Search Results (183)

This article aims to investigate the impact of sustainable assets on dynamic portfolio optimization under varying levels of investor risk aversion, particularly during turbulent market conditions. The analysis compares the performance of two portfolio types: (i) portfolios composed of non-sustainable assets such as fossil energy commodities and conventional equity indices, and (ii) mixed portfolios that combine non-sustainable and sustainable assets, including renewable energy, green bonds, and precious metals using advanced Deep Reinforcement Learning models (including TD3 and DDPG) based on risk and transaction cost- sensitive in portfolio optimization against the traditional Mean-Variance model. Results show that incorporating clean and sustainable assets significantly enhances portfolio returns and reduces volatility across all risk aversion profiles. Moreover, the Deep Reinforcing Learning optimization models outperform classical MV optimization, and the RTC-LSTM-TD3 optimization strategy outperforms all others. The RTC-LSTM-TD3 optimization achieves an annual return of 24.18% and a Sharpe ratio of 2.91 in mixed portfolios (sustainable and non-sustainable assets) under low risk aversion (λ = 0.005), compared to a return of only 8.73% and a Sharpe ratio of 0.67 in portfolios excluding sustainable assets. To the best of the authors’ knowledge, this is the first study that employs the DRL framework integrating risk sensitivity and transaction costs to evaluate the diversification benefits of sustainable assets. Findings offer important implications for portfolio managers to leverage the benefits of sustainable diversification, and for policymakers to encourage the integration of sustainable assets, while addressing fiduciary responsibilities. Full article

Many transportation structures collapse or sustain severe damage as a result of natural disasters such as earthquakes, floods, wars, and similar attacks. These collapsed or severely damaged structures must be rebuilt and returned to service as quickly as possible. Water is used in the mix for cement-bound concrete roads. It is known that drought problems are emerging due to climate change and that water resources are rapidly depleting. Significant amounts of water are used in concrete production, further depleting water resources. In order to contribute to the elimination of these two problems, the usability of polyurethane resin binder in road pavement construction was investigated. Polyurethane resin binder road pavement is a new type of pavement that does not contain cement or bitumen as binders and does not contain water in its mixture. This new type of road pavement can be opened to traffic within 5–15 min. After determining the aggregate and binder mixture ratios, four different curing methods were applied to the created samples. After the curing, the samples were subjected to compression test, flexural test, Bohme abrasion test, freeze–thaw test, bond strength by pull-off test, ultrasonic pulse velocity (UPV) test, SEM-EDX analysis, XRD analysis, and FT-IR analysis. The new type of road pavement created within the scope of this study exhibited a compression strength of 41.22 MPa, a flexural strength of 25.32 MPa, a Bohme abrasion value of 0.99 cm³/50 cm², a freeze–thaw test mass loss per unit area of 0.77 kg/m², and an average bond strength by pull-off value of 4.63 MPa. It was observed that these values ensured the road pavement specification limits. Full article

This study examines links between global financial stress and cryptocurrency returns from 1 January 2017 to 31 January 2025, while explicitly accounting for commodity markets. We use an econometric toolkit: unit-root and cointegration testing, ARDL bounds, Toda–Yamamoto causality, and a two-state Markov Switching model to trace long-run equilibrium and transmission mechanisms across cryptocurrencies (BGCI), systemic stress (OFR-FSI), volatility measures (VIX, VVIX, VSTOXX, VVSTOXX, MOVE), major equities and bonds, and three commodities (gold, oil, copper). Results show robust long-run cointegration between BGCI and several financial variables, including S&P/ASX 200 and the Bloomberg Barclays Bond Index; models that include commodities continue to support these long-term links. Toda–Yamamoto tests reveal that stress and volatility indices unidirectionally transmit shocks to cryptocurrencies and commodities, while gold displays a bidirectional relationship with BGCI, indicating a conditional safe haven interaction. Markov Switching estimates show amplified co-movement among BGCI, gold and bonds in stress regimes, with the model predominantly remaining in a normal state. Overall, cryptocurrencies are embedded within the broader financial system; commodities, especially gold, are used to moderate the stress crypto transmission and offer conditional diversification value during turmoil. Full article

This systematic review examines how recent scientific literature addresses place attachment among rural youth, emphasizing the central role of emotional bonds with place in decisions to stay, leave, or return to rural areas. Based on an analysis of studies published between 2019 and 2023, it considers factors such as country of publication, study participants, methodology, research approach, theoretical framework, and main findings. A systematic search was conducted in Scopus and Web of Science, applying inclusion criteria based on type of research, year of publication, language, and article relevance. The review includes 19 peer-reviewed articles. Methodologically, the reviewed articles employ both quantitative and qualitative approaches, with questionnaires and semi-structured interviews as the primary data collection techniques. Key themes include urban migration and the relationship between place attachment and environmental awareness. Despite limitations such as regional disparities in study coverage, the findings highlight the challenges faced by rural youth under urbanormative cultural pressures. The review underscores the need for nuanced approaches that are sensitive to gender and other axes of oppression in addressing rural issues, and it advocates for a holistic understanding of rural youth experiences that takes into account intergenerational dynamics shaping their aspirations and decision-making. Full article

Reliable multiscale models of thrombosis require platelet-scale fidelity at organ-scale cost, a gap that scientific machine learning has the potential to narrow. We trained a DeepONet surrogate on platelet dynamics generated with LAMMPS for platelets spanning ten elastic moduli and capillary numbers (0.07–0.77). The network takes as input the wall shear stress, bond stiffness, time, and initial particle coordinates and returns the full three-dimensional deformation of the membrane. Mean-squared-error minimization with Adam and adaptive learning-rate decay yields a median displacement error below 1%, a 90th percentile below 3%, and a worst case below 4% over the entire calibrated range while accelerating computation by four to five orders of magnitude. Leave-extremes-out retraining shows acceptable extrapolation: the held-out stiffest and most compliant platelets retain sub-3% median error and an 8% maximum. Error peaks coincide with transient membrane self-contact, suggesting improvements via graph neural trunks and physics-informed torque regularization. These results represent a first demonstration of how the surrogate has the potential for coupling with continuum CFD, enabling future platelet-resolved hemodynamic simulations in patient-specific geometries and opening new avenues for predictive thrombosis modeling. Full article

This study develops an integrated risk modeling framework to assess capital adequacy and optimize portfolio performance for Thai life and non-life insurers. Leveraging ARMA–GJR–GARCH models with skewed Student-t innovations, extreme value theory, and dynamic R-vine copulas, the framework effectively captures volatility, tail risks, and evolving asset interdependencies. Utilizing daily data from 2014 to 2024, the models generate value-at-risk forecasts consistent with international standards such as Basel III’s 10-day 99% VaR and rolling Sharpe ratios for portfolios integrating green bonds compared to traditional asset allocations. The results demonstrate that green bonds, fixedincome instruments funding renewable energy and other environmental projects, significantly improve risk-adjusted returns and have the potential to reduce capital requirements, particularly for life insurers with long-term sustainability mandates. These findings underscore the importance of portfolio-level capital assessment and support the proactive integration of ESG considerations into supervisory investment guidelines to enhance financial resilience and align the insurance sector with Thailand’s sustainable finance agenda. Full article

Bitcoin and other cryptocurrency returns show higher volatility than equity, bond, and other asset classes. Increasingly, researchers rely on machine learning techniques to forecast returns, where different machine learning algorithms reduce the forecasting errors in a high-volatility regime. We show that conventional time series modeling using ARMA and ARMA GARCH run on a rolling basis produces better or comparable forecasting errors than those that machine learning techniques produce. The key to achieving a good forecast is to fit the correct AR and MA orders for each window. When we optimize the correct AR and MA orders for each window using ARMA, we achieve an MAE of 0.024 and an RMSE of 0.037. The RMSE is approximately 11.27% better, and the MAE is 10.7% better compared to those in the literature and is similar to or better than those of the machine learning techniques. The ARMA-GARCH model also has an MAE and an RMSE which are similar to those of ARMA. Full article

This research examines the challenges of issuing green bonds due to a lack of established benchmarks. We compare regional differences between the U.S. and the E.U., hypothesizing that issuers of green bonds stand to benefit from comparing them to conventional (black) bonds. As most investors prioritize net positive returns as opposed to intangible sustainability metrics, the existence of a “green premium”, defined as the opportunity to price green bonds differently, remains to be proven. To this end, we employ a time-varying parameter vector autoregression (TVP-VAR), first deriving dynamic variance–covariance matrices and then conducting variance decomposition analysis to gauge connectedness and spillover effects of various bond benchmarks. Implementing multivariate portfolio construction strategies, we investigate the hedging capabilities of green and black bonds. Our findings show that both green and black bonds contribute to portfolio diversification as a risk management strategy. The paper highlights the role played by green bonds in promoting financial stability. Full article

Fiber–cement composites have been increasingly studied for sustainable construction applications, but durability issues—particularly fiber degradation in alkaline environments—remain a challenge. This study aimed to evaluate the individual and combined effects of furfurylated sisal fibers and nanoclay additions on the physical and mechanical performance of autoclaved fiber–cement composites, seeking to enhance fiber durability and matrix compatibility. All the composites were formulated with CPV-ARI cement and partially replaced with agricultural limestone to reduce the environmental impact and production costs. Sisal fibers (2 wt.%) were chemically modified using furfuryl alcohol, and nanoclays—both hydrophilic and surface-functionalized—were incorporated at 1% and 5% of cement weight. The composites were characterized for physical properties (density, water absorption, and apparent porosity) and mechanical performance (flexural and compressive strength, toughness, and modulus). Furfurylation significantly improved fiber–matrix interaction, leading to higher flexural strength and up to 100% gain in toughness. Nanoclay additions reduced porosity and increased stiffness, particularly at 5%, though excessive content showed diminishing returns. The combination of furfurylated fibers and functionalized nanoclay provided the best results in maintaining a compact microstructure, reducing water absorption, and improving mechanical resilience. Optical microscopy confirmed improved fiber dispersion and interfacial bonding in composites containing furfurylated fibers and functionalized nanoclay. These findings highlight the effectiveness of integrating surface-treated natural fibers with pozzolanic additives to enhance the performance and longevity of fiber–cement composites. Full article

I show that house prices can be modeled using machine learning (kNN and tree-bagging) and a small dataset composed of macroeconomic factors (MEF), including an inflation metric (CPI), US Treasury rates (10-yr), Gross Domestic Product (GDP), and portfolio size of central banks (ECB, FED). This set of parameters covers all the parties involved in a transaction (buyer, seller, and financing facility) while ignoring the intrinsic properties of each asset and encompassing local (inflation) and liquidity issues that may impede each transaction composing a market. The model here takes the point of view of a real estate trader who is interested in both the financing and the price of the transaction. Machine learning allows for the discrimination of two periods within the dataset. First, and up to 2015, I show that, although the US Treasury rates level is the most critical parameter to explain the change of house-price indices, other macroeconomic factors (e.g., consumer price indices) are essential to include in the modeling because they highlight the degree of openness of an economy and the contribution of the economic context to price changes. Second, and for the period from 2015 to today, I show that, to explain the most recent price evolution, it is necessary to include the datasets of the European Central Bank programs, which were designed to support the economy since the beginning of the 2010s. Indeed, unconventional policies of central banks may have allowed some institutional investors to arbitrage between real estate returns and other bond markets (sovereign and corporate). Finally, to assess the models’ relative performances, I performed various sensitivity tests, which tend to constrain the possibilities of each approach for each need. I also show that some models can predict the evolution of prices over the next 4 quarters with uncertainties that outperform existing index uncertainties. Full article

Introduction: A limited March 2024 Google Scholar search regarding COVID-19 redeployment to emergency care in fourteen medical specialties found no oncologist returns. Identifying oncologist redeployment through a scoping review of peer-reviewed research from several databases investigates this anomaly. Method: Searched are Web of Science, Scopus, PubMed, OVID, Google Scholar, and the Cochrane COVID-19 Study Register with the keywords “burnout AND COVID-19 AND emergencies AND oncologists” concerning the emergency experienced, their burnout response, and patient outcome. Results: Following the PRISMA scoping review process, the assessment is of eight reports from 17,848 results. The finding is that there was a redeployment of oncologists to emergency care. It was defined in various ways and caused oncologist burnout for several internally and externally directed reasons. These reasons negatively affected patient outcomes, contributing to the adoption of different coping techniques by oncologists. Oncologists, uniquely among medical specialists, experienced burnout regarding empathy for the increased mortality risk of their patients and the diminished doctor/patient bond. They also lacked symptom-directed coping. Conclusion: The results of this study may reinforce to oncologists the importance of their doctor/patient dyad and of initiating coping strategies that include symptom-directed health improvement techniques when the redeployment of oncologists is again to emergency care. Full article

This study comparatively examines the determinants of yield spreads for Sukuk and conventional bonds, with a particular focus on the role of firms’ environmental, social, and governance (ESG) performance. Using a dataset comprising 744 bond-year observations from issuers in countries with prominent dual financial systems—namely, Saudi Arabia, UAE, Turkey, Malaysia, and Indonesia—over the period 2008 to 2022, this analysis identifies distinct mechanisms that influence yield spreads in these asset classes. For robustness, the sample excludes financial institutions to prevent industry-weight distortion and to account for their distinct risk–return profiles, which require differentiated valuation approaches for conventional bonds and Sukuk. Drawing primarily on decoupling, information asymmetry, and legitimacy theories, our empirical results reveal that robust ESG performance is significantly associated with lower yield spreads for both Sukuk and conventional bonds. Moreover, the study explores the moderating effect of investment horizons on the ESG–yield spreads relationship, uncovering evidence of differentiated investor behavior in relation to yield curve positioning. These findings, robust across various regression specifications, underscores the pivotal role of ESG factors as firm-level drivers of financing costs, offering new insights for scholars, policymakers, and practitioners in the sustainable finance domain. Full article

The significant capacitive effects of cables can cause resonance stability issues, making it crucial to accurately model cables in the wide frequency range (up to several kilo-Hertz) where resonance typically occurs. To address the complexity and the neglect of cable bonding and earthing arrangements in previous accurate cable modeling, this paper derives a concise analytical method for calculating cable electrical parameters over the wide frequency range, simplifying the prior complex formulas, clarifying the series impedance components, and comprehensively considering three common bonding and earthing arrangements. The case studies of three-core and single-core submarine cables are presented to verify the effectiveness of the improved analytical method. The analysis includes frequency-dependent per-unit-length parameters and the impact of each component on the series impedances. Furthermore, a simplified algorithm is explored, avoiding Bessel function computations based on the impedance component impact study, as well as infinite series calculations by considering the effect of the earth/sea return path position factor on the simplified series accuracy. Full article

This paper explores the hypothesis that the returns of asset classes can be predicted using common, systematic risk factors represented by the level, slope, and curvature of the US interest rate term structure. These are extracted using the Nelson–Siegel model, which effectively captures the three dimensions of the yield curve. To forecast the factors, we applied autoregressive (AR) and vector autoregressive (VAR) models. Using their forecasts, we predict the returns of government and corporate bonds, equities, REITs, and commodity futures. Our predictions were compared against two benchmarks: the historical mean, and an AR(1) model based on past returns. We employed the Diebold–Mariano test and the Model Confidence Set procedure to assess the comparative forecast accuracy. We found that Nelson–Siegel factors had significant predictive power for one-month-ahead returns of bonds, equities, and REITs, but not for commodity futures. However, for 6-month and 12-month-ahead forecasts, neither the AR(1) nor VAR(1) models based on Nelson–Siegel factors outperformed the benchmarks. These results suggest that the Nelson–Siegel factors affect the aggregate stochastic discount factor for pricing all assets traded in the US economy. Full article

Green bonds are a relatively new financial product that offers investors a variety of alternatives. However, many individuals continue to be suspicious about its long-term returns and risks. To clarify this issue, this study employed two global environment events—COP26 and COP27—to influence investor attention and investor yield of green bonds and conventional bonds. The data are collected from 15,188 bonds, including 779 green bonds and 14,409 conventional bonds issued from 2021 to 2023 worldwide. The event study method has been conducted with pre- and post-event data to estimate the impact of green bond issuance before and after COP26 and COP27 on investor returns, as well as the impact of investor attention on investment returns. The research results show that investors should buy shares of companies that issue green bonds after major environmental events to benefit from the higher CAR of these companies. Investors can also use the S&P 1200 index as a measure to assess risk and abnormal returns when making short-term investments in shares of organizations that issue green bonds. Full article