Search Results (27,387)

An efficient legal system facilitates the enforcement of guarantees, enables the recovery of non-performing loans and increases trust between creditors and borrowers. This study examines the effect of the legal environment and the profitability of the Angolan banking sector. Specifically, it analyses the influence of property rights and the rule of law on bank profitability in Angola. The study employs various econometric methods for analyzing panel data, such as Feasible Generalized Least Squares (FGLS), and instrumental variables models such as Two-Stage Least Squares (IV-2SLS), Generalized Method of Moments (IV-GMM) and Quantile Regression (MQREG). The study concludes that improving the legal environment by strengthening property rights and promoting the rule of law favours the profitability of Angolan banks. In terms of practical implications, this study shows that the legal environment in Angola is an important barrier to the promotion of credit in Angola, and, above all, to improving the profitability of banks. This study contributes to the scarce literature highlighting the relationship between the legal system and the Angolan banking sector, a topic that has been little explored in the context of African countries. Furthermore, the study awakens the dormant debate on the legal system and finance. Full article

The multivariate Vasicek model is commonly used to capture mean-reverting dynamics typical for short rates, asset price stochastic log-volatilities, etc. Reparametrizing the discretized problem as a VAR(1) model, the parameters are oftentimes estimated using the multivariate least squares (MLS) method, which can be susceptible to outliers. To account for potential model violations, a maximum trimmed likelihood estimation (MTLE) approach is utilized to derive a system of nonlinear estimating equations, and an iterative procedure is developed to solve the latter. In addition to robustness, our new technique allows for reliable recovery of the long-term mean, unlike existing methodologies. A set of simulation studies across multiple dimensions, sample sizes and robustness configurations are performed. MTLE outcomes are compared to those of multivariate least trimmed squares (MLTS), MLE and MLS. Empirical results suggest that MTLE not only maintains good relative efficiency for uncontaminated data but significantly improves overall estimation quality in the presence of data irregularities. Additionally, real data examples containing daily log-volatilities of six common assets (commodities and currencies) and US/Euro short rates are also analyzed. The results indicate that MTLE provides an attractive instrument for interest rate forecasting, stochastic volatility modeling, risk management and other applications requiring statistical robustness in complex economic and financial environments. Full article

The development of cooperatives represents an effective solution to address the looming issue of “who will harvest the rubber”. Participation in cooperatives has the potential to increase the income of natural rubber farmers, enhance agricultural operational efficiency, and mitigate risks inherent in agricultural production. Livelihood capital and speculative preferences are key factors influencing natural rubber farmers’ decisions to participate in cooperatives. However, the existing literature has largely overlooked the intrinsic relationship between livelihood capital, speculative preferences, and the participation of natural rubber farmers in cooperatives. This study employs data from a field survey of 506 natural rubber farmers in Hainan Province, utilizing a Logit model to assess the impact of livelihood capital on farmers’ participation in cooperatives. The results indicate that (1) Livelihood capital encourages natural rubber farmers’ participation in cooperatives at the 5% significance level; (2) Speculative preferences negatively moderate the effect of livelihood capital on farmers’ participation in cooperatives. Therefore, the government can enhance farmers’ livelihood capital through education and training while providing financial instruments, such as insurance, to reduce speculative demand, thereby encouraging their participation in cooperatives. Full article

As energy efficiency measures have reduced the operational carbon footprint of buildings, the significance of embodied carbon has increased. Efforts by all construction players, including material and component manufacturers, are needed to avoid burdens shifting towards embodied impacts. Environmental Product Declarations (EPDs) can represent useful instruments to push the decarbonization of construction materials. This study examines EPDs to assess the embodied GWP of insulation materials, bricks, concrete, cement, steel, and natural stones. The variance structure of the GWP was studied for each material, the main variation parameters were detected, and statistically significant categories were identified. For each category reference values were calculated (i.e., mean or median values, lower and upper interquartile ranges, and box plot whiskers) which can be useful for manufacturers to reduce the impact of their products, for EPD verifiers to detect outliers, and for designers to determine safety coefficients for using EPD data in the early design stage. Consolidated results were achieved for materials produced through standardized processes whose GWP variability was mainly structured around universal physical properties or production techniques. More localized or artisanal products demonstrate higher decarbonization potential but require further segmentation and additional GWP data to establish more robust reduction benchmarks. Full article

Hair care products that have oxidative hair dye ingredients have been widely used to permanently change hair color for the characteristic and younger appearance of people and/or their companion animals. In the European Union and the Republic of Korea, these ingredients have been carefully used or prohibited for cosmetic products according to their genotoxic potential. There is a growing demand for reliable quantification methods to monitor oxidative hair dye ingredients in hair care products. However, accurately quantifying oxidative dyes in cosmetic samples is challenging due to their high reactivity and chemical instability under both basic and ambient conditions. For this reason, for the quantification methods, elaborate sample preparation procedures should be accompanied by chemical derivatization to avoid chemical reactions between hair dye ingredients, before instrumental analysis. Therefore, this study utilized a gas chromatography–mass spectrometry (GC-MS) method combined with in situ chemical derivatization to quantify 26 oxidative hair dye ingredients in hair care products. In situ derivatization using acetic anhydride provided the characteristic [M-CH₂CO]⁺ ions at m/z (M-42), produced by the loss of a ketene from the hair dye ingredient derivatives. These characteristic ions can be used to establish a selective ion monitoring (SIM) mode of GC-MS. The established method was successfully applied to hair dye products (n = 13) and hair coloring shampoos (n = 12). Most products contained unintended hair dye ingredients including catechol without labeling. It was cautiously speculated that these unintended hair dye ingredients might be caused by biodegradation due to various enzymes in natural product extracts. This study presents a reliable GC-MS method with in situ derivatization to quantify 26 oxidative hair dye ingredients in hair care products, addressing challenges related to their chemical instability. This method is crucial for public health and regulatory compliance. Full article

The accuracy of instrument transformers (ITs) is vital for the accurate measurement of electrical quantities. However, their performance is influenced by various factors during operation, including environmental conditions such as temperature, pressure, and humidity, as well as other factors like positioning, electromagnetic fields, and geometry. Given that IT accuracy is challenging to verify once installed in the field, it is essential to thoroughly understand its performance beforehand. This paper investigates how variations in resin properties affect IT accuracy. Samples prepared with different curing temperatures were subjected to aging tests, which included exposure to temperature and combined temperature–humidity conditions. Throughout the aging process, the dielectric properties of the samples were measured, and their impact on IT accuracy was evaluated. The results clearly demonstrate that the choice of resin properties is critical to ensure reliable IT performance, as improper selection can lead to significant accuracy deviations. Full article

Microfinance aims to promote financial inclusion among underprivileged individuals, particularly through progressive microcredit, which enables borrowers to access increasing loan amounts over time. This study examines the conditions under which progressive microcredit positively impacts both small business performance and household well-being, considering borrower characteristics and business activity conditions. Using a dataset of 278 households across 110 administrative sectors in Tunisia from 2012 to 2020, this study employs two-stage least squares (2SLS) and three-stage least squares (3SLS) econometric techniques to estimate simultaneous equation models. The findings reveal that the cumulative amount of progressive microcredit received is mainly determined by project capital, suggesting that businesses with higher capital requirements tend to secure larger loans over successive cycles. Household well-being is significantly influenced by progressive microcredit, household income, net business benefit, rate of development index, and homeownership. Meanwhile, business profitability is driven by project capital and total fixed assets, highlighting the long-term impact of microcredit. The results highlight the critical role of microfinance in enabling small-scale entrepreneurs to expand their businesses while simultaneously improving household financial security. By promoting sustainable income generation, progressive microcredit serves as a key instrument in poverty alleviation and economic stability. This study underscores the necessity for microfinance institutions (MFIs) to tailor their lending strategies, ensuring optimal loan progression that balances business expansion with financial sustainability. Additionally, policymakers should refine microcredit frameworks to enhance accessibility and long-term economic benefits for low-income borrowers. Overall, these insights contribute to the broader discourse on financial inclusion and sustainable development, emphasizing that progressive microcredit not only facilitates entrepreneurship, but also serves as a driver of socioeconomic mobility. Full article

This study discusses the preparation of biopolymeric hydrogels (a biomaterial) via different techniques, such as casting and extrusion, to compare the effects of the process and the use of citric acid as a crosslinker on the morphology, physicochemical properties, and degree of swelling of the hydrogel. Casting is widely used for its low cost and space-saving nature, but upscaling is problematic. Extrusion offers a way to produce materials in large quantities; these materials can undergo mechanical and thermal energy, which can significantly alter their properties. The samples obtained by extrusion had porous surfaces, which are critical for the water penetration and swelling of superabsorbent hydrogels. In contrast, the hydrogels produced by casting did not form pores, resulting in a lower degree of swelling. Extrusion increased the degree of swelling threefold due to the formation of pores, influencing water absorption and diffusion dynamics, especially in samples with higher starch content, where crosslinking occurred more effectively. Full article

This study calibrates an affordable, solar-powered LoRaWAN air quality monitoring prototype using the research-grade Palas Fidas Frog sensor. Motivated by the need for sustainable air quality monitoring in smart city initiatives, this work integrates low-cost, self-sustaining sensors with research-grade instruments, creating a cost-effective hybrid network that enhances both spatial coverage and measurement accuracy. To improve calibration precision, the study leverages the Super Learner machine learning technique, which optimally combines multiple models to achieve robust PM (Particulate Matter) monitoring in low-resource settings. Data was collected by co-locating the Palas sensor and LoRaWAN devices under various climatic conditions to ensure reliability. The LoRaWAN monitor measures PM concentrations alongside meteorological parameters such as temperature, pressure, and humidity. The collected data were calibrated against precise PM concentrations and particle count densities from the Palas sensor. Various regression models were evaluated, with the stacking-based Super Learner model outperforming traditional approaches, achieving an average test R² value of 0.96 across all target variables, including 0.99 for PM_2.5 and 0.91 for PM_10.0. This study presents a novel approach by integrating Super Learner-based calibration with LoRaWAN technology, offering a scalable solution for low-cost, high-accuracy air quality monitoring. The findings demonstrate the feasibility of deploying these sensors in urban areas such as the Dallas-Fort Worth metroplex, providing a valuable tool for researchers and policymakers to address air pollution challenges effectively. Full article

Knowledge of realistic loads is crucial in the engineering design process of medical devices and for assessing their interaction with the spinal system. Depending on the type of modeling, current numerical spine models generally either neglect the active musculature or oversimplify the passive structural function of the spine. However, the internal loading conditions of the spine are complex and greatly influenced by muscle forces. It is often unclear whether the assumptions made provide realistic results. To improve the prediction of realistic loading conditions in both conservative and surgical treatments, we modified a previously validated forward dynamic musculoskeletal model of the intact lumbosacral spine with a muscle-driven approach in three scenarios. These exploratory treatment scenarios included an extensible lumbar orthosis and spinal instrumentations. The latter comprised bisegmental internal spinal fixation, as well as monosegmental lumbar fusion using an expandable interbody cage with supplementary posterior fixation. The biomechanical model responses, including internal loads on spinal instrumentation, influences on adjacent segments, and effects on abdominal soft tissue, correlated closely with available in vivo data. The muscle forces contributing to spinal movement and stabilization were also reliably predicted. This new type of modeling enables the biomechanical study of the interactions between active and passive spinal structures and technical systems. It is, therefore, preferable in the design of medical devices and for more realistically assessing treatment outcomes. Full article

Buildings account for a significant share of global energy consumption and carbon emissions, making deep renovations essential for climate mitigation. Renovation passports (RPs) are an emerging concept still in the early stages of development, designed to provide structured step-by-step renovation roadmaps that prevent lock-in effects and optimise energy performance over time. However, their large-scale adoption in the European Union (EU) remains limited due to technical, financial, behavioural, and policy challenges. This study conducts a Systematic Literature Review (SLR) to identify key strategies for the successful development and large-scale implementation of RPs in EU. A total of 217 research articles from Scopus and ScienceDirect, along with 99 EU policy documents and 16 Building Performance Institute Europe (BPIE) reports, were analysed to assess the technical, financial, behavioural, and policy dimensions of RP adoption. Our findings highlight the role of digital tools like Building Information Modelling (BIM), digital building logbooks (DBLs), and one-stop shops (OSSs) in improving RP usability and accessibility. Financial barriers, such as high upfront costs and fragmented funding, require harmonised incentives, green loans, and energy performance contracting. Behavioural factors, including homeowner awareness, trust in renovation services, and decision-making complexity, also influence RP adoption. This study underscores the need for stronger policy integration between RPs and energy performance certificates (EPCs), improved financial instruments, and enhanced stakeholder engagement. By addressing these gaps, this research provides actionable recommendations for policymakers and stakeholders to accelerate the adoption of RPs and contribute to the EU’s Renovation Wave strategy and broader climate neutrality objectives. Full article

Between the eighth and ninth centuries, the world entered a second period of strong winter monsoons, which precipitated a series of recurrent natural disasters, including reduced summer rainfall and prolonged droughts. The various types of droughts that occurred in southeastern China are documented in historical records, which also include the official-led ritual prayers to the local deities that were conducted during these challenging periods. As evidenced in these historical records, officials implemented a series of measures to provide solace to the populace, including the restoration of shrines and temples and the offering of sacrifices and prayers to the local deities, such as the Wutang God 吳塘神 and the Chutan God 儲潭神. These actions were intended to leverage the influence of the local deities to mobilise labour and financial resources for the implementation of public works, including the reclamation of barren land and the construction of dikes and ponds. These initiatives ultimately proved instrumental in enabling the populace to withstand the adverse effects of disasters. This approach represents a distinctive strategy for coping with drought in ancient China. It may provide insights into how governments and non-governmental organisations can utilise the influence of religious beliefs to unite people in addressing the climate crisis in the present era. Full article

This article introduces an innovative meteor detection system that integrates high-speed photodiode detectors with traditional camera-based systems. The system employs four photodiodes to record changes in sky brightness at 100 Hz, enabling meteor detection and the observation of their dynamics. This technology serves as a valuable complement to existing imaging techniques, offering a cost-effective solution for measuring meteor ablation at frequencies beyond the capabilities of camera-based systems. We showcase findings from the Perseid meteor shower, demonstrating the potential of our system. Moreover, our system addresses the current limitations in meteor radiometry, where many existing instruments either remain in developmental stages or have not been validated with a substantial number of confirmed meteor events. Our approach successfully addresses these limitations, demonstrating effectiveness across multiple meteor events simultaneously recorded on video. Full article

This study introduces an integrated lower limb robotic orthosis with near-field electrospinning (NFES) piezoelectric sensors and a fuzzy logic-based gait phase detection system to enhance mobility assistance and rehabilitation. The exoskeleton incorporates embedded pressure sensors within the insoles to capture ground reaction forces (GRFs) in real-time. A fuzzy logic inference system processes these signals, classifying gait phases such as stance, initial contact, mid-stance, and pre-swing. The NFES technique enables the fabrication of highly oriented nanofibers, improving sensor sensitivity and reliability. The system employs a master–slave control framework. A Texas Instruments (TI) TMS320F28069 microcontroller (Texas Instruments, Dallas, TX, USA) processes gait data and transmits actuation commands to motors and harmonic drives at the hip and knee joints. The control strategy follows a three-loop methodology, ensuring stable operation. Experimental validation assesses the system’s accuracy under various conditions, including no-load and loaded scenarios. Results demonstrate that the exoskeleton accurately detects gait phases, achieving a maximum tracking error of 4.23% in an 8-s gait cycle under no-load conditions and 4.34% when tested with a 68 kg user. Faster motion cycles introduce a maximum error of 6.79% for a 3-s gait cycle, confirming the system’s adaptability to dynamic walking conditions. These findings highlight the effectiveness of the developed exoskeleton in interpreting human motion intentions, positioning it as a promising solution for wearable rehabilitation and mobility assistance. Full article

A wide band range can cover more of the characteristic spectral lines of substances, and thus analyze the structure and composition of substances more accurately. In order to broaden the band range of spectral instruments, an ultra-wide-band Fourier transform infrared spectrometer is designed. The incident light of the spectrometer is constrained by a secondary imaging scheme, and switchable light sources and detectors are set to achieve an ultra-wide band coverage. A compact and highly stable double-moving mirror swing interferometer is adopted to generate optical path difference, and a controller is used to stabilize the swing of the moving mirrors. A distributed design of digital system integration and analog system integration is adopted to achieve a lightweight and low-power-consumption spectrometer. High-speed data acquisition and a transmission interface are applied to improve the real-time performance. Further, a series of experiments are performed to test the performance of the spectrometer. Finally, the experimental results show that the spectral range of the ultra-wide-band Fourier transform infrared spectrometer covers 0.770–200 μm, with an accurate wave number, a spectral resolution of 0.25 cm⁻¹, and a signal-to-noise ratio better than 50,000:1. Full article