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IR-Induced CO Photodesorption from Pure CO Ice and CO on Amorphous Solid Water
No full textCarbon monoxide (CO) is a key component of the icy mantles that form on the surfaces of dust grains in the interstellar medium. In dense molecular clouds, where grain temperatures are around 10 K, CO freezes out as a nonpolar layer on top of H2O ice. This CO plays an important role in the formation of complex organic molecules (COMs) through reactions with hydrogen atoms. Interstellar grains are also exposed to photons and charged particles that can both drive chemical reactions and promote desorption of molecules, providing an important link between the solid state reservoir of molecules and the gas phase. While several studies have considered UV photon driven desorption mechanisms, the UV component of the interstellar radiation field is strongly attenuated within dense clouds, with the internal cloud field being dominated by IR photons. We have used the FELIX IR Free Electron Laser (FEL) FEL-2 to irradiate a few monolayer film of CO deposited on the top of amorphous solid water (ASW) and compared the CO desorption yields to those obtained for a pure CO film. Infrared spectroscopy, combined with mass spectrometric detection of desorbing CO molecules, reveals that excitation of vibrational modes in the underlying ASW leads to significant CO desorption. This is in contrast to direct excitation of the stretching mode of CO which results in only inefficient desorption. The desorption efficiencies we derive indicate that energy transfer within ices on interstellar grains might provide an important route to IR photon-induced desorption of volatile species, such as CO.</p
Risk aggregation and stochastic dominance for a class of heavy-tailed distributions
No full textWe introduce a new class of heavy-tailed distributions for which any weighted average of independent and identically distributed random variables is larger than one such random variable in (usual) stochastic order. We show that many commonly used extremely heavy-tailed (i.e., infinite-mean) distributions, such as the Pareto, Fréchet, and Burr distributions, belong to this class. The established stochastic dominance relation can be further generalized to allow negatively dependent or non-identically distributed random variables. In particular, the weighted average of non-identically distributed random variables dominates their distribution mixtures in stochastic order
Risk aggregation and stochastic dominance for a class of heavy-tailed distributions
No full textWe introduce a new class of heavy-tailed distributions for which any weighted average of independent and identically distributed random variables is larger than one such random variable in (usual) stochastic order. We show that many commonly used extremely heavy-tailed (i.e., infinite-mean) distributions, such as the Pareto, Fréchet, and Burr distributions, belong to this class. The established stochastic dominance relation can be further generalized to allow negatively dependent or non-identically distributed random variables. In particular, the weighted average of non-identically distributed random variables dominates their distribution mixtures in stochastic order
Dual congruence in live-streaming commerce:A mixed-method to examine the role of virtual influencers and live content on consumer purchase behavior
No full textAs virtual influencers increasingly become a fixture in live-streaming commerce, understanding how their brand congruence influences consumer behaviors is critical. Hence, this research investigates the dual congruence between live content, virtual influencers, and brands and how these congruences impact perceived value, source credibility, and, ultimately, purchase behaviors. Anchored in the congruity theory, perceived value theory, and source credibility theory, a mixed-method approach was employed in this research. Study 1 employs PLS-SEM and ANN to quantitatively demonstrate that utilitarian value and credibility, rather than hedonic content or attractiveness, significantly influence purchases. Study 2 offers qualitative insights to explain these findings, highlighting consumer preferences for informative content and credible influencers over mere entertainment or visual appeal. Theoretically and practically, this research contributes to digital marketing theory by clarifying how congruence mechanisms operate in virtual contexts and offers managerial strategies for brands seeking to leverage virtual influencers effectively in live-streaming commerce
Bio-inspired multi-mode finger mechanism based on Miura-ori unit equivalent linkages
No full textOrigami structures, characterized by predefined crease patterns and configurable properties, offer valuable insights for designing reconfigurable mechanisms. Inspired by diverse grasping states of the human finger and multi-mode characteristics of the Miura-ori unit, this paper proposes a novel finger mechanism capable of four distinct single degree-of-freedom (DOF) motion modes. Each mode corresponds to a distinct finger state, characterized by two interphalangeal joints that are either rotatable or nonrotatable. First, the Miura-ori unit equivalent linkage (PFSFL, plane-symmetric flat-deployable spherical four-bar linkage) is introduced, and its multi-mode characteristics are analyzed through an approach based on dual quaternions. Next, the finger mechanism is constructed by coupling specific links and joints of two PFSFLs, and its multi-mode kinematics are systematically demonstrated. Three such fingers are integrated with an orthogonal Bricard linkage to develop a multi-mode grasping mechanism. A pneumatically actuated, 3D printed gripper based on this mechanism is fabricated, and experimentally confirms its multi-mode grasping capability. The results demonstrate the potential of the proposed finger mechanism for developing reconfigurable grippers or hands with enhanced flexibility, adaptability, and multi-task capability
Characterization of microplastics and associated metals in green mussel cultivation: Estimation of potential health risks
No full textGreen mussels, a popular seafood in Jakarta, have been found to be contaminated with microplastics. Microplastics are hydrophobic, they can adsorb various pollutants, such as metals and persistent organic compounds, onto their surface, thereby increasing the potential for biomagnification through the trophic chain. Microplastic contamination in mussels is a growing concern and may pose health risks to consumers. This research aims to characterize the types of polymers, shape colors, abundance of microplastic, detect heavy metal contaminants on microplastic surfaces in the gills, and estimate the health risks associated with their consumption. The results showed that microplastics were detected in all 120 green mussels sampled, with fragments being the dominant type, followed by fibers and films. The average abundance of microplastics was 18 ± 9.4 particles per individual or 4 ± 2.8 per gram of wet tissue weight and the average wet weight was 4.9 ± 2.15 g. FTIR analysis identified 15 types of polymers, and polymer hazard levels led to risk categories I, II III and V, which is considered very dangerous to human health. The percentages of aluminum and lead on the surface of gill microplastics were 0.15 % and 0.01 %, respectively, while the percentage of aluminum identified in microplastics on the Whatman filter was 0.23 %. The estimated annual quantity of microplastics ingested by humans ranged from 10,192 items to 76,440 items among diverse age ranges. It is estimated that each person in Indonesia ingests 271,313 microplastics annually through the consumption of green mussels. The ingestion of microplastics also leads to the intake of associated heavy metals, posing significant risks to human health
Dynamics of street views and socio-economic conditions in profiling illegal dumping ‘black spots’: An LLM-enabled study in Hong Kong
No full textIllegal dumping remains a persistent urban problem. Previous research has established that a neighborhood’s socioeconomic status and certain urban features, observed from a bird’s-eye view, influence dumping behavior. However, environmental criminologists contend that granular, eye-level street views offer more immediate and relevant environmental cues for potential offenders. This study aims to develop an explanatory model to profile illegal dumping ’black spots’ in urban areas by employing street view analytics. The innovative aspect of this approach lies in leveraging emerging large language models (LLMs) to extract street-level cues, which are then combined with census-based socioeconomic indicators using a spatially adaptive Geographic Random Forest. The model achieved a predictive accuracy of R2 = 0.7574 and an RMSE of 0.9368 on the held-out test set. Local feature analysis revealed that compact hotspot clusters with visible waste or dense vegetation significantly increase illegal dumping risk. Compared to traditional computer vision methods, LLMs proved more efficient in extracting meaningful features without manual annotation or specialized training. These findings demonstrate that integrating scalable, LLM-derived environmental cues with spatial machine learning enables more targeted and effective interventions for urban waste management
Strategies enhancing the implementation of design for adaptability in the Ghanaian construction industry: An exploratory and confirmatory factor analyses
No full textDespite the benefits design for adaptability (DfA) contributes to achieving sustainability and circularity in the construction industry, studies have demonstrated that construction professionals are yet to realize its full potential. This study examines the strategies that can enhance the practice of DfA among design professionals in the Ghanaian construction industry (GCI). A quantitative approach was used to achieve the aim of the study by soliciting the views of 236 design professionals in the GCI through structured questionnaires. Data gathered were analyzed via descriptive and inferential statistics. The findings revealed six key categories of strategies (i.e., management strategies, economic strategies, governmental regulations and policy strategies, design strategies, technological strategies and social strategies) to enhance the implementation of DfA practices in the GCI. This study highlights the theoretical and practical implications of DfA implementation, offering actionable insights for construction stakeholders to foster sustainability and resilience in the built environment. It contributes to academic discourse by categorizing strategies and proposing an implementation framework relevant to developing economies like Ghana
The spatial correlation network and driving factors of economic resilience in the construction sector: Evidence from China
No full textThe Chinese construction sector (CS) is the largest in the world in terms of total output value. Studying the economic resilience of the construction sector (ERCS) is thus of critical importance for the sustainable development of the CS globally. Regional differences in the CS economy have raised concerns about the spatial heterogeneity of the ERCS. In this study, the social network method and random forest models were used to explore the characteristics and driving factors of the ERCS spatial network. The findings reveal that the ERCS spatial network has a clear “center-edge” structure, with direct or indirect relationships across different regions as well as significant spatial spillover effects. The ERCS spatial network also exhibits a significant clustering pattern, with a high degree of internal integration and interdependence. Market size, technological innovation level, and industry scale were the main factors that promoted network formation. These insights provide a deeper understanding of the mechanisms that drive ERCS networks and offer guidance to countries aiming to develop sustainable growth strategies while also providing technical and managerial references to the global CS. This research framework thus serves as an effective complement to the sustainable development of the CS
Natural Deep Eutectic Solvent Integrated with Bulk Liquid Membrane System for Salicylic Acid Removal from Wastewater
No full textThis work describes an innovative approach for salicylic acid (SA) removal from wastewater using a bulk liquid membrane (BLM) technique incorporated with natural deep eutectic solvents (NADES) as the stripping phase. The preparation of NADES was achieved by mixing of choline chloride (ChCl) as a hydrogen bond acceptor (HBA) with lactic acid (LA) as a hydrogen bond donor (HBD). Screening studies were performed to select the best NADES molar ratio as a stripping agent for SA removal. The impact of various parameters such as the pH of the feed phase, initial concentration of SA, carrier concentration, mixing speed and temperature were investigated. The conductor-like screening model for real solvents (COSMO-RS) is used to analyse the SA removal and molecular interaction. The SA achieves an extraction efficacy of 73% and a stripping efficacy of 90.28% under the optimum conditions: HBA: HBD of 1:1, pH 2 of feed, 0.001 M of SA, 4 wt.% of carrier concentration, 150 rpm of mixing speed and at 50 ºC. The SA extraction using the NADES-based BLM technique follows the consecutive first-order kinetic model with an extraction (K1) and stripping rate constants (K2) of 0.0128 and 0.0627 min-1, respectively. The SA transport mechanism through the NADES-based BLM adheres to the film theory for mass transfer. COSMO-RS confirmed the extraction efficiency through molecular interactions validated by a sigma profile and a sigma potential between the SA molecules and NADES. This study provides a new and cleaner route for using NADES as the stripping agent in the BLM system for water treatment
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