Search Results (1,479)

Flexible energy sources such as electric vehicles and the battery energy storage systems of intelligent distribution systems can provide system-wide auxiliary services such as frequency regulation for power systems. This paper proposes an optimal method for operating the local energy community that is based on two-stage scheduling. Firstly, the basic concepts of the local energy community and flexible service are introduced in detail. Taking LEC as the reserve unit of artificial frequency recovery, an energy information interaction model among LEC, balance service providers, and the power grid is established. Then, a two-stage scheduling framework is proposed to ensure the rationality and economy of community energy scheduling. In the first stage, day-ahead scheduling uses the energy community management center to predict the up/down flexibility capacity that LEC can provide by adjusting the BESS control parameters. In the second stage, real-time scheduling aims at maximizing community profits and scheduling LEC based on the allocation and activation of standby flexibility determined in real time. Finally, the correctness of the two-stage scheduling framework is verified through a case study. The results show that the control parameters used in the day-ahead stage can significantly affect the real-time profitability of LEC, and that LEC benefits more in the case of low BESS utilization than in the case of high BESS utilization and non-participation in frequency recovery reserve. Full article

Integrating artificial intelligence (AI) and cause-related marketing has reshaped corporate social responsibility practices while triggering a conflict between technological instrumental rationality and moral value transmission. Building on the Persuasion Knowledge Model (PKM) and AI aversion literature, this research employs two experiments to reveal that AI disclosure exerts a unique inhibitory effect on consumers’ purchase intentions in cause-related marketing contexts compared to non-cause-related marketing scenarios. Further analysis uncovers a chain mediation pathway through consumer skepticism and advertisement attitudes, explaining the psychological mechanism underlying AI disclosure’s impact on purchase intentions. The study also identifies the moderating role of AI aversion within this chain model. The findings provide a new theoretical perspective for integrating AI disclosure, consumer psychological responses, and marketing effectiveness while exposing the “value-instrumentality” conflict inherent in AI applications for cause-related marketing. This research advances the evolution of the PKM in the digital era and offers practical insights for cause-related marketing enterprises to balance AI technology application with optimized disclosure strategies. Full article

The division of the flood season is of great significance for the precise operation of water conservancy projects, flood control and disaster reduction, and the rational allocation of water resources, alleviating the contradiction of the uneven spatial and temporal distribution of water resources. The single weighting method can only determine the weight of the flood season division indicators from a certain perspective and cannot comprehensively reflect the time-series attributes of the indicators. This study proposes a Flood Season Division Model based on the Goose Optimization Algorithm and Minimum Deviation Combined Weighting (FSDGOAMDCW). The model uses the Goose Optimization Algorithm (GOA) to solve the Minimum Deviation Combination model, integrating weights from two subjective methods (Expert Scoring and G1) and three objective methods (Entropy Weight, CV, and CRITIC). Combined with the Set Pair Analysis Method (SPAM), it realizes comprehensive flood season division. Based on daily precipitation data of the Nandujiang River (1961–2022), the study determines its flood season from 1 May to 30 October. Comparisons show that: ① GOA converges faster than the Genetic Algorithm, stabilizing at T = 5 and achieving full convergence at T = 24; and ② The model’s division results have the smallest Intra-Class Differences, avoiding indistinguishability between flood and non-flood seasons under special conditions. This research aims to support flood season division studies in tropical islands. Full article

Technological roadmaps are essential tools for managing and planning complex projects, especially in the rapidly evolving field of space exploration. Defined as dynamic schedules, they support strategic and long-term planning while coordinating current and future objectives with particular technology solutions. Currently, the available methodologies are mostly built on experts’ opinions and in just few cases, methodologies and tools have been developed to support the decision makers with a rational approach. In any case, all the available approaches are meant to draw “ideal” maturation plans. Therefore, it is deemed essential to develop an integrate new algorithms able to decision guidelines on “non-nominal” scenarios. In this context, Politecnico di Torino, in collaboration with the European Space Agency (ESA) and Thales Alenia Space–Italia, developed the Technology Roadmapping Strategy (TRIS), a multi-step process designed to create robust and data-driven roadmaps. However, one of the main concerns with its initial implementation was that TRIS did not account for time and budget estimates specific to the space exploration environment, nor was it capable of generating alternative development paths under constrained conditions. This paper discloses two main significant updates to TRIS methodology: (1) improved time and budget estimation to better reflect the specific challenges of space exploration scenarios and (2) the capability of generating alternative roadmaps, i.e., alternative technological maturation paths in resource-constrained scenarios, balancing financial and temporal limitations. The application of the developed routines to available case studies confirms the tool’s ability to provide consistent planning outputs across multiple scenarios without exceeding 20% deviation from expert-based judgements available as reference. The results demonstrate the potential of the enhanced methodology in supporting strategic decision making in early-phase mission planning, ensuring adaptability to changing conditions, optimized use of time and financial resources, as well as guaranteeing an improved flexibility of the tool. By integrating data-driven prioritization, uncertainty modeling, and resource-constrained planning, TRIS equips mission planners with reliable tools to navigate the complexities of space exploration projects. This methodology ensures that roadmaps remain adaptable to changing conditions and optimized for real-world challenges, supporting the sustainable advancement of space exploration initiatives. Full article

This review surveys recent advances and emerging prospects in phase-transfer catalysis (PTC) for fuel desulfurization. In response to increasingly stringent environmental regulations, the removal of sulfur from transportation fuels has become imperative for curbing SO_x emissions. Conventional hydrodesulfurization (HDS) operates under severe temperature–pressure conditions and displays limited efficacy toward sterically hindered thiophenic compounds, motivating the exploration of non-hydrogen routes such as oxidative desulfurization (ODS). Within ODS, PTC offers distinctive benefits by shuttling reactants across immiscible phases, thereby enhancing reaction rates and selectivity. In particular, PTC enables efficient migration of organosulfur substrates from the hydrocarbon matrix into an aqueous phase where they are oxidized and subsequently extracted. The review first summarizes the deployment of classic PTC systems—quaternary ammonium salts, crown ethers, and related agents—in ODS operations and then delineates the underlying phase-transfer mechanisms, encompassing reaction-controlled, thermally triggered, photo-responsive, and pH-sensitive cycles. Attention is next directed to a new generation of catalysts, including quaternary-ammonium polyoxometalates, imidazolium-substituted polyoxometalates, and ionic-liquid-based hybrids. Their tailored architectures, catalytic performance, and mechanistic attributes are analyzed comprehensively. By incorporating multifunctional supports or rational structural modifications, these systems deliver superior desulfurization efficiency, product selectivity, and recyclability. Despite such progress, commercial deployment is hindered by the following outstanding issues: long-term catalyst durability, continuous-flow reactor design, and full life-cycle cost optimization. Future research should, therefore, focus on elucidating structure–performance relationships, translating batch protocols into robust continuous processes, and performing rigorous environmental and techno-economic assessments to accelerate the industrial adoption of PTC-enabled desulfurization. Full article

The purpose of this paper is to use an informatics-based analysis to develop a rational design approach to the accelerated screening of nano-composite materials. Using existing nano-composite data, we develop a quantitative structure–activity relationship (QSAR) as a function of polymer matrix chemistry and nano-additive volume, with the property predicted being electrical conductivity. The development of a QSAR for the electrical conductivity of nano-composites presents challenges in representing the polymer matrix chemistry and backbone structure, the additive content, and the interactions between the components while capturing the non-linearity of electrical conductivity with changing nano-additive volume. An important aspect of this work is designing chemistries with small training data sizes, as the uncertainty in modeling is high, and potentially the representated physics may be minimal. In this work, we explore two important components of this aspect. First, an assessment via Uniform Manifold Approximation and Projection (UMAP) is used to assess the variability provided by new data points and how much information is contributed by data, which is significantly more important than the actual data size (i.e., how much new information is provided by each data point?). The second component involves assessing multiple training/testing splits to ensure that any results are not due to a specific case but rather that the results are statistically meaningful. This work will accelerate the rational design of polymer nano-composites by fully considering the large array of possible variables while providing a high-speed screening of polymer chemistries. Full article

The shikimate pathway is the fundamental metabolic route for aromatic amino acid biosynthesis in bacteria, plants, and fungi, but is absent in mammals. This review explores how multi-enzyme synergy and allosteric regulation coordinate metabolic flux through this pathway by focusing on three key enzymes: 3-deoxy-d-arabino-heptulosonate-7-phosphate synthase, chorismate mutase, and tryptophan synthase. We examine the structural diversity and distribution of these enzymes across evolutionary domains, highlighting conserved catalytic mechanisms alongside species-specific regulatory adaptations. The review covers directed evolution strategies that have transformed naturally regulated enzymes into standalone biocatalysts with enhanced activity and expanded substrate scope, enabling synthesis of non-canonical amino acids and complex organic molecules. Industrial applications demonstrate the pathway’s potential for sustainable production of pharmaceuticals, polymer precursors, and specialty chemicals through engineered microbial platforms. Additionally, we discuss the therapeutic potential of inhibitors targeting pathogenic organisms, particularly their mechanisms of action and antimicrobial efficacy. This comprehensive review establishes the shikimate pathway as a paradigmatic system where understanding allosteric networks enables the rational design of biocatalytic platforms, providing blueprints for biotechnological innovation and demonstrating how evolutionary constraints can be overcome through protein engineering to create superior industrial biocatalysts. Full article

Docetaxel is a chemotherapeutic agent widely used for breast cancer treatment; however, its efficacy is often limited by drug resistance and associated toxicity. This review examines the molecular mechanisms of docetaxel resistance in breast cancer and discusses research advances and future directions for overcoming this challenge. Key resistance mechanisms include alterations in drug targets (microtubules), increased drug efflux, suppression of apoptosis, activation of survival signalling pathways, epithelial-to-mesenchymal transition (EMT), and cancer stem cell enrichment. An evolutionary perspective distinguishes between intrinsic and acquired resistance, emphasising the need for adaptive therapeutic strategies. Recent advances in genomic profiling, non-coding RNA research, novel drug combinations, and biomarker-guided therapies have also been reviewed. Emerging approaches, such as targeting the tumour microenvironment, harnessing immunotherapy, and implementing adaptive dosing schedules, have been discussed. This review emphasises the understanding of resistance as a multifactorial phenomenon that requires multipronged interventions. Research has aimed to identify predictive biomarkers, develop targeted agents to reverse resistance, and design rational combination strategies to improve patient outcomes. Progress in deciphering and targeting docetaxel resistance mechanisms holds promise for enhancing treatment responses and extending survival in patients with breast cancer. Full article

This essay argues for the reciprocal benefits of joining the new theories of matter emerging out of critical posthumanism and the psychedelic drugs currently experiencing a so-called “renaissance” in global north societies. While the former’s twin emphasis on relationality and embodiment is perfectly suited to capture and ground the ontological, epistemological, and ethical implications of psychedelic experiences of interconnectedness and transformation, these substances are in turn powerful companions through which to enact a “posthuman phenomenology” that helps us with the urgent task to “access, amplify, and describe” our deep imbrication with our more-than-human environments. In other words, I argue that while the “new materialism” emerging out of posthumanism can help elaborate a psychedelic rationality, psychedelics can in turn operate as educators in materiality. It is from this materialist perspective that we can best make sense of psychedelics’ often touted potential for social transformation and the enduring suspicion that they are somehow at odds with the “ontoepistemological order” of modernity. From this point of view, I contend that a crucial critical move is to push against the common trope that this opposition is best expressed as a turn from the narrow scientific and “consumerist materialism” of modern Western societies to more expansive “spiritual” worldviews. Pushing against this science-–spirituality binary, which in fact reproduces modern “indivi/dualism” by confining psychedelic experience inside our heads, I argue instead that what is in fact needed to think through and actualize such potentials is an increased attention to our material transcorporeality. In a nutshell, if we want psychedelics to inform social change, we must be more, not less, materialist—albeit by redefining matter in a rather “weird”, non-reductive way and by redefining consciousness as embodied. By the end of the essay, attaching psychedelics to a new materialism will enable us to formulate a “material spirituality” that establishes psychedelics’ political value less in an idealistic or cognitive “politics of consciousness” and more in a “materialization of critique”. Full article

Cellular senescence is a state of the durable cell cycle arrest of dysfunctional cells, which has been associated with the promotion of tumor cell reprogramming into a stem cell state. We previously reported that the mixed lineage kinase (MLK) inhibitor CEP-1347 promotes the differentiation of glioma stem cells (GSCs)—key contributors to glioblastoma recurrence and therapy resistance—into non-stem tumor cells. However, we also noted that CEP-1347–treated GSCs exhibited a morphological change suggestive of senescence. Therefore, we herein investigated whether CEP-1347 induces senescence in GSCs and, consequently, if senescent GSCs may be eliminated using senolytics. Cell death induced by CEP-1347 in combination with senolytic agents or with the knockdown of anti-apoptotic BCL2 family genes, as well as the effects of CEP-1347 on the expression of senescence markers and anti-apoptotic Bcl-2 family proteins, were examined. The results obtained showed that CEP-1347 induced senescence in GSCs accompanied by the increased expression of Bcl-xL. Among the panel of senolytic agents tested, navitoclax, a BH3 mimetic, efficiently induced cell death in GSCs when combined with CEP-1347 at concentrations clinically achievable in the brain. The knockdown of Bcl-xL resulted in more pronounced GSC death in combination with CEP-1347 than that of Bcl-2. These results suggest that combining CEP-1347 with the targeting of Bcl-xL, the expression of which increases with CEP-1347-induced senescence, is a rational approach to ensure the elimination of GSCs, thereby improving the outcomes of glioblastoma treatment. Full article

Transdermal drug delivery (TDD) is an increasingly important non-invasive method for administering active pharmaceutical ingredients (APIs) through the skin barrier, offering advantages such as improved therapeutic efficacy and reduced systemic side effects. As demand increases for patient-friendly and minimally invasive treatment options, TDD has attracted substantial attention in research and clinical practice. This review summarizes recent advances enhancing skin permeability through chemical enhancers (e.g., ethanol, fatty acids, terpenes), physical (e.g., iontophoresis, microneedles, sonophoresis), and nanotechnological methods (e.g., liposomes, ethosomes, solid lipid nanoparticles, and transferosomes). A comprehensive literature analysis, including scientific publications, regulatory guidelines, and patents, was conducted to identify innovative methods and materials used to overcome the barrier properties of the stratum corneum. Special emphasis was placed on in vitro, ex vivo, and in vivo evaluation techniques for such as Franz diffusion cells for assessing drug permeation and skin interactions. The findings highlight the importance of active physical methods, passive nanostructured systems, and chemical penetration enhancers. In conclusion, integrating multiple analytical techniques is essential for the rational design and optimization of effective transdermal drug delivery systems. Full article

This study evaluated the effects of different doses of limonene essential oil (LEO) and a blend of cinnamaldehyde and carvacrol (BCC) on the fermentative quality and chemical–bromatological composition of total mixed ration (TMR) silages. Two independent trials were conducted, each focused on one additive, using a completely randomized design with four treatments (0, 200, 400, and 600 mg/kg of dry matter), replicated across two seasons (summer and autumn), with five replicates per treatment per season. The silages were assessed for their chemical composition, fermentation profile, aerobic stability (AS), and storage losses. In the LEO trial, the dry matter (DM) content increased significantly by 0.047% for each mg/kg added. Dry matter recovery (DMR) peaked at 97.9% at 473 mg/kg (p < 0.01), while lactic acid (LA) production reached 5.87% DM at 456 mg/kg. Ethanol concentrations decreased to 0.13% DM at 392 mg/kg (p = 0.04). The highest AS value (114 h) was observed at 203.7 mg/kg, but AS declined slightly at the highest LEO dose (600 mg/kg). No significant effects were observed for the pH, neutral detergent fiber (NDF), acid detergent fiber (ADF), crude protein (CP), or non-fiber carbohydrates (NFCs). In the BCC trial, DMR reached 98.2% at 548 mg/kg (p < 0.001), and effluent losses decreased by approximately 20 kg/ton DM. LA production peaked at 6.41% DM at 412 mg/kg (p < 0.001), and AS reached 131 h at 359 mg/kg. BCC increased NDF (from 23.27% to 27.73%) and ADF (from 35.13% to 41.20%) linearly, while NFCs and the total digestible nutrients (TDN) decreased by 0.0007% and 0.039% per mg of BCC, respectively. In conclusion, both additives improved the fermentation efficiency by increasing LA and reducing losses. LEO was more effective for DM retention and ethanol reduction, while BCC improved DMR and AS, with distinct effects on fiber and energy fractions. Full article

This article investigates the political cost of poverty in democracies marked by deep social divisions. We develop a probabilistic voting model that incorporates clientelism as a strategic tool employed by elite political parties to secure electoral support from non-elite voters. Unlike models based on ideological proximity, our framework conceptualizes party competition as structured by the socioeconomic composition of their constituencies. We demonstrate that in contexts of high inequality and widespread poverty, elite parties face structural incentives to deploy clientelistic strategies rather than universalistic policy agendas. Our model predicts that clientelistic expenditures by elite parties increase proportionally with both inequality (GINI index) and poverty levels, rendering clientelism a rational and cost-effective mechanism of political control. Empirical evidence from a cross-national panel (2013–2019) confirms the theoretical predictions: an increase of the 1 percent in the GINI index increase a 1.3 percent in the clientelism, even after accounting for endogeneity and dynamic effects. These findings suggest that in divided democracies, poverty is not merely a condition to be alleviated, but a political resource that elites strategically exploit. Consequently, clientelism persists not as a cultural residue or institutional failure, but as a rational response to inequality-driven constraints within democratic competition. Full article

Polyvinyl alcohol (PVA) hydrogels have emerged as versatile materials due to their exceptional biocompatibility and tunable mechanical properties, showing great promise for flexible sensors, smart wound dressings, and tissue engineering applications. However, rational design remains challenging due to complex structure–property relationships involving multiple formulation parameters. This study presents an interpretable machine learning framework for predicting PVA hydrogel tensile strain properties with emphasis on mechanistic understanding, based on a comprehensive dataset of 350 data points collected from a systematic literature review. XGBoost demonstrated superior performance after Optuna-based optimization, achieving R² values of 0.964 for training and 0.801 for testing. SHAP analysis provided unprecedented mechanistic insights, revealing that PVA molecular weight dominates mechanical performance (SHAP importance: 84.94) through chain entanglement and crystallization mechanisms, followed by degree of hydrolysis (72.46) and cross-linking parameters. The interpretability analysis identified optimal parameter ranges and critical feature interactions, elucidating complex non-linear relationships and reinforcement mechanisms. By addressing the “black box” limitation of machine learning, this approach enables rational design strategies and mechanistic understanding for next-generation multifunctional hydrogels. Full article

Corrosion, combined with cyclic loading, is inevitable and becomes a challenging problem, even when inherently corrosion-protected materials have been selected and applied based on established in-house experience. Aero engine mount structures are exposed to dusty and salty environmental conditions during both operational and non-operational periods. It is becoming tough to predict the remaining useful corrosion fatigue life due to the unascertainable material strength degradations under service conditions. As such, a rationalized approach is currently being used to assess their structural integrity, which produces more wastages of the flying parts. This paper presents a novel approach for predicting corrosion fatigue by proposing a random-parameter model in combination with validated experimental data. The two-random-parameter model is employed here with the probability method to determine the time-independent corrosion fatigue life of a magnesium structural casting, which is used heavily in engine front-mount aircraft systems. This is also correlated with experimental data from the literature, validating the proposed stochastic corrosion fatigue model that addresses the technical variances that occur during service to increase optimal mount structure usage using an automated data system. Full article