Search Results (1,724)

Idelalisib (IDE) is a marketed chiral anticancer drug administered as the S-enantiomer, requiring sensitive monitoring of the R-enantiomer to ensure enantiomeric purity. However, no dedicated capillary electrophoresis (CE) method has been reported for trace-level quantification of R-IDE. In this study, a cyclodextrin-mediated CE method was developed for reliable detection of the R-enantiomer at the 0.1% level (LOD 2 µg/mL; LOQ 5 µg/mL). Systematic screening identified hydroxypropyl-β-cyclodextrin (HP-β-CD) with an intermediate degree of substitution (DS~6.8) as the optimal chiral selector, providing efficient enantioseparation (R_s up to 4.3). The method was validated according to ICH Q2(R2) guidelines, demonstrating suitable precision, accuracy, and robustness. Complementary NMR studies revealed hindered rotation of the 3-phenyl moiety and elucidated the molecular basis of enantioselectivity. Complexation with β-CD and HP-β-CD produced clear diastereomeric differentiation in both ¹H and ¹⁹F NMR spectra, while the simplified ¹⁹F NMR profiles enabled direct enantiomer discrimination. NOESY and ROESY experiments demonstrated distinct inclusion modes, with HP-β-CD accommodating both the fluorinated aromatic ring and the 3-phenyl moiety. These interactions may account for the superior enantioseparation observed with HP-β-CD of intermediate DS. Our validated CE method addresses the distomer determination while NMR insights provide mechanistic understanding of the chiral recognition. Full article

China’s transition from strict fertility control toward a pronatalist regime raises important questions regarding how institutional regime changes are associated with wage outcomes across different policy stages. While previous studies primarily examine contemporaneous labor-market outcomes, this study evaluates wage associations across fertility-policy regime stages using a dynamic panel specification. Using five waves of the China Family Panel Studies (CFPS) from 2014 to 2022, the analysis constructs a short, unbalanced panel and estimates a wage equation using two-step System Generalized Method of Moments (System GMM) with collapsed instruments and restricted lag depth to address dynamic endogeneity and unobserved heterogeneity. To improve representativeness and mitigate observed wage-observability differences, survey weights are combined with inverse-probability weighting. The preferred baseline estimates indicate a positive but modest lagged-wage coefficient. Monte Carlo and sensitivity analyses further suggest that the persistence estimate is fragile and may overstate the degree of true persistence in this short-panel setting. Accordingly, the findings do not support strong intertemporal wage persistence and instead indicate only limited dependence of current wages on past wage realizations. The dynamic specification is therefore informative primarily as a diagnostic framework for assessing whether regime-stage wage associations exhibit meaningful persistence. Additional exposure-based heterogeneity analyses show negative interaction coefficients for Female and childbearing women (CBW). Married women aged 20–39 experience additional negative wage associations during fertility-policy regime stages, with similar results obtained under a narrower CBW20–35 robustness definition. These findings suggest that positive aggregate regime-stage associations may conceal relative wage disadvantages among women in demographic groups more plausibly exposed to fertility-policy-related labor-market conditions. The CBW indicator is interpreted as a demographic-exposure proxy rather than as a direct measure of employer expectations, fertility intentions, or discrimination. Overall, the results highlight exposure-based heterogeneity in regime-stage wage associations while emphasizing that the estimates should be interpreted as conditional associations embedded within broader institutional transitions rather than as causal fertility-policy effects. Full article

As the world’s largest labor market, China exhibits a range of distinctive and, at times, puzzling employment phenomena. Using an online survey on working conditions and the best–worst scaling method across 13 dimensions of job values, this study examines variations in working conditions and job value preferences among Chinese workers. The results reveal systematic differences in working conditions, with workers holding second-generation urban hukou and college degrees from better universities enjoying superior job amenities, while female workers face some inferior job attributes. On average, monetary job benefits primarily influence job choice decisions, though job amenities also play a crucial role. Additionally, our findings demonstrate substantial heterogeneity in job values among individuals. Interestingly, expressed job preferences align with actual job choices, suggesting that job values are effective indicators of labor market dynamics. Understanding these variations in working conditions and job value preferences provides essential insights into the Chinese labor market. Full article

Nigeria has pursued exchange rate unification as a deliberate macroeconomic policy objective since the Structural Adjustment Programme (SAP) of 1986, culminating in the full unification of June 2023. Despite the centrality of this policy to Nigeria’s economic governance, its poverty implications have received insufficient empirical examination. This study investigates the effect of exchange rate unification on poverty in Nigeria over the period 1986–2024, using the Autoregressive Distributed Lag (ARDL) bounds testing approach. Exchange rate unification is operationalised as the ratio of the official to the parallel market exchange rate, a continuous measure of the degree of rate convergence achieved at each point in time, which is mathematically the inverse of the conventional parallel market premium used to measure exchange rate distortion. While the parallel market premium measures the degree of exchange rate distortion, that is, how far apart the two rates are, the ERU ratio employed in this study measures the degree of exchange rate unification achieved, that is, how close the two rates are to convergence. Poverty is measured using the Multidimensional Poverty Index (MPI). The MPI is constructed using Principal Component Analysis (PCA) from per capita income, life expectancy at birth, agricultural value-added per worker, and household consumption expenditure per capita. The study finds that exchange rate unification significantly worsens poverty in both the short and long run, consistent with the sharp naira depreciation, inflationary pass-through, and deterioration in cost of living observed following Nigeria’s exchange rate reforms. GDP growth reduces poverty marginally in the long run, confirming a modest pro-poor growth effect. These findings establish that exchange rate unification, while necessary for long-run macroeconomic efficiency, imposes significant poverty costs that require deliberate complementary fiscal and social protection policies to mitigate. Full article

Carbon emission reduction decisions are subject to risks for shipping carriers. These include policy uncertainty (an upcoming policy may be stringent or lenient) and cost uncertainty (the operation cost may increase or decrease in the future). This paper develops a two-period game model to study the carbon emission reduction strategy choices of two risk-averse shipping carriers facing both policy uncertainty and cost uncertainty, with the goal of advancing sustainable maritime transport. They can choose a high- or low-carbon emission reduction strategy in period 1. Whether they need to upgrade in period 2 depends on the strategy they choose in period 1 and the policy implemented in period 2. The results show that in a deterministic environment, a high-cost strategy translates directly into a high-price strategy. However, in period 2, when the policy is lenient, adopting a high-carbon emission reduction strategy does not always result in a higher price than adopting a low-carbon emission reduction strategy. This result is counterintuitive. In addition, the carrier adopting a high-carbon emission reduction strategy does not necessarily set a higher price than the competitor who adopts a low-carbon emission reduction strategy. The market share plays an important role in shaping the equilibrium. When the possibility of a stringent policy is extremely low or extremely high, both carriers will choose an identical strategy. However, when the possibility is medium, they will choose differentiated strategies. The carrier with a bigger market share can tolerate a higher possibility of an upcoming stringent policy than the competitor. The degree of cost volatility also has a significant impact on the equilibrium. Its influence is particularly pronounced under a moderate probability of a stringent policy. Shippers’ carbon emission sensitivity also has a positive effect on encouraging carriers to choose a greener strategy. Our findings provide actionable insights for policymakers and industry stakeholders to facilitate the sustainability transition of the shipping sector through appropriate policy design. Full article

This study investigates whether shareholder value creation, defined as beta-adjusted outperformance relative to a market benchmark, can be effectively predicted in an emerging market using a sequential machine learning framework. While prior research has predominantly focused on profitability forecasting or stock return prediction, the prediction of risk-adjusted shareholder value creation remains relatively underexplored, particularly in emerging economies such as Morocco. To address this gap, the study develops a predictive framework that combines market-based indicators, macroeconomic variables, and accounting fundamentals using only information realistically available to investors at each decision date. These variables are organized into firm-level temporal sequences based on a monthly decision-date panel of non-financial firms listed on the Casablanca Stock Exchange over the period 2010–2024. To capture nonlinear relationships and temporal dependencies in financial data, the empirical analysis compares baseline models with deep learning architectures, including GRU, LSTM, and CNN1D. The results indicate that deep learning models consistently outperform naïve and linear benchmark models, suggesting that shareholder value creation exhibits a measurable degree of predictability. With an AUC of 0.700 and a PR-AUC of 0.727, CNN1D achieves the strongest performance in the final evaluation setting and ranks as the best-performing model according to the primary AUC criterion. The findings also reveal that macroeconomic variables generate the strongest standalone predictive signal, whereas market-based variables exhibit comparatively weaker predictive power when considered in isolation. By extending financial prediction toward a risk-adjusted, benchmark-based, and investor-oriented framework, and by providing new empirical evidence on the value of temporal modeling and multi-source financial information for forecasting shareholder value creation in an emerging market context, this study contributes to the growing literature at the intersection of financial forecasting and artificial intelligence. Full article

In the fuzzy front end of innovation, firms often lack sufficient citation, market, and performance data, which limits the usefulness of outcome-based approaches to screening early-stage product innovation opportunities. To address this problem, this study develops a text co-occurrence network-based measurement system for assessing early-stage product innovation opportunities in new product development. We first preprocess idea texts through concept extraction and semantic cleaning, and then construct an integrated semantic network by combining market-related texts with ideation data. The Leiden algorithm is applied to detect latent knowledge communities in the network. Building on this structure, we assess early-stage product innovation opportunities along two complementary dimensions: cross-domain knowledge recombination, capturing the extent to which an idea draws on concept communities that are otherwise weakly connected, and network structural perturbation, capturing the degree to which an idea reconfigures existing semantic boundaries and connection patterns. Based on community entropy and modularity change, we construct a composite indicator for the ex ante assessment of early-stage ideas with stronger product innovation potential. Compared with traditional approaches relying on patent citations, market outcomes, or expert judgments, the proposed method enables earlier screening of ideas that deviate from dominant semantic trajectories and may warrant further development attention. The framework is explicitly positioned as an ex ante screening and attention-allocation tool for early-stage product innovation opportunities, not as a deterministic predictor of later market success. Full article

In the last fifteen years, Mexico’s National Electric System (Sistema Eléctrico Nacional, SEN) has undergone significant structural changes, including the 2013 energy reform, the 2020 health contingency, ongoing geopolitical pressures, and the 2024 constitutional energy reform. Over this period, electricity consumption grew at an average annual rate of 3.1%, while the generation mix shifted substantially, with solar and wind capacity expanding from negligible levels to a combined output of 38,627 GWh by 2024. Despite these advances, supply reliability remains under pressure, and the growth of renewable deployment has not kept value with declared decarbonization commitments. This study quantifies the gap between the historical growth trajectory of the SEN and the targets established in the national expansion plan, using linear and second-degree polynomial regression models applied to official data series for the period 2010–2024 to assess whether current structural inertia is consistent with Mexico’s declared energy transition commitments. The results indicate that under a trend scenario, renewable installed capacity would reach approximately 34.3% by 2030, with an estimated generation of 112,136 GWh—insufficient to close the gap to sectoral decarbonization goals. The analysis further reveals that the Expansion Plan requires installing nearly twice the annual capacity historically added, posing a financing and institutional challenge that market signals alone cannot resolve. These findings demonstrate that structural inertia, rather than policy ambition, is currently the dominant driver of the evolution of Mexico’s electricity system, and that its energy transition will require deliberate acceleration beyond historical trends. Full article

Optics is the science of light, which supports disciplines like biology, medicine, engineering, materials science, chemistry, physics and more. Optics helps to improve diagnostic speed, portable and user-friendly devices, cost efficiency, and sensitivity. Through time, optical components have been made with hard and non-deformable materials. However, traditional optical elements can no longer meet the needs of the market, and new optical elements are needed, such as materials with higher degrees of freedom. A candidate that has been proposed to replace traditional optical materials is polydimethylsiloxane (PDMS or silicone) because it presents suitable characteristics like biocompatibility, nontoxicity, flexibility, non-biodegradability, high transparency in the UV–visible range, low scattering and absorption, easy fabrication, cost-effective relation and more. Many articles have reported the fabrication of optical components with silicone and the use of these components in optical devices. Unfortunately, there is no review that comprehensively covers the field of optics in relation to the application of silicone. The present work is intended as a descriptive overview to provide a clear and accessible review of the topic, rather than a comparative analysis. Articles describing the use of silicone in the fabrication of optical components during the past 20 years were reviewed. Full article

Sustainable innovation constitutes the cornerstone of firms’ long-term competitive edge, yet the underlying mechanisms via which patient capital facilitates corporate sustainable innovation remain understudied. Based on a sample of Chinese A-share listed firms spanning 2013 to 2024, this study operationalizes patient capital through two proxies: relational debt and stable institutional ownership. We systematically investigate the impact of patient capital on sustainable innovation, alongside the mediating pathway of internal control quality and the moderating role of climate policy uncertainty. The empirical outcomes indicate that both forms of patient capital exert a significant positive effect on sustainable innovation, with internal control quality serving as a partial mediator in this relationship. Additionally, climate policy uncertainty reinforces the promotional influence of patient capital on sustainable innovation. We further stratify heterogeneity analyses into two dimensions: firm-inherent heterogeneity and external environmental heterogeneity. From the perspective of endogenous firm attributes, the innovation-stimulating effect of patient capital differs markedly across enterprises with distinct ownership types, life-cycle stages, and total asset sizes. Externally, the observed positive impact varies considerably conditional on industrial factor intensity and the regional marketization degree of the firm’s location. These findings expand the existing literature concerning long-term capital and sustainable innovation, and yield actionable implications for corporate management, institutional investors, and policymakers. Full article

Metallic particle contaminants have been shown to have a detrimental effect on the reliability, performance and capacity of lithium-ion battery cells. In addition, they pose a significant safety risk. Typical contaminants, such as iron (Fe), copper (Cu) and aluminium (Al), often enter the cell via mechanical abrasion from production equipment, as burrs during electrode cutting, or through environmental exposure during handling. In such instances, the degradation mechanisms are known to accelerate, dendrite formation is increased, and, in the most unfavourable circumstances, thermal runaway is the likely outcome. Contaminants that do not affect cell behavior during formation and the initial cycles, yet only compromise safety at a subsequent stage, are of particular concern. Affected cells are known to pass end-of-line testing and make their way into the market as latent safety risks. Consequently, there is an urgent requirement for non-destructive diagnostic methods that are capable of identifying latent defects. The issue under discussion is approached in the present paper through the utilization of an innovative methodology that integrates the distribution of relaxation time (DRT) analysis of electrochemical impedance spectroscopy (EIS) data with machine learning techniques. The objective of this integrated approach is to facilitate the detection of critically contaminated pouch cells with a high degree of sensitivity. Full article

Rapid demographic change across countries, with people from diverse ethnic backgrounds and preferences, mandates new nectarine (Prunus persica var. nucipersica) cultivars with varying skin and flesh colors and times of availability. After a 4-year initial screening with 21 cultivars, long-term phenological characteristics, yield, and fruit quality attributes of several cultivars of yellow- and white-fleshed nectarine, harvested at two intervals (Harvest 1 and Harvest 2) and stored for two storage durations (Period 1 and Period 2), were studied over four years. ‘Royal Bright’ consistently exhibited the latest bloom (higher Julian day) and, together with ‘Giant Pearl’, required greater cumulative growing degree days (GDD) than other cultivars to reach both Harvest 1 and Harvest 2 each year. Fruit GDD differences at Harvest 2 as compared to Harvest 1 in ‘Candy Pearl’ were the longest, and those of ‘Giant Pearl’, ‘BN7’, and ‘Royal Bright’ were shorter among all cultivars. ‘Burnectseven’ (‘BN7’), ‘Flame’, and ‘Royal Bright’ always had higher yield, while all “Pearl” series (‘Giant Pearl’, ‘Majestic Pearl’, ‘Candy Pearl’) were in the low-yielding cultivars. ‘Candy Pearl’, ‘Majestic Pearl’, and ‘BN7’ nectarines often had larger fruit than other cultivars. Fruit picked at the second harvest had lower firmness than that picked at the first harvest in all cultivars every year. Fruit of ‘Candy Pearl’ often had higher firmness, but those of ‘BN7’ and ‘Royal Bright’ had lower firmness, at the times of both Harvest 1 and Harvest 2. Fruits of ‘BN7’ and ‘Candy Pearl’ often had higher soluble solids concentrations at Harvest 1 and Harvest 2, and after keeping the fruit in storage for Period 1 and Period 2. According to this study, ‘Candy Pearl’ is recommended as a good choice for the early market, as the fruit in this cultivar was mild with high flavor and attractive red skin and white flesh. Also, ‘Majestic Pearl’, ‘BN7’, and ‘Flame’ can be grown for the mid-to-late August market. ‘Majestic Pearl’ and ‘Flame’ had large fruit with a moderate level of russet and a split pit in some years, and thus, any cultural practices that may contribute to fruit russet and split pit should be avoided. Details on recommendations for suitable cultivars, harvest stages, and storage durations are provided in this study. Full article

Tourism plays an increasingly important role in promoting economic growth and rural revitalization in ecologically fragile regions. However, tourism systems in Tibet–related areas of Sichuan, China, are highly vulnerable to natural disasters, ecological degradation, and regional development imbalances, posing challenges to sustainable tourism development. This study aims to evaluate tourism system resilience and identify its key influencing factors from a sustainability perspective. Based on the regional characteristics of Tibet-related areas in Sichuan, a comprehensive evaluation framework is constructed covering four subsystems: tourism infrastructure and scale, economy, society, and ecology. An integrated entropy weight–analytic hierarchy process (AHP) model, coupling coordination model, and obstacle degree model are employed to assess tourism system resilience and examine subsystem interactions using panel data from 2011 to 2020. The results indicate that: (1) the resilience levels of tourism subsystems show no clear spatial or temporal regularity across the study areas; (2) ecological resilience remains significantly lower than tourism, economic, and social resilience, representing the weakest component of the tourism system; (3) the coupling coordination among subsystems remains at a low level, suggesting insufficient synergy for sustainable regional development; and (4) ecological constraints are the primary limiting factors affecting overall tourism system resilience. This study contributes to sustainable tourism research by revealing the critical role of ecological governance and subsystem coordination in enhancing tourism resilience in ecologically sensitive regions. Policy implications include strengthening ecological protection, improving tourism infrastructure, promoting digital tourism marketing, and advancing rural revitalization to achieve long-term sustainable development. However, this study is limited by data availability and the spatial scope of the selected case-study areas, which may affect the generalizability of the findings. Full article

This article analyses trading-performance patterns in a stock market simulation conducted with 134 second-year students at the University of Mons (Belgium) on 11 December 2025. Participants had a virtual capital of 100,000 euros and were free to trade CAC 40 securities without any restrictions on the number or volume of transactions. An academic incentive scheme, combining a participation bonus and bonuses for the three best portfolios, created a tournament-style environment with continuous ranking feedback. This feature is considered as part of the experimental context rather than as a separately identified causal mechanism. We estimate a quadratic model linking performance to activity, measured by the number of mean-centered transactions to reduce the collinearity between the first-degree term and its square, and control exposure via the average percentage of cash in the portfolio, portfolio variability (measured as the standard deviation of portfolio value) and the average trade size. Breusch–Pagan and White tests indicate heteroscedasticity, justifying a robust inference. The results highlight a convex relationship between activity and performance: the marginal association is initially negative but becomes positive above a model-implied upper-tail level corresponding to approximately 46 transactions. This value should not be interpreted as a behavioral level or as a trading rule. The percentage of cash in the portfolio and the average trade size are negatively associated with performance, while the portfolio variability does not show a statistically significant association with performance. Overall, the results indicate heterogeneous trading patterns rather than a single activity–performance profile. Full article

The quality of rice grain impacts milling yield, market acceptance, and product value. Physical quality is determined by many traits, such as chalkiness, whiteness, vitreous whiteness, caryopsis length, and width. Breeding for these traits is challenging due to their quantitative nature, environmental effects, and time and labor requirements to evaluate these traits. The digital grain analyzer (S21) equipment determines rice grain physical quality by image-based analysis; however, its use remains restricted. Thus, here we aimed to evaluate S21 efficiency to determine the physical quality of rice grains and estimate the genetic diversity of the trait using a Brazilian panel of 152 irrigated rice genotypes as a working model. We accessed total whiteness, vitreous whiteness, chalkiness degree, chalky grain rate, white belly, grain length, width, and length/width ratio. Our results demonstrated that S21 allowed the characterization of the genotypes according to physical traits, facilitating grouping and separation of accessions and correlation analyses between quality traits. It was also possible to estimate the heritability of quality traits. S21 was efficient in characterizing the physical quality of rice grains and determining their genetic diversity. The equipment is an effective tool exhibiting potential application by breeder programs. Full article