Research Articles

Despite extensive research on anaerobic digestion (AD), the combined effects of intermittent stirring energy demand and substrate co-digestion on overall energy performance have remained insufficiently explored. This study evaluated the effects of intermittent stirring duration and the AD performance of cattle dung (CD), poultry droppings (PD), rabbit droppings (RD), and their mixtures on methane production and net energy recovery.  Experiments were conducted in 30 L laboratory-scale mechanically stirred batch anaerobic digesters operated under mesophilic conditions and intermittent stirring durations of 1, 2, and 3 h/day. Co-digestion clearly outperformed mono-digestion, achieving higher methane yields (up to 223 L/kg VS), improved biodegradability (>90%), methane-rich biogas, and shorter digestion times (18 days). Intermittent stirring duration significantly influenced performance; moderate stirring (2 h/day) was optimal for most binary mixtures, while higher stirring (3 h/day) enhanced methane yield in balanced ternary systems. Optimized co-digestion could reach up to 7.96 MJ/kg VS despite increased mixing energy use. The statistical analyses revealed strong interrelationships among process variables, identifying stirring duration and digestion time as key drivers of methane yield and net energy production. Artificial neural network (ANN) modeling successfully predicted methane production and net energy based on C/N ratio, stirring rate, and waste composition. The optimal ANN showed high accuracy (R=0.99).

To address the issue of insufficient desktop space in modern office and home environments, this study developed a side-mounted desktop organizer based on a pegboard. Comparative experiments on printing accuracy and mechanical performance were conducted using three commonly used 3D printing filaments in the market: polylactic acid (PLA), polyethylene terephthalate glycol (PETG), and acrylonitrile-butadiene-styrene (ABS). The results showed that among the X, Y, and Z directions, PLA specimens exhibited the smallest dimensional errors, PETG showed intermediate values, and ABS displayed the largest. In terms of tensile strength and elastic modulus, PLA specimens demonstrated the highest values, PETG ranked second, and ABS had the lowest. Therefore, PLA filament was selected as the printing material for the desktop organizer, and fused deposition modeling (FDM) technology was used to complete the 3D printing of the prototype. The 3D-printed desktop organizer features good surface quality and high assembly accuracy, effectively expanding the vertical storage space of the desk and enabling organized storage of small items, such as stationery and cables, demonstrating strong potential for application and promotion.

In the wood industry, warehouse layout decisions have a strong impact on production efficiency and workplace safety. This study presents a methodology that combines ERP-based historical movement data with measured process times to evaluate alternative warehouse layout versions. As a first step, products were grouped, their packaging and storage types were identified, and stock demand was calculated based on average and percentile values. A movement model was then used to assess layout options based on measured handling times and transport distances. Four layout versions were presented to the company management. For each version, the total daily net material handling time and required storage capacity were determined. The results showed that the originally planned block storage layout (V2) had a medium handling time and good feasibility but raised safety concerns. The V4 layout provided the most balanced option in terms of safety and capacity, while the finally selected V3 version—with a 4-meter aisle width—offered the lowest total handling time, which was a priority due to high production volume and fast warehouse servicing needs. This research demonstrates how can the Excel-based numerical modeling built on ERP data and process measurements, support warehouse layout decisions and improve operational performance and strategic planning.

Traditional furniture design relies heavily on designers’ prior knowledge and limited individual capacity, which often results in insufficient innovation capability and low product development efficiency. To overcome these limitations, this study introduces advanced Artificial Intelligence Generated Content (AIGC) technology and proposes an integrated creative design and evaluation framework for new Chinese-style furniture that combines Stable Diffusion (SD) with the CRITIC–VIKOR method. The proposed method aims to enhance research and development efficiency and creativity while enabling a comprehensive assessment of generated furniture design alternatives. Specifically, the SD model within the AIGC technology is employed to train and generate innovative furniture design images. After identifying affective words that represents user needs, the CRITIC–VIKOR method is applied to calculate the objective weights of user needs and to conduct a multi-criteria evaluation of the creative schemes, thereby determining the optimal scheme. The proposed method effectively integrates the strengths of generative technologies and quantitative decision-making approaches. It facilitates the rapid generation of diverse creative concepts while systematically selecting the optimal scheme that best satisfies user requirements, thereby promoting the development of the furniture industry and fostering the inheritance and innovative advancement of traditional culture.