Search Results (1,373)

Monoclonal antibodies (mAbs) are widely used as therapeutic molecules for the treatment of serious diseases, primarily cancer. The market for mAbs is one of the fastest-growing segments of the biopharmaceuticals industry. Purification is a crucial stage in the production of mAbs. While staphylococcal protein A (SpA) affinity chromatography remains the gold standard in industrial mAb purification, its limitations—low alkaline stability and insufficient binding capacity of SpA, as well as the need for harsh acidic elution conditions—have driven extensive efforts for novel progressive affinity ligands. This review focuses on the development and performance of bacterial protein-based affinity resins for the purification of class G immunoglobulins (IgGs), including conventional proteins A and G, the promising protein L, and the more recently discovered protein M (from M. genitalium), each offering unique specificities for different antibody fragments and species. Hybrid ligands combining domains from multiple bacterial proteins are also discussed, along with next-generation synthetic alternatives such as affibodies, affimers, nanobodies, etc., as well as peptide-based or mixed-mode ligands. The key finding is that the reliable and time-tested resins like those based on protein A continue to dominate the market, although future trends also point toward smaller, more stable, and cost-effective synthetic ligands for specific applications. Full article

Consumers’ risk attitudes shape how they evaluate and trade off product attributes, yet most preference estimation approaches overlook risk attitudes, assuming risk neutrality. The resulting preference structures may fail to capture the heterogeneity essential for effective market segmentation and product strategy. We propose a risk-driven preference learning framework that reconceptualizes risk attitude as an integral factor shaping preference structures. Its core is a risk-contingent value function in which risk attitude determines how attributes are traded off. This function infers risk attitudes through deviations from full compensation among attributes under risk neutrality, endogenizing risk-attitude inference within preference estimation, and yields preference structures from behavioral data. The method also identifies distinct preference subtypes within the same risk-attitude category. Validated on 114,317 vehicle reviews from Edmunds, an automotive e-commerce platform, the approach achieves strong in-sample fit to behavioral data. Results show that risk-averse, risk-neutral, and risk-seeking consumers exhibit different preference structures, and the risk-averse group shows pronounced internal heterogeneity. The proposed framework offers a structurally interpretable tool for intelligent decision support in consumer segmentation and product strategies. Full article

Ornamental aquaculture represents a high-value segment of global aquaculture and plays an important role in income diversification for small-scale producers. Despite its economic relevance, investment decisions in ornamental fish farming are often supported by limited economic information and rarely incorporate risk and uncertainty into economic assessments. This study developed and applied a stochastic framework to evaluate the economic performance and investment risk of intensive ornamental aquaculture using Betta splendens production as a representative case study. A representative production system was developed from technical and economic surveys conducted on 20 commercial family operated farms located in one of Brazil’s major ornamental fish production clusters. The production system comprised three greenhouse units with an estimated annual output of 162,288 marketable fish. Production costs were estimated using conventional cost-accounting procedures, whereas economic risk was assessed through Monte Carlo simulation incorporating uncertainty in biological, productive, and market variables. The estimated total production cost was US$0.14 fish⁻¹, while the weighted average selling price reached US$0.18 fish⁻¹, resulting in a benefit–cost ratio of 1.33, a profitability of 25.09%, and an annual return on invested capital (ROIC) of 23.90% under the deterministic baseline scenario. Monte Carlo simulation estimated a mean annual ROIC of 22.46%, with a 98.53% probability of positive economic returns. Sensitivity analysis identified labor demand, the selling price of premium males, and reproductive productivity as the principal drivers of economic risk. The results indicate that economic returns in B. splendens farming are primarily influenced by managerial efficiency, labor requirements, and market conditions rather than production volume alone. The proposed framework provides a robust tool for evaluating economic viability and investment risk in ornamental fish farming under uncertainty and may support decision-making in small-scale aquaculture systems. Full article

In this study, which is the result of a thesis defended in 2020, we evaluate how the market and the state have allied themselves in promoting popular and social interest housing in the city of São Paulo through a set of municipal regulatory frameworks and financing programs from Caixa Econômica Federal, “Caixa”, respectively. The market and the state have adapted and integrated; this association is both politically and economically valuable for the state—despite it having given up on greater urban regulation—and opportune and profitable for the market. This thesis yielded an important result: in 2018, most of the developments launched in the municipality were financed through “Caixa’s” Minha Casa, Minha Vida Program. In other words, the market and the state are allied to address the housing deficit in the municipality, with quantitatively expressed results. We highlight that this formal establishment of private housing, focused on the popular market, was facilitated by not only current municipal laws but also the participation of the state in the federal sphere through funding mechanisms like “Caixa” that have a monopoly on financing this segment. Studying this real estate production, as well as municipal regulatory changes and economics in national terms, enables the creation of an urban space generated through intervention by the state—which acts simultaneously as a regulating and financing agent—and the development of the market as an encouraging and productive agent. Full article

Robert Shiller proposed perpetual futures in 1993 to create derivative markets for assets that are illiquid or whose price cannot be observed directly. Cryptocurrency markets later built the instrument under a different funding rule. We give a single no-arbitrage result that nests both designs: the perpetual price is the present value of a benchmark flow discounted at the funding rate, so the funding rule fixes both the benchmark and the discount. A random time change represents the price as the expected spot at the first event of a clock whose intensity is the funding rate. This yields the main structural result, that stochastic volatility moves the basis only through the carry, so a volatility risk premium, and not volatility itself, can break the peg. We then read price discovery as nonlinear filtering in which the funding rule is a feedback observer whose gain is the funding intensity and the peg the fixed point of a stochastic approximation, and we give a segmented market equilibrium under which the pre-listing premium is structural rather than behavioral. In the June 2026 SpaceX market, the last pre-listing closes were $172.84 on Hyperliquid and $170.82 on Binance, compared with the listed equity’s $185 close on 18 June and the $135 bookbuilt offer. Simulation matches the pricing results to their closed forms. Generative Bayesian computation recovers the funding intensity sharply but not the softness of the anchor. Full article

The value-creation capacity of active asset management remains one of the most debated issues within modern portfolio theory. While the relationship between costs and performance is typically negative in developed markets, in smaller and less liquid markets—where information asymmetry is more pronounced—higher fees may also be interpreted as signals of managerial ability. This study investigates this apparent contradiction in the context of the Hungarian equity mutual fund market, using a panel dataset covering 79 funds over the period 2017–2024, with a particular focus on identifying non-linear effects among performance determinants. The methodological framework combines fixed-effects panel regression with Driscoll-Kraay robust standard errors, complemented by quantile regression estimates to examine different segments of the return and alpha distributions. The results indicate that growth dynamics (NAV_change) and cumulative historical performance (Yield_from_start) consistently enhance fund performance, while the negative effect of past returns suggests the dominance of mean reversion. The impact of the total expense ratio (TER) proves to be non-linear and specification-dependent—a finding con-firmed by an extensive battery of robustness checks—thereby rejecting the cost-signalling hypothesis with respect to risk-adjusted excess returns (Jensen’s alpha). Quantile estimates further reveal that the effects of economies of scale and cost structure differ significantly between underperforming and top-performing funds, confirming that analyses based on average effects obscure the heterogeneity of market dynamics. By jointly modelling returns and risk-adjusted performance across the full conditional distribution, the study contributes a distribution-sensitive theoretical account of active management’s limitations in a small, less liquid market, showing that these limitations are conditional on fund size and cost structure rather than uniform across the fund population. Full article

The rapid growth of distributed renewable generation and electric vehicles has increased the demand for flexible peak-shaving resources. Battery swapping stations (BSSs), which centrally manage standardized batteries under the battery-as-a-service model, can provide station-to-grid (S2G) services when aggregated by virtual power plants (VPPs). However, S2G adoption is influenced by contract design, market returns, subsidies, battery degradation, and heterogeneous consumer attitudes. This study develops a complex-network evolutionary diffusion model for VPP–BSS cooperation. The framework integrates a VPP profit-accounting module, a segmented Hotelling demand model, and an evolutionary game on a Newman–Watts small-world network. BSS strategies are updated through a partial asynchronous Fermi rule to reflect bounded rationality and investment inertia. Numerical simulations examine contract parameters, subsidy policies, consumer structures, exogenous variables, and network characteristics. The results show that S2G adoption follows an S-shaped trajectory but does not automatically reach full penetration. Successful diffusion requires a feasible combination of electricity prices, revenue sharing, settlement mechanisms, subsidies, consumer acceptance, and available battery capacity. The findings also reveal a trade-off between promoting BSS participation and maintaining VPP profitability, while robustness tests confirm the stability of the main conclusions. Full article

Geopolitical disruptions of natural gas supply have repeatedly forced importing countries to reorganize their electricity systems, yet it remains unclear whether such disruptions are followed by movement of structurally different economies toward comparable energy-security and sustainability outcomes. This study compares Germany, a high-income economy exposed to the 2022 curtailment of Russian pipeline gas, with Jordan, a developing import-dependent economy exposed to the repeated sabotage of the Arab Gas Pipeline after 2011, using harmonized generation-mix and carbon intensity data for Germany over 1985–2024 and Jordan over 2000–2022, supplemented by weekly German market data. The generation fuel mix concentration is measured with a Herfindahl-based Supply Concentration Index (SCI), structural change is estimated with segmented interrupted time series (ITS) regressions inferred through Newey–West heteroskedasticity- and autocorrelation-consistent standard errors, and the joint security–sustainability position of each country is summarized with a newly proposed Energy Vulnerability–Transition Index (EVTI) that combines diversification, renewable penetration, and carbon intensity performance. The results show that Jordan’s 2011 disruption was associated with a baseline estimated change in its carbon intensity trajectory from +3.32 to −12.63 gCO₂/kWh per year and with renewable growth of +2.39 percentage points per year from a near-zero base, while Germany’s 2022 disruption was associated with a temporary carbon intensity shock, visible in a coal reactivation index that peaked at 1.26 and a sixfold wholesale price increase, followed by a policy-supported return to the pre-existing decarbonization pathway. The Germany–Jordan EVTI ratio narrowed from 6.5× in 2014 to 1.8× in 2022, and this convergence is robust to alternative component weightings. The findings indicate that geopolitical gas disruptions, despite their high short-run costs, were followed in both contexts by measurable movement toward more diversified and lower-carbon electricity systems, with direct implications for Sustainable Development Goal (SDG) 7. Full article

Objective: This study investigates the relationship between social media use and consumer purchase intentions or out-of-home food choices in the Polish gastronomic market, focusing specifically on variations across generations. Methodology: Data were collected between May and July 2024 through a diagnostic survey using the Computer-Assisted Web Interviewing (CAWI) technique (N = 1099). Respondents were recruited via a non-probability quota sampling approach based on strict demographic inclusion criteria. To test the research hypotheses regarding generational variations in market behaviour, a multivariate discriminant function analysis was performed using Statistica 13.1 PL. A preliminary pilot study (N = 30) confirmed the initial questionnaire readability and overall consistency (Cronbach’s alpha = 0.87), while subscales were treated independently during the main analysis. Results: Multivariate models were found to be highly significant, revealing clear differences between age groups. The youngest cohort (aged 18–35) relies heavily on Instagram and TikTok, showing distinct patterns regarding visual triggers such as “instagrammable” aesthetics, digital validation, and menu uniqueness alongside weight loss claims. Conversely, seniors (aged 61 and older) lean unexpectedly towards X. This older segment displays pragmatic, utility-driven motives, searching for detailed textual data about ingredients and the health-promoting properties of food. General product quality and calorie control were identified as universal factors that do not vary by generation. Conclusions and Managerial Implications: The digital transformation of the Polish restaurant industry does not follow a single path. Social media is closely linked to modern customer journeys, with physical dining spots frequently serving as spaces for socialisation. Consequently, restaurant operators should move away from mass communication and adopt a selective omnichannel strategy, where message formats shift from visual appeal to factual, nutrition-oriented text that is tailored to the digital literacy and dietary needs of each generation. Full article

Vietnam’s domestic gold price has persistently exceeded the world price by a wide margin, even as recent reforms have begun to relax the state’s historical monopoly over gold-bar production and imports. This paper asks why the gap persists, and whether it is confined to normal market conditions or extends into periods of extreme price movement. Using daily data spanning 2 January 2019 to 31 July 2026 (1856 trading days), covering the reform introduced by Decree No. 232/2025/ND-CP we decompose the domestic premium into a currency component and a pure physical-gold component, and use a copula-based framework to separately assess average price linkage and tail (extreme-event) co-movement between the domestic and world markets. Domestic gold bars traded at an average premium of 16.0% over import-parity world prices, of which 13.8 percentage points reflect the physical-gold component driven by constrained arbitrage, while currency factors account for only about 2 percentage points. The average linkage between the two markets is weak, indicating persistent segmentation, and this segmentation extends into the tails of the distribution for most of the sample. The premium itself carries substantial latent risk: a reversion to price parity would imply a one-off loss of about 9.6% of value, roughly eight to ten times the historical one-day 5% Value-at-Risk. Following the reform’s effective date, however, we find early evidence of emerging co-movement specifically in extreme upside price movements, even though the physical premium itself has not yet narrowed—consistent with a reform that has been enacted in law but remains at an early stage of operational implementation. The results indicate that administrative restrictions on the physical gold supply chain, rather than currency controls, are the principal source of Vietnam’s persistent gold-price gap, with direct implications for how the ongoing liberalization process should be sequenced. Full article

The Internet of Things (IoT) market is currently undergoing a period of unprecedented, rapid evolution, leading to the enabling of novel and diverse applications spanning both the civilian and industrial sectors. A significant proportion of these emerging use cases, particularly those in domains such as maritime communications and forestry management, require service continuity and connectivity within geographically remote regions, where conventional terrestrial infrastructure is often absent or economically unfeasible. To bridge this coverage gap and achieve truly ubiquitous connectivity, the recent 3GPP initiative to extend 5G services into Non-Terrestrial Segments (NTNs) holds substantial promise. This expansion is crucial for ensuring that massive Machine-Type Communication (mMTC) services can be reliably provisioned globally. This paper aims to detail the progress in standardization and academic activities towards the design and deployment of NTN-based Narrowband IoT (NB-IoT) systems, which are the leading NTN mMTC enabler in the 3GPP portfolio. We will specify the challenges faced by these systems and outline the solutions proposed thus far. We conclude the paper with a discussion on already operational systems and lessons learned from their deployment and operation. Full article

Photoreceptors work in a biologically demanding compartment of the eye. They consume large amounts of energy, receive continuous light and oxygen, and renew outer-segment membranes enriched in polyunsaturated lipids. These conditions are necessary for vision, but they also make the outer retina poorly tolerant to persistent mitochondrial dysfunction and oxidative stress. Photobiomodulation (PBM), mainly based on red and near-infrared light, has been investigated as a way to support retinal cells that are functionally impaired but not yet irreversibly lost. The field has also acquired new clinical relevance after the 2024 De Novo marketing authorization by the United States Food and Drug Administration (FDA) of the Valeda Light Delivery System for dry age-related macular degeneration (AMD). This narrative review examines the mitochondrial, redox, inflammatory, and neurovascular mechanisms proposed for PBM, and discusses preclinical and clinical evidence across nonexudative AMD, inherited retinal degeneration, diabetic retinal disease, and light-induced damage. Current findings are encouraging, but devices, doses, schedules, endpoints, and sponsorship patterns differ substantially among studies. PBM therefore deserves further investigation, especially in early or intermediate disease, but its clinical use should remain linked to tested protocols, rigorous safety monitoring, and biomarkers of residual retinal functional reserve. Full article

As Feng-flavor Baijiu increasingly shifts toward consumer-oriented product development, clarifying consumers’ shared sensory priorities and age-related differences is essential for aligning its traditional style with evolving market demands. This study collected 1523 questionnaires across China, of which 1357 valid responses were analyzed. A hierarchical sensory system comprising three primary dimensions—aroma, taste, and drinking perception—and 24 secondary attributes was evaluated using hierarchical weighting, a proportional sensory flavor wheel, machine learning, and SHAP interpretation. Drinking perception received the highest weight (35.23%), although it was only slightly higher than aroma (33.54%) and taste (31.22%). Jiuhai aroma, sweetness, comfort, and purity were identified as the leading attributes within their respective dimensions. Machine learning further identified purity, persistence, saltiness, comfort, and harmony as attributes with relatively high contributions to age-group classification. Age-group differences were not concentrated in any single sensory attribute but were reflected in the classification information jointly provided by multiple sensory attributes. Descriptive group comparisons showed decreasing trends in the relative weights of saltiness and sourness across the 18–30-, 31–45-, and ≥46-year groups, although neither difference remained statistically significant after Benjamini–Hochberg correction. Comfort, bitterness, and honey aroma were relatively more prominent in the ≥46-year group, whereas grain aroma was relatively more prominent in the 31–45-year group. Sweetness and Jiuhai aroma had relatively high overall weights but contributed less to age-group classification, suggesting that they may represent more broadly shared sensory priorities across age groups. These findings identify candidate sensory priorities and segmentation indicators for subsequent validation using actual products, consumer liking tests, and ideal-intensity assessments. Full article

Digitization is reshaping how banks operate, yet whether it improves corporate financial performance remains unsettled. This study examines how the digital transformation that banks disclose relates to the structure of their key financial performance indicators. Using a balanced panel of 73 listed Gulf Cooperation Council (GCC) banks over 2020–2025 (438 bank–year observations), digital transformation is measured by a text-mined digital disclosure index (DDI, 0–100) constructed from annual reports and decomposed into nine themes. Bank fixed-effects regressions with Driscoll–Kraay standard errors, one-year-lagged specifications, and two-step system GMM are estimated across profitability, net interest margin, cost efficiency, credit risk and capital adequacy. Disclosed digitization more than doubled over the window, but its associations with performance are conditional rather than uniformly positive. Within banks, a higher DDI value is associated with wider net interest margins, yet also with lower profitability, higher cost-to-income ratios, modestly higher credit risk, and thinner capital buffers. This pattern is consistent with an investment or build-out phase in which the costs of digital transformation are visible before any efficiency or stability dividend and in which margins are the single offsetting benefit. The six-year window observes only this cost-bearing segment and not any later recovery, so the study documents the investment-phase drag rather than a completed cycle. The theme decomposition indicates that the margin association is closest to regulatory technology, cybersecurity, broad transformation and payments. Because the design is observational, the results are interpreted as within-bank associations rather than causal effects, and, although precisely estimated, these associations are economically modest. The study contributes a transparent theme-decomposed measure of bank digitization and evidence on its limits for corporate financial performance in an emerging-market banking region. Full article

Customer segmentation and purchase behavior prediction are fundamental tasks in intelligent e-commerce systems, enabling personalized marketing strategies and data-driven customer relationship management. However, conventional machine learning and graph neural network approaches primarily model pairwise interactions and often fail to capture higher-order relationships among customers, products, brands, and purchase contexts, limiting predictive performance and model interpretability. To address these challenges, this study proposes an Explainable Hypergraph Neural Network (EHGNN) framework that integrates higher-order hypergraph representation learning with post hoc explainability using SHAP. The proposed framework constructs a heterogeneous hypergraph from customer transaction data, learns informative customer embeddings via hypergraph convolution, segments customers via clustering, and predicts purchase behavior using an embedding fusion network. Comprehensive experiments were conducted to compare the proposed framework with conventional clustering algorithms, deep clustering methods, graph neural networks, and hypergraph neural networks. Experimental results demonstrate that the proposed EHGNN consistently achieved superior performance, obtaining a Silhouette Coefficient of 0.824, Davies–Bouldin Index of 0.336, and Calinski–Harabasz Index of 2815 for customer segmentation. For purchase behavior prediction, the proposed framework achieved an Accuracy of 97.30%, Precision of 97.00%, Recall of 96.80%, F1-score of 96.90%, Area Under the Receiver Operating Characteristic Curve (AUC) of 99.20%, and a Matthews Correlation Coefficient (MCC) of 0.942, outperforming all benchmark methods. These findings demonstrate that modeling higher-order customer relationships using hypergraph learning substantially improves both customer segmentation quality and purchase behavior prediction, while maintaining model transparency via explainable artificial intelligence. The proposed EHGNN framework provides an effective, robust, and interpretable solution for intelligent customer analytics and personalized decision support in modern e-commerce environments. Full article