Search Results (316)

The continuous detection of emerging pollutants (EPs) in water poses potential threats to aquatic environmental safety and human health, and their efficient removal is a frontier in environmental engineering research. This review systematically summarizes research progress from 2005 to 2025 on the application of activated carbon (AC) and its composites for removing EPs from water and analyzes the development trends in this field using bibliometric methods. The results indicate that research has evolved from the traditional use of AC for adsorption to the design of novel materials through physical and chemical modifications, as well as composites with metal oxides, carbon-based nanomaterials, and other functional components, achieving high adsorption capacity, selective recognition, and catalytic degradation capabilities. Although AC-based materials demonstrate considerable potential, their large-scale application still faces challenges such as cost control, adaptability to complex water matrices, material regeneration, and potential environmental risks. Future research should focus on precise material design, process integration, and comprehensive life-cycle sustainability assessment to advance this technology toward highly efficient, economical, and safe solutions, thereby providing practical strategies for safeguarding water resources. Full article

In modern financial markets, decision-makers increasingly rely on quantitative methods to navigate complex trade-offs among multiple, often conflicting objectives. This paper addresses constrained multi-objective optimization (MOO) with an application to portfolio optimization for minimizing risk and maximizing return. To this end, and to address existing gaps, we propose a novel reinforcement learning (RL)-guided non-dominated sorting genetic algorithm II (NSGA-II) enhanced with gray relational coefficients (GRC), termed RL-NSGA-II-GRC, which combines an RL agent controller and GRC-based selection to improve the convergence and diversity of the Pareto-optimal fronts. The agent adapts key evolutionary parameters online using population-level metrics of hypervolume, feasibility, and diversity, while the GRC-enhanced tournament operator ranks parents via a unified score simultaneously considering dominance rank, crowding distance, and geometric proximity to ideal reference. We evaluate the framework on the Kursawe and CONSTR benchmark problems and on a NASDAQ portfolio optimization application. On the benchmarks, RL-NSGA-II-GRC achieves convergence metric improvements of about 5.8% and 4.4% over the original NSGA-II, while preserving a well-distributed set of non-dominated solutions. In the portfolio application, the method produces a smooth and densely populated efficient frontier that supports the identification of the maximum Sharpe ratio portfolio (with annualized Sharpe ratio = 1.92), as well as utility-optimal portfolios for different risk-aversion levels. The main contributions of this work are three-fold: (1) we propose an RL-NSGA-II-GRC method that integrates an RL agent into the evolutionary framework to adaptively control key parameters using generational feedback; (2) we design a GRC-enhanced binary tournament selection operator that provides a comprehensive performance indicator to efficiently guide the search toward the Pareto-optimal front; (3) we demonstrate, on benchmark MOO problems and a NASDAQ portfolio case study, that the proposed method delivers improved convergence and well-populated efficient frontiers that support actionable investment insights. Full article

Background: This study investigates the global burden of early-onset bladder cancer (EOBC) from 1990 to 2021, highlighting regional disparities and the growing role of metabolic risk factors. Early-onset bladder cancer (EOBC), diagnosed before age 50, is an emerging global health concern. While less common than kidney cancer, EOBC contributes substantially to mortality and disability-adjusted life years (DALYs), with marked sex disparities. Its global epidemiology remains unassessed systematically. Methods: Using GBD 1990–2021 data, we analyzed EOBC incidence, prevalence, mortality, and DALYs across 204 countries in individuals aged 15–49. Trends were examined via segmented regression, EAPC, and Bayesian age-period-cohort modeling. Inequality was quantified using SII and CI. Decomposition and SDI-efficiency frontier analyses were introduced. Results: From 1990 to 2021, EOBC incidence rose 62.2%, prevalence 73.1%, deaths 15.3%, and DALYs 15.8%. Middle-SDI regions bore the highest burden. Aging drove trends in high-SDI areas and population growth in low-SDI regions. Over 25% of high-SDI countries underperformed in incidence/prevalence control. Smoking remained the leading risk factor, with rising hyperglycemia burdens in high-income areas. Males carried over twice the female burden, peaking at age 45–49. Conclusions: EOBC shows sustained global growth with middle-aged concentration and significant regional disparities. Structural inefficiencies highlight the need for enhanced screening, early warning, and tailored resource allocation. Full article

Ports are the major gateways of cities. Background: Sustainable growth requires ports to prioritize efficiency while balancing economic, social, and environmental goals. There is limited synthesized evidence on the sustainability evaluation of ports, including those of North America. In this paper, we propose a multi-step approach based on fuzzy DEA to evaluate the environmental performance of ports. Methods: In the first step, we identify indicators for environmental performance evaluation. The second step involves application of fuzzy DEA using the identified indicators to measure the environmental efficiency of ports. In the third step, a numerical illustration is provided using open data. The proposed model incorporates undesirable outputs and employs one set of constraints to make a production frontier. Results: The findings show wide differences in performance, ports reach higher scores when they use resources wisely plus keep emissions low, not merely when they expand. Conclusions: The proposed methodology provides a robust and comparable measurement of port environmental efficiency under uncertainty. Full article

With the rapid advancement of Transformer-based large language models (LLMs), these models have found widespread applications in industrial domains such as code generation and non-functional requirement (NFR) classification in software engineering. However, recent research has primarily focused on optimizing linear matrix operations, while nonlinear operators remain relatively underexplored. This paper proposes hardware-efficient approximation and acceleration methods for the Softmax and RMSNorm operators to reduce resource cost and accelerate Transformer inference while maintaining model accuracy. For the Softmax operator, an additional range reduction based on the SafeSoftmax technique enables the adoption of a bipartite lookup table (LUT) approximation and acceleration. The bit-width configuration is optimized through Pareto frontier analysis to balance precision and hardware cost, and an error compensation mechanism is further applied to preserve numerical accuracy. The division is reformulated as a logarithmic subtraction implemented with a small LOD-driven lookup table, eliminating expensive dividers. For RMSNorm, LOD is further leveraged to decompose the reciprocal square root into mantissa and exponent parts, enabling parallel table lookup and a single multiplication. Based on these optimizations, an FPGA-based pipelined accelerator is implemented, achieving low operator-level latency and power consumption with significantly reduced hardware resource usage while preserving model accuracy. Full article

Feature selection is a critical preprocessing step for mitigating the curse of dimensionality in machine learning. Existing methods present a difficult trade-off: filter methods are fast but often suboptimal as they evaluate features in isolation, while wrapper methods are powerful but computationally prohibitive due to their iterative nature. In this paper, we propose LAFS (Learnable Attention for Feature Selection), a novel, end-to-end differentiable framework that achieves the performance of wrapper methods at the speed of simpler models. LAFS employs a neural attention mechanism to learn a context-aware importance score for all features simultaneously in a single forward pass. To encourage the selection of a sparse and non-redundant feature subset, we introduce a novel hybrid loss function that combines the standard classification objective with an information-theoretic entropic regularizer on the attention weights. We validate our approach on real-world high-dimensional benchmark datasets. Our experiments demonstrate that LAFS successfully identifies complex feature interactions and handles multicollinearity. In general comparison, LAFS achieves very close and accurate results to state-of-the-art RFE-LGBM and embedded FSA methods. Our work establishes a new point on the accuracy-efficiency frontier, demonstrating that attention-based architectures provide a compatible solution to the feature selection problem. Full article

(1) Background: Ecuador is the fourth largest milk producer in Latin America, where ap-proximately 80% of production originates from small family farms located in the Andean region. Despite their socioeconomic importance, these farms face challenges related to low technical efficiency. While there are specific studies on efficiency in dairy systems from other regions, a knowledge gap persists regarding the temporal evolution of technical efficiency (TE) in Ecuadorian Andean dairy farms, especially during crisis periods such as the COVID-19 pandemic. The objective of this study was to evaluate the evolution of TE of family dairy farms in the Ecuadorian Andean region during the period 2018–2024 and to analyze the impact of the pandemic on said efficiency. (2) Methods: Data Envelopment Analysis (DEA) with input orientation and bootstrap simulation was employed to estimate TE, using data from a representative sample that included between 2370 and 2987 farms per year (approximately 25% of the national database of the Ministry of Agriculture and Livestock). Farms were selected based on the availability of complete information on key variables: number of milking cows, area dedicated to forage, family and hired labor (annual hours), and total annual milk production. Statistical analysis included ANOVA to compare mean TE values between years, post-hoc tests to identify specific differences between periods, and the identification of factors related to the TE. (3) Results: The mean TE of Andean dairy farms increased significantly from 0.37 in 2018 to 0.44 in 2024 (p < 0.10), evidencing sustained improvement, although the mean is still distant from the efficiency frontier. The analysis revealed a notable decrease in TE during 2020–2021, coinciding with the period of greatest impact of the COVID-19 pandemic, followed by progressive recovery in subsequent years. The TE distribution showed that between 70% and 75% of farms remained below 0.50 throughout the analyzed period, while only 8–12% achieved levels above 0.70. The main sources of technical inefficiency identified were relative excesses of labor and forage area in relation to milk production obtained. When compared with international studies, Ecuadorian farms present TE levels substantially lower than those reported in the European Union (>0.80) and similar to or slightly lower than those found in Turkey (0.61–0.71). (4) Conclusions: Family dairy farms in the Ecuadorian Andean region operate with technical efficiency levels considerably below their potential and international standards, suggesting substantial scope for improvement through the optimization of productive resource use, particularly labor and land. The COVID-19 pandemic impacted the sector’s efficiency negatively but temporarily, demonstrating resilience and recovery capacity. These findings are relevant to the design of public policies and technical assistance programs aimed at sustainable intensification of family dairy production in the Andes, with an emphasis on improving labor productivity and the efficient use of forage area. Full article

Background: This study examines how national innovation performance shapes the diffusion of advanced diagnostic technologies across European healthcare systems. Strengthening technological capacity through innovation is increasingly essential for resilient and efficient health services. The analysis quantifies the influence of innovation capacity on the availability of medical imaging technologies in 26 EU Member States between 2018 and 2024. Methods: A balanced panel dataset was assembled from Eurostat, the European Innovation Scoreboard, and World Bank indicators. Dynamic relationships between innovation performance and the adoption of CT, MRI, gamma cameras, and PET scanners were estimated using a two-step approach combining General-to-Specific (GETS) outlier detection with Robust Least Squares regression to address heterogeneity and specification uncertainty. Results: Higher innovation scores significantly increase the diffusion of R&D-intensive technologies such as MRI and PET, while CT availability shows limited responsiveness due to market maturity. Public health expenditure supports frontier technologies when strategically targeted, whereas GDP growth has no significant effect. Population size consistently enhances technological capacity through scale and system-integration effects. Conclusions: The findings show that innovation ecosystems, rather than economic growth alone, drive the modernization of diagnostic infrastructure in the EU. Integrating innovation metrics into health-technology assessments offers a more accurate basis for designing innovation-oriented investment policies in European healthcare. Full article

Home energy management systems have emerged as a crucial solution for enhancing energy efficiency, reducing carbon emissions, and facilitating the integration of renewable energy sources into homes. To fully realize their potential, these systems’ performance must be optimized, which involves addressing multiple objectives, such as minimizing costs and environmental impact. The Pareto frontier is a tool widely adopted in multi-objective optimization within home energy management systems’ operation, where a range of optimal solutions are produced. This study uses the Pareto curve to optimize the operational performance of home energy management systems, considering the state health of the battery to determine the best answer among the optimal solutions in the curve. The main reason for considering the state of health is the effects of the battery’s operation on the performance of energy systems, especially for long-term optimization outcomes. In this study, the performance of the battery is measured through a physical model named PyBaMM that is tuned based on swarm intelligence techniques, including the Whale Optimization Algorithm, Grey Wolf Optimization, Particle Swarm Optimization, and the Gravitational Search Algorithm. The proposed framework automatically identifies the optimal solution out of the ones in the Pareto curve by comparing the performance of the battery through the tuned physical model. The effectiveness of the proposed algorithm is demonstrated for a home, including four distinct energy carriers along with a 12 V 128 Ah LFP chemistry Li-ion battery module, where the overall cost and carbon emissions are the metrics for comparisons. Implementation results show that tuning the physical model based on the Whale Optimization Algorithm reaches the highest accuracy compared to the other methods. Moreover, considering the state of health of the battery as the selecting criterion will improve home energy management systems’ performance, particularly in long-term operation models, because it guarantees a longer battery lifespan. Full article

This paper tested the use of the surplus efficient frontier (a minimum tracking error portfolio selection method) to select the optimal hedging portfolio that replicates the best Mexican #4 lemon price in a t + 1 and t + 4 week hedging scenario. Using data on the nine most traded agricultural futures in the US from January 2000 to February 2025, we tested hedging effectiveness across 502 futures portfolios in a weekly backtest. The results suggest that a corn and wheat portfolio increases the hedging effectiveness of the lemon price by 0.7033 or 70.33%. A result that, including the impact of trading fees and taxes, leads to a reduction in income risk to a lemon seller in a t + 1 week hedging horizon. The results suggest that a public or private financial institution could take a short position in such a portfolio to provide a hedge at a price that finances the spot/future price difference at minimum cost to Mexican taxpayers. Full article

This study develops a tri-level adversarial robust optimization framework for cyber–physical scheduling in smart grids, addressing the intertwined challenges of coordinated cyberattacks, defensive resource allocation, and stochastic operational uncertainties. The upper level represents the attacker’s objective to maximize system disruption and conceal detection, the middle level models the defender’s optimization of detection and redundancy deployment under budgetary constraints, and the lower level performs economic dispatch given tampered data and uncertain renewable generation. The model integrates Distributionally Robust Optimization (DRO) based on a Wasserstein ambiguity set to safeguard against worst-case probability distributions, ensuring operational stability even under unobserved adversarial scenarios. A hierarchical reformulation using Karush–Kuhn–Tucker (KKT) conditions and Mixed-Integer Second-Order Cone Programming (MISOCP) transformation converts the nonconvex tri-level problem into a tractable bilevel surrogate solvable through alternating direction optimization. Numerical case studies on multi-node systems demonstrate that the proposed method reduces system loss by up to 36% compared to conventional stochastic scheduling, while maintaining 92% dispatch efficiency under high-severity attack scenarios. The results further reveal that adaptive defense allocation accelerates robustness convergence by over 50%, and that the risk–reward frontier stabilizes near a Pareto-optimal equilibrium between cost and resilience. This work provides a unified theoretical and computational foundation for adversarially resilient smart grid operation, bridging cyber-defense strategy, uncertainty quantification, and real-time economic scheduling into one coherent optimization paradigm. Full article

Heavy-metal contamination poses a significant global threat to soil environments, underscoring the necessity for effective and sustainable remediation technologies. This review methodically summarizes advances in the field of microbial remediation of heavy metal-contaminated soils, organized around four major dimensions: remediation mechanisms, synergistic technologies, field applications, and future prospects. Firstly, the remediation mechanisms are elucidated, encompassing molecular interactions, cellular adaptation, and community-level cooperative responses. Secondly, the integration of microbes with functional materials and bioelectrochemical systems (BESs) is evaluated, with these materials providing support, electron mediation, and micro-environment regulation that markedly improve remediation efficiency and stability. Moreover, illustrative field cases demonstrate pivotal technological pathways and cost-effectiveness when transitioning from laboratory- to field-scale applications. Finally, emerging frontiers such as synthetic biology-engineered microbes, AI-driven microbial design, circular-economy value recovery, and policy-governance innovations are discussed, proposing essential elements for building a “predictable-controllable-sustainable” microbial remediation platform. This review aims to provide a comprehensive knowledge framework for researchers and to offer decision-making guidance for practitioners and policymakers, thereby advancing microbial remediation toward higher efficiency, reliability, and scalability. Full article

Background/Objectives: Cell-mediated and peptide-assisted delivery systems have emerged as powerful platforms at the intersection of chemistry, nanotechnology, and molecular medicine. By leveraging the intrinsic targeting, transport, and signaling capacities of living cells and bioinspired peptides, these systems facilitate the delivery of therapeutic agents across otherwise restrictive biological barriers such as the blood–brain barrier (BBB) and the tumor microenvironment. This review aims to summarize recent advances in engineered cell carriers, peptide vectors, and hybrid nanostructures designed for enhanced intracellular and tissue-specific delivery. Methods: We surveyed recent literature covering molecular design principles, mechanistic studies, and in vitro/in vivo evaluations of cell-mediated and peptide-enabled delivery platforms. Emphasis was placed on neuro-oncology, immunotherapy, and regenerative medicine, with particular focus on uptake pathways, endosomal escape mechanisms, and structure–function relationships. Results: Analysis of current strategies reveals significant progress in optimizing cell-based transport systems, peptide conjugates, and multifunctional nanostructures for the targeted delivery of drugs, nucleic acids, and immunomodulatory agents. Key innovations include improved BBB penetration, enhanced tumor homing, and more efficient cytosolic delivery enabled by advanced peptide designs and engineered cellular carriers. Several platforms have progressed toward clinical translation, underscoring their therapeutic potential. Conclusions: Cell-mediated and peptide-assisted delivery technologies represent a rapidly evolving frontier with broad relevance to next-generation therapeutics. Despite notable advances, challenges remain in scalability, manufacturing, safety, and regulatory approval. Continued integration of chemical design, molecular engineering, and translational research will be essential to fully realize the clinical impact of these delivery systems. Full article

Global climate change is exerting a growing impact on agricultural ecosystems. Accurately assessing the spatiotemporal dynamics of agricultural ecosystem respiration and its response mechanisms to climate has therefore emerged as a critical issue in agricultural carbon cycle research and climate change response. It should be noted that the ‘agro-ecosystem’ referred to in this study covers two major types: one is the farmland agro-ecosystem dominated by crop planting (such as farmland, orchard and other artificial management systems), and the other is the grassland agro-ecosystem dominated by herbaceous plants and managed by humans (such as grazing grassland and mowing grassland). Remote sensing technology provides a new way to break through the limitations of traditional ground observation by virtue of its advantages of large-scale and continuous monitoring. Based on the CiteSpace bibliometric method, this study focused on the key time window of 2021–2025, systematically searched the core collection of Web of Science, and finally included 222 related literature. This period marks the initial stage of the rise and rapid development of this interdisciplinary field, enabling us to capture the formation of its knowledge structure and the evolution of its research paradigm from the source. Through the quantitative analysis of this literature, it aims to reveal the research hotspots, development paths and frontier trends in this field. The results show that China occupies a dominant position in this field (135 articles). The evolution of research shows a three-stage development characterized by “technology-driven-method fusion-system coupling,” which is divided into the initial development period (2021–2022), the rapid growth period (2023–2024) and the deepening development period (2025) (because 2025 has not yet ended, this stage is a preliminary discussion). Keyword clustering analysis identified 13 important research directions, including machine learning (# 0 clustering), permafrost (# 1 clustering) and carbon flux (# 2 clustering). It is found that the deep integration of artificial intelligence and remote sensing data is promoting the transformation of research methods from traditional inversion to intelligent modeling. At the same time, the attention to alpine grassland and other ecosystems also reflects the trend that the research frontier extends to the interaction zone between the agricultural ecosystem and the natural environment. Future research should prioritize three key directions: building multi-scale monitoring networks, developing “grey box” models that integrate mechanisms and data fusion, and evaluating the carbon emission reduction efficiency of agricultural management practices. These efforts will provide a theoretical basis for carbon management and climate adaptation in agricultural ecosystems, as well as scientific and technological support for achieving global agricultural sustainable development goals (specifically, SDG13 on climate action and SDG15 on terrestrial ecosystem conservation). Full article

Energy management in smart microgrids is critical to achieving sustainable and efficient energy utilization. This study introduces a hybrid optimization framework combining neural networks (NNs) and multi-objective genetic algorithms (MOGAs) (hybrid NN-MOGA) to address the dual objectives of minimizing total energy cost and maximizing customer satisfaction. The hybrid NN-MOGA approach leverages NNs for predictive modeling of load and renewable energy generation, feeding accurate inputs to the MOGA for enhanced Pareto-optimal solutions. The performance of the proposed method is benchmarked against traditional optimization techniques, including MOGA, multi-objective particle swarm optimization (MOPSO), and the multi-objective firefly algorithm (MOFA). The simulation results demonstrate that hybrid NN-MOGA outperforms the alternative model. The proposed method produces uniformly distributed and highly convergent Pareto frontiers, ensuring robust trade-offs of USD 48.2817 and 81.7898 for total cost and customer satisfaction, respectively. Convexity analysis and the satisfaction of Karush–Kuhn–Tucker (KKT) conditions further validate the optimization model. Full article