Microbial electrochemical techniques describe a variety of emerging technologies that use electrode–bacteria interactions for biotechnology applications including the production of electricity, waste and wastewater treatment, bioremediation and the production of valuable products. Central in each application is the ability of the microbial catalyst to interact with external electron acceptors and/or donors and its metabolic properties that enable the combination of electron transport and carbon metabolism. And here also lies the key challenge. A wide range of microbes has been discovered to be able to exchange electrons with solid surfaces or mediators but only a few have been studied in depth. Especially electron transfer mechanisms from cathodes towards the microbial organism are poorly understood but are essential for many applications such as microbial electrosynthesis. We analyze the different electron transport chains that nature offers for organisms such as metal respiring bacteria and acetogens, but also standard biotechnological organisms currently used in bio-production. Special focus lies on the essential connection of redox and energy metabolism, which is often ignored when studying bioelectrochemical systems. The possibility of extracellular electron exchange at different points in each organism is discussed regarding required redox potentials and effect on cellular redox and energy levels. Key compounds such as electron carriers (e.g., cytochromes, ferredoxin, quinones, flavins) are identified and analyzed regarding their possible role in electrode–microbe interactions. This work summarizes our current knowledge on electron transport processes and uses a theoretical approach to predict the impact of different modes of transfer on the energy metabolism. As such it adds an important piece of fundamental understanding of microbial electron transport possibilities to the research community and will help to optimize and advance bioelectrochemical techniques.
Meriem Guellout, Zineb Guellout, Hani Belhadj
et al.
Arid and semi-arid soils represent extreme habitats where microbial life is constrained by high temperature, low water availability, salinity, and nutrient limitation, yet these ecosystems harbor unique bacterial communities that sustain key ecological processes. To explore the diversity and functional potential of prokaryotic assemblages in Algerian drylands, we compared soils from three contrasting sites: The Oasis of Djanet (RM1), the hyper-arid Tassili of Djanet desert (RM2), and the semi-arid El Ouricia forest in Sétif (RM3). Physicochemical analyses revealed strong environmental gradients: RM2 exhibited the highest pH (8.66), electrical conductivity (11.7 dS/m), and sand fraction (56%), whereas RM3 displayed the greatest moisture (10.9%), organic matter (7.6%), and calcium carbonate (20.7%) content, with RM1 generally showing intermediate levels. High-throughput 16S rRNA gene sequencing generated >60,000 effective reads per sample with sufficient coverage (>0.99). Alpha diversity indices indicated the highest bacterial richness and diversity in RM2 (Chao1 = 3144, Shannon = 10.0), while RM3 showed lower evenness and the dominance of a few taxa. Across sites, 66 phyla and 551 genera were detected, dominated by <i>Actinobacteriota</i> (38–45%) and <i>Chloroflexi</i> (13–44%), with <i>Proteobacteria</i> declining from RM1 (17.5%) to RM3 (3.3%). Venn analysis revealed limited overlap, with only 58 operational taxonomic units shared among all sites, suggesting highly habitat-specific communities. Predictive functional profiling (PICRUSt2, Tax4Fun, FAPROTAX) indicated metabolism as the dominant functional category (≈50% of KEGG Level-1), with carbohydrate and amino acid metabolism forming the metabolic backbone. Notably, transport functions (ABC transporters), lipid metabolism, and amino acid degradation pathways were enriched in RM2–RM3, consistent with adaptation to osmotic stress, nutrient limitation, and energy conservation under aridity. Collectively, these findings demonstrate that Algerian arid and semi-arid soils host diverse, site-specific bacterial communities whose functional repertoires are strongly shaped by soil chemistry and climate, highlighting their ecological and biotechnological potential.
Dimitrios Koemtzopoulos, Georgia Zournatzidou, Konstantina Ragazou
et al.
Fintech prioritizes the progression of issues related to environmental conservation and the consequences of climate change. This study is among the first investigations exploring the relationship between fintech and sustainable energy. It presents potential financial models that might be developed to assist companies in remaining operational via the use of renewable and clean energy sources. We employ a bibliometric analysis as the statistical methodology to address the study topic. We extract bibliometric data from the Scopus database employing the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) approach, thereafter analyzing the data with the R statistical programming language and the bibliometric applications Biblioshiny and VOSviewer. The results of the research indicate that fintech companies are committed to achieving carbon neutrality and investing in strategies such as environmental, social, and corporate governance (ESG) which may help them reduce their carbon footprint and enhance their eco-efficiency. In contrast to the United Kingdom, which is frequently regarded as the world’s preeminent financial center, Chinese fintech enterprises appear to demonstrate a more fervent dedication to the improvement of their ecological transition. However, the results, ultimately, emphasize the transition of fintech to an alternative paradigm, namely greentech. Greentech is a new fintech-dependent paradigm which will help cryptocurrencies and fintech reduce their environmental impact and promote carbon-neutral financial institutions via investment. Greentech aims to decarbonize the financial industry by investing in renewable resources and clean energy, therefore enhancing the sector’s environmental sustainability.
Yustisia Serfiyani Cita, Hermono Budi, Sulistiyowati Eny
et al.
AI data centers are a vital necessity that is inevitable in the era of AI development, which is growing very rapidly today, even though the aspects of technological development and environmental preservation are two sides that often cannot go hand in hand if not accompanied by a commitment to ecological conservation. The integration of Renewable Energy Certificates (RECs) into the management of AI data centers in Indonesia is a progressive idea but not easy to implement because it clashes with climate crisis issues, especially related to the use of clean drinking water that is used massively by data center operator companies everywhere, including in Indonesia, so that it has the potential to cause a clean water crisis if not balanced with the strengthening of government policies that involve the environment. A comparative study of the concept of sustainable AI data center management applied to Australia. This study recommends REC as a mandatory certification that AI data center operators must possess before being permitted to invest in AI data centers in Indonesia. The REC requirement mechanism includes the ability or commitment to provide energy sources other than clean water, which technological readiness or cooperation contract documents can prove.
Abstract Cultural memory fundamentally shapes urban collective identity, , yet it is seldom quantified at fine spatial scales. This study proposes the Heritage–Memory Symbiosis Loop (HMSL) as an analytical framework to examine Guangzhou, a historic trading hub in China with 446 state-listed heritage units. Each heritage unit is systematically classified within a “two representations–six memory-space” matrix, and a Cultural Memory Index (CMI) is computed and visualized as a spatialfield-energy surface. Subsequently, Kernel-density estimation, Moran’s I, and LISA analyses illuminate memory hotspots centered around the Yuexiu–Liwan core, while revealing the attenuation of spirituality-based memories in fringe districts undergoing gentrification. Field-energy gradients underpin the delineation of three protection zones: high-intensity “living museums” along dynastic trade routes, medium-intensity multipurpose belts, and low-intensity rural nodes. The CMI map constitutes the first point-level quantification of cultural memory for Guangzhou, elucidates the interplay between material and spiritual domains within the human–land system, and supplies a replicable methodology—including heritage inventory, memory zoning, and field-energy mapping— tailored for conservation strategies in rapidly urbanizing Asian cities.
Mian Ibad Ali Shah, Marcos Eduardo Cruz Victorio, Maeve Duffy
et al.
The integration of renewable energy resources in rural areas, such as dairy farming communities, enables decentralized energy management through Peer-to-Peer (P2P) energy trading. This research highlights the role of P2P trading in efficient energy distribution and its synergy with advanced optimization techniques. While traditional rule-based methods perform well under stable conditions, they struggle in dynamic environments. To address this, Multi-Agent Reinforcement Learning (MARL), specifically Proximal Policy Optimization (PPO) and Deep Q-Networks (DQN), is combined with community/distributed P2P trading mechanisms. By incorporating auction-based market clearing, a price advisor agent, and load and battery management, the approach achieves significant improvements. Results show that, compared to baseline models, DQN reduces electricity costs by 14.2% in Ireland and 5.16% in Finland, while increasing electricity revenue by 7.24% and 12.73%, respectively. PPO achieves the lowest peak hour demand, reducing it by 55.5% in Ireland, while DQN reduces peak hour demand by 50.0% in Ireland and 27.02% in Finland. These improvements are attributed to both MARL algorithms and P2P energy trading, which together results in electricity cost and peak hour demand reduction, and increase electricity selling revenue. This study highlights the complementary strengths of DQN, PPO, and P2P trading in achieving efficient, adaptable, and sustainable energy management in rural communities.
Olzhaev Dilshod, Nurmurodov Tulkin, Ganiev Kahramon
et al.
At present, the composition of steam and condensate in periodic motion during 45кthe continuous operation of thermal power plants is getting worse and worse. Great importance is attached to the prevention of industrial wastewater pollution. In industry, water is used as a raw material and energy source, as a coolant or heater, as a solvent and extractant, and it is purified in wastewater treatment plants and discharged back into water bodies. Therefore, it is important to further improve the engineering work of wastewater treatment in water conservation. Water is the main technological raw material for obtaining steam in thermal power plants. It is the most widely used product in the continuous operation of the plant.
Hicham Mangach, Younes Achaoui, Muamer Kadic
et al.
Recently, metamaterials have driven advancements in wave propagation and polarization control. Chiral elastic metamaterials, in particular, have attracted considerable attention due to their distinctive properties, such as acoustical activity and auxeticity. Such characteristics arise from the additional degrees of freedom for tuning the embedded micro- and macro-rotations. In this study, we demonstrate an unusual energy exchange between longitudinal and in-plane shear waves in a 3D chiral mechanical metamaterial. The structural design is capable of inducing up to a 90 ^∘ rotation in the plane of polarization. Additionally, this capacity for conversion is achieved by employing both an arrangement of chiral cells and a single meta-atom. This peculiar behavior enables a seamless switch between the three polarization states existing within a solid material, namely, the longitudinal state, the shear horizontal state, and the shear vertical state. Furthermore, a 2D discrete mono-atomic mass-spring model featuring inclined connectors is used to characterize the distinctive energy exchange between modes. This characterization is based on the retrieval of the pertinent elastic coefficients. The engineered chiral metamaterial polarization converter stands as a promising device for momentum conservation conversions and applications in elasto-dynamic polarimetry.
Imtiyaz Hussain, Uzair Sajjad, Naseem Abbas
et al.
This study addresses the durability and performance challenges in Proton Exchange Membrane Fuel Cell (PEMFC) technology, primarily focusing on optimal humidity management. Acknowledging that, beyond technical aspects, the broader commercialization of PEMFCs is critically influenced by factors such as the cost and availability of hydrogen, this research aims to provide a comprehensive solution to enhance PEMFC applicability. Utilizing Nafion (NR-212), reverse osmosis (RO), and pervaporation (PV) membranes, the study optimizes five key performance metrics: pressure drop (∆P), dew point approach temperature (DPAT), water recovery ratio (WRR), Water Flux (J), and coefficient of performance (COP). These optimizations are conducted considering variables like temperature, humidity, flowrate, and membrane material. A deep neural network (DNN) model, incorporating Bayesian surrogacy with Gaussian process, gradient boost regression trees, and random forest, is developed using experimental data. With a correlation coefficient of 0.986, the model demonstrates high accuracy in predicting performance metrics, subsequently guiding genetic algorithms for effective PEMFC humidity control. The results show significant improvements in all metrics, with optimal values achieved for NR-212, RO, and PV membranes. This study thus presents a novel, practical deep learning approach, considering both technological advancements and external economic factors, for enhancing PEMFC operations.
Despite the large number of publications on symmetry analysis of the geopotential forecast equation, its group foliations laws have not been considered previously. The present publication aims to address this shortcoming. First, group foliations are constructed for the equation, and based on them, invariant solutions are derived, some of which generalize previously known exact solutions. There is also a discussion of the pros and cons of the group foliation approach. In addition, the rest of the paper is dedicated to a comprehensive discussion of conservation laws. All possible second-order conservation laws of the geopotential forecast equation are obtained through direct calculations, and a number of higher-order conservation laws are derived using the known symmetries of the equation.
Missing data are frequently observed by practitioners and researchers in the building energy modeling community. In this regard, advanced data-driven solutions, such as Deep Learning methods, are typically required to reflect the non-linear behavior of these anomalies. As an ongoing research question related to Deep Learning, a model's applicability to limited data settings can be explored by introducing prior knowledge in the network. This same strategy can also lead to more interpretable predictions, hence facilitating the field application of the approach. For that purpose, the aim of this paper is to propose the use of Physics-informed Denoising Autoencoders (PI-DAE) for missing data imputation in commercial buildings. In particular, the presented method enforces physics-inspired soft constraints to the loss function of a Denoising Autoencoder (DAE). In order to quantify the benefits of the physical component, an ablation study between different DAE configurations is conducted. First, three univariate DAEs are optimized separately on indoor air temperature, heating, and cooling data. Then, two multivariate DAEs are derived from the previous configurations. Eventually, a building thermal balance equation is coupled to the last multivariate configuration to obtain PI-DAE. Additionally, two commonly used benchmarks are employed to support the findings. It is shown how introducing physical knowledge in a multivariate Denoising Autoencoder can enhance the inherent model interpretability through the optimized physics-based coefficients. While no significant improvement is observed in terms of reconstruction error with the proposed PI-DAE, its enhanced robustness to varying rates of missing data and the valuable insights derived from the physics-based coefficients create opportunities for wider applications within building systems and the built environment.
Optimally scheduling multi-energy flow is an effective method to utilize renewable energy sources (RES) and improve the stability and economy of integrated energy systems (IES). However, the stable demand-supply of IES faces challenges from uncertainties that arise from RES and loads, as well as the increasing impact of cyber-attacks with advanced information and communication technologies adoption. To address these challenges, this paper proposes an innovative model-free resilience scheduling method based on state-adversarial deep reinforcement learning (DRL) for integrated demand response (IDR)-enabled IES. The proposed method designs an IDR program to explore the interaction ability of electricity-gas-heat flexible loads. Additionally, the state-adversarial Markov decision process (SA-MDP) model characterizes the energy scheduling problem of IES under cyber-attack, incorporating cyber-attacks as adversaries directly into the scheduling process. The state-adversarial soft actor-critic (SA-SAC) algorithm is proposed to mitigate the impact of cyber-attacks on the scheduling strategy, integrating adversarial training into the learning process to against cyber-attacks. Simulation results demonstrate that our method is capable of adequately addressing the uncertainties resulting from RES and loads, mitigating the impact of cyber-attacks on the scheduling strategy, and ensuring a stable demand supply for various energy sources. Moreover, the proposed method demonstrates resilience against cyber-attacks. Compared to the original soft actor-critic (SAC) algorithm, it achieves a 10% improvement in economic performance under cyber-attack scenarios.
<p><b>Over the past decades, Laos has experienced an increase in household energy consumption due to population growth and economic expansion. As the country continues to develop, the demand for energy is expected to grow, presenting a significant challenge for Laos in achieving its target of carbon neutrality by 2050. To achieve a transition towards a low-carbon future, increasing the uptake of energy efficiency and conservation in the home is considered to be a key strategy. However, there is a lack of in-depth research on the factors that motivate households in Laos to engage in energy conservation behaviour. Understanding the key determinants of people’s willingness to engage in energy-saving behaviour is vital in informing future policy interventions that aim to encourage households to reduce their energy use. </b></p> <p>People’s willingness to engage in household energy-saving behaviours can be associated with many factors. Using the Value-Belief-Norm theory (Stern, 2000) as a theoretical framework, this study examined two broad factors that have been recognised in the literature as important in explaining energy conservation behaviour: socio-demographic (such as age, gender, level of education and household characteristics) and psychological factors (such as values, environmental beliefs, and personal norms). The study further explored the role of these factors in predicting the willingness for households to adopt energy conservation behaviour. A sample of 304 residents in Laos took part in a survey. The results of the quantitative analysis indicated that psychological factors, especially personal norms, awareness of consequences and self-transcendence values, play a significant role in predicting people’s willingness to engage in energy-saving behaviour. While a correlation between socio-demographic factors and the willingness to adopt energy-saving behaviour was observed, these factors were not found to be significant in predicting behavioural intentions when the other variables were controlled for in a regression analysis. The findings of this study then, provide useful insights for policy development, particularly in the design of interventions to promote behaviour change related to energy efficiency and conservation.</p>
Energy saving is a hot topic due to the proliferation of climate changes and energy challenges globally. However, people's perception about using smart technology for energy saving is still in the concept stage. This means that people talk about environmental awareness readily, yet in reality, they accept to pay the given energy bill. Due to the availability of electricity and its integral role, modulating consumers’ attitudes towards energy savings can be a challenge. Notably, the gap in today's smart technology design in smart homes is the understanding of consumers’ behaviour and the integration of this understanding into the smart technology. As part of the Paris Climate change agreement (2015), it is paramount for Singapore to introduce smart technologies targeted to reduce energy consumption. This paper focused on the perception of Singapore households on smart technology and its usage to save energy. Areas of current research include: (1) energy consumption in Singapore households, (2) public programs and policies in energy savings, (3) use of technology in energy savings, and (4) household perception of energy savings in smart homes. Furthermore, three case studies are reviewed in relation to smart homes and smart technology, while discussing the maturity of existing solutions.
With the increase of harmful substances and greenhouse gases that need to be discharged from the traditional thermal power in industrial production in China, the phenomenon of climate warming is becoming more and more prominent. Clean energy will continue to increase in China's future energy consumption structure and market share, hydropower, nuclear power, and other energy as China's main clean energy, the future in China still has a huge market development and use of space. The new policies further adopted by the central bank of China include: continuously optimizing the structure of reasonable credit fund allocation and risk fund application for electric power enterprises to enhance the return rate of assets of electric power enterprises; continuously supporting the development of smart grid and strengthening the linkage between network and electric power; reasonably and categorically guiding the source of clean utilization of electric power, actively supporting large hydropower generation and solar and nuclear power generation, and investing funds in a controlled manner to support large thermal power generation, promote the upgrading of the thermal power generation industry structure, cautiously guide funds into large biomass power generation, wind power generation and small and medium-sized micro-hydro power, strictly control small and medium-sized thermal power, as soon as possible to withdraw from the implementation of the national preferential policies for small and medium-sized power industry management system, energy conservation and reduction of harmful emissions of environmental gases of enterprises is not possible to meet the standards and there are financial risks business efficiency situation Small and medium-sized electric power enterprises that continue to seriously deteriorate.
We develop a cosmological theory in which the evolution of the universe is controlled by the cosmological constant and dominated by the associated vacuum energy. The universe starts as a classical de Sitter space with an infinite effective vacuum energy density, which decreases subsequently like 1/t^3. The corresponding Friedmann-Robertson-Walker (FRW) scale factor also decreases over time, showing that the common assumption that it describes the expansion of the universe is incorrect and should be abandoned. Instead, the (cubic) expansion of the universe is needed to satisfy energy conservation. Once the vacuum energy density has decreased to the Planck level the first elementary particles can be created through a direct conversion of vacuum energy. After this epoch, the enormous kinetic energy enables a quick magnification of the number of particles through ordinary production processes in tandem with the expansion of space. The dominance of vacuum (dark) energy is supposed to persist into later epochs, which enables a perturbative treatment of matter and radiation leading to linear equations, which replace the usual FRW equations. The presence of matter changes the vacuum metric, inducing a secondary matter term which might explain the phenomenon of dark matter. Together with a similar induced radiation term, it provides a possible explanation for the recent acceleration of the expansion of the universe. The theory unifies particle physics and cosmology by expressing particle physics units in terms of the gravitational and cosmological constant. This relationship also explains a number of numerical coincidences which have long puzzled physicists.