Search Results (154)

Amid the growing debate over how cryptocurrencies are reshaping global finance, this study explores the nexus between Bitcoin, Brent Crude Oil, Gold and the U.S. Dollar Index. We used a time-varying vector autoregressive (tvVAR) model to examine the connection among these four assets during the Trump (2017–2020) and Biden (2021–2024) governments. The 48-week return forecast of the Bitcoin–Gold correlation was also conducted by using the Bayesian Structural Time Series (BSTS) model. Results indicate that Bitcoin was the most volatile asset, while the U.S. Dollar remained the least volatile under both regimes. Under Trump, U.S. Dollar significantly influenced Oil and Bitcoin while Bitcoin and Gold were negatively linked to Oil and positively associated with U.S. Dollar. An inverse relationship between Bitcoin and Gold also emerged. Under Biden, Bitcoin, Gold, and U.S. Dollar all significantly affected Oil with Bitcoin showing a positive impact. Bitcoin and Gold remained negatively correlated though not significantly, and the Dollar maintained positive ties with both. Forecasts show a positive link between Bitcoin and Gold in the coming year. However, Bitcoin does not exhibit consistent characteristics of a safe-haven asset during the U.S. presidential transitions examined, largely due to its high volatility and unstable correlations with a traditional safe-haven asset, Gold. This study contributes to the understanding of shifting relationships between digital and traditional assets across political regimes. Full article

Globalization and the spread of technological innovations have made world markets and economies increasingly unified and conditioned by international trade, not only for sales markets but above all for the supply of raw materials necessary for the functioning of the production complex of each country. Alongside oil and gold, the main commodities traded include industrial metals, such as aluminum and copper, mineral products such as gas, electrical and electronic components, agricultural products, and precious metals. The conflict between Russia and Ukraine tested the unification of markets, given that these are countries with notable raw materials and are strongly dedicated to exports. This suggests that commodity prices were able to influence the stock markets, especially in the countries most closely linked to the two belligerents in terms of import-export. Given the importance of industrial metals in this period of energy transition, the aim of our study is to analyze whether Industrial Metals volatility affects G7 stock markets. To this end, the BEKK-GARCH model is used. The sample period spans from 3 January 2018 to 17 September 2024. The results show that lagged shocks and volatility significantly and positively influence the current conditional volatility of commodity and stock returns during all periods. In fact, past shocks inversely influence the current volatility of stock indices in periods when external events disrupt financial markets. The results show a non-linear and positive impact of commodity volatility on the implied volatility of the stock markets. The findings suggest that the war significantly affected stock prices and exacerbated volatility, so investors should diversify their portfolios to maximize returns and reduce risk differently in times of crisis, and a lack of diversification of raw materials is a risky factor for investors. Full article

As sustainability becomes an essential approach in the USA hospitality sector, green certifications like Leadership in Energy and Environmental Design (LEED) are increasingly adopted by hotel developers. However, the extent to which different LEED certification levels influence guest satisfaction remains unclear. This study investigates how the LEED certification level interacts with the relationship between a hotel’s sustainability performance and guest satisfaction in the United States. A mixed-methods approach was used, combining Random Forest Regression and the Process macro on a dataset of LEED-certified USA hotels with normalized guest satisfaction scores. The Random Forest model identified Energy and Atmosphere (EA) and Indoor Environmental Quality (EQ) as the most influential LEED categories in predicting satisfaction. Additionally, the results reveal that the positive effect of sustainability on satisfaction is strongest at the lower LEED levels (Certified and Silver), but shows diminishing returns at higher levels (Gold and Platinum), suggesting that an increased sustainability performance does not uniformly improve guest experience. These findings support all three hypotheses and offer practical insights for hotel developers, operators, and certification bodies seeking to align sustainability strategies with guest expectations. Full article

Objectives: Tibial shaft fractures (TSFs) represent the most common diaphyseal fractures in adults. The gold-standard treatment is intramedullary nailing. Recently, the suprapatellar technique has been increasingly adopted due to its ability to reduce complications associated with the infrapatellar approach. Currently, no clinical score for leg fractures comprehensively assesses the entire lower limb. Therefore, we reviewed the main lower-limb scores available in the literature and developed a new clinical evaluation tool for tibial shaft fractures. The aim of our study was to report our experience with both techniques, to compare the outcomes of our prospective study with the international literature, and to propose a new, easy-to-apply, and reproducible clinical score that evaluates the specific functions of the entire lower limb. Methods: We conducted a prospective analysis of 920 tibial shaft fractures treated with intramedullary nailing via either a suprapatellar or infrapatellar approach. Patients were divided into two groups: Group A, including 420 patients treated with the infrapatellar approach; Group B, including 500 patients treated with the suprapatellar approach. Follow-up included clinical and radiographic assessments at 1, 3, and 6 months, and annually thereafter. We evaluated differences in patient positioning, operation time, radiation exposure, healing rate, incidence of pseudarthrosis and infection, return to ambulation, residual knee pain and fracture site, persistent lameness, and deformities. For the clinical assessment, we devised a new score—the Catania Hospital Score (CHS)—by integrating the most relevant clinical items from existing lower-limb evaluation tools. The CHS includes anterior knee pain (20 points), lameness (5 points), swelling (10 points), stair-climbing ability (10 points), tibial pain (15 points), the ability to perform daily activities (20 points), and evaluation of deformities (varus/valgus, shortening, rotation, and recurvatum/procurvatum (40 points)), for a total of 120 points. Results: Statistically significant differences were observed in Group B regarding a shorter surgical time, a reduced patient positioning time, and decreased radiation exposure. The CHSs were significantly better for Group B at the 3- and 6-month follow-ups. No statistically significant differences were found in infection or pseudarthrosis rates between the two groups. Notably, no cases of chronic knee pain were reported in patients treated with the suprapatellar approach. Conclusions: Both surgical approaches are valid and effective. However, our findings indicate that the suprapatellar approach reduces the complications of the infrapatellar technique, improves postoperative outcomes, and does not result in chronic knee pain. The CHS provides a comprehensive, practical, and reproducible tool to assess functional recovery in patients treated with intramedullary tibial nailing. Full article

This study investigates the multifractal behavior of four leading semiconductor stocks—Intel (INTC), Advanced Micro Devices (AMD), Nvidia (NVDA), and Broadcom (AVGO)—in relation to key financial assets, including the Dow Jones Industrial Average (DJI), the Euro–U.S. Dollar exchange rate (EUR), gold (XAU), crude oil (WTI), and Bitcoin (BTC), using Multifractal Asymmetric Detrended Cross-Correlation Analysis (MF-ADCCA). The analysis is based on daily price return time series from January 2015 to January 2025. Results reveal consistent evidence of multifractality across all asset pairs, with the generalized Hurst exponent exhibiting significant variability, indicative of complex and nonlinear stock price dynamics. Among the semiconductor stocks, NVDA and AVGO exhibit the highest levels of multifractal cross-correlation, particularly with DJI, WTI, and BTC, while AMD consistently shows the lowest, suggesting comparatively more stable behavior. Notably, cross-correlation Hurst exponents with BTC are the highest, reaching approximately 0.54 for NVDA and AMD. Conversely, pairs with EUR display long-term negative correlations, with exponents around 0.46 across all semiconductor stocks. Multifractal spectrum analysis highlights that NVDA and AVGO exhibit broader and more pronounced multifractal characteristics, largely driven by higher fluctuation intensities. Asymmetric cross-correlation analysis reveals that stocks paired with DJI show greater persistence during market downturns, whereas those paired with XAU demonstrate stronger persistence during upward trends. Analysis of multifractality sources using surrogate time series confirms the influence of fat-tailed distributions and temporal linear correlations in most asset pairs, with the exception of WTI, which shows less complex behavior. Overall, the findings underscore the utility of multifractal asymmetric cross-correlation analysis in capturing the intricate dynamics of semiconductor stocks. This approach provides valuable insights for investors and portfolio managers by accounting for the multifaceted and asset-dependent nature of stock behavior under varying market conditions. Full article

Objectives: Hamstring strain injuries are common in elite athletes and affect return-to-sport timelines. Although magnetic resonance imaging (MRI) is the gold-standard method for assessing injury severity, ultrasonography (US) is a more accessible and cost-effective alternative. This study aimed to evaluate the agreement between US and MRI in the diagnosis of hamstring injuries and their prognostic value in predicting recovery. Methods: This retrospective study included elite athletes who sustained acute first-time hamstring strains and underwent both MRI and US within five days after injury. The injuries were classified according to location (muscle belly, musculotendinous junction, or tendon) and severity (modified Peetron’s classification). The agreement between the imaging findings and return-to-sports timelines was analyzed. Results: US demonstrated a 70% agreement with MRI in identifying injury locations, showing the highest concordance for muscle belly injuries (90%), followed by musculotendinous junction (80%) injuries, but a lower accuracy for tendon injuries (60%). Recovery times differed significantly by location and severity (p < 0.01), with tendon grade 3 injuries requiring the longest recovery (383 days) and muscle belly injuries requiring the shortest recovery (16 days). Musculotendinous junction grade 2, tendon grade 1, and tendon grade 2 injuries had similar recovery durations (57–65 days). Conclusions: High-resolution US is a reliable diagnostic tool for muscle belly and musculotendinous junction injuries. However, MRI remains essential for high-grade tendon injuries. US serves as the first-line imaging modality, with MRI reserved for cases that require a detailed prognostic assessment. These findings provide guidance for optimizing imaging strategies for hamstring injury management. Full article

This study examines the volatility and hedging effectiveness of commodities, specifically gold and oil, on the Indian stock market, focusing on both aggregate and sectoral indices. Data have been collected from 1 January 2021 to 31 December 2024 to cover the post-COVID-19 period. Utilizing the Asymmetric Dynamic Conditional Correlation Generalized Autoregressive Conditional Heteroskedasticity (ADCC-GARCH) model, we analyze the volatility spillovers and time-varying correlations between commodity and stock market returns. The analysis of spillover connectedness reveals that both commodities exhibit limited and inconsistent hedging potential. Gold demonstrates low and stable spillovers in most sectors, indicating its diminished role as a reliable safe-haven asset in Indian markets. Oil shows relatively higher but volatile spillover effects, particularly with sectors closely tied to energy and industrial activities, reflecting its dependence on external economic and geopolitical factors. This study contributes to the literature by providing a sector-specific perspective on commodity–stock market interactions, challenging conventional assumptions of hedging efficiency of gold and oil. It also emphasizes the need to explore alternative hedging mechanisms for risk management in the post-crisis phase. Full article

This study analyses the impact of the Geopolitical Risk Index (GPR) on the volatility of commodity futures returns from 4 January 2010 to 30 June 2023, using Exponential Generalized Autoregressive Conditional Heteroskedasticity (EGARCH) models. It expands the research scope to include precious metals, agricultural products, energy, and industrial metals. The study differentiates between the impacts of geopolitical threat events and actions using GPRACT and GPRTHREAT indicators. Findings reveal that negative geopolitical shocks increase commodity returns’ volatility more than positive shocks. Specifically, gold, silver, and natural gas are negatively affected, while wheat, corn, soybeans, cotton, zinc, nickel, lead, WTI oil, and Brent oil experience positive effects. Platinum, cocoa, coffee, and copper show no significant impact. These insights highlight the importance of geopolitical risks on commodity market volatility and returns, aiding in risk management and portfolio diversification. Policymakers, financial market stakeholders, and investors can leverage these findings to better understand the GPR’s relationship with commodity markets and develop effective strategies. Full article

Mainstream strategies for protecting wealth from inflation involve diversification into traditional assets like common stocks, gold, fixed-income securities, and real estate. However, a significant contributor to inflation has been the rising energy prices, which have been the main underlying cause of several past recessions and high inflation periods. Investments in distributed generation with solar photovoltaics (PV) present a promising opportunity to hedge against inflation, considering non-taxed profits from PV energy generation. To investigate that potential, this study quantifies the return on investment (ROI), internal rate of return (IRR), payback period, net present cost, and levelized cost of energy of PV by running Solar Alone Multi-Objective Advisor (SAMA) simulations on grid-connected PV systems across different regions with varying inflation scenarios. The case studies are San Diego, California; Boston, Massachusetts; Santiago, Chile; and Buenos Aires, Argentina. Historical inflation data are also imposed on San Diego to assess PV system potential in dynamic inflammatory conditions, while Boston and Santiago additionally analyze hybrid PV-battery systems to understand battery impacts under increasing inflation rates. Net metering credits vary by location. The results showed that PV could be used as an effective inflation hedge in any region where PV started economically and provided increasingly attractive returns as inflation increased, particularly when taxes were considered. The varying values of the ROI and IRR underscore the importance of region-specific financial planning and the need to consider inflation when evaluating the long-term viability of PV systems. Finally, more capital-intensive PV systems with battery storage can become profitable in an inflationary economy. Full article

Following the re-opening of the Rasa Trulnang Tsuklhakhang, the central temple in Lhasa, all of the new images of Buddhas, bodhisattvas, and famous lamas were placed behind glass except for those in the sancta sanctorum, the “Jokhang”. When a pilgrim approaches the central figure, the Jowo Śākyamuni, she climbs a ladder on his right side, lays a ceremonial scarf across his lap, and then lays her head there, like a child seeking solace from her mother. A wealthy pilgrim might return in the late afternoon, when the temple is closed to visitors, to sponsor a regilding ceremony, in which the sponsor can spend up to an hour nearly alone with the Jowo watching his whole body be repainted in gold. Based on participant observation, pilgrimage guides, and verses of praise offered to the Jowo, this paper considers how the cult of the Jowo uses moments of private intimacy to bridge the distance, both physically and historically, between a devotee and the Buddha. Full article

The co-movement of multi-asset markets in emerging markets has become an important determinant for investors seeking diversified portfolios and enhanced portfolio returns. Despite this, studies have failed to examine the determinants of the co-movement of multi-asset markets such as investor sentiment and changing market conditions. Accordingly, this study investigates the effect of investor sentiment on the co-movement of South African multi-asset markets by introducing alternating market conditions. The Markov regime-switching autoregressive (MS-AR) model and Markov regime-switching vector autoregressive (MS-VAR) model impulse response function are used from 2007 March to January 2024. The findings indicate that investor sentiment has a time-varying and regime-specific effect on the co-movement of South African multi-asset markets. In a bull market condition, investor sentiment positively affects the equity–bond and equity–gold co-movement. In the bear market condition, investor sentiment has a negative and significant effect on the equity–bond, equity–property, bond–gold, and bond–property co-movement. Similarly, in a bull regime, the co-movement of South African multi-asset markets positively responds to sentiment shocks, although this is only observed in the short term. However, in the bear market regime, the co-movement of South African multi-asset markets responds positively and negatively to sentiment shocks, despite this being observed in the long run. These observations provide interesting insights to policymakers, investors, and fund managers for portfolio diversification and risk management strategies. That being, the current policies are not robust enough to reduce asset market integration and reduce sentiment-induced markets. Consequently, policymakers must re-examine and amend current policies according to the findings of the study. In addition, portfolio rebalancing in line with the findings of this study is essential for portfolio diversification. Full article

The anterior cruciate ligament (ACL) is one of the most injured ligaments, with approximately 100,000 ACL reconstructions taking place annually in the United States. In order to successfully manage ACL rupture, it is of the utmost importance to understand the anatomy, unique physiology, and biomechanics of the ACL, as well as the injury mechanisms and healing capacity. Currently, the “gold standard” for the treatment of ACL ruptures is surgical reconstruction, particularly for young patients or athletes expecting to return to pivoting sports. Although ACL reconstruction boasts a high success rate, patients may face different, serious post-operative complications, depending on the type of graft and technique used in each one of them. Tissue engineering is a multidisciplinary field that could contribute to the formation of a tissue-engineered ACL graft manufactured by a combination of the appropriate stem-cell type, a suitable scaffold, and specific growth factors, combined with mechanical stimuli. In this review, we discuss the aspects that constitute the creation of a successful tissue-engineered graft while also underlining the current drawbacks that arise for each issue. Finally, we highlight the benefits of incorporating new technologies like artificial intelligence and machine learning that could revolutionize tissue engineering. Full article

Forecasting stock markets is challenging due to the influence of various internal and external factors compounded by the effects of globalization. This study introduces a data-driven approach to forecast S&P 500 returns by incorporating macroeconomic indicators including gold and oil prices, the volatility index, economic policy uncertainty, the financial stress index, geopolitical risk, and shadow short rate, with ten technical indicators. We propose three hybrid deep learning models that sequentially combine convolutional and recurrent neural networks for improved feature extraction and predictive accuracy. These models include the deep belief network with gated recurrent units, the LeNet architecture with gated recurrent units, and the LeNet architecture combined with highway networks. The results demonstrate that the proposed hybrid models achieve higher forecasting accuracy than the single deep learning models. This outcome is attributed to the complementary strengths of convolutional networks in feature extraction and recurrent networks in pattern recognition. Additionally, an analysis using the Shapley method identifies the volatility index, financial stress index, and economic policy uncertainty as the most significant predictors, underscoring the effectiveness of our data-driven approach. These findings highlight the substantial impact of contemporary uncertainty factors on stock markets, emphasizing their importance in studies analyzing market behaviour. Full article

We analyze the predictive effect of monthly global, regional, and country-level financial uncertainties on daily gold market volatility using univariate and multivariate GARCH-MIDAS models, with the latter characterized by variable selection. Based on data over the period of July 1992 to May 2020, we highlight the role of the global financial uncertainty factor in accurately forecasting gold price volatility relative to the benchmark GARCH-MIDAS-realized volatility model, with a dominant role of European financial uncertainties, and 36 out of the 42 regional financial market uncertainties. The forecasting performance of the global financial uncertainty factor is as good as an index of global economic conditions, with results based on a combination of these two models depicting evidence of complementary information. Moreover, the GARCH-MIDAS model with global financial uncertainty cannot be outperformed by the multivariate version of the GARCH-MIDAS framework, estimated using the adaptive LASSO, involving the top five developed and developing countries each, chosen based on their ability to explain the movements of overall global financial uncertainty. Our results imply that as financial uncertainties can improve the accuracy of the forecasts of gold returns volatility, it would help investors to design optimal portfolios to counteract financial risks. Also, as gold returns volatility reflects financial uncertainty, accurate forecasts of it would provide information about the future path of economic activity, and assist policy authorities in preventing possible economic slowdowns. Full article

Gold is a significant revenue source for custom copper smelters facing profitability challenges due to low treatment and refining charges, stricter regulations, and rising costs. Gold is also often blended with copper concentrates, but precise recovery rates from smelting processes are poorly documented despite gold critical economic importance. This paper aims to provide the first comprehensive estimates of gold first-pass recovery across various operational units within the copper sulfide concentrate processing flowsheet. It evaluates the effectiveness of different copper smelting and converting technologies in recovering gold. Optimizing gold first-pass recovery is especially important to enhance immediate financial returns and responsiveness to market dynamics, allowing companies to capitalize on favorable gold prices without delays. Given the absence of direct measurements for gold recovery rates, this research develops an estimation method based on understanding gold loss mechanisms during smelting. This study identifies and analyzes key input and output parameters by examining data from various copper producers. By correlating these parameters with gold loss, the research estimates gold first-pass recovery rates within the copper smelting process. Among integrated smelting-converting routes, the flash smelting to Peirce–Smith converting route achieves the highest gold first-pass recovery (98.8–99.5%), followed by the Mitsubishi continuous smelting and converting process (94.3–99.8%), bottom-blowing smelting to bottom-blowing converting (95.8%), flash smelting to flash converting (95.5%), Teniente smelting to Peirce–Smith converting (95.2%), and the Noranda continuous smelting and converting process (94.8%). The final recovery rates are expected to be higher considering the by-products’ internal recirculation and post-processing within the copper flow sheet. Additionally, superior gold recoveries are attributed to advanced metallurgical practices and control systems, which vary even among companies with similar technologies. This research demonstrates that copper smelting can effectively recover over 99% of gold from sulfide concentrates. Gold accumulates up to 1000 times its original concentration in anode slime during electrolytic refining, generating 5–10 kg of slime per ton of copper, which is further processed to recover gold and other by-products. Major smelters operate precious metal plants where recovering gold from highly concentrated anode slime is both cost-effective and efficient. Full article