Search Results (95)

The present study implements novel innovative geostatistical imaging using precision agriculture (PA) under sugarbeet field conditions. Two driplines layout designs (d.l.d.) and soil water content (SWC)–irrigation treatments (A: d.l.d. = 1.00 m driplines spacing × 0.50 m emitters inline spacing; B: d.l.d. = 1.50 m driplines spacing × 0.50 m emitters inline spacing) were applied, with two subfactors of clay loam and clay soils (laboratory soil analysis) for modeling (evaluation of seven models) TDR multi-sensor network measurements. Different sensor calibration methods [method 1(M1) = according to factory; method 2 (M2) = according to Hook and Livingston] were applied for the geospatial two-dimensional (2D) imaging of accurate GIS maps of rootzone soil moisture profiles, soil apparent dielectric Ka profiles, and granular and hydraulic parameters profiles, in multiple soil layers (0–75 cm depth). The modeling results revealed that the best-fitted geostatistical model for soil apparent dielectric Ka was the Gaussian model, while spherical and exponential models were identified to be the most appropriate for kriging modelling, and spatial and temporal imaging was used for accurate profile SWC ${\theta v}_{TDR}$(m³·m⁻³) M1 and M2 maps using TDR sensors. The resulting PA profile map images depict the spatio-temporal soil water and apparent dielectric Ka variability at very high resolutions on a centimeter scale. The best geostatistical validation measures for the PA profile SWC ${\theta v}_{TDR}$ maps obtained were MPE = −0.00248 (m³·m⁻³), RMSE = 0.0395 (m³·m⁻³), MSPE = −0.0288, RMSSE = 2.5424, ASE = 0.0433, Nash–Sutcliffe model efficiency NSE = 0.6229, and MSDR = 0.9937. Based on the results, we recommend d.l.d. A and sensor calibration method 2 for the geospatial 2D imaging of PA GIS maps because these were found to be more accurate, with the lowest statistical and geostatistical errors, and the best validation measures for accurate profile SWC imaging were obtained for clay loam over clay soils. Visualizing sensors’ soil moisture results via geostatistical maps of rootzone profiles have practical implications that assist farmers and scientists in making informed, better and timely environmental irrigation engineering decisions, to save irrigation water, increase water use efficiency and crop production, optimize energy, reduce crop costs, and manage water resources sustainably. Full article

The earth’s surface has an uneven solar energy density that is sufficient to stimulate solar photovoltaic (PV) production. This causes variations in a solar plant’s output, which are impacted by geometrical elements and atmospheric conditions that prevent it from passing. Motivated by the focus on encouraging increased PV production efficiency, the goal was to use machine learning models (MLM) to map the distribution of solar energy in Mozambique in a regressive literal and geospatial–temporal manner on a short measurement scale. The clear-sky index $\left(K_t^{*}\right)$ theoretical approach was applied in conjunction with MLM that emphasized random forest (RF) and artificial neural networks (ANNs). Solar energy mapping was the result of the methodology, which involved statistically calculating $K_t^{*}$ for the analysis of solar energy in correlational and causal terms of the space-time distribution. Utilizing data from PVGIS, NOAA, NASA, and Meteonorm, a sample of solar energy was gathered at 11 measurement stations in Mozambique over a period of 1 to 10 min between 2012 and 2014 as part of the FUNAE and INAM measurement programs. The statistical findings show a high degree of solar energy incidence, with increments $\left(∆K_t^{*}\right)$ in the average order of −0.05 and $K_t^{*}$ mostly ranging between 0.4 and 0.9. In 2012 and 2014, $K_t^{*}$ was 0.8956 and 0.6986, respectively, because clear days had a higher incident flux and intermediate days have a higher frequency of ${∆K}_t^{*}$ on clear days and a higher occurrence density. There are more cloudy days now 0.5214 as opposed to 0.3569. Clear days are found to be influenced by atmospheric transmittance because of their high incident flux, whereas intermediate days exhibit significant variations in the region’s solar energy. Full article

As industries come under growing pressure to minimize carbon emissions without compromising the efficiency of operations, the integration of energy-aware production scheduling with emerging energy markets, renewable energy, and policy mechanisms is critical. This paper identifies critical shortcomings in current academic and industrial approaches—namely, an excessive reliance on deterministic assumptions, a limited focus on dynamic operational realities, and the underutilization of regulatory mechanisms such as carbon trading. We advocate for a paradigm shift to more robust, adaptable, and policy-compliant scheduling systems that provide space for on-site renewable generation, battery energy storage systems (BESSs), demand-response measures, and real-time electricity pricing schemes like time-of-use (TOU) and real-time pricing (RTP). By integrating recent advances and their critical analysis of limitations, we map out a future research agenda for the integration of uncertainty modeling, machine learning, and multi-level optimization with policy compliance. In this paper, we propose the need for joint efforts from researchers, industries, and policymakers to collectively develop industrial scheduling systems that are both technically efficient and adherent to sustainability and regulatory requirements. Full article

The selection of the peak power of a photovoltaic system to meet the energy demand of a building is a key task in the energy transformation. This article presents an algorithm for assessing the correctness of the selection of a photovoltaic system with a peak power of 50 kWp for the needs of a university administration building. This is made possible due to the use of an advanced photovoltaic inverter, which is a device of the Internet of Things and the smart metering system. At the beginning of the review, the authors employed the naked eye measurement data of the time series related to the power production by the photovoltaic system and its consumption by the university building. Then, traditional statistical analyses were performed, characterizing the generated power divided into self-consumption power and that fed into the power grid. The analysis of the total consumed power was performed with the division into the power produced by the photovoltaic system and that taken from the power grid. The analyses conducted were subjected to expert validation aimed at explaining the nature of the behavior of the power generation and consumption systems. The main goal of this article is to determine the signatures of the power generated by the photovoltaic system and consumed by the administration building. As a result of unsupervised clustering, the power generation and consumption space were divided into states. These were then termed based on their nature and their usefulness in managing the power produced and consumed. Presentation of clustering results in the form of heatmaps allows for localization of specific states at specific times of the day. This leads to their better understanding and quantification. The signatures of power generated by the photovoltaic system and consumed by the university building confirmed the possibility of using an energy storage system. The presented computational algorithm is the basis for determining the correctness of the photovoltaic system selection for the current energy needs of the building. It can be the basis for further analysis related to the prediction of both the power generated by Renewable Energy Sources and the energy consumed by diverse types of buildings. Full article

Low-emission hydrogen generation systems require large amounts of energy from renewable energy sources. This article characterizes the production of low-emission hydrogen, emphasizing its scale and the necessity for its continuity. For hydrogen production defined in this way, it is possible to select the appropriate renewable energy sources. The research part of the article presents a case study of the continuous production of large amounts of hydrogen. Daily production capacities correspond to the demand for the production of industrial chemicals and artificial fertilizers or for fueling a fleet of hydrogen buses. The production was placed in the Lublin region in Poland, where there is a large demand for low-emission hydrogen and where there are favorable conditions for the production of energy from a photovoltaic–wind mix. Statistical and probabilistic analyses were performed related to the generation of power by a photovoltaic system with a peak power of 3.45 MWp and a wind turbine with an identical maximum power. The conducted research confirmed the complementarity and substitutability relationship between one source and another within the energy mix. Then, unsupervised clustering was applied using the k-Means algorithm to divide the state space generated in the power mix. The clustering results were used to perform an operational analysis of the low-emission hydrogen generation system from a renewable energy sources mix. In the analyzed month of April, 25% of the energy generated in the photovoltaic–wind mix came from the photovoltaic system. The low-emission hydrogen generation process was in states (clusters), ensuring that the operation of the electrolyzer with nominal power amounted to 57% of the total operating time in that month. In May, the share of photovoltaics in the generated power was 45%. The low-emission hydrogen generation process was in states, ensuring that the operation of the electrolyzer with nominal power amounted to 43% of the total time in that month. In the remaining states of the hydrogen generation process, the power must be drawn from the energy storage system. The cluster analysis also showed the functioning of the operating states of the power generation process from the mix, which ensures the charging of the energy storage. The conducted research and analyses can be employed in planning and implementing effective climate and energy transformations in large companies using low-emission hydrogen. Full article

Rice hulls, a traditional ingredient in Chinese light-flavor Baijiu, contribute to bran and furfural flavors but may adversely affect the aroma and taste. This study explores fresh pomegranate peel as a sustainable alternative to rice hulls in Baijiu fermentation. The flavor profiles in jiupei and Baijiu were interpreted by employing head-space solid-phase microextraction coupled with gas chromatography–mass spectrometry (HS-SPME-GC-MS), while their physicochemical characteristics were systematically assessed. Statistical evaluations, such as correlation analysis and cluster analysis, were conducted to interpret the data. The results showed that compared with rice hull, pomegranate peel reduced furfural content in jiupei by 90%, increased the alcohol distillation rate (alcohol distillation rate: this refers to the weight percentage of 50% alcohol by volume (ABV) Baijiu produced from a unit amount of raw material under standard atmospheric pressure at 20 °C (also known as Baijiu yield)) by 30%, enhanced antioxidant capacity by 24.38%, and improved starch efficiency by 3%. Notably, the Baijiu complied with the premium Baijiu standards specified in the Chinese National Standard for light-flavor Baijiu. Additionally, under the experimental conditions of this study, the optimal Baijiu yield (optimal Baijiu yield: the maximum achievable Baijiu production under defined constraints (e.g., energy input, time, cost)) (48% ± 3.41%) correlated with the pomegranate peel particle size. This research highlights the viability of using pomegranate peel as a sustainable and environmentally friendly adjunct in the fermentation of light-flavor Baijiu, offering valuable perspectives for exploring alternative brewing ingredients. Full article

Fractured-vuggy marine carbonate reservoirs, as an unconventional energy resource, hold significant potential for exploration and development. In this study, the Manshen block of the Furman oilfield in the Tarim Basin, China, was selected as the research object. A systematic investigation was conducted on the types of marine carbonate reservoir bodies, production characteristics, and both static and dynamic connectivity. Static connectivity analysis was performed using the heat diffusion equation and the multi-source potential field method. Dynamic connectivity evaluation was carried out by combining the dynamic time warping (DTW) algorithm with the analytic hierarchy process (AHP). Well logging, core analysis, and cast-thin section experiments were utilized to determine the types of reservoir spaces. The results indicate that the main types of reservoir spaces in the study area are caves, pores, and fractures. The fractures are primarily structural, with secondary development of dissolution fractures, weathering fractures, and sutures. The productivity changes in oil wells in the study area are classified into three types: slow decline, rapid decline, and high-speed decline. Based on the connectivity coefficients, wells were divided into three connectivity groups, with the A32 well group having the highest connectivity, followed by B5 well group 1, and B5 well group 2 having the lowest connectivity. The research provides technical support for the accurate evaluation of marine carbonate reservoirs and contributes to enhancing the efficiency of oil and gas exploration and development. Full article

In June 2024, the Fermi Gamma-Ray Space Telescope (FGST) celebrated its 16th year of operations. The Fermi Large Area Telescope (Fermi-LAT) is the main instrument onboard the FGST satellite and is designed to be sensitive to $\gamma$-rays in the energy range from about $20\mathrm{MeV}$ up to the $\mathrm{TeV}$ regime. From its launch, the Fermi-LAT has collected more than $4.53\mathrm{billion}$ photon events, providing crucial information to improve our understanding of particle acceleration and $\gamma$-ray production phenomena in astrophysical sources. The most abundant in the last 4FGL-data release 4 (4FGL-DR4), most powerful and persistent $\gamma$-ray emitters in the sky are the Active Galactic Nuclei (AGNs). These sources are extremely luminous galaxy cores powered by a super massive black hole (SMBH) with a mass ranging from millions to billions of times the mass of the Sun. The ASI-SSDC, a facility of the Agenzia Spaziale Italiana (ASI), plays a pivotal role in supporting Fermi-LAT by providing the essential infrastructure for the storage, processing, and analysis of the vast amounts of data generated by the mission. As a key asset to various space missions, ASI-SSDC contributes significantly to advancing research in high-energy astrophysics and $\gamma$-ray observations. Full article

Hydrogen (H2) offers a green medium for storing the excess from renewables production instead of dumping it, thus being crucial to decarbonisation efforts. Hydrogen also offers a storage medium for the grid’s cheap electricity to be used during grid peak demand or grid power cutoffs. Funded by the Scottish Government’s Emerging Energy Technologies, this paper presents the design and performance analysis of a hydrogen uninterruptible power supply (H2GEN) for Cygnas Solutions Ltd., which is intended to enable continuity of supply in the residential sector while eradicating the need for environmentally and health risky lead–acid batteries and diesel generator backup. This paper presents the design, optimal sizing and analysis of two H2Gen architectures, one powered by the grid alone and the other powered by both the grid and a renewable (PV) source. By developing a model of each architecture in the HOMER space and using residential location weather data, the home yearly load–demand profile, and the grid yearly power outages profile in the developed models, the optimal sizing of each H2Gen design was realised by minimising the costs while ensuring the H2Gen meets the home power demand during grid outages To enable HOMER to optimise its selection, the sizes, technical specifications and costs of all the market-available H2GEN components were added in the HOMER search space. Moreover, the developed models were also used in assessing the sensitivity of the simulation outputs to several changes in the modelled system design and settings. Using a residential home with frequent power outages in New Delhi, India as a case study, it was found that the optimal sizing of H2Gen Architecture 1 is comprised of a 2 kW electrolyser, a 0.2 kg type-I tank, and a 2 kW water-cooled fuel cell directly connected to the AC bus, offering an operational lifetime of 14.3 years. It was also found that the optimal sizing of Architecture 2 is comprised of a 1 kV PV utilised with the same 2 kW electrolyser, 0.2 kg type-I tank and 2 kW water-cooled fuel cell connected to the AC bus. While the second design was found to have a higher capital cost due to the added PV, it offered a more cost-effective and environmentally friendly architecture, which contributes to the ongoing energy transition. This paper further investigated the capacity expansion of each H2GEN architecture to meet higher load demands or increased grid power outages. From the analysis of the simulation results, it has been concluded that the most feasible and cost-effective H2GEN system expansion for meeting increased power demands or increased grid outages can be realised by using the developed models for optimally sizing the expanded H2Gen on a case-by-case basis because the increase in these profiles is highly time-dependent (for example, an increased load demand or increased grid outage in the morning can be met by the PV, while in the evening, it must be met by the H2GEN). Finally, this paper investigated the impact of other environmental variables, such as the temperature and relative humidity, on the H2GEN’s performance and provided further insights into increasing the overall system efficiency and cost benefit through utilising the H2GEN’s exhaust heat in the home space for heating/cooling and selling the electrolyser exhaust’s O₂ as a commodity. Full article

The ability to control process parameters over time and build space in electron beam powder bed fusion (PBF-EB) opens up unprecedented opportunities to tailor the process and use materials of a different nature in the same build. The present investigation explored the various methods used to adapt the PBF-EB process for the production of functionally graded materials (FGMs). In this way, two pre-alloyed powders—a stainless steel (SS) powder and a highly alloyed cold work tool steel (TS) powder—were combined during processing in an S20 Arcam machine. Feasibility experiments were first carried out in a downscaled build setup, in which a single powder container was installed on top of the rake system. In the container, one powder was placed on top of the other (SS/TS) so that the gradient materials were produced as the powders were spread and intermixed during the build. The process was later scaled up to an industrial machine setup, where a similar approach was implemented using two configurations of powder disposal: SS/SS + TS/TS and TS/TS + SS/SS. Each configuration had an intermediate layer of powder blend. The FGMs obtained were characterized in terms of their microstructure and local and macromechanical properties. For the microstructural analysis, optical microscopy, scanning electron microscopy (SEM), and energy-dispersive X-ray spectroscopy (EDX) were performed on the polished cross-sections. This provided evidence of gradual microstructural and compositional transitions in the samples, with a shift from SS to TS and vice versa. Nanoindentation experiments confirmed that there was a consequent gradient in the hardness, stiffness, and wear ratio from the softer and ductile SS to the harder and stiff TS. Scratch experiments revealed gradual evolution in the sliding wear behavior of the printed materials. A “progressive spring” and a “hardness-tailored punching tool” were fabricated as demonstrators. The results obtained demonstrate the great potential to gradually tailor the composition, microstructure, mechanical properties, and wear resistance by combining different powders, and they suggest that any PBF-EB system can be repurposed to build gradient materials without hardware modification. Potential applications include the tooling industry, where hard and wear-resistant materials are needed for the surfaces of tools, with tougher and more ductile materials used in the cores of tools. Full article

Macroscopic structures consisting of two or more materials are called composites. The decreasing reserves of the world’s oil reserve and the environmental pollution of existing energy and production resources made the use of recycling methods inevitable. There are mechanical, thermal, and chemical recycling methods for the recycling of thermosets among composite materials. The recycling of thermoset composite materials economically saves resources and energy in the production of reinforcement and matrix materials. Due to the superior properties such as hardness, strength, lightness, corrosion resistance, design width, and the flexibility of epoxy/vinylester/polyester fibre formation composite materials combined with thermoset resin at the macro level, environmentally friendly sustainable development is happening with the increasing use of composite materials in many fields such as the maritime sector, space technology, wind energy, the manufacturing of medical devices, robot technology, the chemical industry, electrical electronic technology, the construction and building sector, the automotive sector, the defence industry, the aviation sector, the food and agriculture sector, and sports equipment manufacturing. Bonded joint studies in composite materials have generally been investigated at the level of a single composite material and single joint. The uncertainty of the long-term effects of different composite materials and environmental factors in single-lap bonded joints is an important obstacle in applications. The aim of this study is to investigate the effects of single-lap bonded GFRP (glass fibre-reinforced polymer) and CFRP (carbon fibre-reinforced polymer) specimens on the material at the end of seawater exposure. In this study, 0/90 orientation twill weave seven-ply GFRP and eight-ply CFRP composite materials were used in dry conditions (without seawater soaking) and the hand lay-up method. Seawater was taken from the Aegean Sea, İzmir province (Selçuk/Pamucak), in September at 23.5 °C. This seawater was kept in different containers in seawater for 1 month (30 days), 2 months (60 days), and 3 months (90 days) separately for GFRP and CFRP composite samples. They were cut according to ASTM D5868-01 for single-lap joint connections. Moisture retention percentages and axial impact tests were performed. Three-point bending tests were then performed according to ASTM D790. Damage to the material was examined with a ZEISS GEMINESEM 560 scanning electron microscope (SEM). The SEM was used to observe the interface properties and microstructure of the fracture surfaces of the composite samples by scanning images with a focused electron beam. Damage analysis imaging was performed on CFRP and GFRP specimens after sputtering with a gold compound. Moisture retention rates (%), axial impact tests, and three-point bending test specimens were kept in seawater with a seawater salinity of 3.3–3.7% and a seawater temperature of 23.5 °C for 1, 2, and 3 months. Moisture retention rates (%) are 0.66%, 3.43%, and 4.16% for GFRP single-lap bonded joints in a dry environment and joints kept for 1, 2, and 3 months, respectively. In CFRP single-lap bonded joints, it is 0.57%, 0.86%, and 0.87%, respectively. As a result of axial impact tests, under a 30 J impact energy level, the fracture toughness of GFRP single-lap bonded joints kept in a dry environment and seawater for 1, 2, and 3 months are 4.6%, 9.1%, 14.7%, and 11.23%, respectively. At the 30 J impact energy level, the fracture toughness values of CFRP single-lap bonded joints in a dry environment and in seawater for 1, 2, and 3 months were 4.2%, 5.3%, 6.4%, and 6.1%, respectively. As a result of three-point bending tests, GFRP single-lap joints showed a 5.94%, 8.90%, and 12.98% decrease in Young’s modulus compared to dry joints kept in seawater for 1, 2, and 3 months, respectively. CFRP single-lap joints showed that Young’s modulus decreased by 1.28%, 3.39%, and 3.74% compared to dry joints kept in seawater for 1, 2, and 3 months, respectively. Comparing the GFRP and CFRP specimens formed by a single-lap bonded connection, the moisture retention percentages of GFRP specimens and the amount of energy absorbed in axial impact tests increased with the soaking time in seawater, while Young’s modulus was less in three-point bending tests, indicating that CFRP specimens have better mechanical properties. Full article

The implantation of wind farms in the European territory is being deployed at an accelerated pace. In the proposed framework, the province of La Coruña in the autonomous community of Galicia is tested, with a wide deployment of this type of infrastructure in the territory initiated in the 80s, representing the third autonomous community with the largest exploitation of wind resources, which provides sufficient information, extrapolated to the entire community, to demonstrate the practical usefulness and potential of the method of obtaining the territorial model proposed in this article The regional has been used as the basic administrative subunit of the study variables, considering that the territory thus delimited could have common physical and cultural characteristics. The methodology presented in this article involves the collection and processing of public cartographic data on various factors most repeatedly or agreed upon in the consulted bibliography based on studies by experts in the technical, environmental, and environmental areas, including explanatory variables of risk in a broader context of climate change as the first contribution of this study. Another contribution is the inclusion in the model of the synergistic impact measured as the distance to wind farms in operation (21% of the total area of the sample) to which an area of influence of 4 times the rotor diameter of each of the wind turbines im-planted has been added as a legal and physical restriction. On a solid basis of selection of explanatory variables and with the help of Geographic Information Systems (GIS) and multi-criteria analysis (MCDM), techniques widely documented in the existing literature for the determination of optimal areas for the implementation of this type of infrastructure, a methodological proposal is presented for the development of a strategic, long-term territorial model, for the prioritization of acceptable areas for the implementation of wind farms, including forecasts of increased energy demand due to the effect of climate change and the population dynamics of the study region that may influence energy consumption. This article focuses on the use of multivariate clustering techniques and spatial analysis to identify priority areas for long-term sustainable wind energy projects. With the proposed strategic territorial model, it has been possible to demonstrate that it is not only capable of discriminating between three categories of acceptable areas for the implementation of wind farms, taking into account population and climate change forecasts, but also that it also locates areas that could require conservationist measures to protect new spaces or to recover the soil because they present high levels of risk due to natural or anthropic disasters considered. The results show acceptable areas for wind energy implementation, 23% of the total area of the sample, 3% conservation as ecological spaces to be preserved, and 7% recovery due to high-risk rates. The findings show that coastal regions generally show a more positive carrying capacity, likely due to less dense development or regulatory measures protecting these areas. In contrast, certain inland regions show more negative values, suggesting these areas might be experiencing higher ecological disturbance from construction activities. This information highlights the importance of strategic site analysis to balance energy production with conservation needs. The study provides insights into wind farm deployment that considers the visual and ecological characteristics of the landscape, promoting sustainability and community acceptance. For this reason, these insights can be effectively used for advancing renewable energy infrastructures within the European Union’s energy transition goals, particularly under the climate and energy objectives set for 2030. Full article

Hydrothermal liquefaction (HTL) is an effective biomass thermochemical conversion technology that can convert organic waste into energy products. However, the HTL process is influenced by various complex factors such as operating conditions, feedstock properties, and reaction pathways. Machine learning (ML) methods can utilize existing HTL data to develop accurate models for predicting product yields and properties, which can be used to optimize HTL operation conditions. This paper presents a bibliometric review on ML applications in HTL from 2020 to 2024. CiteSpace, VOSviewer, and Bibexcel were used to analyze seven key bibliometric attributes: annual publication output, author co-authorship networks, country co-authorship networks, co-citation of references, co-citation of journals, collaborating institutions, and keyword co-occurrence networks, as well as time zone maps and timelines, to identify the development of ML in HTL research. Through the detailed analysis of co-occurring keywords, this study aims to identify frontiers, research gaps, and development trends in the field of ML-aided HTL. Full article

Microorganisms that colonize in or on a host play significant roles in regulating the host’s immunological fitness and bioenergy production, thus controlling the host’s stress responses. Radiation elicits a pro-inflammatory and bioenergy-expensive state, which could influence the gut microbial compositions and, therefore, the host–microbe bidirectional relationship. To test this hypothesis, young adult mice were exposed to total body irradiation (TBI) at doses of 9.5 Gy and 11 Gy, respectively. The irradiated mice were euthanized on days 1, 3, and 9 post TBI, and their descending colon contents (DCCs) were collected. The 16S ribosomal RNAs from the DCCs were screened to find the differentially enriched bacterial taxa due to TBI. Subsequently, these data were analyzed to identify the metagenome-specific biofunctions. The bacterial community of the DCCs showed increased levels of diversity as time progressed following TBI. The abundance profile was the most divergent at day 9 post 11 Gy TBI. For instance, an anti-inflammatory and energy-harvesting bacterium, namely, Firmicutes, became highly abundant and co-expressed in the DCC with pro-inflammatory Deferribacteres at day 9 post 11 Gy TBI. A systems evaluation found a diverging trend in the regulation profiles of the functional networks that were linked to the bacteria and metabolites of the DCCs, respectively. Additionally, the network clusters associated with lipid metabolism and bioenergy synthesis were found to be activated in the DCC bacteria but inhibited in the metabolite space at day 9 post 11 Gy. Taking these results together, the present analysis indicated a disrupted mouse–bacteria symbiotic relationship as time progressed after lethal irradiation. This information can help develop precise interventions to ameliorate the symptoms triggered by TBI. Full article

Due to tremendous mining operations, large quantities of abandoned mines with considerable underground excavated space have formed in China during the past decades. This provides huge potential for geothermal energy production from mine water in abandoned coal mines to supply clean heating and cooling for buildings using heat pump technologies. In this study, an analytical model describing the injection pressure of mine water recharge for water source heat pumps in abandoned coal mines is developed. The analytical solution in the Laplace domain for the injection pressure is derived and the influences of different parameters on the injection pressure are investigated. This study indicates that a smaller pumping rate results in a smaller injection pressure, while smaller values of the hydraulic conductivity and the thickness of equivalent aquifer induce larger injection pressures. The well distance has insignificantly influenced the injection pressure at the beginning, but a smaller well distance leads to a larger injection pressure at later times. Additionally, the sensitivity analysis, conducted to assess the behavior of injection pressure with concerning changes in each input parameter, shows that the pumping rate and the hydraulic conductivity have a large influence on injection pressure compared with other parameters. Full article