Reduction Framework Research Articles

Exploring the application of machine‐learning techniques in the next generation of long‐term hydropower‐thermal scheduling

AbstractThis paper introduces a shape‐based inflow scenarios reduction framework applied in long‐term hydro‐thermal scheduling. This scheduling problem involves strategically managing the limited stored hydro energy in coordination with the electricity system. The scenario fan problem, selected as a long‐term hydro‐thermal scheduling approach, can effectively represent the individual hydropower plants and their short‐term flexibility within the long‐term scheduling process, making it well‐suited for capturing the disaggregated physical characteristics of hydro stations. However, the computational burden of scenario fan problem increases due to the complexity of the cascaded system and the large number of inflow scenarios. To tackle these challenges, a machine learning clustering method is developed for extracting shape‐based features to capture the energy and shape attributes of hydro inflows. A comparative analysis was conducted to evaluate the effectiveness of the proposed method, comparing it with four established clustering methods. This evaluation was carried out on a hydro‐thermal test case, utilizing both in‐sample and out‐of‐sample data analysis. The findings demonstrate that the proposed framework outperforms other methods in terms of computational time by 42.6% and optimal reservoir trajectories by 12.1% considering K‐means using Euclidean distance as the benchmark. Additionally, this study showcases the feasibility of performing long‐term hydro‐thermal scheduling at a granular level, with detailed topological information and a shorter time scale. This expanded approach opens up new possibilities for addressing the integration of renewable energy resources within the realm of long‐term hydro‐thermal scheduling.

Community-Based Strategies for Disaster Preparedness in Mauritius

Mauritius is Small Island Developing State (SIDS) of volcanic origin which lies in the South-West Indian Ocean Basin and is vulnerable to the consequences of global environmental challenges. The fourth priority action of the Sendai Framework for Disaster Risk Reduction 2015-2030 is to consolidate disaster preparedness for an effective response and to have a Build Back Better agenda that will improve recovery, rehabilitation, and reconstruction. This research aimed at examining the disaster management system in Mauritius through community-based approaches. The National Disaster Risk Reduction and Management Centre’s (NDRRMC) involvement with the community living in high-risk zones was investigated. This study used quantitative data gathered from secondary sources and included the number of field training simulation exercises, training of volunteers under the Community Disaster Response Programme (CDRP), awareness campaigns and formulation of contingency plans based on documentation across Mauritius. The NDRRMC identified 109 high-risk zones across Mauritius out of which 13.7 per cent benefitted from all four forms of mitigation activities. Results indicated that contingency plans in Mauritius were evidence-based and had been developed after identification of disaster risk in selected regions, and therefore, they were limited to some types of disasters only. Findings also revealed that flooding was the most common type of simulated disaster exercise undertaken. The greatest number of field simulation exercises was carried out in Poste de Flacq. Results also demonstrated that only 38.5% of the community were exposed to disaster-related awareness campaigns, making it the least common form of disaster risk reduction (DRR) activity in Mauritius.

Indigenous designed metal-organic framework for electrocatalytic reduction of CO2—a review

Indigenous designed metal-organic framework for electrocatalytic reduction of CO2—a review

Data-driven model order reduction for sensor positioning and indirect reconstruction with noisy data: Application to a Circulating Fuel Reactor

Sensor positioning and real-time estimation of non-observable fields is an open question in the nuclear sector, especially for advanced nuclear reactors. In Circulating Fuel Reactors (CFR), liquid fuel and coolant are homogeneously mixed, and thus these reactors will not have internal structures, making sensor positioning in the primary circuit, including the core, an unresolved problem, making most of the core blind to sensors. Thus, the possibility of estimating the system state in the whole domain using a few local measurements has important implications for safety, monitoring, and control both in nominal and accidental conditions. In this context, the integrated Model Order Reduction and Data Assimilation framework offers intriguing opportunities to reliably combine experimental data and background knowledge from a reduced mathematical model. This work discusses and applies innovative methods within this framework, based on the Generalized Empirical Interpolation and the Indirect Reconstruction algorithms, to a proposed concept of CFR. This work aims to identify the optimal sensor positioning within the core and assess the feasibility of reconstructing the quantities of interest starting only from transient sparse data on fuel temperature, possibly noisy, and testing the predictive capabilities of the discussed methods.

Model-Free Statistical Inference on High-Dimensional Data

This article aims to develop an effective model-free inference procedure for high-dimensional data. We first reformulate the hypothesis testing problem via sufficient dimension reduction framework. With the aid of new reformulation, we propose a new test statistic and show that its asymptotic distribution is χ 2 distribution whose degree of freedom does not depend on the unknown population distribution. We further conduct power analysis under local alternative hypotheses. In addition, we study how to control the false discovery rate of the proposed χ 2 tests, which are correlated, to identify important predictors under a model-free framework. To this end, we propose a multiple testing procedure and establish its theoretical guarantees. Monte Carlo simulation studies are conducted to assess the performance of the proposed tests and an empirical analysis of a real-world dataset is used to illustrate the proposed methodology. Supplementary materials for this article are available online including a standardized description of the materials available for reproducing the work.

High‐performance BCN‐C heterostructured electrocatalyst derived from covalent triazine frameworks for nitrogen reduction

AbstractDesigning and synthesizing heterostructure‐based catalysis combining two‐dimensional (2D) materials has attracted enormous attention in materials chemistry, especially for electrocatalysis, due to their tunable electronic properties and structure. In this work, a boron carbon nitride (BCN)‐coupled carbon (BCN‐C) heterostructure with improved electronic performance was synthesized starting from C‐ and N‐enriched 2D covalent triazine frameworks (CTFs) via a simple thermal transformation process using NaBH4 as the B source. The characterizations results showed that the B released from NaBH4 reacted with N and C in CTF, forming the BCN‐C heterostructure coupling the BCN and carbon with a strong interfacial effect. The electrocatalytic NRR experiments and density functional theory (DFT) calculation revealed that the interfacial electronic properties and large numbers of active sites of the BCN‐C heterostructure benefited the NRR. The BCN‐C‐1 catalyst showed a high Faradaic efficiency of 27.8% and an NH3 yield of 4.3 μg h−1 mgcat.−1 in 0.1 M HCl electrolyte, owing to its high specific surface area (288 m2/g) with a large number of exposed active sites and the Mott–Schottky effect from the BCN‐C heterostructure. This work highlights the design and synthesis of metal‐free catalysts with high activity and selectivity and makes them excellent candidates for electrocatalytic applications.

Hydrological drought forecasts using precipitation data depend on catchment properties and human activities

Hydrological Drought Early Warning Systems play a crucial role in effective drought planning and management, as the impacts of drought are more closely associated with hydrological droughts than meteorological ones. However, current systems primarily focus on meteorological drought forecasts due to the limited access to hydrological data. Here we assess the feasibility of forecasting drought in streamflow and groundwater by solely using precipitation data. The results demonstrate that meteorological drought forecasts derived from the Standardized Precipitation Index with 6-month accumulation periods and various lag times hold the potential to predict streamflow and groundwater droughts. This study also highlights the importance of catchment properties in hydrological drought predictions. Our findings present an opportunity for developing hydrological drought early warning system globally to reach the goal of the Sendai framework for disaster risk reduction by 2030 and support the initiative of early warnings for all.

Fabrication of p-n Heterostructured Photocatalysts with Triazine-Based Covalent Organic Framework and CuInS2 for High-Efficiency CO2 Reduction.

The application of covalent organic frameworks (COFs) for the photocatalytic reduction of CO2 is mostly limited by severe charge recombination and low sunlight utilization. Herein, a triazine-based COF with an electron-rich and large π-conjugated system (TCOF) was employed as a building block and integrated with CuInS2 (CIS) to construct a noble-metal-free and high-efficiency photocatalyst for CO2 reduction. The in situ growth of CIS nanosheets on TCOF creates a p-n heterojunction, named CIS@TCOF. Compared with TCOF, the CIS@TCOF heterostructure exhibits a dramatically boosted photocatalytic performance in the reduction of CO2. The produced HCOOH yield over 10 wt % CIS@TCOF can be up to 171.2 μmol g-1 h-1 under visible light irradiation with good reproducibility, which is about 3 times as high as that over TCOF. Further in-depth studies indicate that the introduction of CIS not only enhances the visible light utilization but also restrains the recombination of photogenerated electron-hole pairs efficiently and facilitates the photoinduced charge transfer via the p-n heterojunction system due to the unique structural and compositional features. This research shows the great potential of COFs as efficient photocatalytic carbon fixation materials and provides a versatile route to construct semiconductor-COF heterostructures for photocatalysis.

Nickel Nanoparticles Confined in Core–Shell Derived from Covalent Organic Framework for the Efficient Electrocatalytic NO Reduction to NH3

Nickel Nanoparticles Confined in Core–Shell Derived from Covalent Organic Framework for the Efficient Electrocatalytic NO Reduction to NH₃

On Doob h-transformations for finite-time quantum state reduction

The paper develops a finite-time quantum state reduction framework via the use of Lévy random bridges (LRBs) that can be understood as Doob h-transformations on Lévy processes. Building upon the non-anticipative semimartingale representation of LRBs, we propose a family of energy-driven stochastic Schrödinger equations that go beyond the purely-continuous Brownian motion setup, and enter the scope of quantum systems involving discontinuities and heavy-tails. In doing so, we allow collapse dynamics to be governed intrinsically by the Markovian statistics of LRBs. The framework can host progressively convoluted stochastic state reduction dynamics in a tractable way and encapsulates jump-diffusion Schrödinger evolutions by use of the Lévy-Itô decomposition. Our construct motivates the usage of more general Markov processes and Doob h-transformations in producing consistent wave function collapse dynamics in finite-time.

Obtaining Lower Query Complexities Through Lightweight Zeroth-Order Proximal Gradient Algorithms.

Zeroth-order (ZO) optimization is one key technique for machine learning problems where gradient calculation is expensive or impossible. Several variance, reduced ZO proximal algorithms have been proposed to speed up ZO optimization for nonsmooth problems, and all of them opted for the coordinated ZO estimator against the random ZO estimator when approximating the true gradient, since the former is more accurate. While the random ZO estimator introduces a larger error and makes convergence analysis more challenging compared to coordinated ZO estimator, it requires only O(1) computation, which is significantly less than O(d) computation of the coordinated ZO estimator, with d being dimension of the problem space. To take advantage of the computationally efficient nature of the random ZO estimator, we first propose a ZO objective decrease (ZOOD) property that can incorporate two different types of errors in the upper bound of convergence rate. Next, we propose two generic reduction frameworks for ZO optimization, which can automatically derive the convergence results for convex and nonconvex problems, respectively, as long as the convergence rate for the inner solver satisfies the ZOOD property. With the application of two reduction frameworks on our proposed ZOR-ProxSVRG and ZOR-ProxSAGA, two variance-reduced ZO proximal algorithms with fully random ZO estimators, we improve the state-of-the-art function query complexities from Omindn1/2ε2,dε3 to O˜n+dε2 under d>n12 for nonconvex problems, and from Odε2 to O˜nlog1ε+dε for convex problems. Finally, we conduct experiments to verify the superiority of our proposed methods.

Progressing the research on systemic risk, cascading disasters, and compound events

Disaster risk in the 21st century differs from the experience of past generations and is defined by its evolving systemic nature. The increased role of technological networks, societal interdependencies and climate change dynamics make crises more complex and unpredictable. Cascading and compounding dynamics are becoming the new “business as usual”, challenging emergency management to maintain operations in face of complex disruptions while requiring the development of good practices and strategy for facilitate the recovery process. Our special issue aimed to support the development of a paradigm shift in the understanding of complex events, utilizing a network-based, cross-disciplinary approach to resilience. This editorial introduces and summarizes 18 papers across four thematic areas: 1) Resilience Challenges; 2) Area Studies and the Sendai Framework for Disaster Risk Reduction; 3) Community and Health; 4) Enhancing New Methodologies. The conclusions highlight open research challenges for future exploration.

Liquid-Liquid interfacial approach for rapid synthesis of Well-Crystalline Two-Dimensional Metal-Organic frameworks for nitro reduction

Two-dimensional metal–organic frameworks (2D MOFs) have garnered significant attention due to their exceptional properties across diverse research domains, including catalysis, biomedicine, gas adsorption, separation, energy storage, and sensing. Despite these promising attributes, the facile and large-scale synthesis of 2D MOFs with high crystallinity remains a formidable challenge. In this study, we propose a novel approach based on a modified liquid–liquid interfacial method for the rapid synthesis of well-crystalline 2D MOFs. By vigorously mixing water, a hydrophobic solvent, and an emulsifier, we generate micrometer-sized droplets that create substantial curved two-phase interfaces. This method is compatible with various ligands and metal ions, enabling the rapid formation of 2D MOFs at these curved interfaces under additional pressure. The subsequent separation of oil and water phases via centrifugal force promotes moderate π-π stacking within the 2D MOF, thereby stabilizing the lamellar structure and enriching the material at the two-phase interface. Remarkably, the entire synthesis process requires a mere 3 min, yielding grams of product in a single run. Furthermore, solvent reuse does not compromise the quality of the resulting 2D MOFs. As a practical application, we synthesized phthalocyanine (Pc)-based structures using this method for the catalytic reduction of nitroaromatics. Among these, Cu-CoPc (2D MOFs with Cu2+ coordinated to CoPc) emerged as the most active catalyst, achieving a turnover frequency (TOF) value of 4726 h−1 for the reduction of 4-nitrophenol (4-NP) to 4-aminophenol (4-AP). Notably, this TOF value surpasses that of non-precious catalysts and is 180 times higher than that of commercial Pd/C (10 wt%).

Enhanced humification via lignocellulosic pretreatment in remediation of agricultural solid waste

Stover and manure are the main solid waste in agricultural industry. The generation of stover and manure could lead to serious environmental pollution if not handled properly. Composting is the potential greener solution to remediate and reduce agricultural solid waste, through which stover and manure could be remediated and converted into organic fertilizer, but the long composting period and low efficiency of humic substance production are the key constraints in such remediation approach. In this study, we explore the effect of lignocellulose selective removal on composting by performing chemical pretreatment on agricultural waste followed by utilization of biochar to assist in the remediation by co-composting treatment and reveal the impacts of different lignocellulose component on organic fertilizer production. Aiming to discover the key factors that influence humification during composting process and improve the composting quality as well as comprehensive utilization of agricultural solid waste. The results demonstrated that the removal of selective lignin or hemicellulose led to the shift of abundances lignocellulose-degrading bacteria, which in turn accelerated the degradation of lignocellulose by almost 51.2%. The process also facilitated the remediation of organic waste via humification and increased the humic acid level and HA/FA ratio in just 22 days. The richness of media relies on their lignocellulose content, which is negatively correlated with total nitrogen content, humic acid (HA) content, germination index (GI), and pH, but positively correlated with fulvic acid (FA) and total organic carbon (TOC). The work provides a potential cost effective and efficient framework for agricultural solid waste remediation and reduction.

Sea-level rise and mental health among coastal communities: A quantitative survey and conditional process analysis

This is the first large-scale empirical study examining the impact of sea-level rise induced by climate change on mental health outcomes among coastal communities. The study focuses on Bangladesh, a country severely affected by salinity ingress, flood risks, and agricultural damage due to sea-level changes. Participants (n = 1,200) randomly selected from three coastal regions each having high, moderate, or low vulnerability to sea-level rise were surveyed during the pre-monsoon season in 2021. The cross-sectional survey included validated measures of psychological distress, depression, anxiety, stress, environmental stressors, resource loss, and demographics. The results indicated significantly higher levels of psychological distress, depression, anxiety, and stress in residents of high-vulnerability areas compared to moderate or low-vulnerability regions. Resource loss served as a mediating variable between environmental stressors and mental health outcomes. Furthermore, demographic analyses showed that older adults and women reported higher levels of psychological distress. These findings align with the Sendai Framework for Disaster Risk Reduction, highlighting urgent need for targeted mental health interventions and sustainable models of care in coastal areas increasingly threatened by sea-level rise.

Sexual health variation among gang-involved youth in Washington state: Social ecological implications for research and practice.

Gang-involved youth experience greater disparities in sexual health compared to non-gang-involved youth. Yet, little is known about how and why sexual behaviors vary within the youth gang population. Developing relevant and effective service approaches requires an understanding of this variation and the environmental factors that influence patterns of sexual health risk. Using latent class analysis, we identified four sexual behavior classes within a school-based sample of gang-involved youth in Washington State (N = 2060): Non-Sexually Active (54%), Limited Partners with Condom Use (14%), Multiple Partner with Sexting (19%), and High Sexual Vulnerability (13%). These classes were distinguished by age at sexual debut, number of sexual partners, condom use, and sexting. Interpersonal and macrosocial factors differentiated the classes, including multiform violence exposures, limited social support, and socioeconomic instability. We also found differences according to sexual identity and substance use. Findings highlight the need for service approaches that are responsive to both the individual needs of gang-involved youth and the factors that shape their living environments. We discuss the implications for research and practice, including the potential utility of a harm reduction framework to promote sexual health and reduce disparities in the youth gang population.

Ligand Mediated Assembly of CdS Colloids in 3D Porous Metal–Organic Framework Derived Scaffold with Multi‐Sites Heterojunctions for Efficient CO2 Photoreduction

AbstractThe inclusion of colloidal particles into 3D porous constructs to realize heterostructure ensembles enhances surface interactions and energy transfer that can transform the catalytic properties of conventional catalysts. However, assembling hydrophobic colloidal nanoparticles within hydrophilic 3D porous matrices to create tailored heterostructures and catalytic properties is particularly challenging. Here, a modified ligand grafting method is presented to spontaneously assemble CdS inorganic colloidal quantum dots within a metal‐organic framework (MOF) derived porous framework for efficient photocatalytic CO2 reduction. The grafted long chain molecular ligands effectively suppress phase separation and endow a molecular‐recognition effect to form ordered assembly microstructures. The proof‐of‐concept assembly of CdS and Prussian blue analogues (PBA) derived Co3O4 facilitates a high density of heterojunctions formation, improving charge transfer and extended lifetimes of photo‐induced electrons. Consequently, CdS/Co3O4 assembly exhibits exceptionally active and stable photocatalytic CO2 reduction activity, with a CO production rate of 73.9 µmol g−1 h−1 and CO selectivity of 98.9%. Importantly, the ligand grafting method can be generalized to achieve spontaneous assembly of quantum dots within various metal organic framework derived porous scaffolds. This work provides a rational strategy for precise self‐assembly of colloidal nanoparticles within porous microstructures, allowing tailored heterointerfaces for functional materials and applications.

Trinuclear Cu-based covalent organic framework: π-conjugated framework regulating electron delocalization to promote photoreduction CO2

Sunlight-driven CO2 reduction to value-added chemicals is an effective strategy to promote carbon recycling. The exploration of catalysts with efficient charge separation is crucially important for highly efficient CO2 photoreduction. In this work, the preparation of metal-cluster-based covalent organic framework (CuABD) integrated features from both metal organic frameworks (MOFs) and covalent organic frameworks (COFs) through the condensation of diamines and functionalized trinuclear copper clusters demonstrate a thoughtful design strategy. The reported yield of 1.3 mmol g−1 h−1 for formic acid (HCOOH) under simulated solar irradiation is impressive, surpassing the performance of many COF- and MOF-based catalysts previously reported. Compared to its isomorphic metal-free structure (named BDFTD) and bare trinuclear Cu cluster which present extremely poor catalytic activities, CuABD displays remarkably enhanced CO2 reduction activity. Experimental and theoretical investigations reveal that the efficient charge transfer between diamine monomer and cyclic trinuclear copper (I) units, and the electron delocalization of the π-conjugated framework are responsible for the appealing catalytic performance. In summary, the work presents a well-structured and scientifically sound exploration of a metal-cluster-based covalent organic framework for efficient CO2 reduction under sunlight.

Attribute reductions based on δ-fusion condition entropy and harmonic similarity degree in interval-valued decision systems

This paper defines an improved similarity degree based on inclusion degree as well as advanced information system based on interval coverage and credibility, and thus an attribute reduction framework embodying 4×2 = 8 reduct algorithms is systematically constructed for application and optimization in interval-valued decision systems. Firstly, a harmonic similarity degree is constructed by introducing interval inclusion degree and harmonic average mechanism, which has better semantic interpretation and robustness. Secondly, interval credibility degree and coverage degree are defined for information fusion, and they are combined to propose a δ-fusion condition entropy. The improved condition entropy achieves the information reinforcement and integrity by dual quantization fusion of credibility and coverage, and it obtains measure development from granularity monotonicity to non-monotonicity. In addition, information and joint entropies are also constructed to obtain system equations. Furthermore, 8 reduct algorithms are designed by using attribute significance for heuristic searches. Finally, data experiments show that our five novel reduct algorithms are superior to the three contrast algorithms on classification performance, which also further verify the effectiveness of proposed similarity degree, information measures and attribute reductions.

Women in Disaster Policy: A Bibliometric Mapping of the Research Literature 2015-2023

The research regarding women in disaster policy discourse received attention from scholars and policymakers as the current leading international disaster policy had a shortfall in mentioning women’s rights. Although many studies have deliberated how women engage with disaster, research that systematically maps how the literature regarding women in disaster policy develops has not yet been conducted. Per the result through the bibliometric mapping, the 58 pieces of literature included by the PRISMA (Preferred Items for Systematic Reviews and Meta-Analyses) framework are clustered into health, environment, economic, and socio-political contexts. The discourse discussion revolves around women’s vulnerability, capacity and capability, and strategy and policy initiatives. The study contributes to the literature by mapping the research trajectory regarding women in the disaster risk reduction policy framework following the Sendai Framework for Disaster Risk Reduction 2015-2030 establishment. A recommendation model of the disaster policy framework and prospective research agenda is developed through the study.