WuFeng University

Abstract Proton exchange membrane fuel cell systems are increasingly recognized as a promising alternative to conventional energy technologies, including electric batteries and other renewable energy sources, in the global transition away from fossil fuel dependence. However, their practical deployment remains constrained by challenges such as water accumulation and contaminant buildup within the fuel cell stack, which obstruct gas diffusion pathways and degrade performance. The dead-end anode (DEA) configuration, while widely employed for its structural simplicity, introduces risks that include hydrogen depletion, localized fuel starvation, and carbon corrosion of the cathode catalyst, issues frequently exacerbated by insufficient purge control. In this study, we present an experimental investigation aimed at optimizing the operational parameters of proton exchange membrane fuel cells (PEMFCs) operating under DEA mode. Specifically, the effects of hydrogen supply pressure, purge duration, and purge interval are systematically evaluated across a range of load currents. The objectives are to mitigate water flooding, maintain voltage stability during purge intervals, facilitate rapid voltage recovery after purge, and enhance overall energy efficiency.

Finding optimal reaction conditions is crucial for chemical synthesis in the pharmaceutical and chemical industries. However, due to the vast chemical space, conducting experiments for all the possible combinations is impractical. Thus, quantitative structure-activity relationship (QSAR) models have been widely used to predict product yields, but evaluating all combinations is still computationally intensive. In this work, we demonstrate the use of Digital Annealer Unit (DAU) can tackle these large-scale optimization problems more efficiently. Two types of models are developed and tested on high-throughput experimentation (HTE) and Reaxys datasets. Our results suggest that the performance of models is comparable to classical machine learning (ML) methods (i.e., Random Forest and Multilayer Perceptron (MLP)), while the inference time of our models requires only seconds with a DAU. In active learning and autonomous reaction condition design, our model shows improvement for reaction yield prediction by incorporating new data, meaning that it can potentially be used in iterative processes. Our method can also accelerate the screening of billions of reaction conditions, achieving speeds millions of times faster than traditional computing units in identifying superior conditions.

Incorporating an input direct injection into a versatile compact oscillator featuring a switchable resistive-loaded cross-coupled pair, this work presents a low-cost radio frequency divider (FD) with a wide input range, attributing to its hybrid multimode operations. First-cut design guides, exclusively in terms of its key component parameters, on implementing a wide inductive-band span and choosing its optimal operating frequency with the peak quality factor (QF) are provided. The measured results of the RF FD in 0.18-<inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mu $ </tex-math></inline-formula>m CMOS show a wide input-range range (<inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\approx 180$ </tex-math></inline-formula>%), while drawing 2.366 mA in low band (LB) and 4.533 mA in high band (HB), respectively, from <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${V}_{\mathrm { DD}} =1\text {.}5~\text {V}$ </tex-math></inline-formula>.

In organizations adopting Internet of Things (IoT) solutions, managing diverse systems with incompatible APIs poses significant challenges to sustainability and social responsibility. To address this, we introduce AUtalk, an artificial intelligence (AI)-enhanced integrated operations center (IOC) framework for centralized management of heterogeneous IoT systems. AUtalk offers a low-code, no-code environment for seamless IoT integration, enabling applications that support sustainable development goals (SDGs). Deployed in smart campuses, it optimizes resources, conserves energy, and reduces waste through AI-driven insights. Its predictive capabilities enhance maintenance efficiency and minimize resource consumption (SDG 12). Beyond sustainability, AUtalk enhances social responsibility by improving workplace safety, system transparency, and accountability. Its advanced analytics ensure over 99% message delivery accuracy, reducing downtime and resource waste. A case study of an AUtalk-managed IOC with two IoT systems highlights its effectiveness in promoting sustainability and responsible IoT operations.

This research explores how Corporate Accounting Disclosure (CAD) affects the corporate reputation (CR) of publicly traded Taiwan companies, with special emphasis on the moderating influence of social learning (SL). While Corporate Accounting Disclosure and social learning serve as the independent and moderating variables, respectively, corporate reputation is the study's dependent variable. A sample of 125 companies, listed on the Taiwan Stock Exchange Corporation (TWSE), was selected for this study. Our findings suggest that Corporate Accounting Disclosure positively correlates with corporate reputation, as does social learning. Interestingly, the anticipated moderating effect of Social Learning on the association between Corporate Accounting Disclosure and Corporate Reputation was not observed. Future research endeavors may delve deeper into the underlying reasons for this phenomenon by adopting a multifaceted approach of longitudinal studies and cross-cultural analysis. The study also offers in-depth discussions of its findings and clearly outlines its theoretical and practical implications. It concludes by offering recommendations and charting potential avenues for subsequent research.

This study used Levilactobacillus brevis LUC 247 to ferment black rice sourdough, made into Type III black rice sourdough powder to produce black rice sourdough bread. The composition analysis, antioxidant capacity, and storage stability of the black rice sourdough bread with different proportions of black rice powder (0–60%) and fermented for different lengths of time (0–48 h) were discussed. The results showed that the black rice sourdough had the maximum lactic acid bacteria count (9 Log CFU/g) during 12 to 36 h of fermentation. The titratable acid, lactic acid, and acetic acid yields increased with the fermentation time and the proportion of black rice powder. The total anthocyanin content and antioxidant capacity increased with the fermentation time. The hardness and chewiness of the black rice sourdough bread were positively correlated with the black rice sourdough powder content and increased with storage time. In addition, the growth of fungi was significantly slowed as the additional level of black rice sourdough powder increased.

There are many published cup anteversion measurements for postoperative total hip arthroplasty (THA), including Liaw's, Lewinnek's, and Murray's methods. However, most measurements ignore the potential pelvic rotation on radiographs except in Liaw's method. Without considering pelvic rotation, clinicians can miscalculate cup anteversion. Therefore, we aimed to quantify the mean degree of pelvic rotation. Herein, we collected 388 radiographs of 98 postoperative THA hips of 77 patients and measured pelvic rotation by measuring h, the horizontal displacement of the sacrococcygeal junction associated with the upper pole of the symphysis pubis, and ssd, the distance between the sacrococcygeal junction and pubic symphysis. The angle θ of pelvic rotation was defined as θ = arc sin (h/ssd) × (180°/π). The mean degree of pelvic rotation was then calculated. The standard deviation of h was 7.84 mm, and the mean ssd was 158 mm. The potential pelvic rotation was 2.50°. The p-values from the paired t-test were all &gt;0.05 when interobserver and intraobserver errors were assessed. This is the first study to quantify the potential pelvic rotation in the coronal plane on postoperative plain radiographs. The potential pelvic rotation was too large to be neglected during the measurement of cup anteversion.