Search Results (3,510)

The behavioral theory of the firm explains how firms react to performance feedback, yet little is known about how firms integrate backward-looking feedback with forward-looking assessments of market opportunity. This study proposes and tests a retrenchment model grounded in SWOT-based behavioral logic via the TOWS matrix. Changes in market share are conceptualized as an internal strength or weakness, and market attractiveness, as an external opportunity or threat. Using prefecture-level panel data on Japanese life insurance companies (2006–2019), the analysis showed that market attractiveness served as a cognitive frame that shapes a firm’s response to performance signals. In attractive markets (opportunity), firms reduced retrenchment, as share gains (strength) were leveraged and losses (weakness) triggered problem-solving. Conversely, in unattractive markets (threat), firms accelerated retrenchment, as losses (weakness) confirmed the need to exit and gains (strength) enabled a profitable withdrawal. The study extends behavioral theory by showing that the strategic meaning of an internal strength or weakness depends on the external context of an opportunity or threat. This mechanism helps explain why firms sometimes persist after failure and retrench after success. Practically, the findings offer a diagnostic framework that helps managers assess market portfolios and mitigate behavioral biases in resource allocation decisions. Full article

Coalbed methane (CBM) resources hold broad development prospects in China, with deep CBM reservoirs increasingly becoming a focal point for exploration. However, compared to shallow CBM, the factors influencing the productivity of deep CBM are more complex and less studied. This study integrates statistical methods—grey correlation analysis and principal component analysis—with the machine learning approach of random forests, and further employs a fuzzy mathematics-based comprehensive evaluation method to propose a systematic evaluation framework for identifying key controlling factors of productivity. Using field data from the No. 8+9 coal seam in the eastern margin of the Ordos Basin, the results indicate that the primary geological factors affecting cumulative gas production are gas content and coal seam thickness, while the key engineering factors are proppant intensity and proppant volume. These findings align with practical field experience and provide a rational basis for the design of fracturing strategies in deep CBM reservoirs. Full article

Sugar amino acids (SAAs) represent a privileged class of molecular chimeras that uniquely merge the structural rigidity of carbohydrates with the functional display of amino acids. These hybrid molecules have garnered significant attention as programmable conformational constraints, offering a powerful strategy to overcome the inherent limitations of peptide-based therapeutics, such as proteolytic instability and conformational ambiguity. The strategic incorporation of SAAs into peptide backbones, particularly within cyclic frameworks, allows for the rational design of peptidomimetics with pre-organized secondary structures, enhanced metabolic stability, and improved physicochemical properties. This review provides a comprehensive analysis of the synthetic methodologies developed to access the diverse structural landscape of SAAs, with a focus on modern, stereoselective strategies that yield versatile building blocks for peptide chemistry. A critical examination of the structural impact of SAA incorporation reveals their profound ability to induce and stabilize specific secondary structures, such as β- and γ-turns. Furthermore, a comparative analysis positions SAAs in the context of other widely used peptidomimetic scaffolds, highlighting their unique advantages in combining conformational control with tunable hydrophilicity. We surveyed the application of SAA-containing cyclic peptides as therapeutic agents, with a detailed case study on gramicidin S analogs that underscores the power of SAAs in elucidating complex structure–activity relationships. Finally, this review presents a forward-looking perspective on the challenges and future directions of the field, emphasizing the transformative potential of computational design, artificial intelligence, and advanced bioconjugation techniques to accelerate the development of next-generation SAA-based therapeutics. Full article

We develop a rigorous algebraic–analytic framework for multidual complex numbers ${\mathbb{DC}}_n$ within the setting of Clifford analysis and establish a comprehensive theory of hyperholomorphic multidual complex-valued functions. Our main contributions are (i) a fully coupled multidual Cauchy–Riemann system derived from the Dirac operator, yielding precise differentiability criteria; (ii) generalized conjugation laws and the associated norms that clarify metric and geometric structure; and (iii) explicit operator and kernel constructions—including generalized Cauchy kernels and Borel–Pompeiu-type formulas—that produce new representation theorems and regularity results. We further provide matrix–exponential and functional calculus representations tailored to ${\mathbb{DC}}_n$, which unify algebraic and analytic viewpoints and facilitate computation. The theory is illustrated through a portfolio of examples (polynomials, rational maps on invertible sets, exponentials, and compositions) and a solvable multidual boundary value problem. Connections to applications are made explicit via higher-order automatic differentiation (using nilpotent infinitesimals) and links to kinematics and screw theory, highlighting how multidual analysis expands classical holomorphic paradigms to richer, nilpotent-augmented coordinate systems. Our results refine and extend prior work on dual/multidual numbers and situate multidual hyperholomorphicity within modern Clifford analysis. We close with a concise summary of notation and a set of concrete open problems to guide further development. Full article

Rapid and green detection of soil nutrients is essential for soil fertility and plant growth. However, traditional methods cannot meet the needs of rapid detection, and the reagents easily cause environmental pollution. Hence, we proposed a multivariable output weighting-network (MW-Net) combined with laser-induced breakdown spectroscopy (LIBS) to achieve rapid and green detection for three soil nutrients. For a better spectral signal-to-background ratio (SBR), the two important parameters of delay time and gate width were optimized. Then, the spectral noise was removed by the near-zero standard deviation method. Three common quantitative models were investigated for single-element prediction, which are usually applied in LIBS analysis. Also, multi-element prediction was investigated using MW-Net. The results showed that MW-Net outperformed other models generally with very good quantification for soil total N and K (the determination coefficients in the prediction set (R_p²) of 0.75 and 0.83 and the relative percent difference in the prediction sets (RPD) of 2.05 and 2.43) and excellent indirect determination for soil exchangeable Ca (R_p² of 0.93 and RPD of 3.91). Finally, the interpretability was realized through feature extraction from MW-Net, indicating its design rationality. The preliminary results indicated that MW-Net combined with LIBS technology could quantify the three soil nutrients simultaneously, improving the detection efficiency, and it could possibly be deployed on a LIBS portable instrument in the future for precision agriculture. Full article

Agricultural soils are hotspots of nitrous oxide (N₂O) emissions, where carbon substrates act as a critical factor influencing microbial community composition. However, how carbon availability modulates microbial denitrifying pathways and further influences N₂O emissions remains poorly understood. Here, we conducted anaerobic incubations to investigate North China vegetable soil N₂O production and consumption in response to varied glucose concentrations (0, 0.5 (Glu_0.5), 1.0 (Glu_1.0), and 2.0 (Glu_2.0) g C kg⁻¹ d.w. of soil). In this study, the δ¹⁵N^SP/δ¹⁸O mapping approach (δ¹⁵N^SP/δ¹⁸O MAP) and acetylene inhibition technique (AIT) were used to quantify the residual N₂O ratio (r_N2O) and the relative contributions of bacterial (f_BD) and fungal (f_FD) denitrification to N₂O production. The results showed that increasing glucose concentrations significantly increased CO₂ and N₂O emissions, with peak fluxes observed at Glu_2.0 on day 1 (116.22 ± 2.80 mg CO₂-C kg⁻¹ and 1.08 ± 0.02 mg N₂O-N kg⁻¹). Concurrently, δ¹⁵N^SP was also significantly elevated (p < 0.001), indicating enhanced f_FD, which was further corroborated by positive correlations between f_FD and glucose concentration (r = 0.48–0.56, p < 0.001). Nevertheless, bacterial denitrification (BD) still dominated N₂O production throughout the incubation period, except on day 1 in Glu_1.0 and Glu_2.0 of Case 2. Bland–Altman analysis with 95% limits of agreement (LoA) demonstrated strong agreement between the MAP and AIT in r_N2O estimation, particularly under Glu_2.0. All the above revealed glucose-induced denitrifying microbial shifts from BD to fungal denitrification (FD), which consequently modulated N₂O emissions and promoted incomplete denitrification. These findings collectively demonstrate that in vegetable cropping systems, rational carbon management strategies can promote N₂O reduction to N₂, thereby achieving effective N₂O mitigation. Full article

Background/Objectives: Cofilin, a key regulator of actin cytoskeleton dynamics, contributes to neuroinflammation, synaptic damage, and blood–brain barrier disruption in ischemic stroke. Despite its established role in stroke pathology, cofilin remains largely untargeted by existing therapeutics. This study aimed to identify potential cofilin-binding molecules by repurposing LIMK1 inhibitors through an integrated computational strategy. Methods: A cheminformatics pipeline combined QSAR modeling with four molecular fingerprint sets and multiple machine learning algorithms. The best-performing QSAR model (substructure–Random Forest) achieved R²_train = 0.8747 and R²_test = 0.8078, supporting the reliability of compound prioritization. Feature importance was assessed through SHAP analysis. Top candidates were subjected to molecular docking against cofilin, followed by 300 ns molecular dynamics simulations, MM-GBSA binding energy calculations, principal component analysis (PCA), and dynamic cross-correlation matrix (DCCM) analyses. Network pharmacology identified overlapping targets between selected compounds and stroke-related genes. Results: Three compounds, CHEMBL3613624, ZINC000653853876, and Gandotinib, were prioritized based on QSAR performance, binding affinity (−6.68, −6.25, and −5.61 Kcal/mol, respectively), and structural relevance. Docking studies confirmed key interactions with Asp98 and His133 on cofilin. Molecular dynamics simulations supported the stability of these interactions, with Gandotinib showing the highest conformational stability, and ZINC000653853876 exhibiting the most favorable energetic profile. Network pharmacology analysis revealed eight intersecting targets, including MAPK1, PRKCB, HDAC1, and serotonin receptors, associated with neuroinflammatory and vascular pathways in strokes. Conclusions: This study presents a rational, integrative repurposing framework for identifying cofilin-targeting compounds with potential therapeutic relevance in ischemic stroke. The selected candidates warrant further experimental validation. Full article

Endolysins, phage-derived enzymes capable of lysing bacterial cell walls, hold significant promise as novel antimicrobials against resistant Gram-positive and Gram-negative pathogens. In this study, we undertook an integrative approach combining extensive in silico analyses and experimental validation to characterize the novel endolysin LysPALS22. Initially, sixteen endolysin sequences were selected based on documented lytic activity and enzymatic diversity, and subjected to multiple sequence alignment and phylogenetic analysis, which revealed highly conserved catalytic and binding domains, particularly localized to the N-terminal region, underscoring their functional importance. Building upon these sequence insights, we generated three-dimensional structural models using Swiss-Model, EBI-EMBL, and AlphaFold Colab, where comparative evaluation via Ramachandran plots and ERRAT scores identified the Swiss-Model prediction as the highest quality structure, featuring over 90% residues in favored conformations and superior atomic interaction profiles. Leveraging this validated model, molecular docking studies were conducted in PyRx with AutoDock Vina, performing blind docking of key peptidoglycan-derived ligands such as N-Acetylmuramic Acid-L-Alanine, which exhibited the strongest binding affinity (−7.3 kcal/mol), with stable hydrogen bonding to catalytic residues ASP46 and TYR61, indicating precise substrate recognition. Visualization of docking poses using Discovery Studio further confirmed critical hydrophobic and polar interactions stabilizing ligand binding. Subsequent molecular dynamics simulations validated the stability of the LysPALS22–NAM-LA complex, showing minimal structural fluctuations, persistent hydrogen bonding, and favorable interaction energies throughout the 100 ns trajectory. Parallel to computational analyses, LysPALS22 was heterologously expressed in Escherichia coli (E. coli) and Pichia pastoris (P. pastoris), where SDS-PAGE and bicinchoninic acid assays validated successful protein production; notably, the P. pastoris-expressed enzyme displayed an increased molecular weight (~45 kDa) consistent with glycosylation, and achieved higher volumetric yields (1.56 ± 0.31 mg/mL) compared to E. coli (1.31 ± 0.16 mg/mL), reflecting advantages of yeast expression for large-scale production. Collectively, these findings provide a robust structural and functional foundation for LysPALS22, highlighting its conserved enzymatic features, specific ligand interactions, and successful recombinant expression, thereby setting the stage for future in vivo antimicrobial efficacy studies and rational engineering efforts aimed at combating multidrug-resistant Gram-negative infections. Full article

Our goal was to identify and understand perspectives of different stakeholders in the field of climate policy and test a process of co-creative policy development to support the implementation of climate protection measures. As the severity of climate change grows globally, perceptions of climate science and climate-based policy have become increasingly polarized. The one-solution consensus or compromise that has encapsulated environmental policymaking has proven insufficient or unable to address accurately or efficiently the climate issue. Because climate change is often described as a wicked problem (multiple causes, widespread impacts, uncertain outcomes, and an array of potential solutions), a clumsy solution that incorporates ideas and actions representative of varied and divergent worldviews is best suited to address it. This study used the Theory of Plural Rationality, which uses a two-dimensional spectrum to identify four interdependent worldviews as well as a fifth autonomous perspective to define the differing perspectives in the field of climate policy in Austria. Stakeholder inputs regarding general worldviews, climate change, and climate policy were evaluated to identify agreeable actions representative of the multiple perspectives. Thus, we developed and tested a co-creative process for developing clumsy solutions. This study concludes that while an ideological consensus is unlikely, agreement is more likely to occur on the practical level of concrete actions (albeit perhaps for different reasons). Findings suggested that creating an ecological tax reform was an acceptable policy action to diverse stakeholders. Furthermore, the study illuminated that the government is perceived to have the most potential influence on climate protection policy and acts as a key “broker”, or linkage, between other approaches that are perceived to be more actualized but less impactful. Full article

This work presents a generative design algorithm for the semi-automatic reconstruction of sweepable surfaces from point clouds obtained through three-dimensional scanning. The proposed algorithm enables, starting from a 3D acquisition dataset, the correct automatic orientation of the mesh, the selection of a suitable cutting edge, and the specification of the number of transversal sections for an effective 3D model reconstruction. Additionally, it suggests a maximum number of points to be used for reconstructing the sectional curves. The mesh reconstruction is performed through a lofting operation, resulting in a non-uniform rational B-spline (NURBS) surface. The algorithm has been applied to a case study involving the front wing surface of a foil from the Olympic class iQFoil, which has recently garnered significant attention from researchers in the field of performance analysis. The obtained reconstructed surface exhibits very low deviation values when compared to the original mesh. This demonstrates the reliability of the results obtained with the proposed approach, which provides sufficient accuracy and is obtained in a considerably shorter time compared to the traditional manual reconstruction approach, enabling the reconstruction of a 3D model in just a few semi-automatic steps, ready for subsequent numerical analyses if needed. Full article

Bioactive peptides have significant potential for applications in pharmaceuticals, the food industry, and cosmetics due to their wide spectrum of biological activities. However, their pronounced structural and functional heterogeneity complicates the classification and prediction of biological activity. This study uses data from the PepLab platform, comprising 2748 experimentally confirmed bioactive peptides distributed across 15 functional classes, including ACE inhibitors, antimicrobial, anticancer, antioxidant, toxins, and others. For each peptide, the amino acid sequence and key physicochemical descriptors are provided, calculated via the integrated DMPep module, such as GRAVY index, aliphatic index, isoelectric point, molecular weight, Boman index, and sequence length. The dataset exhibits class imbalance, with class sizes ranging from 14 to 524 peptides. An innovative methodology is proposed, combining descriptive statistical analysis, structural modeling via DEMATEL, and structural equation modeling with neural networks (SEM-NN), where SEM-NN is used to capture complex nonlinear causal relationships between descriptors and functional classes. The results of these dependencies are integrated into a multi-class machine learning model to improve interpretability and predictive performance. Targeted data augmentation was applied to mitigate class imbalance. The developed classifier achieved predictive accuracy of up to 66%, a relatively high value given the complexity of the problem and the limited dataset size. These results confirm that integrating structured dependency modeling with artificial intelligence is an effective approach for functional peptide classification and supports the rational design of novel bioactive molecules. Full article

As urbanization progresses rapidly, cities face growing challenges of land resource scarcity and the pressure on green ecological spaces. This not only affects the sustainable development of cities but also presents a major challenge to the resilience of urban ecosystems (UER). As an emerging land use model, land financialization (LF), which involves the circulation and financing of land as a financial asset, has become an important means to promote UER. Therefore, this paper examines 221 prefecture-level cities across mainland China to explore the impact of land financialization on urban ecological resilience and aims to reveal the specific pathways through which land financialization improves urban ecological resilience through mechanisms like resource reallocation, industrial structure rationalization, green innovation, green signals, and environmental regulation. This paper employs a two-way fixed effects model, robustness tests, and endogeneity tests, supplemented by mechanism and heterogeneity analysis, to explore the impact of LF on UER. The findings show that LF plays a significant role in improving UER. Mechanism analysis reveals that LF significantly boosts UER by optimizing the distribution of land and financial resources, as well as enhancing the rationalization of the industrial structure. Additionally, enterprise green technology innovation, green value, and the intensity of environmental regulation play a positive moderating role in this process. In addition, the heterogeneity analysis reveals the inclusive characteristics of LF on urban ecological transformation. In cities with higher levels of land price distortion, as well as in old industrial cities, small cities, and peripheral cities with poorer resource characteristics and administrative resources, LF has a more significant impact on promoting the improvement of UER. Based on the findings, this paper proposes policy recommendations to promote the improvement of urban green ecology and support the innovation of land financialization. These insights contribute to the theoretical discourse on greenization and provide essential, practical guidance for optimizing the allocation of land and financial resources, as well as establishing a framework for green and high-quality development. Full article

Examples from urban infrastructure in the United States show that high-level policy reports focused on investment neglect performance improvement, as well as changes in society and technology. A study methodology using systems approaches, institutional analysis, and examples from US situations was used to probe causes and remedies of this policy shortcoming. A conceptual systems model of services and the Maslow hierarchy of needs identified essential services spanning water, energy, transit, and streets management. Drinking water services have greater clarity and were selected to assess actor roles, responsibilities, and actions. The institutional analysis and development framework was used to organize the actors, settings, norms, incentives, rules, and action arenas. Data from the drinking water sector indicated that infrastructure policy reports mix issues and obscure significant impacts on specific sectors. They assume a static view and do not consider transformations in social contracts, alternative technologies, and service delivery methods. Without policy reform, public trust in government services will diminish, but political and administrative realities constrain rational and comprehensive approaches. The drinking water social contract is unlikely to change, but partnerships can incentivize reforms like performance assessment and agency accreditation. Development of a road map for urban infrastructure policy reform will require research by task forces of leading-edge practitioners within categorical arenas like drinking water, electric power, transit, and public works. Full article

The purpose of this study is to systematically examine the impact of technological innovation on the coordinated development of regional economies and its internal mechanism. It is aimed at revealing whether and how technological innovation promotes the coordinated development of regional economies, and further identifying its heterogeneity characteristics and boundary conditions in the space–time dimension. The research was conducted using panel data for 258 prefecture-level cities in China from 2011 to 2021. This study found that technological innovation significantly promoted the coordinated development of regional economies; this effect was more prominent in China’s eastern region and the Yangtze River Economic Belt. The mechanism test shows that technological innovation can optimize regional resource allocation and narrow the development gap by promoting industrial structure upgrades and rationalization. Further analysis shows that the level of marketization has a nonlinear regulatory effect on the coordination effect of technological innovation, with two threshold levels. A heterogeneity analysis reveals significant differences in the effects of technological innovation in different regions in China, especially in the western region and the northwest side of the Hu Changyong line. The research leads to four key policy recommendations. First, it is important to strengthen the core driving role of technological innovation and implement regionally differentiated innovation support policies. Second, industrial structure upgrades should be encouraged through industrial chain coordination. The third recommendation is to improve the market-oriented institutional environment and minimize barriers to factor flow. Fourth, supporting coordinated policies, such as optimizing human capital and introducing high-quality foreign capital, is necessary to establish a sustainable long-term mechanism for regional coordinated development. Full article

Meningococcal meningitis is a deadly acute bacterial infection caused by the Neisseria meningitidis bacterium that affects the membrane covering the brain and spinal cord. The World Health Organization launched the “Defeating bacterial meningitis by 2030” initiative in 2018, which relies on recent discoveries of cheap and effective vaccines. Here, we consider one important factor—human behavior—which is often neglected by immunization campaigns. We constructed a game-theoretic model of meningitis in the meningitis belt, where individuals make selfish rational decisions whether to vaccinate based on the assumed costs and the vaccination decisions of the entire population. We identified conditions when individuals should vaccinate, and we found the optimal (equilibrium) population vaccination rate. We conclude that voluntary compliance significantly reduces the endemic levels of meningitis if the cost of vaccination relative to the cost of the disease is sufficiently low, but it does not eliminate the disease. We also performed uncertainty and sensitivity analysis on our model. Full article