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Background: Consistent use of effective contraceptives is directly associated with a lower risk of unintended pregnancies, a significant public health burden in the U.S. The Asian American population is heterogeneous and fast-growing. However, patterns and disparities in contraceptive use among Asian American women, particularly within racial/ethnic subgroups, have been understudied, hindering effective family planning. Objectives: This study aimed to identify the prevalence of contraceptive use and its pattern in Asian American women using the 2015–2016 California Health and Interview Survey (CHIS) data, with a focus on different Asian ethnic subgroups. Study Design: A composite score of acculturation level (0–5) was created based on place of birth, years in the U.S., and language spoken at home. Data on demographics, self-rated health, contraceptive use, and related information were collected from women aged 18–44 years who were at risk of unintended pregnancy. Adjusted multivariable logistic regressions were conducted to examine contraceptive use and patterns in relation to race/ethnicity and other factors. Results: Over 18.20% of the overall sample (pop estimated N = 16,177,759) were Asian Americans, and among them, 24.62% were Chinese, followed by other Asian subgroups (28.83%), Filipina (25.49%), Korean (11.25%), and Vietnamese (9.80%). Overall, Filipina, Korean, and Vietnamese women were less likely to use contraception compared to their non-Hispanic White (NHW) peers, whereas acculturation level was positively associated with contraceptive use. Among different types of contraceptives, Filipina, Korean, and Vietnamese women were less likely to use long-acting reversible contraceptives compared to NHW. Such racial/ethnic disparities were not observed with less or moderately effective contraceptives. Conclusions: Patterns of contraceptive use and associated disparities varied among Asian American subgroups. Providers working with Asian American women should be aware of these racial disparities in contraceptive use and seek ways to address barriers to effective contraception use in this diverse population in order to provide culturally competent family planning services.

The application of far-infrared blankets has shown certain benefits in health promotion and therapy, such as improving blood circulation and alleviating muscle pain. However, the effects of such blankets on increasing deep sleep, reducing blood pressure, enhancing memory, dilating microvessels for blood flow, reducing chronic inflammation, and decreasing fatigue remain to be studied. We aim to investigate the effects of the DAZZEON αSleep® far-infrared blanket on these indicators. This study adopted a double-blind design, recruiting 24 male participants aged over 45 years, divided into two groups of 12 each: (A) a placebo group and (B) a DAZZEON αSleep® group. The participants used the blanket every night for two weeks, with sleep records taken using a wearable device and blood pressure, blood oxygen levels, arterial stiffness, and surface temperature measured before and after the intervention. Blood samples were collected for an analysis of inflammation and sleep-related blood indicators (serotonin and melatonin), and exercise tests were conducted to assess fatigue improvement. Compared with before the intervention, the blanket significantly increased changes in grip strength and reaction time. Additionally, it significantly increased blood serotonin, melatonin, and nitric oxide concentrations (p < 0.05), thus significantly increasing deep sleep and REM sleep durations (p < 0.05) and improving subjective sleep quality (p < 0.05). This study confirmed that using the DAZZEON αSleep® far-infrared blanket for 14 consecutive days helps to improve blood circulation, reduce vascular age and arterial stiffness, increase serotonin and melatonin levels, and improve sleep quality, as well as enhances muscle strength and reaction time.

In the digital era, cloud computing stands as a pivotal tool in the transformative journey of enterprises, with China’s tech landscape serving as a prime exemplar. However, when enterprises embrace cloud computing, they face complex hurdles, mainly technical ones. To understand how the complexity of cloud migration affects their digital transformation, our research meticulously constructed business process models for business deployment in both non-cloud and cloud contexts, spanning the IaaS, PaaS, and SaaS levels. By harnessing China’s public cloud market data, we constructed the Cloud Migration Complexity Index, providing a tangible metric to gauge the intricacies of cloud migration and their implications on digital transformation. The findings illustrate that a decrease in cloud migration complexity significantly accelerates digital transformation, with the reduction in SaaS complexity having the most profound impact. Analyzing businesses of varying scales, the diminishing complexity of SaaS predominantly boosts digital transformation for non-SME enterprises, while reduced PaaS complexity is most beneficial for SMEs. This study advocates for the government to expand the PaaS market and suggest that cloud providers develop more PaaS-based products to optimize cloud migration both technically and economically.

Acetaminophen (APAP) overdose can induce hepatocyte necrosis and acute liver failure in experimental rodents and humans. APAP is mainly metabolized via hepatic cytochrome P450 enzymes to generate the highly reactive metabolite N-acetyl-p-benzoquinone imine (NAPQI), which forms acetaminophen protein adducts (APAP-adducts) and damages mitochondria, triggering necrosis. APAP-adducts and damaged mitochondria can be selectively removed by autophagy. Increasing evidence implies that the activation of autophagy may be beneficial for APAP-induced liver injury (AILI). In this minireview, we briefly summarize recent progress on autophagy, in particular, the pharmacological targeting of SQSTM1/p62 and TFEB in AILI.

The rise of the Internet of things has catalyzed extensive research in the realm of flexible wearable sensors. In comparison with conventional sensor power supply methods that are reliant on external sources, self-powered sensors offer notable advantages in wearable comfort, device structure, and functional expansion. The energy-harvesting modes dominated by piezoelectric nanogenerators (PENGs), triboelectric nanogenerators (TENGs), and pyroelectric nanogenerators (PyENGs) create more possibilities for flexible self-powered sensors. This paper meticulously examines the progress in flexible self-powered devices harnessing TENG, PENG, and PyENG technologies and highlights the evolution of these sensors concerning the material selection, pioneering manufacturing techniques, and device architecture. It also focuses on the research progress of sensors with composite power generation modes. By amalgamating pivotal discoveries and emerging trends, this review not only furnishes a comprehensive portrayal of the present landscape but also accentuates avenues for future research and the application of flexible self-powered sensor technology.

The goal of this study is to explore the effectiveness of applying multi-objective particle swarm optimization (MOPSO) algorithms in the design of infinite impulse response (IIR) filters. Given the widespread application of IIR filters in digital signal processing, the precision of their design plays a significant role in the system’s performance. Traditional design methods often encounter the problem of local optima, which limits further enhancement of the filter’s performance. This research proposes a method based on multi-objective particle swarm optimization algorithms, aiming not just to find the local optima but to identify the optimal global design parameters for the filters. The design methodology section will provide a detailed introduction to the application of multi-objective particle swarm optimization algorithms in the IIR filter design process, including particle initialization, velocity and position updates, and the definition of objective functions. Through multiple experiments using Butterworth and Chebyshev Type I filters as prototypes, as well as examining the differences in the performance among these filters in low-pass, high-pass, and band-pass configurations, this study compares their efficiencies. The minimum mean square error (MMSE) of this study reached 1.83, the mean error (ME) reached 2.34, and the standard deviation (SD) reached 0.03, which is better than the references. In summary, this research demonstrates that multi-objective particle swarm optimization algorithms are an effective and practical approach in the design of IIR filters.

Rotator cuff repair is a common orthopaedic procedure. Despite advancements in the mechanics of rotator cuff repair, the re-tear rate post repair remains significant. This review assesses the available literature on usage of collagen bio-inductive scaffolds for rotator cuff repairs. Augmentation of biology is a key strategy to improving success of rotator cuff repair. Current evidence suggests that augmentation of rotator cuff repairs with a collagen bio-inductive scaffold improves the thickness of the rotator cuff. There is a favourable safety profile, and its usage may improve re-tear rates. However, there is currently no consensus on whether clinical outcomes are improved by the usage of collagen bio-inductive scaffolds. Further research is necessary to increase our understanding of the clinical effects of using collagen bio-inductive scaffolds and to determine which patient profiles will best benefit from its usage.

The relationship between online review types and their outcomes is dynamic. However, it remains unclear how the influence of the three prominent kinds of online reviews (ratings, photos, and text) evolves from the initial to the phases of the restaurant visit cycle. To address this gap in the literature, this study administers a survey in mainland China using two time-lag intervals. Based on the data collection methodology proposed in the consumption-system approach, this survey separates the pre- (T1) and post- (T2) stages of specific restaurant visits. While rating reviews’ direct impact on behavioral intentions increases during the visit cycle, that of photo reviews does not change before and after restaurant visits. As for text reviews, these do not directly influence behavioral intentions before a restaurant visit; however, the impact increases after a visit, highlighting a difference in behavioral responses between the pre- and post-restaurant-visit phases. Rating reviews’ direct effect on review skepticism is negatively significant after visiting a restaurant; moreover, review skepticism is important in mediating the relationship between rating reviews and behavioral intentions after a visit.

Objectives: This study’s objectives were to explore the potential of wideband acoustic immittance (WAI) as a diagnostic tool, examining its accuracy and efficiency in pediatric audiology. Methods: A narrative review of the contemporary literature was conducted, focusing on studies that assessed the use of WAI in diagnosing pediatric auditory conditions. Key variables such as diagnostic accuracy, efficiency, and clinical outcomes were considered. Results: This review highlighted that WAI offers a broader range of test frequencies and more comprehensive diagnostic information compared with traditional tympanometry. The studies indicated that WAI has the potential to improve diagnostic accuracy and efficiency in pediatric audiology. Distinct patterns of wideband absorbance were identified, enabling more detailed and accurate diagnostic evaluations. Conclusions: WAI shows substantial potential as a diagnostic tool in pediatric audiology, offering improvements in diagnostic accuracy and efficiency over traditional methods. While the initial findings are promising, further research is needed to fully understand its applicability and benefits across different pediatric populations. Future studies should aim to validate the clinical utility of WAI to ensure its widespread adoption in pediatric audiological assessments.

Low-grade body inflammation is a major cause of osteoarthritis (OA), a common joint disease. Gut dysbiosis may lead to systemic inflammation which can be prevented by probiotic administration. The Lactobacillus delbrueckii subsp. lactis 557 (LDL557) has been demonstrated to have beneficial effects for anti-inflammation. This study investigated the effects of LDL557 on OA progress using monosodium iodoacetate (MIA)-induced OA of rats. Live or heat-killed (HK)-LDL557 of a low or high dose was administrated for two weeks before MIA-induced OA, and then continuously administrated for another six weeks. After taking supplements for eight weeks, OA progress was analyzed. Results showed that MIA induced knee joint swelling, chondrocyte damage, and cartilage degradation, and supplementation with a high dose of LDL557 reduced MIA-induced knee joint swelling, chondrocyte damage, and cartilage degradation. Additionally, MIA increased serum levels of the matrix-degrading enzyme MMP-13, while a high dose of HK-LDL557 decreased it for the controls. Simultaneously, bone turnover markers and inflammatory cytokines of serum were assayed, but no significant differences were found except for a TNF-α decrease from a low dose of live LDL557. These results demonstrated that supplementation with high doses of live LDL557 or HK-LDL557 can reduce the progression of MIA-induced OA in rats.

Background/Objectives: Relapsed B-cell acute lymphoblastic leukemia (B-ALL) remains an unresolved matter of concern regarding adverse outcomes. This case study aimed to evaluate the effectiveness of blinatumomab, with or without door lymphocyte infusion (DLI), in treating measurable residual disease (MRD)-positive B-ALL. Methods: All patients who received blinatumomab salvage therapy were included in this study. Eleven patients were included in the study. All patients were evaluated for MRD-negativity. Results: Before starting blinatumomab therapy, seven patients tested positive for MRD, three tested negative, and one had refractory disease. Hematopoietic cell transplantation (HCT) was reserved for five patients with persistent MRD. Six patients became MRD-negative and subsequent HCT was not performed. Only two patients relapsed; one patient died of relapse, and the other one received carfilzomib-based therapy and was MRD-negative thereafter. Nine patients were MRD-negative at a median follow-up of 28 months (15–52 months). Two of three MRD-positive post-transplant patients remained in complete molecular remission after preemptive DLI at the last follow-up date. In the first salvage, blinatumomab may achieve complete remission and bridging to HCT in pediatric patients with end-of-induction MRD-positive B-cell precursor ALL. Conclusions: The decision on how to treat post-transplant relapse continues to affect survival outcomes. Blinatumomab combined with DLI may extend the armamentarium of release options for high-risk pediatric patients. This approach is encouraging for high-risk ALL patients who are MRD-positive post-transplantation.

Petri nets are a graphical notation for describing a class of discrete event dynamic systems whose behaviours are characterised by concurrency, synchronisation, mutual exclusion and conflict. They have been used over the years for the modelling of various distributed systems applications. With the advent of pervasive systems and the Internet of Things, the Calculus of Context-aware Ambients (CCA) has emerged as a suitable formal notation for analysing the behaviours of these systems. In this paper, we are interested in comparing the expressive power of Petri nets to that of CCA. That is, can the class of systems represented by Petri nets be modelled in CCA? To answer this question, an algorithm is proposed that maps any Petri net onto a CCA process. We prove that a Petri net and its corresponding CCA process are behavioural equivalent. It follows that CCA is at least as expressive as Petri nets, i.e., any system that can be specified in Petri nets can also be specified in CCA. Moreover, tools developed for CCA can also be used to analyse the behaviours of Petri nets.

To alleviate social problems in agriculture such as aging and labor force shortages, automatic growth monitoring based on image measurement has been introduced to tomato cultivation in greenhouses. The overlap of leaves and fruits makes precise observations challenging. In this study, we applied context recognition to tomato growth monitoring by using a Bayesian network. The proposed method combines image recognition using convolutional networks and context recognition using Bayesian networks. It enables not only the recognition of individual tomatoes but also the evaluation of tomato plants. An accurate number of tomatoes and the condition of the stocks can be estimated based on the number of ripe and unripened tomatoes in addition to their density information. The verification experiments clarified that a more accurate number of tomatoes could be estimated than with simple tomato detection, and the stock states could also be evaluated correctly. Compared to conventional methods, the method used in this study has improved tomato decision accuracy by 23%.

This entry examines Lorenz’s error growth models with quadratic and cubic hypotheses, highlighting their mathematical connections to the non-dissipative Lorenz 1963 model. The quadratic error growth model is the logistic ordinary differential equation (ODE) with a quadratic nonlinear term, while the cubic model is derived by replacing the quadratic term with a cubic one. A variable transformation shows that the cubic model can be converted to the same form as the logistic ODE. The relationship between the continuous logistic ODE and its discrete version, the logistic map, illustrates chaotic behaviors, demonstrating computational chaos with large time steps. A variant of the logistic ODE is proposed to show how finite predictability horizons can be determined, emphasizing the continuous dependence on initial conditions (CDIC) related to stable and unstable asymptotic values. This review also presents the mathematical relationship between the logistic ODE and the non-dissipative Lorenz 1963 model.

We aimed to provide a realistic portrayal of customer sentiment and product competitiveness, as well as to inspire businesses to optimise their products and enhance their services. This paper uses 119,190 pairs of real composite review data as a corpus to examine customer sentiment analysis and product competitiveness. The research is conducted by combining TF-IDF text mining with a time-phase division through the k-means clustering method. The study identified ‘quality’, ‘taste’, ‘appearance packaging’, ‘logistics’, ‘prices’, ‘service’, ‘evaluations’, and ‘customer loyalty’ as the commodity dimensions that customers are most concerned about. These dimensions should therefore serve as the primary entry point for improving the commodity and understanding customers. A review of customer feedback reveals that the composite reviews can be divided into three time stages. Furthermore, the sentiment expressed by customers can become increasingly negative over time. The product competitiveness based on the composite review can be characterised by four regional quadrants, such as ‘Advantage Area’, ‘Struggle Area’, ‘Opportunity Area’, and ‘Waiting Area’, and merchants can target these areas to improve product competitiveness according to the dimensional distribution. In the future, it will also be possible to take customer demographics into account in order to gain a deeper understanding of the customer base.

AI is considered a key driver of industrial transformation and a strategic technology that will shape future development. With AI services continuing to permeate various sectors, concerns have emerged about the ethics of AI. This study investigates the effects of different types of AI services (mechanical, thinking, and affective AI services) on consumers’ attitudes through offline and online AI service experiments. We also construct a model to explore the mediating roles of identity threat and perceived control. The findings reveal that mechanical AI services negatively affect consumers’ attitudes while thinking and affective AI services have a positive effect. Additionally, we explore how consumers’ attitudes vary across different service scenarios and ethical judgments (utilitarianism and deontology). Our findings could offer practical guidance for enterprises providing AI services.

Autoantibodies against apolipoprotein A-I (ApoA-I) are associated with cardiovascular disease risks. We aimed to examine the 4-hydroxy-2-nonenal (HNE) modification of ApoA-I in coronary artery disease (CAD) and evaluate the potential risk of autoantibodies against their unmodified and HNE-modified peptides. We assessed plasma levels of ApoA-I, HNE-protein adducts, and autoantibodies against unmodified and HNE-peptide adducts, and significant correlations and odds ratios (ORs) were examined. Two novel CAD-specific HNE-peptide adducts, ApoA-I251–262 and ApoA-I70–83, were identified. Notably, immunoglobulin G (IgG) anti-ApoA-I251–262 HNE, IgM anti-ApoA-I70–83 HNE, IgG anti-ApoA-I251–262, IgG anti-ApoA-I70–83, and HNE-protein adducts were significantly correlated with triglycerides, creatinine, or high-density lipoprotein in CAD with various degrees of stenosis (70%). The HNE-protein adduct (OR = 2.208-fold, p = 0.020) and IgM anti-ApoA-I251–262 HNE (2.046-fold, p = 0.035) showed an increased risk of progression from >30% stenosis in CAD. HNE-protein adducts and IgM anti-ApoA-I251–262 HNE may increase the severity of CAD at high and low levels, respectively.

Computer-aided diagnosis systems play a crucial role in the diagnosis and early detection of breast cancer. However, most current methods focus primarily on the dual-view analysis of a single breast, thereby neglecting the potentially valuable information between bilateral mammograms. In this paper, we propose a Four-View Correlation and Contrastive Joint Learning Network (FV-Net) for the classification of bilateral mammogram images. Specifically, FV-Net focuses on extracting and matching features across the four views of bilateral mammograms while maximizing both their similarities and dissimilarities. Through the Cross-Mammogram Dual-Pathway Attention Module, feature matching between bilateral mammogram views is achieved, capturing the consistency and complementary features across mammograms and effectively reducing feature misalignment. In the reconstituted feature maps derived from bilateral mammograms, the Bilateral-Mammogram Contrastive Joint Learning module performs associative contrastive learning on positive and negative sample pairs within each local region. This aims to maximize the correlation between similar local features and enhance the differentiation between dissimilar features across the bilateral mammogram representations. Our experimental results on a test set comprising 20% of the combined Mini-DDSM and Vindr-mamo datasets, as well as on the INbreast dataset, show that our model exhibits superior performance in breast cancer classification compared to competing methods.

In this investigation, the effects of different annealing temperatures (180, 200, 220, 240, 260, and 280 °C) on the microstructure evolution and properties of an extruded Mg–2.0Zn–1.0Y–0.5Zr (wt%) magnesium alloys were determined. Optical microscopy (OM), scanning electron microscopy (SEM), immersion corrosion, electrochemical corrosion experiments, and tensile testing were performed. Research has found that combining hot extrusion with subsequent low-temperature annealing significantly improves the strength, plasticity, and corrosion resistance of alloys due to grain refinement and a reduced dislocation density. The alloy was completely recrystallized at an annealing temperature of 240 °C for 4 h after solid solution extrusion, and the grains were fine and uniform, demonstrating the best comprehensive properties. Its corrosion rate, ultimate tensile strength, yield strength, and elongation were 0.454 ± 0.023 mm/y, 346.7 ± 8.9 MPa, 292.4 ± 6.9 MPa, and 19.0 ± 0.4%, respectively. The corrosion mechanism of the specimens under extruded and annealed conditions was analyzed. After annealing at 240 °C for 4 h, the dislocation and bimodal grain structure of the samples were almost eliminated, resulting in uniform and fine grains, which were conducive to the formation of a more uniform and denser oxide film, thus improving the corrosion resistance of the alloy.

Background: Chronic obstructive pulmonary disease (COPD), characterized by high-energy metabolism, often leads to malnutrition and is linked to exacerbations. This study investigates the association of malnutrition-related body composition and handgrip strength changes with exacerbation frequencies in COPD patients. Methods: We analyzed 77 acute exacerbation COPD (AECOPD) patients and 82 stable COPD patients, categorized as frequent and infrequent exacerbators. Assessments included body composition, handgrip strength, nutritional risk, dyspnea scale, and COPD assessment. Results: Among AECOPD patients, there were 22 infrequent and 55 frequent exacerbators. Infrequent exacerbators showed better muscle parameters, extracellular water ratio, phase angle, and handgrip strength. Significant differences in intracellular water, total cellular water, protein, and body cell mass were observed between groups. Logistic regression indicated that extracellular water ratio (OR = 1.086) and phase angle (OR = 0.396) were independently associated with exacerbation risk. Thresholds for exacerbation risk were identified as 0.393 for extracellular water ratio and 4.85° for phase angle. In stable COPD, 13 frequent and 69 infrequent exacerbators were compared, showing no significant differences in weight, muscle, and adipose parameters, but significant differences in extracellular water ratio, phase angle, and handgrip strength. Conclusions: These findings suggest that increased exacerbations in COPD patients correlate with higher extracellular water ratios and lower phase angles.

In this study, we report the effect of Zr4+ doping on the optical energy gap and microwave dielectric properties of rutile TiO2. Rietveld analysis explicitly confirmed that Zr4+ occupies the octahedral site, forming a single-phase tetragonal structure below the solubility limit (x < 0.10). Notably, at x = 0.025, a significant enhancement in Q × fo was observed. This enhancement was attributed to the reduction in dielectric loss, associated with a decrease in oxygen vacancies and a lower concentration of Ti3+ paramagnetic centers. This conclusion was supported by Raman and electron paramagnetic resonance spectroscopy, respectively. The origin of high τf in rutile Ti1−xZrxO2 is explained on the basis of the octahedral distortion/tetragonality ratio, covalency, and bond strength.

In light of increasingly prominent environmental issues, inspiring green supply chain (GSC) members to engage in collaborative innovation is crucial to improve environmental performance. In this paper, in relation to a two-level GSC consisting of manufacturers and suppliers, differential equations involving the greenness of intermediate and final products as state variables are constructed considering the effect of digital capability on green innovation. Subsequently, designs for three incentive mechanisms—the greenness reward, the R&D effort reward, and the digital construction reward—are presented, and their long-term dynamic effects on the economic, environmental, and social benefits are compared and analyzed. Finally, the impacts of consumer green preference and the contribution of digital capability to the advancement of green innovation are explored. The findings show that all these incentives can boost economic, environmental, and social benefits while motivating the supplier. To achieve the best incentive effect, the reward coefficient should fall within a specific range. The digital construction reward mechanism is the most favourable in the initial stage, while the R&D effort reward mechanism is the most appropriate in the long term. The promotion effects of digital capability on green innovation and consumer green preference have the potential to enhance economic, environmental, and social performance.

Visualizing multivariate data can be challenging, especially for the general public. The difficulties extend beyond determining how to present the data; they also involve comprehension. Early literature has identified various methods, including Chernoff’s face, but these methods often have significant drawbacks, making them challenging to interpret. Subsequently, other techniques, such as scatterplots, parallel coordinate plots, and dynamic graphics, have been introduced. However, many of these methods can be intricate to create and interpret, particularly when visualizing high-dimensional data. Additionally, simultaneously representing discrete aspects (including “space”) and continuous aspects (including “time”) presents another challenge. This study proposes a novel approach named the “Δ table” (delta table), which transforms space–time multivariate data consisting of discrete and continuous variables into a tabular format. The Δ table is believed to be more user-friendly for the general public, which is its most significant advantage compared to previous methods. Finally, we used a case study of the decoupling of the world’s developed, newly industrialized, and developing economies in recent decades as an example of an attempt to apply the Δ table.

The conjugate of rhein and artesunate have shown promising effects in inducing immunogenic cell death (ICD) and inhibiting tumor growth. Rhein, a natural anthraquinone derivative found in various medicinal plants such as Rheum palmatum, possesses diverse pharmacological properties including anti-inflammatory and anticancer activities. Artesunate, a sesquiterpene lactone extracted from Artemisia annua, exhibits potent antimalarial efficacy and has garnered attention for its potential anticancer properties. Through rational drug design, the conjugation of rhein with artesunate has yielded compounds capable of selectively targeting mitochondria of cancer cells, inducing oxidative stress-mediated ICD, and enhancing the immunogenicity of tumor cells. The conjugate leverages the inherent cytotoxicity of artesunate while incorporating the capability to selectively target the mitochondria of rhein, thereby fostering a special approach to immunotherapy for cancer. Upon accumulation in the mitochondria, these compounds induce the generation of reactive oxygen species (ROS), leading to mitochondrial membrane potential (ΔΨm) reduction and endoplasmic reticulum (ER) stress. Notably, the conjugate exhibits far more potent ICD-inducing properties than their parent compounds. In vivo studies have demonstrated that the vaccine, when treated with the conjugate, effectively suppresses tumor growth.

Recently, the construction of the trisubstituted olefin-type probe molecules has elicited the attention of many researchers. However, the synthesis of the trisubstituted olefin-type probes containing two N-heterocycles simultaneously has been rarely reported. In this study, starting from the inexpensive mucobromic acid 1 and N-heterocyclic compound 2, we first utilized a simple one-step reaction to synthesize a series of trisubstituted olefin-type compounds 3 simultaneously bearing with the structure of two N-heterocyclic rings in the absence of transition metal catalysts with a yield of 62–86%. The optimal reaction conditions were systematically explored, and the structure of the obtained compounds 3 were well characterized with 1H NMR, 13C NMR, X-ray single-crystal and HR-MS. The preliminary observation showed that, in the presence of base, mucobromic acid 1 reacts as its ring-opening structure, and the successive nucleophilic substitution reaction and Michael addition reaction can generate the target product 3. Considering that the aldehyde group in the molecular structure of the trisubstituted olefin-type compounds 3 may react with malononitrile, we carried out some relevant investigations so as to realize the visual detection of malononitrile. Interestingly, among the products, compounds 3a–3c can be prepared in portable test strips through a simple process and used to achieve the naked-eye detection of malononitrile in environmental systems as designed.

Taking advantage of fact that the surface electrons of metallic nanoparticles (NPs) can be effectively released even at a low voltage bias, we demonstrate an improvement in the electrochemical performance of nanosized Prussian Blue (PB)-based secondary batteries through the incorporation of bare Ag or Ni NPs in the vicinity of the working PB NPs. It is found that the capacity for electrochemical energy storage of the 17 nm PB-based battery is significantly higher than the capacity of 10 nm PB-based, 35 nm PB-based or 46 nm PB-based batteries. There is a critical PB size for the highest electrochemical energy storage efficiency. The full specific capacity CF of the 17 nm PB-based battery stabilized to 62 mAh/g after 130 charge–discharge cycles at a working current of IW = 0.03 mA. The addition of 14 mass percent of Ag NPs in the vicinity of the PB NPs gave rise to a 32% increase in the stabilized CF. A 42% increase in the stabilized CF could be obtained with the addition of 14 mass percent of Ag NPs on the working electrode of the 35 nm PB-based battery. An enhancement in CF was also found for electrodes incorporating bare Ni NPs but the effect was smaller.

This study examined the risk of low energy availability (LEA) and eating disorders among elite adolescent female athletes from a mixed-sport cohort and ballet dancers in Singapore, where the accelerated biological needs of adolescent growth and maturation overlap with the pursuit of sport/ballet excellence and high-stakes academic testing. All of these are competing demands for adequate fuelling and seeking timely treatment, which consequently affect the risk of LEA. Eighty-nine participants (41 athletes, 48 dancers; age 16.00 [3.00] years old) were screened for the risk of LEA and eating disorders using the Low Energy Availability in Females Questionnaire (LEAF-Q) and Brief Eating Disorder in Athletes Questionnaire (BEDA-Q), respectively. The main effects of athlete/ballet status and age (≤15 years old versus 15.1 years–18 years old) on LEAF-Q and BEDA-Q scores was be determined via Spearman’s correlation coefficient and linear regression analyses. The Mann–Whitney U test and Fisher’s exact test were used to compare the groups for the risk of LEA and ED. We observed that adolescent athletes and ballet dancers had a similar prevalence of being at risk of LEA (61.98% versus 54.17%, respectively, p = 0.529), with the risk of eating disorders absent in most of them. It appeared that the risk of LEA is likely of an unintentional nature in this study as the risk of ED was absent in 90.2% of the adolescent athletes/ballet dancers that were at risk of LEA. The age of the participants was significantly correlated with the risk of LEA, while the number of training hours was negatively correlated with the same factor. Age was also found to be negatively correlated with the number of training hours. The findings suggest that the risk of LEA in Singapore youth athletes and dancers are multifaceted, involving factors such as type of activity, age, and training hours. Targeted and tailored interventions and programmes are thus needed to promote adequate energy availability for optimal physical and psychological growth in sport and dance.

Oxygen evolution reactions (OER) are often the decisive step in determining the water electrolysis rate. The first row of transition metals and their derivatives, represented by Ni and Fe, have attracted much attention due to their excellent OER performance. Here, we develop a one-step strategy for preparing oxygen-evolving electrodes, in which the NiFeOOH-modified NiFe layered double hydroxide (NiFe-LDH) nanosheet is supported by nickel foam. At 100 mA·cm−2, the overpotential of NiFeOOH-NiFe-LDH was just 227 mV, and the duration times were over 200 h in 1 mol·L−1 KOH. Furthermore, the co-existence of LDH and hydroxyl oxides helps the oxygen evolution reaction. These results suggest the potential for this synthesis strategy to provide a low-cost, highly active OER electrocatalyst for industrial water splitting.

The rpl1001 gene encodes 60S ribosomal protein L10, which is involved in intracellular protein synthesis and cell growth. However, it is not yet known whether it is involved in the regulation of cell mitosis dynamics. This study focuses on the growth, spore production, cell morphology, the dynamics of microtubules, chromosomes, actin, myosin, and mitochondria of fission yeast (Schizosaccharomyces pombe) to investigate the impact of rpl1001 deletion on cell mitosis. RNA-Seq and bioinformatics analyses were also used to reveal key genes, such as hsp16, mfm1 and isp3, and proteasome pathways. The results showed that rpl1001 deletion resulted in slow cell growth, abnormal spore production, altered cell morphology, and abnormal microtubule number and length during interphase. The cell dynamics of the rpl1001Δ strain showed that the formation of a monopolar spindle leads to abnormal chromosome segregation with increased rate of spindle elongation in anaphase of mitosis, decreased total time of division, prolonged formation time of actin and myosin loops, and increased expression of mitochondrial proteins. Analysis of the RNA-Seq sequencing results showed that the proteasome pathway, up-regulation of isp3, and down-regulation of mfm1 and mfm2 in the rpl1001Δ strain were the main factors underpinning the increased number of spore production. Also, in the rpl1001Δ strain, down-regulation of dis1 caused the abnormal microtubule and chromosome dynamics, and down-regulation of hsp16 and pgk1 were the key genes affecting the delay of actin ring and myosin ring formation. This study reveals the effect and molecular mechanism of rpl1001 gene deletion on cell division, which provides the scientific basis for further clarifying the function of the Rpl1001 protein in cell division.

The circadian system, a vital temporal regulator influencing physiological processes, has implications for cancer development and treatment response. Our study assessed circadian timing’s impact on whole-brain radiotherapy outcomes in brain metastases for personalized cancer therapy insights. The aim of the study was to evaluate circadian influence on radiation treatment timing and its correlation with clinical outcomes and to identify patient populations benefiting from interventions synchronizing circadian rhythms, considering subgroup differences and potential disparities. An IRB-approved retrospective analysis of 237 patients undergoing whole-brain radiotherapy for brain metastases (2017–2021), receiving over 80% of treatments in the morning or afternoon, was performed. Survival analyses utilized Kaplan–Meier curves. This was a single-institution study involving patients receiving whole-brain radiotherapy. Demographic, disease, and socioeconomic parameters from electronic medical records were collected. Morning treatment (n = 158) showed a trend toward improved overall survival vs. afternoon (n = 79); the median survival was 158 vs. 79 days (p = 0.20, HR = 0.84, CI95% 0.84–0.91). Subgroup benefits for morning treatment in females (p = 0.04) and trends in controlled primary disease (p = 0.11) and breast cancer metastases (p = 0.08) were observed. Black patients exhibited diminished circadian influence. The present study emphasized chronobiological factors’ relevance in brain metastases radiation therapy. Morning treatment correlated with improved survival, particularly in specific subgroups. Potential circadian influence disparities were identified, laying a foundation for personalized cancer therapy and interventions synchronizing circadian rhythms for enhanced treatment efficacy.

Consumers’ personality traits significantly influence their perceptions regarding social media advertising. While prior research on consumers’ purchasing intentions in social networking sites advertising has mainly focused on advertising valence antecedents, it is crucial to recognize that consumers’ susceptibility to advertising persuasion, particularly in terms of empathic expression, varies based on a key criterion: whether consumers are driven to attain a specific desired state or are more inclined to avoid an undesirable state. Regulatory Focus Theory (RFT) posits that individuals operate under distinct motivational mechanisms that govern their determination to achieve desired goals, influencing how they process and evaluate advertising messages. In light of RFT, we conducted an online survey with 524 valid responses, utilizing partial least squares (PLS) for research model analysis. The findings revealed that promotion-focused individuals have positively influenced perceptions of social media ad effectiveness (informativeness, ad creativity, perceived relevance, and emotional appeal). In contrast, prevention-focused individuals negatively perceived social media ad effectiveness. Furthermore, this study highlighted that perceived relevance and emotional appeal have a more significant impact on attitudes toward expressing empathy than informativeness and ad creativity.

The main challenges in 3D metrology involve measuring TSVs etched with very high aspect ratios, where the via depth to diameter ratio approaches 10:1–20:1. In this paper, we introduce an innovative approach to enhance our in-house spectroscopic reflectometer module by integrating aperture technology, resulting in a substantial amplification of interference signals. Our system offers the flexibility to conduct measurements on an average number of TSVs, individual TSVs, or specific periodic arrays of TSVs. Additionally, we demonstrate the utility of the spectroscopic reflectometer as a non-destructive, high-speed metrology solution for in-line monitoring of TSV etch uniformity. Through a series of experimental trials in a reactive ion etch (RIE) process, we show that leveraging feedback data from the reflectometer leads to marked improvements in etch depth uniformity.

This work applied three machine learning (ML) models—linear regression (LR), random forest (RF), and support vector regression (SVR)—to predict the lattice parameters of the monoclinic B19′ phase in two distinct training datasets: previously published ZrO2-based shape-memory ceramics (SMCs) and NiTi-based high-entropy shape-memory alloys (HESMAs). Our findings showed that LR provided the most accurate predictions for ac, am, bm, and cm in NiTi-based HESMAs, while RF excelled in computing βm for both datasets. SVR disclosed the largest deviation between the predicted and actual values of lattice parameters for both training datasets. A combination approach of RF and LR models enhanced the accuracy of predicting lattice parameters of martensitic phases in various shape-memory materials for stable high-temperature applications.

Polyurethane foam (PUF) pads are widely used in semiconductor manufacturing, particularly for chemical mechanical polishing (CMP). This study prepares PUF composites with microcrystalline cellulose (MCC) and nanocrystalline cellulose (NCC) to improve CMP performance. MCC and NCC were characterized using scanning electron microscopy (SEM) and X-ray diffraction (XRD), showing average diameters of 129.7 ± 30.9 nm for MCC and 22.2 ± 6.7 nm for NCC, both with high crystallinity (ca. 89%). Prior to preparing composites, the study on the influence of the postbaked step on the PUF was monitored through Fourier-transform infrared spectroscopy (FTIR). After that, PUF was incorporated with MCC/NCC to afford two catalogs of polyurethane foam composites (i.e., PUFC-M and PUFC-N). These PUFCs were examined for their thermal and surface properties using a differential scanning calorimeter (DSC), thermogravimetric analysis (TGA), dynamic mechanical analyzer (DMA), and water contact angle (WCA) measurements. Tgs showed only slight changes but a notable increase in the 10% weight loss temperature (Td10%) for PUFCs, rising from 277 °C for PUF to about 298 °C for PUFCs. The value of Tan δ dropped by up to 11%, indicating improved elasticity. Afterward, tensile and abrasion tests were conducted, and we acquired significant enhancements in the abrasion performance (e.g., from 1.04 mm/h for the PUF to 0.76 mm/h for a PUFC-N) of the PUFCs. Eventually, we prepared high-performance PUFCs and demonstrated their capability toward the practical CMP process.

Background: Hepatocellular carcinoma (HCC) remains a significant concern for patients with chronic hepatitis C (HCV), even after achieving a sustained virological response (SVR) with direct-acting antivirals (DAAs) or interferon (IFN)-based therapies. This study compared the risk of HCC in patients with HCV who achieved SVR through the DAA versus IFN regimens. Methods: A retrospective analysis was conducted on 4806 HCV patients, without coinfection nor prior HCC history, treated at the Chang Gung Memorial Hospital, Taiwan (DAA: 2825, IFN: 1981). Kaplan–Meier and Cox regression analyses with propensity score matching (PSM) were used to adjust for baseline differences. Results: DAA-treated patients exhibited a higher incidence of HCC than IFN-treated patients before and after PSM (after PSM: annual: 1% vs. 0.5%; 6-year: 6% vs. 3%, p = 0.01). Both DAA and IFN patients had a decreased HCC incidence during follow-up (>3 vs. p < 0.05). HCC incidence was higher in the first three years post-SVR in DAA-treated ACLD patients and then decreased (3.26% vs. 1.39% per year, p < 0.01). In contrast, HCC incidence remained constant in the non-ACLD and IFN-treated groups. Multivariate Cox regression identified age ≥ 60, male sex, BMI, AFP ≥ 6 ng/mL, FIB-4, and ACLD status as independent risk factors for HCC, but antiviral regimens were not an independent factor for HCC. Conclusion: DAA treatment significantly affects HCC risk primarily within three years post-treatment, especially in younger HCV patients with ACLD. HCC incidence was reduced after three years in ACLD patients treated by DAA, but continued surveillance was still necessary. However, patients under 60 without advanced liver disease may require less intensive follow-up.

The rapid proliferation of network applications has led to a significant increase in network attacks. According to the OWASP Top 10 Projects report released in 2021, injection attacks rank among the top three vulnerabilities in software projects. This growing threat landscape has increased the complexity and workload of software testing, necessitating advanced tools to support agile development cycles. This paper introduces a novel test prioritization method for SQL injection vulnerabilities to enhance testing efficiency. By leveraging previous test outcomes, our method adjusts defense strength vectors for subsequent tests, optimizing the testing workflow and tailoring defense mechanisms to specific software needs. This approach aims to improve the effectiveness and efficiency of vulnerability detection and mitigation through a flexible framework that incorporates dynamic adjustments and considers the temporal aspects of vulnerability exposure.

With the rapid development of urban rail transit networks, constructing shield tunnels often requires passing underneath existing buildings, which can potentially impact their safety. This study examined the impact of constructing a double-line shield tunnel underneath a railway bridge on the adjacent pile foundation via numerical simulation. Protective measures, including construction parameter control, grouting methods, monitoring, and early warning systems, were implemented to mitigate impacts. The results indicated that the bridge deformation fell within acceptable limits, with maximum horizontal and longitudinal displacements of 0.06 mm and a maximum vertical displacement of −0.31 mm. The railway bridge pile foundation experienced maximum horizontal and longitudinal displacements of 0.47 mm and vertical displacements of −0.23 mm during construction. Enhanced construction quality control and monitoring effectively controlled deformation to ensure the railway safety. This study provides valuable guidance for similar projects and future urban rail transit developments.

A comprehensive study was conducted at a wave energy device test site located off the northeastern coast of Taiwan to assess the influence of oceanic currents on experimental equipment. A bottom-mounted 600 kHz acoustic Doppler current profiler, equipped with integrated temperature and pressure sensors, was deployed at a depth of approximately 31 m. This study, spanning from 6 June 2023 to 11 May 2024, recorded ocean current profiles by assembling data from 50 pings every 10 min, with a resolution of one meter per depth layer. The findings reveal that variations in water levels were predominantly influenced by the M2 tidal constituent, followed by the O1, K1, and S2 tides. Notably, seawater temperature fluctuations at the seabed were modulated by tides, especially the M2 tide. A significant drop in seawater temperature was also observed as the typhoon passed through the south of Taiwan. In terms of sea surface currents, the measured maximum current speed was 71.89 cm s−1, but the average current speed was only 15.47 cm s−1. Tidal currents indicated that the M4 and M2 tides were the most significant, with semimajor axes and inclination angles of 8.48 cm s−1 and 102.60°, and 7.00 cm s−1 and 97.76°, respectively. Seasonally, barotropic tidal currents were the strongest in winter. Additionally, internal tides were identified, with the first baroclinic mode being dominant. The zero-crossing depths varied between 14 and 18 m. During the summer, the M2 baroclinic tidal current displayed characteristics of the second baroclinic mode, with zero-crossing depths at approximately 7 m and 22 m. This node aligns with results from the empirical orthogonal function analysis and correlates with the depths’ significant shifts in seawater temperature as measured by a conductivity, temperature, and depth instrument. Despite the velocities of internal tides not being strong, the directional variance between surface and bottom flows presents critical considerations for the deployment and operation of moored wave energy devices.

Mitochondrial damage is an early and key marker of neuronal damage in prion diseases. As a process involved in mitochondrial quality control, mitochondrial biogenesis regulates mitochondrial homeostasis in neurons and promotes neuron health by increasing the number of effective mitochondria in the cytoplasm. Sirtuin 1 (SIRT1) is a NAD+-dependent deacetylase that regulates neuronal mitochondrial biogenesis and quality control in neurodegenerative diseases via deacetylation of a variety of substrates. In a cellular model of prion diseases, we found that both SIRT1 protein levels and deacetylase activity decreased, and SIRT1 overexpression and activation significantly ameliorated mitochondrial morphological damage and dysfunction caused by the neurotoxic peptide PrP106–126. Moreover, we found that mitochondrial biogenesis was impaired, and SIRT1 overexpression and activation alleviated PrP106–126-induced impairment of mitochondrial biogenesis in N2a cells. Further studies in PrP106–126-treated N2a cells revealed that SIRT1 regulates mitochondrial biogenesis through the PGC-1α-TFAM pathway. Finally, we showed that resveratrol resolved PrP106–126-induced mitochondrial dysfunction and cell apoptosis by promoting mitochondrial biogenesis through activation of the SIRT1-dependent PGC-1α/TFAM signaling pathway in N2a cells. Taken together, our findings further describe SIRT1 regulation of mitochondrial biogenesis and improve our understanding of mitochondria-related pathogenesis in prion diseases. Our findings support further investigation of SIRT1 as a potential target for therapeutic intervention of prion diseases.

Colorectal cancer (CRC) is a prevalent and escalating health issue in Taiwan. This nationwide study delves into the relationship between Human Papillomavirus (HPV) infection and CRC risk, employing population datasets from 2007 to 2017. Cox regression analyses revealed a statistically significant hazard ratio (HR) of 1.73 (95% CI: 1.63–1.83) for CRC in HPV-positive patients, indicating a considerably elevated risk compared to non-infected individuals. Further, stratification by sex showed males with HPV have a higher CRC risk (HR = 1.49, 95% CI: 1.40–1.58) compared to females. Age-related analysis uncovered a progressive increase in CRC risk with advancing age (HR = 34.69 for over 80 years). The study of specific CRC subtypes showed varying risks: HR = 1.74 for the colon, HR = 1.64 for the rectum, and a notably higher HR = 4.72 for the anus. Comorbid conditions such as hypertension (HR = 1.26), diabetes mellitus (HR = 1.32), and abnormal liver function (HR = 1.18) also correlate with significantly increased CRC risks. These findings suggest that HPV is a significant risk factor for CRC, with disparities in risk based on anatomical location, demographic characteristics, and comorbidities, highlighting the need for intervention strategies and targeted prevention.

Industrial Control Systems (ICSs) have faced a significant increase in malware threats since their integration with the Internet. However, existing machine learning-based malware identification methods are not specifically optimized for ICS environments, resulting in suboptimal identification performance. In this work, we propose an innovative method explicitly tailored for ICSs to enhance the performance of malware classifiers within these systems. Our method integrates the opcode2vec method based on preprocessed features with a conditional variational autoencoder–generative adversarial network, enabling classifiers based on Convolutional Neural Networks to identify malware more effectively and with some degree of increased stability and robustness. Extensive experiments validate the efficacy of our method, demonstrating the improved performance of malware classifiers in ICSs. Our method achieved an accuracy of 97.30%, precision of 92.34%, recall of 97.44%, and F1-score of 94.82%, which are the highest reported values in the experiment.

The Colorado potato beetle (Leptinotarsa decemlineata) is a major pest of potato crops. While Knickkopf (Knk) genes are essential for insect cuticle formation, their roles in pests like L. decemlineata remain unclear. This study aims to identify and characterize Knk genes in L. decemlineata and explore their functions in larval development and cuticle integrity. We used genomic and transcriptomic databases to identify LdKnk-family genes, validated through RT-PCR and RACE. Gene expression was analyzed at various developmental stages and tissues using qRT-PCR. RNA interference (RNAi) and Transmission electron microscopy (TEM) were applied to determine the functional roles of these genes. Four LdKnk-family genes were identified. Spatio-temporal expression analysis indicated significant gene expression during larval molting and pupal stages, especially in the epidermis. RNAi experiments showed that silencing LdKnk and LdKnk3-5′ led to reduced larval weight, cuticle thinning, and increased mortality, while LdKnk3-FL knockdown caused abnormal cuticle thickening and molting disruptions. LdKnk2 knockdown increased epicuticle and endocuticle thickness without visible phenotypic changes. The study highlights the essential roles of LdKnk-family genes in maintaining cuticle structure and integrity, suggesting their potential as targets for RNAi-based pest control.

Coupling solar-induced chlorophyll fluorescence (SIF) with gross primary productivity (GPP) for ecological function integration research presents numerous uncertainties, especially in ecologically fragile and climate-sensitive arid regions. Therefore, evaluating the suitability of SIF data for estimating GPP and the feasibility of improving its accuracy in the northern region of Xinjiang is of profound significance for revealing the spatial distribution patterns of GPP and the strong coupling relationship between GPP and SIF in arid regions, achieving the goal of “carbon neutrality” in arid regions. This study is based on multisource SIF satellite data and GPP observation data from sites in three typical ecosystems (cultivated and farmlands, pasture grasslands, and desert vegetation). Two precision improvement methods (canopy and linear) are used to couple multiple indicators to determine the suitability of multisource SIF data for GPP estimation and the operability of accuracy improvement methods in arid regions reveal the spatial characteristics of SIF (GPP). The results indicate the following. (1) The interannual variation of GPP shows an inverted “U” shape, with peaks values in June and July. The cultivated and farmland areas have the highest peak value among the sites (0.35 gC/m2/month). (2) The overall suitability ranking of multisource SIF satellite products for GPP estimation in arid regions is RTSIF > CSIF > SIF_OCO2_005 > GOSIF. RTSIF shows better suitability in the pasture grassland and cultivated and farmland areas (R2 values of 0.85 and 0.84, respectively). (3) The canopy method is suitable for areas with a high leaf area proportion (R2 improvement range: 0.05–0.06), while the linear method is applicable across different surface types (R2 improvement range: 0.01–0.13). However, the improvement effect of the linear method is relatively weaker in areas with high vegetation cover. (4) Combining land use data, the overall improvement of SIF (GPP) is approximately 0.11%, and the peak values of its are mainly distributed in the northern and southern slopes of the Tianshan Mountains, while the low values are primarily found in the Gurbantunggut Desert. The annual mean value of SIF (GPP) is about 0.13 mW/m2/nm/sr. This paper elucidates the applicability of SIF for GPP estimation and the feasibility of improving its accuracy, laying the theoretical foundation for the spatiotemporal coupling study of GPP and SIF in an arid region, and providing practical evidence for achieving carbon neutrality goals.

Prostate cancer (PCa) is a multifactorial disease influenced by genetic, environmental, and immunological factors. Genetic polymorphisms in the interleukin-10 (IL-10) gene have been implicated in PCa susceptibility, development, and progression. This study aims to assess the contributions of three IL-10 promoter single nucleotide polymorphisms (SNPs), A-1082G (rs1800896), T-819C (rs3021097), and A-592C (rs1800872), to the risk of PCa in Taiwan. The three IL-10 genotypes were determined using PCR-RFLP methodology and were evaluated for their contributions to PCa risk among 218 PCa patients and 436 non-PCa controls. None of the three IL-10 SNPs were significantly associated with the risks of PCa (p all > 0.05) in the overall analyses. However, the GG at rs1800896 combined with smoking behavior was found to significantly increase the risk of PCa by 3.90-fold (95% confidence interval [95% CI] = 1.28–11.89, p = 0.0231). In addition, the rs1800896 AG and GGs were found to be correlated with the late stages of PCa (odds ratio [OR] = 1.90 and 6.42, 95% CI = 1.05–3.45 and 2.30–17.89, p = 0.0452 and 0.0003, respectively). The IL-10 promoter SNP, A-1082G (rs1800896), might be a risk factor for PCa development among smokers and those at late stages of the disease. These findings should be validated in larger and more diverse populations.

Advancements in assisted driving technologies are expected to enable future passengers to use a wide range of multimedia applications in electric vehicles (EVs). To address the bandwidth demands for high-resolution and immersive videos during peak traffic, this study introduces a bandwidth-management algorithm to support differentiated streaming services in heterogeneous vehicle-to-everything (V2X) networks. By leveraging cellular 6G base stations, along with Cell-Free (CF) Massive Multi-Input Multi-Output (mMIMO) Wi-Fi 7 access points, the algorithm aims to provide a high-coverage, high-speed, and low-interference V2X network environment. Additionally, Li-Fi technology is employed to supply extra bandwidth to vehicles with limited connectivity via V2V communication. Importantly, the study addresses the urgency and prioritization of different applications to ensure the smooth execution of emergency applications and introduces a pre-downloading mechanism specifically for non-real-time applications. Through simulations, the algorithm’s effectiveness in meeting EV users’ bandwidth needs for various multimedia streaming applications is demonstrated. During peak-bandwidth-demand periods, users experienced an average increase in bandwidth of 47%. Furthermore, bandwidth utilization across the V2X landscape is significantly improved.

A web-based Java programming learning assistant system (JPLAS) has been developed for novice students to study Java programming by themselves while enhancing code reading and code writing skills. One type of the implemented exercise problem is code writing problem (CWP), which asks students to create a source code that can pass the given test code. The correctness of this answer code is validated by running them on JUnit. In previous works, a Python-based answer code validation program was implemented to assist teachers. It automatically verifies the source codes from all the students for one test code, and reports the number of passed test cases by each code in the CSV file. While this program plays a crucial role in checking the correctness of code behaviors, it cannot detect code plagiarism that can often happen in programming courses. In this paper, we implement a code plagiarism checking function in the answer code validation program, and present its application results to a Java programming course at Okayama University, Japan. This function first removes the whitespace characters and the comments using the regular expressions. Next, it calculates the Levenshtein distance and similarity score for each pair of source codes from different students in the class. If the score is larger than a given threshold, they are regarded as plagiarism. Finally, it outputs the scores as a CSV file with the student IDs. For evaluations, we applied the proposed function to a total of 877 source codes for 45 CWP assignments submitted from 9 to 39 students and analyzed the results. It was found that (1) CWP assignments asking for shorter source codes generate higher scores than those for longer codes due to the use of test codes, (2) proper thresholds are different by assignments, and (3) some students often copied source codes from certain students.

In the paper, we first develop a novel automatically energy-preserving scheme (AEPS) for the undamped and unforced single and multi-coupled Duffing equations by recasting them to the Lie-type systems of ordinary differential equations. The AEPS can automatically preserve the energy to be a constant value in a long-term free vibration behavior. The analytical solution of a special Duffing–van der Pol equation is compared with that computed by the novel group-preserving scheme (GPS) which has fourth-order accuracy. The main novelty is that we constructed the quadratic forms of the energy equations, the Lie-algebras and Lie-groups for the multi-coupled Duffing oscillator system. Then, we extend the GPS to the damped and forced Duffing equations. The corresponding algorithms are developed, which are effective to depict the long term nonlinear vibration behaviors of the multi-coupled Duffing oscillators with an accuracy of O(h4) for a small time stepsize h.

The apple is a delicious fruit with high nutritional value that is widely grown around the world. Apples are traditionally picked by hand, which is very inefficient. The development of advanced fruit-picking robots has great potential to replace manual labor. A major prerequisite for a robot to successfully pick fruits the accurate identification and positioning of the target fruit. The active laser vision systems based on structured algorithms can achieve higher recognition rates by quickly capturing the three-dimensional information of objects. This study proposes to combine the laser active vision system with the YOLOv5 neural network model to recognize and locate apples on trees. The method obtained accurate two-dimensional pixel coordinates, which, when combined with the active laser vision system, can be converted into three-dimensional world coordinates for apple recognition and positioning. On this basis, we built a picking robot platform equipped with this visual recognition system, and carried out a robot picking experiment. The experimental findings showcase the efficacy of the neural network recognition algorithm proposed in this study, which achieves a precision rate of 94%, an average precision mAP% of 92.86%, and a spatial localization accuracy of approximately 4 mm for the visual system. The implementation of this control method in simulated harvesting operations shows the promise of more precise and successful fruit positioning. In summary, the integration of the YOLOv5 neural network model with an active laser vision system presents a novel and effective approach for the accurate identification and positioning of apples. The achieved precision and spatial accuracy indicate the potential for enhanced fruit-harvesting operations, marking a significant step towards the automation of fruit-picking processes.

Lymphoma diagnoses in the US are substantial, with an estimated 89,380 new cases in 2023, necessitating innovative treatment approaches. Phase 1 clinical trials play a pivotal role in this context. We developed a binary predictive model to assess trial adherence to expected average durations, analyzing 1089 completed Phase 1 lymphoma trials from clinicaltrials.gov. Using machine learning, the Random Forest model demonstrated high efficacy with an accuracy of 0.7248 and an ROC-AUC of 0.7677 for lymphoma trials. The difference in the accuracy level of the Random Forest is statistically significant compared to the other alternative models, as determined by a 95% confidence interval on the testing set. Importantly, this model maintained an ROC-AUC of 0.7701 when applied to lung cancer trials, showcasing its versatility. A key insight is the correlation between higher predicted probabilities and extended trial durations, offering nuanced insights beyond binary predictions. Our research contributes to enhanced clinical research planning and potential improvements in patient outcomes in oncology.

Medium optimization and development for selective bacterial cultures are essential for isolating and functionalizing individual bacteria in microbial communities; nevertheless, it remains challenging due to the unknown mechanisms between bacterial growth and medium components. The present study first tried combining machine learning (ML) with active learning to fine-tune the medium components for the selective culture of two divergent bacteria, i.e., Lactobacillus plantarum and Escherichia coli. ML models considering multiple growth parameters of the two bacterial strains were constructed to predict the fine-tuned medium combinations for higher specificity of bacterial growth. The growth parameters were designed as the exponential growth rate (r) and maximal growth yield (K), which were calculated according to the growth curves. The eleven chemical components in the commercially available medium MRS were subjected to medium optimization and specialization. High-throughput growth assays of both strains grown separately were performed to obtain thousands of growth curves in more than one hundred medium combinations, and the resultant datasets linking the growth parameters to the medium combinations were used for the ML training. Repeated rounds of active learning (i.e., ML model construction, medium prediction, and experimental verification) successfully improved the specific growth of a single strain out of the two. Both r and K showed maximized differentiation between the two strains. A further analysis of all the data accumulated in active learning identified the decision-making medium components for growth specificity and the differentiated, determinative manner of growth decisions of the two strains. In summary, this study demonstrated the efficiency and practicality of active learning in medium optimization for selective cultures and offered novel insights into the contribution of the chemical components to specific bacterial growth.