Refractory organic wastewater treatment via thermal activation of molecular oxygen on a confined Al2O3@Cu catalyst.

Flow-electrode capacitive deionization using citric-acid-modified biochar for efficient treatment of acidic electroplating wastewater.

Graphene/thermally expandable microsphere composite films with switchable thermal conductivity for intelligent thermal management.

Multi-functional purine nucleoside-modified chitosan polymer micelles for improving the oral absorption of doxorubicin based on nucleoside transporter mediation.

Surface etching vs. internal disruption: Unraveling the drying mechanisms of apple slices under cold plasma, ultrasound, and microwave pretreatments.

The rapid growth in global population necessitates efficient energy management solutions for sustainable living. Smart Building Energy Management Systems (SBEMS) play a crucial role in achieving this goal by leveraging automation and advanced analytics. This study proposes a novel Deep Learning and IoT-based SBEMS approach to predict energy consumption, classify buildings into energy-demand clusters, and optimize the monitoring and operation of electrical equipment. While traditional statistical methods have been widely used for load forecasting, recent advancements in deep learning provide robust alternatives to address the inherent complexity of nonlinear energy consumption patterns. This research employs regression analysis and state-of-the-art neural network architectures, including Single-Step and Multi-Step Dense Models, Convolutional Neural Networks (CNNs), and Long Short-Term Memory (LSTM) networks, to enhance prediction accuracy. Additionally, K-means clustering is introduced to segment buildings into distinct energy-demand categories, ensuring optimal energy utilization. Unlike prior studies that often lack a comprehensive approach, this work integrates all critical features under a unified framework. By applying these advanced methodologies to a unique dataset, the proposed system demonstrates improved accuracy in energy load forecasting and clustering, providing a significant contribution to the field of smart building energy management. The experimental results demonstrate that CNN and LSTM models significantly outperform conventional statistical approaches in capturing nonlinear energy consumption patterns. These outcomes support proactive energy scheduling, peak-demand mitigation, and scalable smart building energy management, offering practical value for facility managers, utility operators, and policymakers.

Shape-stabilised phase change materials for reinforced concrete roof cooling: Thermophysical performance study

This study performed a multifactorial carbon footprint assessment and sensitivity analysis of colonoscopy. This was a 1-week, single-center, prospective study including all outpatient diagnostic colonoscopies (n = 66). A cradle-to-grave life-cycle assessment methodology evaluated all essential supplies (accessories #1-15), endoscopic procedure (energy consumption, carbon dioxide [CO2] insufflation, bowel preparation, sedation), staff and patient transport, and waste management. The impact assessment was based on sensitivity analysis in different scenarios (base, best, worst) to calculate the Global Warming Potential over 100 years (GWP-100 measured in kg of CO2 equivalent [kgCO2e]). GWP-100 of a single colonoscopy was estimated to be 18.09 kgCO2e in the base scenario, with patient (8.61 kgCO2e) and staff (5.09 kgCO2e) transport, colonoscope manufacture and reprocessing (2.1 kgCO2e), and supplies (1.91 kgCO2e) contributing 47.6%, 28.1%, 11.7%, and 10.6%, respectively. Nitrile gloves, underpads, and a disposable peripheral oxygen saturation sensor accounted for nearly 50% of the total carbon footprint of all supplies. Patient preparation (bowel preparation, sedation, and CO2 insufflation) and energy consumption contributed only 2.0%. Staff and patient travel showed significant variations among worst, base, and best scenarios, with 18.8, 13.7, and 10.2 kgCO2e, respectively. The use of different amounts of medical supplies raised the carbon footprint to 2.38 kgCO2e in the worst scenario or diminished it to 1.56 kgCO2e in the best case. This analysis and multifactorial colonoscopy procedure assessment confirmed patient and staff transport as the main carbon footprint contributors. More sustainable and smarter transport could considerably lower the environmental impact of colonoscopy.

Artificial light at night (ALAN) is a growing environmental pressure linked to socioeconomic development. This study examines ALAN trends from 2012 to 2024 across 165 countries using harmonized VIIRS satellite data. Globally, ALAN increased at an annual rate of 3.2%. Highest radiance levels occur in developed regions (Europe, North America, and East Asia), while remote areas remain dark. A few countries, including France and Venezuela, show declines. The study extends the VIIRS time series and applies a calibrated Kaya-identity framework integrating the human development index (HDI) and Gini coefficient. It introduces radiance density (RD) as a normalized metric linked to per-capita development for cross-national comparison. ALAN shows associations with macroeconomic indicators. Absolute ALAN correlates with total GDP (log), energy consumption, and CO2 emissions ( -0.81). RD and light pollution density better reflect development quality, correlating with HDI ( ), GDP per capita (log) ( ), and life expectancy ( ). Predictive models achieve high explanatory power ( -0.89), with population and GDP per capita as the strongestdeterminants.

The Agile Earth Observation Satellite Scheduling Problem is a multi-objective optimization task involving conflicting goals. These objectives include task priority, imaging quality, and energy consumption, which are highly correlated and often in conflict. Pareto optimisation is a key measure for evaluating the quality of complex multi objective solutions. However, conventional linear weighting methods neglect interactions among sub-objectives, generating suboptimal Pareto fronts with uneven trade-off distributions. This study proposes a multi-objective scheduling method for agile satellites based on Nonlinear Utility and Deep Reinforcement Learning. Within this framework, a Unified Scheduling Model (USM) was developed to provide a unified representation of constrained multi-objective observation tasks. Building on the USM, a contrastive learning–based nonlinear utility network was designed to capture inter-objective relationships via cosine similarity, thereby providing adaptive and diversified guidance for multi-objective trade-offs. Subsequently, a distributed training strategy combined with the Proximal Policy Optimization algorithm was employed to improve learning efficiency and policy stability under complex operational constraints. Results show that, compared with multi-objective deep reinforcement learning and multi-objective heuristic baseline algorithms, the proposed method achieved significant improvements in the Pareto volume and outperformed existing baseline methods in multiple performance indicators.

The combination of osmotic dehydration (OD) and microwave radiation (MW), referred to as microwave-assisted osmotic dehydration (MWOD), has emerged as a promising hybrid strategy for optimizing food processing by enhancing mass transfer and preserving bioactive compounds. This study aimed to provide a comprehensive overview of the synergistic effects of MWOD, focusing on the interaction mechanisms between MW heating and mass transfer, key operational parameters, and their impacts on the quality of dehydrated foods. A systematic literature review was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, using the Scopus and Web of Science databases. Studies were selected through predefined inclusion and exclusion criteria based on document type, relevance to food processing, and experimental application of MWOD. Data related to raw materials, process conditions, mass transfer behavior, quality attributes, and energy aspects were extracted and qualitatively synthesized. From 178 records initially identified, 25 experimental studies specifically addressing MWOD were included in the final analysis. Overall, most of the analyzed experimental studies reported that MWOD enhances water removal rates, reduces solid gain, and improves the retention of color, texture, and antioxidant compounds when compared to conventional OD under the evaluated processing conditions. In addition, several studies indicated a reduction in energy consumption, particularly at laboratory scale. Despite these advances, literature still lacks studies integrating diffusional modeling with physicochemical and sensory analyses, as well as investigations at pilot and industrial scales. These findings highlight the strong potential of MWOD as a sustainable and efficient technology high-quality dehydrated foods.

Environmental sustainability represents an emerging priority for cardiology, owing to the close interconnection between planetary health and human health, with cardiovascular diseases constituting the main clinical outcome. The global healthcare sector accounts for approximately 4-5% of total greenhouse gas emissions, and cardiology contributes substantially to this burden because of its high resource intensity in diagnostic testing, interventional procedures, and energy consumption. At the same time, environmental factors such as air pollution, extreme temperatures, defined as values significantly above or below the regional average caused by climate change, and exposure to emerging contaminants, including heavy metals (lead, cadmium, arsenic) and micro- and nanoplastics are increasingly recognized as major determinants of cardiovascular risk. Chronic exposure to these pollutants is associated with oxidative stress, systemic inflammation, and accelerated progression of atherosclerosis. Strategies for sustainable cardiology primarily aim to reduce emissions related to energy use and supply chains. Priority actions include adopting circular economy principles (reduce, reuse, recycle), improving the appropriateness and optimization of diagnostic testing favoring lower environmental impact modalities, such as echocardiography, over carbon-intensive techniques and implementing telemedicine to reduce patient and provider travel. Furthermore, primary cardiovascular prevention can be considered an effective "double-benefit strategy", capable of simultaneously reducing disease burden and the demand for emission-intensive healthcare. In this context, healthcare professionals and scientific societies, including ANMCO, are called upon to lead a cultural shift by integrating environmental sustainability as a core ethical principle of contemporary cardiology practice.

Carbon emissions from the construction industry have attracted significant public attention, and the sector's low-carbon transition is crucial for achieving carbon neutrality. Although extensive research has been conducted in this field, limited studies have investigated the combined effects of structural performance design and seismic safety technologies on carbon emission reduction. This study aims to simultaneously enhance structural safety and reduce carbon emissions using the seismic energy dissipation technology. Eight reinforced concrete frame structures with varying numbers of floors are selected as case studies in accordance with the Chinese design code. By incorporating additional energy dissipation devices, the required dimensions of structural components in structures with damper (SWD) are reduced compared with structures without damper (SWOD), thereby lowering carbon emissions and improving seismic performance. Both SWOD and SWD systems are designed for each of the eight reinforced concrete frame structures for comparative analysis. Structural component dimensions are calculated using SAUSG software, and key performance indicators, including the natural period and inter-story drift ratio, are analyzed to verify compliance with code-specified safety requirements. Engineering quantities and life-cycle energy consumption are quantified, including the production and transportation of materials, as well as construction and dismantling stages. The results indicate that SWDs reduce material and energy consumption, with average carbon emissions 17.4% lower than those of SWODs. This study provides a novel perspective on carbon emission reduction during the design phase and offers an effective technical pathway for the coordinated development of low-carbon buildings and seismic resilience.

Enhancing carbon allocation and utilization via a sludge-bypass anaerobic/oxic/anoxic process for ultra-efficient nutrient removal from municipal wastewater under low C/N and temperature conditions.

Valorization of spent coffee grounds: techno-economic and environmental assessment of a multi-product biorefinery.

Inhibiting enzymatic browning by inactivating peroxidase and polyphenol oxidase using a novel natural gas-produced catalytic infrared treatment.

A sensitive pH fluorescent probe with large Stokes shift and narrow transition for lysosome imaging during cell cycle.

• Formulated a ship decarbonization design integrating PCC with the ship energy system. • Developed a hybrid model to capture the PCC dynamics under varying ship engine loads. • Designed an EMPC for energy-efficient PCC operation with high carbon capture rate. • Employed cross-entropy to efficiently solve the complex EMPC optimization problem. • Conducted extensive simulations verifying superior modeling and control performance. Implementing carbon capture technology on-board ships holds promise as a solution to facilitate the reduction of carbon intensity in international shipping, as mandated by the International Maritime Organization. In this work, we address the energy-efficient operation of shipboard carbon capture processes by proposing a hybrid modeling-based economic predictive control scheme. Specifically, we consider a comprehensive shipboard carbon capture process that encompasses the ship engine system and the shipboard post-combustion carbon capture plant. To accurately and robustly characterize the dynamic behaviors of this shipboard plant, we develop a hybrid dynamic process model that integrates available imperfect physical knowledge with neural networks trained using process operation data. An economic model predictive control approach is proposed based on the hybrid model to ensure carbon capture efficiency while minimizing energy consumption required for the carbon capture process operation. The cross-entropy method is employed to efficiently solve the complex non-convex optimization problem associated with the proposed hybrid model-based economic model predictive control method. Extensive simulations, analyses, and comparisons are conducted to verify the effectiveness and illustrate the superiority of the proposed framework. The proposed hybrid model-based economic model predictive control reduced the overall economic cost by 8.07% compared to conventional optimal set-point tracking nonlinear model predictive control and achieved a 4.20% lower economic cost with a 9.10% higher carbon capture rate than the imperfect first-principles model-based economic model predictive control.

Does the new energy storage policy reduce the enterprise energy consumption intensity? Evidence from China

Porosity–grouting–luminescence coupling mechanism and durability of semi-flexible self-luminous pavements