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AbstractIn the past few decades, companies have begun to pay attention to the issue of whether companies that are engaged in CSR activities enhance firm value remains a subject of debate. From the perspective of agency theory, high-CSR companies can encounter poor financial performance and a reduction in firm value because of managers’ bad project selection. According to stakeholder theory, high-CSR companies face a positive relationship to financial performance and vament as a result of consideration for the benefits of stakeholders. This study collects CSR data from CommonWealth Magazine and examines how Taiwanese companies engaged in CSR activities affect corporate financial performance. The empirical finding shows that CSR is significantly positive to corporate financial performance. This result suggests that companies actively engaged in CSR activities bring a positive effect on firm value. Therefore, our evidence supports the stakeholder theory that high CSR companies benefit their stakeholders.

Roseomonas mucosa (R. mucosa) is a pink-pigmented, aerobic, nonfermentative, slow-growing Gram-negative coccus typically isolated from the natural environment, human skin, and hospital environment. This pathogen, in most circumstances, leads to infections in immunocompromised hosts, but it may sometimes invade immunocompetent individuals. Bacteraemia is the most common form of infection caused by R. mucosa. In contrast, only two case reports have described R. mucosa-related epidural abscess formation and infective spondylitis. In this case report, we shared the history and treatment experience of a 76-year-old female who was diagnosed with infective spondylitis and epidural abscess caused by R. mucosa. She received a local transdermal injection into the lower back to relieve her back pain two months before symptom onset, which was considered to be associated with this infection episode. After admission to the hospital, neurosurgeons performed emergent decompression and debridement. She was treated with intravenous ceftriaxone for four weeks, followed by oral ciprofloxacin for another eight weeks. The patient recovered well without any sequelae and had no relapse of infection at least six months after the end of treatment. In addition to the case report, we reviewed the literature for reported cases caused by R. mucosa. Our experience suggests that clinicians should include R. mucosa as one of the possible healthcare-associated pathogens among individuals who have undergone transdermal procedures. We believe that this article will help clinicians better recognize R. mucosa infection.

Faults in solar photovoltaic (PV) modules often result from component damage, leading to voltage fluctuations and decreased stability in the power system. In this study, the original voltage signals of different PV modules show little variation. Therefore, a solution that combines symmetrized dot pattern (SDP) and AlexNet for fault detection in PV modules was proposed. This solution investigates three common faults: poor welding, cracking, and bypass diode failure, which can be applied to fault-free modules. First, a high-frequency signal was input into the PV module, and the raw signal was captured using an NI PXI-5105 high-speed data acquisition card. Next, we used SDP to process the signal and create images with specific snowflake-like features. These images were used as a basis for fault diagnosis. Finally, deep-learning algorithms were used to perform status detection on the PV module. This research also used 3200 training samples and 800 test samples (200 for each type) to evaluate a new method for diagnosing faults in PV modules. The results show that the accuracy of the new method reached 99.8%, surpassing traditional convolutional neural networks (CNN) and extension neural networks (ENN), whose accuracies were 99.5% and 91.75%, respectively. Furthermore, this study compares the proposed method with more traditional numerical fault diagnosis methods. SDP effectively extracts fault signals and presents them as images. With AlexNet used for fault identification, the method excels in accuracy, training time, and testing time, thereby enhancing the stability and reliability of future energy systems.

This study aims to provide analytical solutions for circular plates coated or undercoated with functionally graded materials (FGMs) having Young’s modulus functionality through the radius. The circular plates are subjected to thermal loads in radial and thickness directions. Because of the uncoupled stretching–bending problem of the radially FGM circular plate, the bending equilibrium equations in terms of displacements of the FGM-coated or -undercoated circular plates with Young’s modulus based on the power–law function were established individually. General solutions for the homogeneous portion or FGM ring of the radially FGM-coated or -undercoated circular plate were developed separately. Subsequently, analytical thermal solutions for the radially FGM-coated or -undercoated circular plate were evaluated by solving the simultaneous boundary and continuity conditions equations. The analytical results were validated by comparing them with finite element solutions. When degenerated, they coincided with those of the homogeneous circular plate in the literature, enhancing the obtained solutions’ reliability. These analytical solutions provide valuable insights into the plates’ responses and expand the understanding of their mechanical behaviors under thermal loads. Furthermore, the effects of the FGM thickness, the material index, and the thermal loading conditions on the mechanical behaviors were under investigation. This parameter study offers valuable perspectives into the influence of these factors on the plate’s structural response and aids in the optimization and design of FGM-coated or -undercoated circular plates.

Copper in the international market has been priced by copper futures contracts from the London Metal Exchange (LME) and the New York Commodity Exchange (COMEX). Copper prices initially showed a downward trend until March 2020, but after the outbreak of COVID-19, they continued to rise and reached a record high in May 2021. The rise in copper demand also stimulated the continuous growth of copper production. However, a significant amount of smelting flue gas is produced in the copper smelting process. The main component of the flue gas is SO2 and other acid gases, which pollute the environmental atmosphere. At the same time, due to the Chinese government’s effective control of the pandemic, China’s economy continued to grow. Therefore, as one of the world’s largest copper consumers and producers, China’s futures market has attracted attention for its influence on copper pricing and the pollution caused by copper smelting. In this paper, we used the grey entropy method to compare the influence of copper prices on the three futures markets and changes in China’s air pollution in recent years. Our results show that before the pandemic, the influence of the LME futures copper price was the same as the COMEX but greater than the Shanghai Futures Exchange (SHFE). After the outbreak of the pandemic, the influence of the SHFE copper futures price significantly improved and slightly exceeded the LME and COMEX. This result echoes our finding that SO2 has caused serious air pollution in recent years.

The photovoltaic (PV) module is a key technological advancement in renewable energy. When the PV modules fail, the overall generating efficiency will decrease, and the power system’s operation will be influenced. Hence, detecting the fault type when the PV modules are failing becomes important. This study proposed a hybrid algorithm by combining the symmetrized dot pattern (SDP) with a convolutional neural network (CNN) for PV module fault recognition. Three common faults are discussed, including poor welding, breakage, and bypass diode failure. Moreover, a fault-free module was added to the experiment for comparison. First, a high-frequency square signal was imported into the PV module, and the original signal was captured by the NI PXI-5105 high-speed data acquisition (DAQ) card for the hardware architecture. Afterward, the signal was imported into the SDP for calculation to create a snowflake image as the image feature for fault diagnosis. Finally, the PV module fault recognition was performed using CNN. There were 3200 test data records in this study, and 800 data records (200 data records of each fault) were used as test samples. The test results show that the recognition accuracy was as high as 99.88%. It is better than the traditional ENN algorithm, having an accuracy of 91.75%. Therefore, while capturing the fault signals effectively and displaying them in images, the proposed method accurately recognizes the PV modules’ fault types.

Background/Aim: Gout, characterized by acute inflammatory symptoms and monosodium urate crystal deposits in joints, is prevalent among males and the elderly. Total knee arthroplasty (TKA) is a common treatment for knee osteoarthritis (OA), but its impact on the risk of developing gout is unclear. This study examined the risk of gout in patients undergoing TKA. Patients and Methods: Utilizing the TriNetX research network, a retrospective cohort study was conducted on OA patients without prior gout. The TKA cohort was compared to a non-TKA control group using propensity score matching to balance covariates. The primary outcome was the incidence of gout over a five-year follow-up period. Results: The study included 38,834 matched pairs of TKA and non-TKA patients. TKA patients had a 15.6% higher risk of developing gout (HR=1.156; 95%CI=1.042-1.284) compared to controls. Sensitivity analyses confirmed the increased risk across various models and follow-up durations. Conclusion: TKA is associated with a higher risk of developing gout, particularly in females and older adults. Clinicians should monitor and manage gout risk in TKA patients, emphasizing preventive measures and early intervention. Further research is needed to understand the underlying mechanisms and improve patient care post-TKA. [ABSTRACT FROM AUTHOR]

This study aims to measure the impact of Education, Health, Housing, Dependence and Income Growth on the Average Propensity to Consume (APC) of the Urban Chinese Households by Income Level. We use the Quantile Regression for analysis. Results show that Health has a negative impact on the APC; and that Education and Dependence has positive relation. For less wealthy households, “Income Growth” is positive related to the APC. “Housing Prices to Income” (PTI) has a negative impact on the APC for the majority of less wealthy households and national average. Therefore, policies that decrease uncertainties, the household expending on health and housing, and encourage stable growth, redistribute the existing wealth and increase of the size of the family may help to increase the APC in a long run. The government should take into account of issues of housing markets, education and taxation for future policy making of social and economic development. [ABSTRACT FROM AUTHOR]

Understanding switching mechanisms is very important for resistive random access memory (RRAM) applications. This letter reports an investigation of Al/Mg0.5Ca0.5TiO3 (MCTO)/ITO RRAM, which exhibits bipolar resistive switching behavior. The filaments that connect Al electrodes with indium tin oxide electrodes across the MCTO layer at a low-resistance state are identified. The filaments composed of In2O3 crystals are observed through energy-dispersive X-ray spectroscopy, high-resolution transmission electron microscopy, nanobeam diffraction, and comparisons of Joint Committee on Powder Diffraction Standards (JCPDS) cards. Finally, a switching mechanism resulting from an electrical field induced by In3+ ion migration is proposed. In3+ ion migration forms/ruptures the conductive filaments and sets/resets the RRAM device. [ABSTRACT FROM AUTHOR]