuli and domain behavior in molecular ferroelectrics, offering insights for designing tunable ferroelectric devices.

This work aims to enhance the accuracy and efficiency of corporate strategic decision-making, particularly in rapidly changing and highly competitive market environments. Traditional strategic decision-making methods rely on managers’ experiential judgment and exhibit limitations when handling complex data and high-frequency market fluctuations. To address this issue, this work proposes a hybrid optimization model combining transformer models and reinforcement learning algorithms, designed to optimize corporate strategic decision-making processes and improve competitiveness. First, relevant studies on strategic decision-making and corporate competitiveness are reviewed, clarifying the potential and advantages of artificial intelligence (AI) in decision support. Second, the hybrid model is developed and trained through steps including data collection and preprocessing, algorithm selection and model construction, as well as model training and validation. Finally, real-world data are applied to evaluate model performance across indicators such as training time, convergence speed, and prediction effectiveness. The results demonstrate that the hybrid model successfully converges within 150 iterations and exhibits substantial advantages over traditional algorithms, particularly in prediction accuracy for market share (92%), profit growth rate (91%), and customer satisfaction (89%). Implementing the model leads to notable improvements in corporate market position, brand influence, and technological innovation capabilities. The work shows that the hybrid model enhances the scientific rigor and accuracy of decision-making. Meanwhile, it strengthens corporate competitiveness and market responsiveness, highlighting the substantial potential of AI technologies in strategic management. This work provides enterprises with an efficient and reliable decision-support tool, facilitating the maintenance of competitive advantages in complex and dynamic market environments.

This article investigates the problem of event-triggered adaptive fault-tolerant consensus control (FTCC) for nonlinear multi-agent systems (MAS) with actuator faults and asymmetric error consensus constraint. Firstly, a log-type transformation function is designed for all the tracking errors of the leader and followers, so that all the tracking errors can be constrained to unified asymmetric constrained boundary function. Then, a generalized relative threshold event triggering mechanism is constructed, which reduces the communication burden of the MAS and avoids Zeno behavior. In the process of controller design, the coupling problem of parameters is successfully solved by introducing fault and trigger parameters into the structured parameter vector, and the designed controller can entirely offset the influence of trigger parameters and multi-actuator faults on the MAS. Finally, the MAS including one leader and three followers is used to explain the availability of presented method.

Electrochemical nitrogen reduction reaction (NRR) has emerged as a promising approach for sustainable ammonia synthesis under ambient conditions, offering a low-energy alternative to the traditional Haber–Bosch process. However, the development of efficient and sustainable electrocatalysts for NRR remains a significant challenge. Noble metals, known for their exceptional chemical stability under electrocatalytic conditions, have garnered considerable attention in this field. In this study, we report the successful synthesis of nanoporous CuAuPtPd quasi-high-entropy alloy (quasi-HEA) prism arrays through “melt quenching” and “dealloying” techniques. The as-obtained alloy demonstrates remarkable performance as an NRR electrocatalyst, achieving an impressive ammonia synthesis rate of 17.5 μg h⁻¹ mg⁻¹ at a potential of −0.2 V vs. RHE, surpassing many previously reported NRR catalysts. This work not only highlights the potential of quasi-HEAs as advanced NRR electrocatalysts but also provides valuable insights into the design of nanoporous multicomponent materials for sustainable energy and catalytic applications.