Search Results (834)

The cryptocurrency landscape underwent significant changes in 2024 with the regulatory approval of spot Bitcoin ETFs, opening the market to institutional investors and millions of new clients. As Bitcoin reached new price peaks, the market attracted many retail traders using speculative approaches, evidenced by the surge in meme coins at the end of 2024. In such an environment, properly examining trading strategies can offer substantial advantages over the majority of market participants. Many traders, however, fail to test their strategies adequately, limiting evaluations to selected time periods and risking overfitting. This paper introduces the Rolling Strategy–Hold Ratio (RSHR), which uses a rolling-window approach to evaluate how strategies would perform from thousands of different starting points. This method helps mitigate recency bias and provides a more comprehensive understanding of strategy performance across diverse market conditions and cycles. By comparing strategies results against buy-and-hold results, traders can make informed decisions about whether to refine their strategies further or opt for index-based investing or alternative analytical methods. This study demonstrates the RSHR’s applications across technical, on-chain, sentiment analysis, and dollar cost averaging strategies, with initial research suggesting potential applications in traditional markets. Full article

Esterases are ubiquitous enzymes found in all living organisms, including animals, plants, and microorganisms. They are involved in several biological processes, including the synthesis and breakdown of biomolecules, such as nucleic acids, lipids, and esters; phosphorus metabolism; detoxification of natural and artificial toxicants; polymer breakdown and synthesis; remodeling; and cell signaling. The present review focuses on the most industrially important esterases, namely lipases, phospholipases, cutinases, and polyethylene terephthalate hydrolases (PETases). Esterases are widely used in industrial and biotechnological applications. Notably, the biotechnological production of esters, including methyl acetate, ethyl acetate, vinyl acetate, polyvinyl acetate, and ethyl lactate, as an alternative to chemical production, represents a multi-billion-dollar industry. Currently, most enzymes (>75%) used in industrial processes are hydrolytic. Among them, lipases and phospholipases are primarily used for lipid modification. Lipases are the third most commercialized enzymes after proteases and carboxyhydrases, and their production is steadily increasing, currently representing over one-fifth of the global enzyme market. Esterases, particularly lipases, phospholipases, and cutinases, are employed in cosmetics, food, lubricants, pharmaceuticals, paints, detergents, paper, and biodiesel, among other industries. Overall, biotechnological production using enzymes is gaining global traction owing to its environmental benefits, high yields, and efficiency, aligning with green economy principles. Full article

Agrostis stolonifera L., commonly known as creeping bentgrass, is an important cool-season turfgrass used in landscaping and sports fields. However, creeping bentgrass is prone to various diseases, including dollar spot, brown patch, and bacterial yellowing, during its maintenance, leading to significant degradation in turf quality, esthetics, and greening functions, resulting in substantial losses in turfgrass production and management. On the other hand, extreme environmental conditions such as high temperatures, drought, and salinity have also caused a decline in the quality of creeping bentgrass. Moreover, creeping bentgrass has a moderately sized genome and is easy to genetically transform, making it an ideal model system for studying grass stress biology. This article provides an overview of the major diseases and stressors in the management of creeping bentgrass and proposes future research directions for the disease resistance and stress tolerance of creeping bentgrass. Full article

In recent years, the correlation mechanisms between geopolitical risks and financial markets have drawn considerable attention from both academic circles and investment communities. However, their multiscale, nonlinear interactive characteristics still require further investigation. To address this, this paper proposes a dynamic nonlinear causal information network combined with a wavelet transform model and the transfer entropy method. We select the geopolitical risk index, the US dollar index, Brent and WTI crude oil prices, COMEX gold futures, and London gold prices time series as the research objects. The results suggest that the network’s structure changes with time at different time scales. On the one hand, COMEX gold (London gold) acts as the major causal information transmitter (receiver) at all scales; both of their highest values appear at the mid-scale. The US dollar index plays a bridging role in information transmission, and this mediating ability decreases with increasing time scales. On the other hand, the fastest speed of causal information transmission is at the short scale, and the slowest speed is at the mid-scale. The complexity and systematic risk of causal network decrease with increasing time scales. Importantly, at the short-scale (D1), the information transmission speed slowed during the Russian–Ukrainian conflict and further decreased after the start of the Israel–Hamas conflict. Systematic risk has increased annually since 2018. This study provides a multiscale perspective to study the nonlinear causal relationship between geopolitical risk and financial markets and serves as a reference for policy-makers and investors. Full article

This study investigated the impact of a Yard Management System (YMS) implemented at a third-party logistics distribution center in the United States. Five years of operational data (2018–2022), including 72 monthly observations of inbound and outbound freight performance (measured in pounds) and detention occurrences (measured in US dollars), were analyzed using one-way ANOVA to assess pre- and post-implementation performance. The results indicated that the YMS significantly improved inbound and outbound freight volume, reduced detention occurrences, and enhanced operational efficiency within the third-party logistics distribution center. These findings suggest that YMS can be an effective tool for enhancing yard-level operational efficiency, reducing delays, and supporting broader supply chain optimization strategies in third-party logistics environments. Full article

Background/Objective: Lymphedema is a disabling condition that is both underdiagnosed and undertreated. Epidemiological data on this disease is sparse. Methods: The prevalence of lymphedema was studied in hospitalized patients registered in the Nationwide Inpatient Sample (NIS) of the United States (US) from 2016 to 2020. ICD-10 codes related to lymphedema were utilized to identify eligible cases. We studied comorbidity burden and outcomes during hospitalizations, including in-hospital fatality, length of stay and total charges per hospitalization. Results: Lymphedema was present in 0.45% (n = 792,475) of all hospitalizations; with prevalence increasing from 0.40% in 2016 to 0.50% in 2020. Lymphedema-mentioning hospitalizations peaked in July. The median age was 67 (IQR: 57–77) years; A total of 60% were female. Most lymphedema-mentioning hospitalizations were emergency admissions (90%). The most frequent comorbidities were arterial hypertension (77%), obesity (58%), diabetes mellitus (42%), phlegmon (38%), renal disease (32%), chronic pulmonary disease (31%), and cancer (26%). The in-hospital fatality rate was 2.3%, the median length of stay was 5 (IQR: 3–8) days, and each hospitalization incurred a median of 36,304 (IQR: 20,431, 67,171) US dollars, roughly three times higher than the average hospitalization costs in the NIS in the same period. Conclusions: This represents the first comprehensive nationwide study of the epidemiological and economic burden of lymphedema among hospitalized patients in the US. The findings highlight that lymphedema, although underdiagnosed, affects a significant number of patients and is associated with a considerable burden of both comorbidities and costs. Full article

Background/Objectives: To evaluate the cost-effectiveness of β-blocker use in addition to standard care compared to standard care alone for women with triple-negative breast cancer (TNBC), with effectiveness measured by years of life lived (YLL), quality-adjusted life years (QALYs), and equal-value life years (evLYs) gained. Methods: A population cohort Markov model was developed to compare clinical and economic outcomes for TNBC treated with 1) lifelong β-blocker prescription initiated at diagnosis in addition to standard care versus 2) standard care alone. Life-table modelling was used to capture mortality over a lifetime horizon for the estimated eligible population of Australian women diagnosed with TNBC in 2022 (n = 767). Costs were derived from Australian healthcare perspective, and measured in Australian dollars (AUD) at 2022 prices with 5 percent annual discounting and AUD 28,000 willingness to pay threshold applied. Results: The model estimated 628 (95% CI 139, 1035) YLL, 526 (116, 865) QALYs, and 566 (125, 932) evLYs gained in the β-blocker group compared to standard care. The difference in health costs between β-blocker and standard care groups was AUD −935,116 (−2,365,417, 405,350). The β-blocker intervention was dominant over standard care in terms of both QALYs and evLYs gained. Conclusions: Preliminary modelling suggests that implementing β-blockers as an adjunct pharmacotherapy in the treatment of TNBC was more effective and less costly than current standard care. Further monitoring of long-term outcomes is recommended to validate the findings of observational and preclinical studies, and define the incidence, severity, and cost of β-blocker associated adverse events in cancer populations. Full article

With the growing advantages and conveniences provided by digital transactions, the financial sectors also face a loss of billions of dollars each year. While the use of credit cards has made life easier and convenient, it has also become a significant threat. Detecting fraudulent transactions in financial sectors, such as banking, is a major issue because existing fraud detection methods are rule-based and unable to detect unknown patterns. The tactics and techniques used by fraudsters are far more advanced than they are, making machine learning (ML) a valuable approach to improve detection efficiency. While numerous studies have explored machine learning models for credit card fraud detection, most have prioritized accuracy metrics alone, offering little attention to how or why models make decisions. This lack of interpretability creates barriers for financial institutions, where regulatory compliance and user trust are critical. In particular, the systematic application of explainable AI (XAI) techniques such as SHAP and LIME to fraud detection remains scarce. This study addresses this gap by combining high-performing ensemble models (Random Forest and XGBoost) with advanced interpretability methods (SHAP and LIME), providing both strong predictive performance and transparent feature-level explanations. Such integration not only improves fraud detection but also strengthens the trustworthiness and deployability of AI systems in real-world financial contexts. A real-world credit card dataset is used to evaluate both models, and experimental results show that Random Forest achieved higher precision (89.09%) and F1 score (0.9159), while XGBoost yielded better recall (95.56%) and ROC AUC (0.9997). To address the crucial need for interpretability, SHAP and LIME analyses were applied, revealing the most influential features behind model predictions and enhancing transparency in decision-making. Overall, this study demonstrates the potential of integrating explainable artificial intelligence (XAI) into fraud detection systems, thereby enhancing trust and reliability in financial institutions. Full article

Manta ray tourism has become a multi-million-dollar industry proposed as a conservation tool in recent decades; however, its impacts remain unclear. We use drones and Markov models to quantify the effects of diver presence on a juvenile population of the recently described Atlantic manta ray (Mobula yarae) off the coast of Florida. We contrast diver effects on behavioral states (avoidance, feeding, and neutral), examine the responses of individual manta rays, and estimate the energetic costs of diver presence. Diver presence significantly influenced manta ray behavior. Manta rays spent 37% of their time avoiding divers, with neutral and feeding manta rays having an increased probability of transitioning to avoidance states in the presence of divers. We found a significant difference in the proportion of time individual manta rays spent in avoidance, with some individuals being highly avoidant (up to 70%), while others were less affected by diver presence (<20% avoidance). While wingbeat frequency did not change in the presence of divers, manta rays with divers spent significantly more time with their cephalic fins unfurled. Our findings suggest that tourism could negatively impact this small, vulnerable population, making it unsuitable for development. We recommend similar behavioral and kinematic assessments to guide sustainable wildlife tourism management. Full article

Antimicrobial resistance (AMR) is one of the most critical challenges to global public health in the 21st century, posing a significant threat to healthcare systems and human health due to treatment failure and high mortality. The World Health Organization (WHO) estimates that, without effective interventions, AMR-associated infections could cause 10 million deaths annually and economic losses of up to 100 trillion US dollars by 2050. The rapid spread of drug-resistant strains, especially in hospital and community settings, has significantly reduced the efficacy of traditional antibiotics. With the continuous advancements in relevant research, bacteriophage (Phage) therapy is constantly innovating in the antimicrobial field. The application of frontier technologies, such as phage cocktails and engineered phages, has significantly enhanced the broad spectrum and high efficiency of phage therapy, which is gradually becoming a new generation of tools to replace antibiotics and effectively combat pathogenic bacteria. However, phage therapy is facing several challenges, including phage inactivation by gastric acid, enzymes, ultraviolet light, and mechanical stress, as well as the potential risk of bacterial phage resistance. Advanced encapsulation technologies such as electrospun fibers, liposomes, chitosan nanoparticles, and electrospray provide solutions to these problems by protecting phage activity and enabling controlled release and targeted delivery. This review addresses phage therapeutic studies of Salmonella, Pseudomonas aeruginosa, Staphylococcus aureus, Escherichia coli, and Listeria monocytogenes, summarizes the recent advances in phage research, and details the current development and applications of encapsulated phage technologies across various delivery modes. Full article

Herbicides are chemical compounds that are toxic to weed plants. Modern agriculture relies heavily on herbicides for the control of weeds to maximize crop yields. Herbicide usage in the Australian grains industry is estimated to have increased by more than 65% from 2014 to 2024, which equates to more than AUD 2.50 billion dollars per year. The increased popularity of herbicides in farming systems has raised concerns about their negative impacts on the environment, human health and agricultural sustainability due to the rapid evolution of herbicide resistance, as well as their behaviour and fate in the soil. Due to excessive use of herbicides, soil and water pollution, reduced biodiversity and depression in soil heterotrophic bacteria (including denitrifying bacteria) and fungi are becoming increasingly common. Biological degradation governed by microorganisms serves as a major natural remediation process for a variety of pollutants including herbicides. This review provides a brief overview of the present status of herbicide residues in Australian farming systems, with a focus on the microbial degradation of herbicides in soil. It highlights key bacterial and fungal strains involved and the environmental factors influencing the biodegradation process. Recent advancements, including the application of omics technologies, are outlined to provide a comprehensive understanding of the biodegradation process. Full article

The exponential growth of e-commerce and advancements in financial technology have escalated credit card fraud into a major threat, resulting in billions of dollars in global losses annually. This necessitates the development of sophisticated fraud detection systems capable of real-time anomaly interception to safeguard financial activities. While federated learning frameworks have been employed to address data privacy concerns in financial applications, existing approaches often fail to account for the heterogeneity in data distributions across different institutions, such as banks, which hinders collaborative model training. In response, this paper introduces the FED-SPFD model, an innovative federated learning framework designed to detect credit card fraud amidst multi-party heterogeneous data. The model employs a share–private segmentation approach to distinguish shared from private data attributes, facilitating unified feature representation learning. It aligns disparate shared features through local sufficient statistics, thus preventing privacy breaches without directly sharing sample data. Additionally, the integration of a “private autoencoder + standard Gaussian alignment” mechanism stabilizes the training process by ensuring consistent private feature distributions. The efficacy of the FED-SPFD model is demonstrated using a real-world dataset from Kaggle, showcasing significant improvements in recall rate compared to state-of-the-art methodologies. Comprehensive evaluation through ablation studies further validates the framework’s robust contributions to accurate and privacy-preserving fraud detection. Practically, this work offers policymakers a compliant cross-institutional risk collaboration paradigm and provides financial institutions with a privacy-protective solution to enhance fraud detection without data sharing violations. Full article

Dollar spot is a severe and widespread turfgrass disease. Ultraviolet-C (UV-C) light treatment offers a promising management strategy, and its integration into autonomous mowers could reduce fungicide use, promoting sustainable and efficient turfgrass management. To ensure effectiveness and optimize intervention timing, monitoring is essential and hyperspectral sensing could represent a valuable resource. This study aimed to develop an innovative approach for the early detection and integrated management of dollar spot in bermudagrass by evaluating (i) the efficacy of an autonomous mower equipped with UV-C lamps in mitigating infections, and (ii) the potential of full-range hyperspectral sensing (350–2500 nm) for disease detection and monitoring. The autonomous mower enabled UV-C treatment with a field capacity of 0.04 ha h⁻¹, requiring 1.3 machines to treat 1 ha day⁻¹, and a primary energy consumption of 55.06 kWh ha⁻¹ for a complete weekly treatment. Full-range canopy hyperspectral data (400–2400 nm) enabled rapid, non-destructive field detection. Permutational multivariate analysis of variance (PERMANOVA) detected significant effects of Clarireedia jacksonii (Cj; dollar spot pathogen) and the Cj × UV-C interaction. Partial least-squares discriminant analysis (PLS-DA) separated Cj+/UV+ and Cj+/UV− plots (Accuracy validation ≈ 0.73; K ≈ 0.69). Investigated spectral indices confirmed Cj × UV-C interactions. Future research should explore how to optimize UV-C application regimes, improve system scalability, and enhance the robustness of hyperspectral models across diverse turfgrass genotypes, growth stages, and environmental conditions. Full article

Conventional wastewater treatment methods typically achieve 70–90% removal efficiency for organic pollutants. However, the global wastewater crisis—with 80% of wastewater discharged untreated—demands innovative solutions to overcome persistent challenges in nutrient removal and resource recovery. This review presents the first systematic analysis of technology integration strategies for algal wastewater treatment, examining synergistic combinations of biofilm reactors, nano-enhancement, artificial intelligence, and 3D printing technologies. Individual technologies demonstrate distinct performance characteristics: algal biofilm reactors achieve 60–90% removal efficiency with biomass productivity up to 50 g/m²/day; nano-enhanced systems reach 70–99% pollutant removal; AI optimization provides 15–35% efficiency improvements with 25–35% energy reductions; and 3D-printed architectures achieve 70–90% removal efficiency. The novel integration framework reveals that technology combinations achieve 85–95% overall efficiency compared to 60–80% for individual approaches. Critical challenges include nanomaterial toxicity (silver nanoparticles effective at 10 mg/L), high costs (U.S. Dollar (USD) 50–300 per m² for 3D components, USD 1500+ per kg for nanomaterials), and limited technological maturity (TRL 4–5 for AI and 3D printing). Priority development needs include standardized evaluation metrics, comprehensive risk assessment, and economic optimization strategies. The integration framework provides technology selection guidance based on pollutant characteristics and operational constraints, while implementation strategies address regional adaptation requirements. Findings support integrated algal systems’ potential for superior treatment performance and circular economy contributions through resource recovery. Full article

Background: HPV infections with high-risk subtypes are a risk factor for developing cervical and anal cancer. Despite HPV vaccination, the incidence of cervical and anal cancer is increasing. There has been substantial research regarding the benefits of screening men who have sex with men (MSM) and those diagnosed with HIV for anal HPV and dysplasia, but little data exists for women diagnosed with cervical cancer. Methods: We constructed a Markov model in Python 3.13 to simulate the outcomes and financial impact of screening women newly diagnosed with cervical cancer for anal HPV and dysplasia. Two matrices were used to represent the screened group and the unscreened group. In the screening group, all women received initial anal HPV screening and high-resolution anoscopy with biopsy. If biopsy results confirmed HSIL, each would receive treatment with electrocautery. The screening group would also undergo annual surveillance and follow-up treatment as necessary. In the unscreened group, women did not receive screening or treatment, and the disease process was allowed to progress naturally. Results: The initial cohort consisted of 5555 women diagnosed with cervical cancer and concurrent anal HPV. The incidence of anal cancer in the screening group was 271 vs. 375 in the unscreened group after three years, 642 vs. 1236 after ten years, and 863 vs. 2039 after twenty years. Moreover, anal cancer deaths were 1236 in the screening group vs. 9041 in the unscreened group after 10 years and 31,118 vs. 51,553 after twenty years. The screened group saved 330.1 million dollars after ten years and 1.33 billion dollars after twenty years when compared to the unscreened group. Over the life of the study, the screened group would also accrue 102,000 discounted QALYs when compared to the unscreened group. Conclusions: Our model strongly suggests that screening women diagnosed with cervical cancer for anal HPV and treating anal dysplasia leads to less anal cancer, less deaths from anal cancer, less economic impact on the healthcare system, and more QALYs for patients. Full article