Ever since oil was discovered in the Persian Gulf Region in the 1920s, the oil and gas industries have been dominant in the area. These countries are responsible for generating approximately 35 percent of the world's natural gas and 25 percent of its crude oil, making them the largest crude oil producers globally. The release of carbon dioxide (CO2) into the atmosphere is directly linked to the use of fossil fuels. CO2 accounts for 58.8 percent of all greenhouse gas emissions caused by human activities and has a detrimental impact on urban residents through the urban heat island effect. A significant portion of these emissions can be attributed to residential buildings. Some nations exacerbate the problem by relying excessively on fossil fuels. Oil-rich countries provide energy subsidies to the public in order to lower energy costs for their citizens. However, this policy can have negative consequences, including wasteful energy usage in countries like Iran.

The building and construction sector has a significant impact on CO2 emissions and pollutants released into the atmosphere, which contribute to climate change. The EPDB Directive mandates the achievement of a minimum energy class E for all residential buildings by 2030 and an energy class D by 2033. Particularly, in Italy, about 86% of the existing building stock predates the enactment of any energy laws and regulations, making it imperative to apply the energy efficiency interventions. This paper provides a support decision tool for the identification of standardized interventions on the envelope, air conditioning system and domestic hot water production. The study is focused on a specific construction period class (1976 - 1990) in six different climatic zones. The methodological approach is based on a cataloguing phase and definition of Ante Operam energy classes, on the case study identification, the energy requalification interventions identification, solutions simulations, cost estimation. By simulating standardized interventions for each climatic zone, a range of possible combinations will be identified. The most advantageous ones will be determined based on a cost-benefit analysis, considering the potential class jump achieved. The research result is a matrix of energy efficiency interventions that is applicable to each climatic zone and can be extended to the existing housing stock.

The construction industry has witnessed the increasing use of digital tools and smart solutions, particularly, in the realm of buildings energy automation within the concept of smart cities. While to truly realize the potential benefits of smart cities, a broader scope of smart initiatives is required to support the transition from smart buildings towards smart neighbourhoods, appreciated as the critical unit of urban development. To support this interplay of smart solutions between buildings and neighbourhoods, this article aims to collect and review all smart solutions presented in existing scientific articles, technical literature, and realized European projects. These solutions are classified under two main sections of buildings and neighbourhoods, which are investigated through five domains: buildings energy-related uses, renewable energy sources, water, waste, and open space management. Showcasing the quantitative outcomes demonstrates the potential benefits of implementing smart solutions from buildings to neighbourhoods. Moreover, this research concludes that the true enhancement of energy conservation goes beyond the buildings’ energy component and can be genuinely achieved by integrating intelligent elements of the neighbourhood, due to their strong interdependencies. Future research is recommended to focus on assessing the effectiveness of these solutions towards resource conservation.

The global demand for energy has been steadily increasing due to population growth, urbanization, and industrialization. Numerous researchers worldwide are striving to create precise forecasting models for predicting energy consumption to manage supply and demand effectively. In this research, a time-series forecasting model based on multivariate multilayered long short-term memory (LSTM) is proposed for forecasting energy consumption and tested using data obtained from commercial buildings in Melbourne, Australia: the Advanced Technologies Center, Advanced Manufacturing and Design Center, and Knox Innovation, Opportunity, and Sustainability Center buildings. This research specifically identifies the best forecasting method for subtropical conditions and evaluates its performance by comparing it with the most used methods at present, including LSTM, bidirectional LSTM, and linear regression. The proposed multivariate multilayered LSTM model was assessed by comparing mean average error (MAE), root-mean-square error (RMSE), and mean absolute percentage error (MAPE) values with and without labeled time. Results indicate that the proposed model exhibits optimal performance with improved precision and accuracy. Specifically, the proposed LSTM model achieved a decrease in MAE by 30%, RMSE by 25%, and MAPE by 20% compared to the LSTM method. Moreover, it outperformed the bidirectional LSTM method with a reduction in MAE by 10%, RMSE by 20%, and MAPE by 18%. Furthermore, the proposed model surpassed linear regression with a decrease in MAE by 2%, RMSE by 7%, and MAPE by 10%. These findings highlight the significant performance increase achieved by the proposed multivariate multilayered LSTM model in energy consumption forecasting.

The global trade and transportation sectors heavily rely on the maritime industry. Still, its dependence on fossil energy sources poses significant environmental challenges and leads to unstable fuel prices that affect the cost of goods transported by sea. This paper aims to evaluate the viability of seaports as energy-intensive entities and explore the feasibility of implementing a Nuclear-Renewable Hybrid Energy System (NRHES). The study presents a case study of the Tanjung Priok Port in Indonesia, focusing on estimating energy consumption, emissions, and the potential impact of carbon taxation on seaport operations. By quantifying these factors, the research provides insights into the energy requirements, environmental effects, and potential costs associated with seaport carbon taxation. A comprehensive analysis of the technical and economic feasibility of implementing an NRHES in the seaport case study is conducted, determining the optimal sizing and composition of components, considering the proportion of nuclear and renewable energy sources. Additionally, the economic analysis considers energy costs, net present cost, cash flow, return on investment, and internal rate of return. The findings aim to inform decision-makers about the benefits and challenges of adopting an NRHES, contributing to a cleaner and more sustainable future for the maritime industry.

This article presents a forecast model that uses a hybrid architecture of recurrent neural networks (RNN) with surface neural networks (ANN), based on historical records of exported active energy (EAE) and weather data. Two types of models were developed: the first type includes six models that use EAE records and weather variables as inputs, while the second type includes eight models that use only weather variables. Different metrics were applied to assess the performance of these models, and the best model of each type was selected. Finally, a comparison of the performance between the selected models of both types is presented, and they are validated with real data provided by a solar plant, achieving acceptable levels of accuracy. The selected model of the first type has an RMSE of 0.19, MSE of 0.03, MAE of 0.09, a correlation coefficient of 0.96, and a determination coefficient of 0.93. The other selected model of the second type showed lower precision in the metrics (RMSE = 0.24, MSE = 0.06, MAE = 0.10, Corr. Coef. = 0.95, and Det. Coef. = 0.90). Both models demonstrated good performance and acceptable accuracy in forecasting the weekly photovoltaic energy production of the solar plant.

Open Inertia Modelling (OpInMod) is a modelling framework designed to create unit commitment and economic inertia dispatch optimisation problems. Present energy system modelling generators do not consider power system inertia in unit commitment and economic dispatch modelling to assess future energy system pathways. However, maintaining sufficient power system inertia in power systems is the foundation for power frequency controllability. The work at hand describes the functionality and approaches of open source tool OpInMod. The softwares universal design approach increases reuse potential. OpInMod is distributed with a set of examples to test and understand OpInMod

A harmful impact of climate change and global warming has concerned various sectors of the international community. Numerous energy policies aiming at climate change mitigation have been implemented on a national and global scale. Renewable Energy Technologies (RET) play a critical role in enhancing sustainable solutions that significantly limit greenhouse gases (GHG) emissions. Such innovative technologies can facilitate energy transition through providing e.g. energy security, sustainable development, or effective usage of indigenous resources. However, the commercialization of RET becomes extremely challenging. The barriers can be of a different nature, although in this study the focus has been put on socio-economic and regulatory issues. In fact, there is ample evidence that energy policies play a central role in supporting renewables adoption. It is also claimed that RET require the whole ecosystem to support their successful diffusion. In this study, we explore multifarious barriers for a widespread RET diffusion in two European Union countries: Finland and Poland, indicating the most common barriers existing in the literature as well as analyzing major bottlenecks in the viewpoint of renewable energy companies’ executives. We also present statistics of the most commonly used RET in these countries in order to express the diffusion issues more appropriately. The outcomes of this study provide useful insight for the researchers in the energy transition field as well as practical managerial and regulatory implications aimed at overcoming these challenges.

Considering the increasing rate of energy consumption and its environmental detrimental effects, as well as considering the use of non-renewable energy sources such as fossil fuels, energy management issues have become more important. Given the 40% share of the building industry's total energy consumption, as well as the 80% share of energy consumed during the operation period, attention to the areas of energy management and optimization during the operation period of the buildings can have a major impact on buildings’ energy performance. In this research, through identifying building energy management tools and studying previous studies and assessing the effects of building energy management systems, the economic and environmental impacts of using building energy management systems on the annual energy consumption in an office building in Tehran as a case study has been investigated. The results indicate a 32 percent reduction in energy consumption and a significant reduction in the release of the environmental pollutants in smart mode compared to the base mode. Moreover, considering the social costs associated with the emitted pollutants as well as the return period, it has been attempted to identify the factors contributing to the economic justification of using smart heating and cooling systems. According to the results, the use of smart energy management systems can be considered as an effective step in optimizing and managing energy consumption in the construction sector.

Generalized stacking fault energies of palladium alloys were calculated using the density functional theory. The stacking fault energy of palladium alloys is correlated with the valence electron of the transition metal element. The twinning tendency is also modified by the presence of an alloying element in the plane of deformation. The obtained results suggest that Pd –transition metal alloys with elements such as Cr, Mo, W, Mn, Re are expected to exhibit high work hardening rate due to the tendency to emit of the partial dislocations and mechanical twins, which results in increased strength and ductility.

While many in-steam tidal energy resource studies have been carried out globally, very few studies have assessed the effect of seabed changes on tidal energy resources. For coastal regions in particular, where the seabed is generally more mobile than in deep waters, bathymetric evolution could have a significant effect on tidal energy production. Here two high-resolution models, one purely hydrodynamic and one morphodynamic, are used to analyse the potential effect of natural morphodynamic evolution on tidal energy resources at two macro-tidal sandy bays, Adaír Bay and San Jorge Bay, in the Upper Gulf of California, Mexico. The high-resolution models are validated using a low-resolution model and ADCP observations to assess the agreement between model predictions and observations of tides at three ADCP moorings within the domain of interest. The models’ skill is evaluated using several error statistics such as the mean relative error, the root mean square error (RMSE), and the correlation coefficient. It was found that the regions with the largest bed changes, and also the largest renewable energy resources, were near the shore. Moreover, the results indicated a good correlation between a) regions with the most significant depth changes, and b) the regions where the difference in annual energy production with and without depth change was largest. Finally, the morphodynamic model was run for two years, and the evolution of a zonal profile (in the west-east direction) off the coast at the southeastern corner of Adaír Bay was inspected. This profile evolved towards a featureless equilibrium profile, in good agreement with the morphological classification for macro-tidal sandy environments and with the model assumptions. But most importantly, this natural evolution would not be detrimental to tidal energy exploitation at the site.

This study refers to the optimization of a Selective Solar Absorber to improve the Sun-to-thermal conversion efficiency at mid temperatures in high vacuum flat thermal collectors. Efficiency has been evaluated by using analytical formula and a numerical thermal model. Both results have been experimentally validated using a commercial absorber in a custom experimental set-up. The optimization procedure aimed at obtaining Selective Solar Absorber is presented and discussed in the case of a metal dielectric multilayer based on Cr2O3 and Ti. The importance of adopting a real spectral emissivity curve to estimate high thermal efficiency at high temperatures in selective solar absorber is outlined. Optimized absorber multilayers can be 8% more efficient than the commercial alternative at 250 °C operating temperatures and up to 27% more efficient at 300 °C. Once the multilayer has been optimized the choice of a very low emissivity substrate such as copper allows to further improve efficiency and to reach stagnation temperature higher than 400 °C without Sun concentration.

The paper presents a new vision on the energy consumption management in the case of the Small and Medium Enterprises (SMEs), integrated into an advanced decision support platform, with technical and economic benefits on increasing the energy efficiency, which contains modules for database management, profiling, forecasting, and production scheduling. Inside each module, Artificial Intelligence and Data Mining techniques were proposed to remove the uncertainties regarding the dynamic of technological flows. Thus, the data management module includes the Data Mining techniques, that extract the technical details on the energy consumption needed in the development of production scheduling strategies, the profiling module uses an original approach based on clustering techniques to determine the typical energy consumption profiles required in the optimal planning of the activities, the forecasting module contains a new approach based on an expert system to forecast the total energy consumption of the SMEs, and production scheduling module integrates a heuristic optimization method to obtain the optimal solutions in flattening the energy consumption profile. The testing was done for a small enterprise from Romania, belonging to the domain of trade and repair of vehicles. The obtained results highlighted the advantages of the proposed decision support platform on the decrease in the intensity of energy consumption per unit of product, reduction of the purchase costs, and modification of the impact whom the energy bills have on the operational costs.

In this study, the values of the interfacial energies of seven different polymer-water systems obtained by Sessile Drop Accelerometry (SDACC) are compared with the values obtained by the Young’s-equation-based Owens-Wendt method. The SDACC laboratory instrument –a combination of a drop shape analyzer with high-speed camera and a microgravity tower- and the evaluation algorithms, are designed to measure the interfacial energies as a function of the geometrical changes of a sessile droplet shape due to the effect of “switching off” gravity during the experiment. The method bases on Thermodynamics of Interfaces and differs from the conventional aproach of the two hundred-years-old Young’s equation in that it assumes a thermodynamic equilibrium between interfaces, rather than a balance of forces on a point of the solid-liquid-gas contour line. A comparison of the mathematical model that supports the SDACC method with the widely accepted Young`s equation is discussed in detail in this study.

A new method, the sessile drop accelerometry (SDACC) for the study and measurement of the interfacial energies of solid-liquid-gas systems, is tested and discussed in this study. The laboratory instrument and technique –a combination of a drop shape analyzer with high-speed camera and a laboratory drop tower- and the evaluation algorithms, were designed to calculate the interfacial energies as a function of the geometrical changes of a sessile droplet shape due to the effect of “switching off” gravity during the experiment. The method bases on the thermodynamic equilibrium of the system interfaces and not on the balance of bi-dimensional tensions on the solid-liquid-gas contour line. A comparison of the mathematical model that supports the method with the widely accepted Young`s equation is discussed in this study.

The main objective of this article was to evaluate the repercussions of the crises resulting from the COVID-19 pandemic and the war in Ukraine on changes in the European Union's reliance on energy raw material imports, particularly from Russia. Furthermore, several specific objectives were outlined: to ascertain the EU's energy balance and any alterations therein, to identify changes in the concentration of energy raw material imports among EU countries during the crises, to determine the level and fluctuations in energy raw material import dependence within the EU and individual member states throughout the crises, and lastly, to assess changes in the concentration of energy raw material imports from Russia in EU countries during the crises. The research encompassed all 27 EU Member States as of 31st December 2022, and the examination period spanned from 2010 to 2022. The information sources included relevant literature on the subject and data sourced from Eurostat. In conducting the analysis and presenting the data, several methodologies were employed, including descriptive, tabular, and graphical techniques. Additionally, quantitative measures such as the Gini structure concentration coefficient and concentration analysis through the Lorenz curve were utilized. During the COVID-19 pandemic, the EU experienced a slight reduction in dependence on energy raw material imports. However, this reduction was short-lived and mainly attributed to the decrease in energy demand caused by the pandemic. Conversely, the war in Ukraine had a substantial impact, leading to a notable surge in energy raw material imports as a result of stockpiling efforts and the need to safeguard against further disruptions in the energy market. Notably, the EU's reliance on energy imports from Russia was already considerable. Following the outbreak of the conflict in Ukraine, imports of oil, petroleum products, and solid fossil fuels experienced significant decline due to imposed sanctions. Nonetheless, restrictions on natural gas imports were comparatively less severe.

This paper reports aligned activities to promote energy and resource-efficient construction practice in Vietnam. First, the governance framework is introduced, including government decrees and technical standards. Then, a laboratory with building physics measurement technology is designed and partly set up at the local partner Vietnam Institute for Building Materials (VIBM), which can be used to determine the essential characteristic values required for the implementation of energy standards. The basis for the prioritization of the characteristic values are the requirements of the National technical regulation on energy efficient buildings of Vietnam – QCVN09:2017/BXD. Furthermore, basic characteristic values from international standards are described, which can also be used for calculations to optimize the energy consumption of buildings. In order to be able to carry out transient hygrothermal computer simulations, special characteristic values were also included. These are particularly useful for the research and development of new building materials and the evaluation of entire buildings in terms of thermal and moisture protection In order to communicate the material properties to the market and enable informed decision making by the stakeholders, a labeling system has been developed. Thereby, the practical means for implementing governance instruments are provided, and the related technical applications are supported.

Background: Breastfeeding is essential for optimal infant health, offering numerous benefits such as infection prevention and enhanced bonding. Despite its importance, challenges exist, particularly for mothers of preterm and very low birth weight (VLBW) infants, necessitating effective lactation support strategies. Initiatives like the Baby Friendly Hospital Initiative (BFHI) and Kangaroo Mother Care (KMC) aim to promote breastfeeding success, while donor human milk (DHM) serves as an alternative bridge while the mother is supported to build up her supply. Adequate lactation support, counseling, and education are vital antenatally, during NICU stays and after discharge to improve breastfeeding rates and infant health outcomes, ultimately contributing to global efforts to reduce neonatal mortality and achieve sustainable development goals. This study explores existing research on factors influencing mothers’ ability to produce breast milk and sustain breastfeeding, with the aim of enhancing lactation support and improving infant health outcomes.Methods/Design: A systematic review will be conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) checklist. Searches will be performed in the following databases: PubMed, Cochrane Library, Cumulative Index to Nursing and Allied Health Literature (CINAHL), MEDLINE Ovid via EBSCOhost, Google Scholar, and WILEY online Library) to identify and incorporate primary research articles related to the topic published between 2014 and 2024. Authors will search peer-reviewed articles, gray literature sources, and reference lists to identify eligible studies. Independent abstract and full-text screening by two reviewers will follow the title screening. The analysis of data will be carried out using Review Manager software (version 5.3.5). The I² test will be used to quantify the amount of unexplained heterogeneity. Potential biases related to study size (such as publication bias) will be examined through the application of several techniques, including funnel plots, Egger’s test, Begg’s test, and Trim and Fill analysis.Discussion: Our review represents a singular exploration into exclusive breastfeeding among very low birth weight (VLBW) infants in low- and middle-income countries (LMICs), focusing on breastfeeding assistance methods. With VLBW defined as less than 1500 g, and most low birth weight (LBW) babies born in LMICs, there is a critical need to address feeding challenges and promote optimal nutrition. Despite updated WHO guidelines in 2022, there is still a lack of comprehensive advice for this vulnerable population. Peer support interventions and health worker counseling have shown promise in increasing breastfeeding continuation rates in LMICs, but challenges persist due to workforce shortages and inadequate documentation. Promoting early postpartum milk expression is crucial for milk production and infant health. Our review aims to fill the gap in understanding VLBW mothers’ breastfeeding experiences in LMICs, shedding light on the effectiveness of breastfeeding support methods and identifying areas for further research. Systematic review registration: CRD42024547864

Once considered relatively benign, superficial vein thrombosis (SVT) of the lower limbs is often linked to deep vein thrombosis (DVT) or pulmonary embolism (PE). Treatment goals include alleviating local symptoms and preventing SVT from recurring or extending into DVT or PE. Fondaparinux 2.5 mg once daily for 45 days is the treatment of choice for most patients with SVT. Potential alternatives include intermediate-dose low-molecular-weight heparin or the direct oral factor Xa inhibitor rivaroxaban. Despite these treatment options, significant gaps remain, including the role of systemic or topical anti-inflammatory agents alone or combined with anticoagulants, and the optimal duration of anticoagulation for patients at varying risk levels. Additionally, the efficacy and safety of factor Xa inhibitors other than rivaroxaban, management of upper extremity SVT, and optimal treatment for SVT near the sapheno-femoral or sapheno-popliteal junctions are not well understood. This narrative review aims to summarize current evidence on anticoagulant treatment for SVT, highlight key unmet needs in current approaches, and discuss how ongoing studies may address these gaps.

In the 21st century, climate change and its consequences are getting more serious. The changes in temperature and precipitation alter the run-off conditions, subsequently influencing the channel processes of rivers. The study aims to analyse the hydrological changes of a small, sub-alpine river (Rába River, Central Europe) and the bank erosional processes since the 1950s. The bank erosion was determined based on topographical maps, aerial photographs, and RTK–GPS surveys. Short (2–3 days) floods were common between 1950–1980, and low stages occurred in 65–81% of a year. However, in the 21st century, extreme regimes developed, as record-high, flashy floods alter with long low stages (91–96% of a year). The bank erosion shows a cyclic temporal pattern, gradually increasing until it reaches a high value (4.1–4.9 m/y), followed by a limited erosional rate (2.2–2.8 m/y). However, the magnitude of the bank erosion is decreasing. It could be explained by (1) the lower transport capacity of the more common low stages and (2) the seasonal shift of the flood waves, appearing in the vegetational period when the riparian vegetation can more effectively protect the banks from erosion.

As the largest contributor of carbon emissions in China, the building sector currently relies mostly on enterprises’ own effort to report carbon emissions, which usually results in challenges related to information transparency and workload for regulatory bodies, who play an otherwise vital role in controlling the building sector’s carbon footprint. In this study, we established a novel regulatory model known as QCEPM (Quantitative Carbon Emission Prediction Model) by conducting multiple linear regression analysis using the quantities of concrete, rebar, and masonry structures as independent variables, and the embodied carbon emissions of a building as the dependent variable. We processed the data in the detailed quantity list of 20 multi-story frame-structure buildings and fed them to QCEPM for solution. Comparison of the QCEPM-calculated results against the time-consuming and error-prone manual calculation results suggested a mean absolute percentage error (MAPE) of 2.26%. Using this simplified model, regulatory bodies can efficiently supervise the embodied carbon emissions in multi-storey frame structures by setting up carbon quota for a project in its approval stage, allowing the construction enterprise to carry out dynamic control over the audited three most important building materials throughout a project’s planning and implementation phase.

Preterm infants have increased body adiposity at term equivalent age and risk for adverse metabolic outcomes. The aim of the study was to define how nutrient intake may impact body composition (BC) of very low birth weight infants fed with early progressive enteral feeding and standard fortification. Eighty-six infants with

In the context of carbon neutrality and carbon peaking, in order to achieve low carbon emissions and promote the efficient utilization of wind energy, hydrogen energy as an important energy carrier is proposed to mix hydrogen and natural gas to form hydrogen enriched compressed natu-ral gas (HCNG). It is also injected into the natural gas pipeline network to realize the transmission and utilization of hydrogen energy. At the same time, the participation of demand response is considered, the load peak and trough period is adjusted, and large-scale consumption of renewa-ble energy and the reduction of carbon emissions are realized. First of all, a fine model of hydro-gen production and hydrogen use equipment is established to analyze the impact of adding hy-drogen mixing on the economy and low-carbon of the system. With green certificates and demand response, the utilization rate of hydrogen energy is improved to further explore the energy utiliza-tion rate and emission reduction capacity of the system. Secondly, on the basis of modeling, the optimal scheduling strategy is proposed with the sum of energy purchase cost, equipment opera-tion cost, carbon emission cost, wind curtailment cost and green certificate income as the lowest objective function. Considering the constraints such as hydrogen blending ratio and flexible load ratio of the pipeline network, a low-carbon economic scheduling model of hydrogen mixed natu-ral gas was established. The model was linearized and solved by using MATLAB and CPLEX solver. By comparing different scenarios, the superiority of the model and the effectiveness of the strategy are verified.

We propose a new methodology for long-time biopotential recording based on MEMS multisensor integrated platform featuring a commercial electrostatic charge transfer sensor. That family of sensors was originally intended for presence tracking in automotive, so the existing setup has been engineered for the acquisition of electrocardiogram, electroencephalogram, electrooculogram and electromyography, designing a dedicated front-end and writing a proper firmware for the specific application. Systematic tests on controls and nocturnal acquisitions from patients in domestic environment will be discussed in detail. The excellent results candidate this technology to provide a low power, unexplored solution to biopotentials acquisition. The technology breakthrough is in that it enables adding this type of functionality to existing MEMS boards at near-zero additional power consumption. For these reasons, they open to additional possibilities for wearable sensing and strengthen the role of MEMS technology in medical wearable applications for long time synchronous acquisition of a wide range of signals.

To date, radiation adaptation response has been reported as a low-dose related phenomenon and has been associated with radiation hormesis. Well-known cancers are caused by non-radiation active reactants, in addition to radiation. A model of suppression for radiation-specific cancers was previously reported, but the model did not target radiation-nonspecific cancers. In this paper, we describe kinetic models of radiation-induced suppressors for general radiation non-specific cancers, estimating the dose that induces the maximum hormesis effect while satisfying the condition that the risk is approximately proportional to dose above 100 mSv. It was found that the radiation hormesis effect is maximal at about 20 mSv when the rate constant for generation is the same as the rate constant for decomposition. When the two rate constants are different, the dose at which the radiation hormesis effect is maximized depends on both rate constants, but the dose increases as the two rate constants approach each other, reaching a maximum of about 20 mSv. This conclusion may lead to the discovery of unknown cancer suppressors at low doses and the development of cancer suppression methods in the future.

This study leverages the relationships between the Great Plains low-level jet (GP-LLJ) and the circumglobal teleconnection (CGT) to enhance the 30-day rainfall forecast skill in the Northern Great Plains (NGP). The assessment of 30-day simulated precipitation using the Climate Forecast System (CFS) is contrasted with the North American Regional Reanalysis, searching for sources of precipitation predictability associated with extended wet and drought events. We analyze the 30-day sources of precipitation predictability using (1) the characterization of dominant statistical modes of variability of 900-mb winds associated with the GP-LLJ, (2) the large-scale atmospheric patterns based on 200-mb geopotential height (HGT), and (3) the use of GP-LLJ and CGT conditional probability distributions using a continuous correlation threshold approach to identify when and where the forecast of NGP precipitation occurs. Two factors contributing to the predictability of precipitation in the NGP are documented. We found that multi-scale geospatial interactions occur at the daily and sub-seasonal time windows. The CFS reforecast suggests that the ability to forecast sub-seasonal precipitation increases in response to the enhanced simulation of the GP-LLJ and CGT. Finally, the multi-dimensional analysis of covariance reveals that high-precipitation forecasting skill is associated with a better prognostic of GP-LLJ and CGT.

For two-dimensional forward-looking sonar imaging, high sidelobes significantly degrade the quality of sonar images. The cosine window function weighting method is often applied to suppress the sidelobe levels in the angular and range dimensions at the expense of the main lobe resolutions. Therefore, the improved spatially variant apodization imaging method for forward-looking sonar is proposed to reduce the sidelobes without degrading the main lobe resolution in angular-range dimensions. The proposed method is a nonlinear post-processing operation in which the raw complex-valued sonar image produced by a conventional beamformer and matched filter is weighted by a spatially variant coefficient. To enhance the robustness of the spatially variant apodization approach, the array magnitude and phase errors are calibrated to prevent beam sidelobe heightening from occurring prior to beamforming operations. The analyzed results of numerical simulations and a lake experiment demonstrate that the proposed method can greatly reduce the sidelobes to approximately -40 dB, while the main lobe width remains unchanged. Moreover, this method has an extremely simple computational process.

The improvement of agricultural low-carbon efficiency is an important path to promote the realization of the "double carbon" goal in the Yellow River Basin. In the context of rapid urbanization development, it is of great significance to explore whether the promotion of urbanization will affect the agricultural low-carbon efficiency. Based on the panel data of 75 cities in the Yellow River Basin from 2000 to 2020, this paper uses super-DEA model, three-dimensional kernel density model and Markov chain model to measure and analyze the spatio-temporal evolution of agricultural low-carbon efficiency in the Yellow River Basin, on this basis, the panel Tobit model is used to analyze the relationship between urbanization and low-carbon efficiency in agriculture. The results show that: (1) The level of agricultural low-carbon efficiency in the Yellow River Basin is low and has not reached the effective state, showing a slow downward trend in general, among which, the agricultural low-carbon efficiency in the lower reaches is higher than that in the middle reaches, and the upper reaches is the lowest. (2) The agricultural low-carbon efficiency in the Yellow River Basin has a negative trend of transferring to the low level on the whole, and tends to maintain the original state level, and it is difficult to realize the leapfrog transfer between states. Agricultural low-carbon efficiency has obvious spatial spillover effect and "club convergence" phenomenon, high efficiency area has a positive driving effect on the neighborhood area, while low efficiency area has a negative impact on the neighborhood area. (3) The level of urbanization has a significant positive impact on the low-carbon efficiency of agriculture in the upper, middle and lower reaches of the Yellow River Basin, which plays an important role in promoting the green development of agriculture. Finally, based on the above research conclusions, relevant policy recommendations were put forward, in order to provide references for promoting the improvement of low-carbon agricultural efficiency and promoting high-quality agricultural development in the Yellow River Basin.

Objective: To analyze the relation between alterations in the growth hormone (GH)/insulin-like growth factor 1 (IGF-1) axis during the first 6 months of life and weight in children born in the Lower Middle São Francisco region. Methods: This is an analytical cohort, with a quantitative approach and a translational perspective. Thirty children with low and normal birth weight were initially identified in a hospital and reapproached at 3 and 6 months of age. Birth weight and alterations in GH/IGF-1 curves at birth, third month, and sixth month of life. Results: Weight gain during the 6 months of follow-up in newborns with low birth weight was greater compared to newborns with normal birth weight. All children who were born with low birth weight had an altered GH/IGF-1 curve at birth (p = 0.002). Most newborns with low birth weight maintained the alteration in the GH/IGF-1 curve at the third month of life (p = 0.027). Regarding the GH/IGF-1 curve at the sixth month, alteration persisted in greater proportion among children with low birth weight. Conclusion: Alterations in insulin resistance markers, demonstrated by increased GH without a proportional increase in IGF-1, were observed to be significant in children with low birth weight, with greater adiposity in this group, which may increase the risk of metabolic diseases in later life.

This study aimed to assess the prevalence and identify risk factors for schistosomiasis among school-aged children in low- and middle-income communities. A retrospective cross-sectional study was conducted, involving the examination of 24 034 children for Schistosoma haematobium ova. Data were analyzed using IBM Statistical Package for the Social Sciences (SPSS) version 27. Logistic regression was employed to determine the factors associated with schistosomiasis. The overall prevalence of schistosomiasis in the study population was 75%, with higher rates observed in rural-dominated areas, particularly Bushbuckridge (73%) and Nkomazi (51%). Age, a history of bilharzia, and the presence of blood in urine were significantly associated with schistosomiasis, while being a female child was found to be a protective factor. This study underscores the importance of implementing robust screening procedures and the necessity for health education to mitigate the high prevalence of schistosomiasis and prevent its further spread.

Rural areas, as a system, face multiple challenges. Among these are population decline and attendant economic and social maladies namely demographic issues considering a scarce population. Pull factors for immigrants to go to other countries are known, nevertheless, to comprehend factors for remaining in rural areas after arriving could support a strategic local policy, and for an informed practice. The purpose of the study is to explore motivations shaping immigrants’ intentions to stay in the Alentejo, a depopulated region in Portugal, using a qualitative approach. Research questions are: What motivates immigrants to remain living in depopulated regions in Portugal? Furthermore, what contributions can practitioners and immigrants give for local policy and practice? Eight practitioners and fifteen non-European Union immigrants living in this region were interviewed between 2020 and 2021. Empirical data were analysed, supported by MaxQDA software. Results point out that the intention to remain in rural areas arise from a progressive construction, it is a process that immigrants experience to become motivated to stay long-term. Factors influencing the process include four components: 1. instrumental and material motivations; 2. emotional and social motivations; 3. motivations based on quality of life; and 4. motivations based on political dimensions. Components are conditioned by the availability of : stable and suitable jobs, access to decent and affordable housing, as well as quality of education and health services for their children; additionally to socio-emotional satisfaction, as family reunification, migrants network, integration and real intercultural living in the host community, close relationships to practitioners from social services; and quality of life, such as lower rents, peaceful and secure living conditions, granted a better future for their children as in any another place in Europe, associated finally to the simplified policy enabling one to become a legal citizen. Conclusions highlight implications for policy and practice, suggesting more investment for rural regions to reverse depopulation reality.

Low Emission Zone (LEZ) is an important mechanism to limit the negative environmental impact of traffic pollution in an urban area. Car traffic in the zone is strictly regulated to meet certain technical and environmental criteria. Such solutions of urban environment protection are gaining popularity, especially in European conurbations. Nowadays there are above 300 zones in Europe, mostly established in Italy and Germany. So far there are no LEZs in Eastern and Central European countries. However, the Warsaw authorities are just discussing launching of a LEZ in the city in 2024. According to the ongoing project, the zone is to be launched gradually, in five phases between 2024 and 2032, with the traffic criteria being tightened accordingly. The progressive emission reductions of the predominant pollutants in LEZ in consecutive phases were estimated on the basis of the Real Driving Emissions (RDE) test, carried out in 2020 by the ICCT. The gradually reduced emissions of NOx and PM were subsequently utilized as the input dataset for the regional CALPUFF model predictions, to calculate concentrations of these main traffic-induced pollutants within the LEZ area. The direct effect of the zone activation is reduction of air pollution in the urban center, which is mostly exposed to the pollution risk due to heavy traffic. Computer simulations reveal that implementation of the zone in the case of Warsaw will result in a quite significant reduction of NOx concentrations within the LEZ, such that its mean concentration in the zone becomes comparable to the rest of the conurbation. However, it will bring only a slight reduction of PM2.5 concentrations. This situation is due to the long-standing dominance of coal combustion in the Polish economy, which also applies to the urban municipal sector. As a consequence, particulate matter emissions of this sector are clearly predominant over the road traffic contribution. Balance can be achieved once the ongoing de-carbonization of the national economy has been completed.

Over a century ago, low-dose-rate (LDR) brachytherapy was introduced to treat prostate cancer (PCa). Since then, it has been widely applied worldwide, including in East Asia. LDR brachythera-py has been performed in 88 institutes in Japan. Beneficial clinical outcomes of LDR brachytherapy for intermediate-to-high-risk PCa have been demonstrated in large clinical trials. These clinical outcomes were achieved through advances in methods, such as urological precise needle puncture and seed placement, and the quantitative decision-making regarding radiological parameters by radiation oncologists. Combined use of LDR brachytherapy with other therapeutical modalities, such as external beam radiation and androgen deprivation therapy, for clinical risk classification of PCa has led to better anticancer treatment efficacy. In this study, we summarized basic LDR brachytherapy findings that should remain unchanged and be passed down in urology depart-ments. We also discussed the applications of LDR brachytherapy for PCa in various clinical settings, including focal and salvage therapies. In addition, we highlighted technologies associated with brachytherapy that are under development.

Oxidative stress (OS) is reported in several chronic diseases. Extra-cellular superoxide dismutase (ec-SOD) catalyzes the dismutation of superoxide anion with a protective role on endothelial cells. In chronic kidney diseases (CKD), OS and thyroid dysfunction (low T3 syndrome) are fre-quently present, but their relationship has not been investigated. This cohort study evaluated ec-SOD activity in CKD patients during haemodialysis, divided into: “acute hemodialytic pa-tients” (AH, 1-3 months of treatment) and “chronic hemodialytic patients” (CH, treated for a longer period). We also evaluated plasmatic total antioxidant capacity (TAC) and the relation-ships with thyroid hormones. Two basal samples (“basal 1”, 3 days after the last dialysis and “basal 2”, 2 days after the last dialysis) were collected. On the same day of basal 2, a sample was collected 5 and 10 min after the standard heparin dose and at the end of the procedure. The ec-SOD values were significantly higher in CH vs AH in all determinations. Moreover, they pre-sented lower TAC values. Dividing CH patients into two subgroups according to fT3 levels (nor-mal or low) we found significantly lower ec-SOD values in the group with low fT3 at basal, 5 and 10 min samples. Finally, a significant correlation was observed between fT3 and ec-SOD in the basal 1 samples. These data, confirming OS and low T3 syndrome in CKD, suggest that low fT3 concentrations can influence ec-SOD activity and therefore contribute to possible endothelial oxi-dative damage in such a situation.

A reduced ability to work with information contained in a text is usually registered in low-achieving students. One important internal factor influencing reading comprehension concerns the child’s executive functioning. The current study investigated whether the domain-specific intervention ExeFun-READ targeting language abilities related to reading comprehension and executive functioning in primary school children would be effective in improving their scholastic performance in their reading domain and executive functioning. ExeFun-READ is designed to address the relations between domain-specific versus thinking-skill-oriented (domain-general) instruction. A child’s active learning is focused on semantic, phonetic, morphological and syntactical comprehension of linguistic material. In total, 151 students attending grade four from seven elementary schools took part in the project. The study concerned a pretest-intervention-posttest experimental design with three conditions: the experimental condition, an active, and a passive control group. To assess the children’s level of EF, the Delis–Kaplan Executive Function System test battery was used; to assess children’s language ability in reading domain, the Cognitive Abilities Test (the verbal battery, Thorndike & Hagan, 1986 [1]), was used. In the current study, the intervention led to improved language abilities related to reading comprehension, but in terms of executive functioning the improvement only extended to switching fluency. Regarding the effects of the ExeFun-READ intervention on children’s reading abilities, specified as language abilities related to reading comprehension, significant improvements were found in vocabulary, completion of sentences, and classification of terms in the group of children that received the ExeFun-READ intervention.

Methicillin-resistant Staphylococcus aureus (MRSA) constitutes a serious public health concern with a considerable impact on patients' health and substantial healthcare costs. In this study, patients, and healthcare workers (HCWs)from six public hospitals in Benin were screened for MRSA. Strains were identified as MRSA using conventional microbiological methods in Benin and confirmed using Matrix-Assisted Laser Desorption/Ionization Time of Flight (MALDI-TOF) mass spectrometry in Belgium. Whole genome sequencing (WGS) was used on confirmed MRSA isolates to characterize their genomic content and study their relatedness. In total,304 and 61 samples were collected from patients and HCWs, respectively. The mecA gene was detected in all isolates. The isolates were assigned to five sequence types (STs), with ST8 (55.6%, n=15), ST152 (18.52%, n=5), and ST121 (18.52%, n=5) being the most common. All isolates carried multiple virulence genes, including genes encoding the Panton-Valentine leukocidin toxin (48.15%, n=13) and the test gene (29.63%, n=8) associated with toxic shock syndrome. The presence of strains carrying numerous genomic features associated with antibiotic resistance and virulence in HCWs is a major public health concern. This study highlights the need to implement a multimodal strategy for reducing the risk of cross-transmission of MRSA in hospitals.

Deep-Brain Stimulation (DBS) is a a highly effective and safe medical treatment that improves the lives of patients with a wide range of neurological and psychiatric deceases, and has been consolidated as a first-line tool in the treatment of these conditionsin the last two decades. Closed Loop Deep-Brain Stimulation (CLDBS) pushes this tool further by automatically adjusting the stimulation parameters to the brain response in real time. In this context, this paper presents a Low-Noise Amplifier (LNA) and a Neurostimulator circuits fabricated in the low-power/low-voltage 65 nm CMOS process from the TSMC, which were designed targeting implantable applications. To achieve the best trade-off between input-referred noise and power consuption, metaheuristic algorithms were employed to determine and optimizes the dimentions of the LNA devices during the design phase. The measurement results showed that the LNA had a gain of 40.6 dB, a 3 dB bandwidth spanning over three decades from 10 Hz to 8.6 kHz, and a power consumption of 6.19 uW. Simulations results indicated an input-referred noise of 4.86 uVrms for the LNA. The circuit of the Neurostimulator is a programmable Howland Current-Pump, whose measurements showed its ability to generate currents with arbitrary shapes ranging from between 325 uA to +318 uA. The simulations showed a quiescent power consumption of 0.13 W with a zero neurostimulation current. The LNA and the Neurostimulator circuits are supplied with 1.2 V voltage and occupy a microdevice area of 145 um x 311 um and 88 um x 89 um, respectively, making them suitable for implantation in applications involving Closed Loop Deep-Brain Stimulation.

Low-cost particulate matter (PM) sensors offer an excellent opportunity to improve our knowledge about this type of pollution. Their size and their cost, which support multi-node network deployment, along with their temporal resolution, enable them to report fine spatio-temporal resolution for a given area. These sensors have known issues across performance metrics. Generally, the literature focuses on the PM mass concentration reported by these sensors but some models of sensors also report particle number concentrations (PNC) segregated into different PM size bins. In this study, 8 units of each Alphasense OPC-R1, Plantower PMS5003 and Sensirion SPS30 have been exposed, under controlled conditions, to short-lived peaks of PM generated using two different combustion sources of PM, exposing the sensors to different particle size distributions to quantify and better understand the low cost sensors performance across a range of relevant environmental ranges. The PNC reported by the sensors were analysed. This study showed that there is added value in directly using the PNC reported by the sensors instead of the mass concentrations, which could aid the efforts to calibrate these sensors to a known accuracy. It demonstrated that all sensors tested here could track the fine temporal variation of PNC, that the Alphasense OPC-R1 could closely follow the variations of size distribution between the two sources of PM, and it showed that particle size distribution and composition are more impactful on the sensors measurement than relative humidity.

A low-resource emotional speech synthesis system for empathetic speech synthesis based on modelling prosody features is presented here. Secondary emotions, identified to be needed for empathetic speech, are modelled and synthesised in this paper. As secondary emotions are subtle in nature, they are difficult to model compared to primary emotions. They are also less explored, and this is one of the few studies that model secondary emotions in speech. Current speech synthesis research uses large databases and deep learning techniques to develop emotion models. There are many secondary emotions, and hence, developing large databases for each of the secondary emotions is expensive. This research presents a proof-of-concept using hand-crafted feature extraction and modelling of these features using a low resource-intensive machine learning approach, thus creating synthetic speech with secondary emotions. Here, a quantitative model-based transformation is used to shape the emotional speech fundamental frequency contour. Speech rate and mean intensity are modelled via rule-based approaches. Using these models, an emotional text-to-speech synthesis system to synthesise five secondary emotions - anxious, apologetic, confident, enthusiastic and worried is developed. A perception test to evaluate the synthesised emotional speech is also conducted.

The increasing regulatory pressure to monitor and reduce GHG emissions and air pollutants requires cost-effective methods for their surveillance. The most common technique used for scientific investigations on gas concentration monitoring in barns are accurate but expensive and with a complex maintenance. This research study analysed the potential use of a low-cost portable measurement devices for the measurement of ammonia (NH3) and carbon dioxide (CO2) concentrations in an open dairy barn. A comparison between gas concentrations acquired at different heights from the floor by using portable devices and those acquired by a photoacoustic infrared multigas spectroscope (i.e., reference measurement) in the same sampling locations was carried out to determine the precision of the low-cost portable devices. The performance of low-cost portable devices was statistically analysed by the application of the one-way analysis of variance, correlation analysis and regression analysis. The results showed a significant difference between gas concentration values at various heights from the floor for both NH3 and CO2. The correlations between concentrations acquired by the low-cost portable device and the INNOVA were statistically significant (r=0.83; P<0.001) for gas concentrations monitored at 0.4 m from the floor. Compared with the reference measurement device, the low-cost devices were effective in the monitoring of NH3 concentrations at 0.40 m from the floor though it underestimated them in the barn at increasing of the height from the floor, whereas the device was not adequate for CO2 concentrations. In detail, the relative measurement error of the low-cost devices compared to INNOVA was reduced close to the floor during NH3 concentration measurements. Within these limitations, this device could be useful for monitoring NH3 concentration in the barn and to assess variation in NH3 concentrations mainly related to the animal occupied zone. Further efforts are needed in this field of research to identify low-cost device that could simplify emission estimation from open dairy barns.

Introduction. Self-sampling has the potential to increase cervical cancer screening (CCS) among women living with HIV (WLWH) in low and middle-income countries (LMICs). However, our understanding of how HPV self-collection studies have been conducted in WLWH is limited. The purpose of this scoping review was to examine the extent to which the HPV self-sampling has been applied among WLWH in LMICs. Method: We conducted multiple searches in several databases for articles published between 2000 and January 2022. With the combination of keywords relating to HPV self-sampling, LMICs, and WLWH, we retrieved over 9,000 articles. We used pre-defined inclusion and exclusion criteria to select relevant studies for this review. Once a study met the inclusion criteria, we created a table to extract each study’s characteristics and classified them under common themes. We used a qualitative descriptive approach to summarize the scoping results. Results: A total of 12 articles were included in the final review. Overall, 3,178 women were enrolled in those studies and 2,105 (66%) of them were WLWH. The self-sampling participation rate was 92.6%. The findings of our study show that 43% of the WLWH in 8 of the studies reviewed tested positive for high-risk HPV (hr-HPV) genotypes, indicating 4 out of 10 WLWH in the studies are at risk of cervical cancer. The prevalence of the hr-HPV in WLWH was 18% higher than that of HIV-negative women. Most women in the study found the self-sampling experience acceptable, easy to use, convenient, and comfortable. Self-sampling performance in detecting hr HPV genotypes is comparable to clinician-performed sampling. However, limited access (i.e., affordability, availability, transportation), limited knowledge about self-screening, doubts about the credibility of self-sampling results, and stigma remain barriers to wide acceptance and implementation of self-sampling. In conclusion, the findings of this review highlight that (a) cervical cancer is a threat to every sexually active woman but for WLWH the threat increases, (b) self-sampling laboratory performance is similar to clinician performed sampling, (c) self-sampling is associated with an increase in cervical cancer screening uptake and (d) WLWH reported a positive experience with self-sampling. However, personal, environmental, and structural barriers challenge the application of self-sampling in LMICs, and these need to be addressed. Keywords: keyword 1; keyword 2; keyword 3 (List three to ten pertinent keywords specific to the article yet reasonably common within the subject discipline.)

Large quantities of real-time particle data are becoming available from low-cost particle monitors. However, it is crucial to determine the quality of these measurements. The largest network of monitors in the United States is maintained by the PurpleAir company, which offers two monitors: PA-I and PA-II. PA-I monitors have a single sensor (PMS1003) and PA-II monitors employ two independent PMS5003 sensors. We determine a new calibration factor for the PA-I monitor and revise a previously published calibration algorithm for PA-II monitors (ALT-CF3). From the PurpleAir API site, we downloaded 83 million hourly average PM2.5 values in the PurpleAir database from Washington, Oregon, and California between January 1, 2017 and September 8, 2021. Daily outdoor PM2.5 means from 194 PA-II monitors were compared to daily means from 47 nearby Federal regulatory sites using gravimetric Federal Reference Methods (FRM). We find a revised calibration factor of 3.4 for the PA-II monitors. For the PA-I monitors, we determined a new calibration factor (also 3.4) by comparing 26 outdoor PA-I sites to 117 nearby outdoor PA-II sites. These results show that PurpleAir PM2.5 measurements can agree well with regulatory monitors when an optimum calibration factor is found.

A right frontoparietal neoplastic lesion was found in a young-aged patient with analgesic refractory headache. Surgical resection was performed, and the pathologist analysis turned out a cellular ependymoma with signs of anaplasia. In the follow-up, a MRI showed a suspicious lesion, so a [18F]Fluorocholine PET/CT was performed. An increased uptake was described in the right parietal region on the margin of residual cystic lesion. The patient got a complete resection which was confirmed later by MR. In the pathology analysis, a focally congestive cerebral parenchyma with a central histiocytic reaction to foreign body area was described. Foreign body reaction in brain tissue is a very rare immune response that has not been well studied. Haemostatic material has been reported as a possible trigger of this response in other organs and could be detected by [18F]FDG PET/CT. Following the experience of the current case report, [18F]Fluorocholine PET/CT could also show a false positive related to foreign body reaction.

The instability of traveling pulses in nonlinear diffusion problems is inspected on the example of Gunn domains in semiconductors. Mathematically the problem is reduced to the calculation of the "energy" of the ground state in Schrödinger equation with a complicated potential. A general method to obtain the bottom-part spectrum of such equations based on the approximation of the potential by square wells is proposed and applied. Possible generalization of the approach to other types of nonlinear diffusion equations is discussed.

High-strength low-alloy steels (HSLAs) are widely used in the structural body components of many domestic motor vehicles owing to their better mechanical properties and greater resistance. The real production process of HSLA steelmaking can be regarded as a model that builds on the relationship between process parameters and product quality attributes. A surrogate modeling method is used, and the resulting production process model can be applied to predict the optimal manufacturing process parameters. We used different methods in this paper to build such a surrogate model, including linear regression, random forests, support vector regression, multilayer perception, and a simplified VGG model. We then applied three bio-inspired search algorithms, namely particle swarm optimization, the artificial bee colony algorithm, and the firefly algorithm, to search for the optimal controllable manufacturing process parameters. Through experiments on 9,000 test samples used for building the surrogate model, and 299 test samples for making the optimal process parameter selection, we found that the combination of a simplified VGG model and the firefly algorithm was the most successful at reaching a success rate of 100%—in other words, when the product quality attributes of all test samples satisfy the mechanical requirements of the end products.

In this paper we perform numerous numerical studies for the problem of low-rank matrix completion. We compare the Bayesian approaches and a recently introduced de-biased estimator which provides a useful way to build confidence intervals of interest. From a theoretical viewpoint, the de-biased estimator comes with a sharp minimax-optimal rate of estimation error whereas the Bayesian approach reaches this rate with an additional logarithmic factor. Our simulation studies show originally interesting results that the de-biased estimator is just as good as the Bayesian estimators. Moreover, Bayesian approaches are much more stable and can outperform the de-biased estimator in the case of small samples. However, we also find that the length of the confidence intervals revealed by the de-biased estimator for an entry is absolutely shorter than the length of the considered credible interval. These suggest further theoretical studies on the estimation error and the concentration for Bayesian methods as they are being quite limited up to present.

The log interpretation and evaluation of Chang 8 tight sandstone reservoir in Longdong West area, Ordos Basin, is facing great challenges due to the existence of low resistivity contrast oil pays. To better guide the exploration of oil resources in this area, the characteristics and control mechanism of low resistivity contrast oil pays were studied in this research. Firstly, according to the relative value of apparent resistivity increase rate of the target formation, the reservoir was divided into two types: low resistivity contrast oil pay (LRCP) and high resistivity oil pay (HRP). Then, the reservoir characteristics were studied by comparing and analyzing the experimental data, formation water data and logging data collected from the two reservoir types. On this basis, the control mechanism of LRCP was studied and summarized into reservoir micro factors and regional macro factors, respectively. It is found that the reservoir rock composition between LRCP and HRP is basically the same. Compared with HRP reservoir, the average porosity and permeability of LRCP reservoir are relatively better, but the pore structure is relatively poorer because of the high content of micro pores. When the filling pressure of crude oil is sufficient enough, the high oil saturation can be formed in LRCP reservoir. The high irreducible water saturation and high formation water salinity are the main micro factors reduced the reservoir resistivity. Besides, the difference of hydrocarbon expulsion capacity of source rock and the regional difference of formation water salinity are the macro factors influenced the distribution of LRCP and HRP from vertical and horizontal of the region. The formation of LRCP is controlled by the comprehensive influence of reservoir micro factors and regional macro factors. And the comprehensive consideration of the influence of micro factors and macro factors on LRCP is suggested during the log interpretation and evaluation.

We propose a novel transfer learning method for speech emotion recognition allowing us to obtain promising results when only few training data is available. With as low as 125 examples per emotion class, we were able to reach a higher accuracy than a strong baseline trained on 8 times more data. Our method leverages knowledge contained in pre-trained speech representations extracted from models trained on a more general self-supervised task which doesn’t require human annotations, such as the wav2vec model. We provide detailed insights on the benefits of our approach by varying the training data size, which can help labeling teams to work more efficiently. We compare performance with other popular methods on the IEMOCAP dataset, a well-benchmarked dataset among the Speech Emotion Recognition (SER) research community. Furthermore, we demonstrate that results can be greatly improved by combining acoustic and linguistic knowledge from transfer learning. We align acoustic pre-trained representations with semantic representations from the BERT model through an attention-based recurrent neural network. Performance improves significantly when combining both modalities and scales with the amount of data. When trained on the full IEMOCAP dataset, we reach a new state-of-the-art of 73.9% unweighted accuracy (UA).

Emerging pollutants as pharmaceuticals have been focusing international attention for few decades. Ciprofloxacin (CIP) is a common drug widely found in effluents from hospitals, industrial and different wastewater treatment plants, as well as rivers. In this work, the lab-scale 3D-BER system was established, and more than 90% of the antibiotic CIP removal from the Low C/N wastewater. Best results were obtained with current intensity, and different C/N ratio significantly improve the removal of CIP and nitrates, when the ideal conditions were; C/N = 1.5-3.5, pH =7.0-7.5, and I = 60 mA. The highest removal efficiency of CIP = 94.20 %, NO3--N= 95.53 % and total nitrogen (TN) = 84.27 %, respectively. In this novel system, the autotrophic-heterotrophic denitrifying bacteria played vital role for the removal of CIP and enhanced denitrification process. Thus, autotrophic denitrifying bacteria uses CO2 and H2 as carbon sources to reduce nitrates to N2. This system has the assortment and prosperous community revealed at the current intensity of 60 mA, and the analysis of bacterial community structure in effluent samples fluctuates under different condition of C/N ratios. According to the results of LC-MS/MS analysis, the intermediate products were proposed after efficient biodegradation of CIP. Microbial community on biodegrading was mostly found at phylum, and class level was dominantly responsible for the NO3--N and biodegradation of CIP. This work can provide some new insights towards the biodegradation of CIP and the efficient removal of nitrates from low C/N wastewater treatment by the novel 3D-BER system.

Abstract The COVID-19 pandemic is thought to began in Wuhan, China in December 2019. Mobility analysis identified East-Asia and Oceania countries to be highly-exposed to COVID-19 spread, consistent with the earliest spread occurring in these regions. However, here we show that while a strong positive correlation between case-numbers and exposure level could be seen early-on as expected, at later times the infection-level is found to be negatively correlated with exposure-level. Moreover, the infection level is positively correlated with the population size, which is puzzling since it has not reached the level necessary for population-size to affect infection-level through herd immunity. These issues are resolved if a low-virulence Corona-strain (LVS) began spreading earlier in China outside of Wuhan, and later globally, providing immunity from the later appearing high-virulence strain (HVS). Following its spread into Wuhan, cumulative mutations gave rise to the emergence of an HVS, known as SARS-CoV-2, starting the COVID-19 pandemic. We model the co-infection by an LVS and an HVS, and show that it can explain the evolution of the COVID-19 pandemic and the non-trivial dependence on the exposure level to China and the population-size in each country. We find that the LVS began its spread a few months before the onset of the HVS, and that its spread doubling-time is $\sim 1.59\pm 0.17$ times slower than the HVS. Although more slowly spreading, its earlier onset allowed the LVS to spread globally before the emergence of the HVS. In particular, in countries exposed earlier to the LVS and/or having smaller population-size, the LVS could achieve herd-immunity earlier, and quench the later-spread HVS at earlier stages. We find our two-parameter (the spread-rate and the initial onset time of the LVS) can accurately explain the current infection levels (R^2$\mathrm{=0.74}$; correlation p-value (p) of 5x10^-13$$). Furthermore, countries exposed early should have already achieved herd-immunity. We predict that in those countries cumulative infection levels could rise by no more than 2-3 times the current level through local-outbreaks, even in the absence of any containment measures. We suggest several tests and predictions to further verify the double-strain co-infection model and discuss the implications of identifying the LVS.

Uniform illumination is a key requirement in different research fields. However, this requirement is often difficult to achieve when high intensity is required at the same time. Recent advancements in LED lamps allow nowadays for compact and economical solutions. In this work we present a suitable solution for various laboratory purposes requiring stable, uniform and high intensity illumination. The system is composed of four identical high power white LED arrays of 30 mm diameter each, placed on a supporting and cooling structure having a minimum volume of 26 cm x 26 cm x 8 cm. A numerical model has been developed, based on a ray tracing software, in order to simulate the performances. These have then been experimentally validated with measurements of the power density map, carried out with a 1% uncertainty pyranometer. Data show that the built system is very stable over time and provides an illumination uniformity higher than 98%, on a surface of 50 mm radius, which reduces to 95% on a surface of 75 mm radius. The power density can be adjusted in the 390-1360 W m-2 range, not affecting uniformity.

In this paper, we draw on recent experiences from the Young Lives study to discuss some of the ethical and practical challenges facing longitudinal cohort studies in low- and middle-income countries in the time of coronavirus. We argue that COVID-19 has instigated an ‘ethics of disruption’ for social researchers across the world, and for longitudinal cohort studies like Young Lives, this requires navigating three core considerations: first, managing research relationships and reciprocity within an observational study design; second, maintaining methodological continuity and consistency across time; and third, balancing an immediate short-term response to COVID-19 against the longer-term perspective. We refer to the study’s plan to implement a new COVID-19 phone survey to illustrate how the team are navigating this altered ethical terrain.

Chronic kidney disease (CKD) affects >10% of the adult population. Each year approximately 120,000 Americans develop end-stage kideny disease and initiate dialysis, which is costly and associated with functional impairments, worse health-related quality of life, and high early-mortality rates exceeding 20% in the first year. Recent declarations by the World Kidney Day and the U.S. Government Executive Order seek to implement strategies that reduce the burden of kidney failure by slowing CKD progression and controlling uremia without dialysis. Pragmatic dietary interventions may have a role in improving CKD outcomes and preventing or delaying dialysis initiation. Evidence suggests that a patient-centered plant-dominant low-protein diet (PLADO) of 0.6-0.8 g/kg/day comprised of >50% plant-based sources, administered by dietitians trained in non-dialysis CKD care, can be promising. The scientific premise of the PLADO is based on the observations that high protein diets with high meat intake are not only associated with higher cardiovascular disease risk but also higher CKD incidence and faster CKD progression due to increased intraglomerular pressure and glomerular hyperfiltration. Meat intake increases production of nitrogenous end-products, worsens uremia, and may increase the risk of hyperkalemia, given constipation from the typical low fiber intake. Plant-dominant, fiber-rich, low-protein diet may lead to favorable alterations in the gut microbiome, which can modulate uremic toxin generation and slow CKD progression, along with reducing cardiovascular risk in CKD patients. PLADO is a heart-healthy, safe, flexible, and feasible diet that could be the centerpiece of a conservative and preservative CKD-management strategy that challenges the prevailing dialysis-centered paradigm.

In order to solve the problem of the poor oil displacement effect of high molecular weight alkali/surfactant/polymer (ASP) solution in low permeability reservoirs, Daqing Oilfield uses a partial quality tool to improve the oil displacement effect in low permeability reservoirs. Without changing the oil displacement capability of high molecular weight ASP solution in high permeability oil layers, the ASP solution is actively sheared in low permeability oil layers by using a partial quality tool to increase the injection capability of the solution and improve the overall oil recovery. In order to study the ability of the partial quality tool to improve the oil displacement effect, firstly, the matching degree of high molecular weight ASP solution to low permeability cores is studied, and the ability of quality control tools to change the molecular weight is studied. Then, experimental research on the pressure and oil displacement effect of high molecular weight ASP solution before and after the actions of the partial quality tool is carried out. The results show that ASP solutions with molecular weights of 1900 × 104 and 2500 × 104 have a poor oil displacement effect in low permeability reservoirs. After the action of the partial quality tool, the injection pressure is reduced by 5.22 MPa, and the oil recovery is increased by 7.79%. The injection pressure of the ASP solution after shearing by the partial quality tool is lower than that of the ASP solution with the same molecular weight and concentration without shearing, but the oil recovery is lower. On the whole, the use of the partial quality tool can obviously improve the oil displacement effect in low permeability reservoirs.

In our previous study, a novel barrier processing on a porous low-dielectric constant (low-k) film was developed: an ultrathin Mn oxide on a nitrogen-stuffed porous carbon-doped organosilica film (p-SiOCH(N)) as a barrier of the Cu film was fabricated. To form a better barrier Mn₂O_3-xN film, an additional annealing at 450°C was implemented. In this study, the electrical characteristics and reliability of this integrated Cu/Mn₂O_3-xN/p-SiOCH(N)/Si structure is investigated. The proposed Cu/Mn₂O_3-xN/p-SiOCH(N)/Si capacitors exhibited poor dielectric breakdown characteristics in the as-fabricted stage, although, less degradation was found after thermal stress. Moreover, its time-dependence-dielectric-breakdown electric-field acceleration factor slightly increased after thermal stress, leading to a larger dielectric lifetime in a low electric-field as compared to other MIS capacitors. Furthermore, its Cu barrier ability under electrical or thermal stress was improved. As a consequent, the proposed Cu/Mn₂O_3-xN/p-SiCOH(N) scheme is a promising integrity for back-end-of-line interconnects.

In the environments of all kinds open coal storage sites , mining-affected coalbeds, and goafs, parts of coal body at low moisture content (≤8%) are prone to spontaneous combustion under the influence of some external environmental factors. In order to examine the influence effect of low moisture content on the spontaneous combustion tendency of coals with different metamorphic grade, we conducted temperature programmed oxidation (TPO) experiment and differential scanning calorimetry (DSC) experiment to study the spontaneous combustion characteristics of coals with different metamorphic grade at four different low moisture content, and comparatively analyzed the change laws of the characteristic parameters of four different metamorphic grade coals at four different low moisture content. The experimental results indicate that: 1) Compared other low moisture content, anthracite and fat coal at low moisture content of 1.2% show stronger tendency to undergo spontaneous combustion, long flame coal and lignite at low moisture content of 3.4% and 5.6% are more prone to spontaneous combustion. 2) Four different metamorphic grade coals at low moisture content of 7.8% are less prone to spontaneous combustion; 3) Coals with different metamorphic grade have different tendency to undergo spontaneous combustion.

CASBEE-City tool determines the city’s Built Environment Efficiency (BEE) by calculating the improvement of Quality of Life (Q) over human activities’ Environmental Load (L) within the city’s hypothetical boundary. A total of 58 variables (57 Q indicators and one variable for L) are used in the worldwide version of CASBEE-City which were grounded using ISO 37120:2014 Sustainable Development of Communities and 17 Sustainable Development Goals (SDGs) by the United Nations (UN). This paper examines the application of CASBEE-City for Malaysian cities using the case of Johor Bahru City and identifies assessment indicators which are customised based on the data availability, reliability and suitability through focus group discussions (FGDs) which involved 36 respondents (researchers, urban planners and stakeholders). This paper reveals Johor Bahru with moderate score B+ in 2010 and 2025. Consensus were also achieved from the 36 FGD respondents for the practicability and future potential of CASBEE-City and BEE framework in Johor Bahru.

This paper analyses and evaluate the precision and the accuracy the capability of low-cost terrestrial photogrammetry by using many digital cameras to construct a 3D model of an object. To obtain the goal, a building façade has imaged by two inexpensive digital cameras such as Canon and Pentax camera. Bundle adjustment and image processing calculated by using Agisoft PhotScan software. Several factors will be included during this study, different cameras, and control points. Many photogrammetric point clouds will be generated. Their accuracy will be compared with some natural control points which collected by the laser total station of the same building. The cloud to cloud distance will be computed for different comparison 3D models to investigate different variables. The practical field experiment showed a spatial positioning reported by the investigated technique was between 2-4cm in the 3D coordinates of a façade. This accuracy is optimistic since the captured images were processed without any control points.

LoRa and its upper layers definition LoRaWAN is one of the most promising LPWAN technologies for implementing the Internet of Things (IoT). Although being a popular technology, several works in the literature have revealed various weaknesses regarding the security of LoRaWAN v1.0 (the official 1st draft). By using all these recommendations from the academia and industry, the LoRa-Alliance has worked on the v1.0 to develop an enhanced version and provide more secure and trustable architecture. The result of these efforts ended-up with LoRaWAN v1.1, which was released on Oct 11, 2017. This manuscript aims at demystifying the security aspects and provide a comprehensive Security Risk Analysis related to latest version of LoRaWAN. Besides, it provides several remedies to the recognized vulnerabilities. To the best of authors’ knowledge, this work is one of its first kind by providing a detailed security analysis related to latest version of LoRaWAN. According to our analysis, end-device physical capture, rogue gateway and replay attacks are found to be threating for safety operation of the network. Eventually, v1.1 of LoRaWAN is found to be less vulnerable to attacks compared to v1.0, yet possesses several security implications that need to be addressed and fixed for the upcoming releases.

This paper focuses on the non-destructive dielectric measurement for low-loss planar materials with a thickness of less than 3 mm using a large coaxial probe with an outer diameter of 48 mm. The aperture probe calibration procedure required only to make a measurement of the half-space air and three offset shorts. The reflection coefficient for the thin material is measured using a Keysight E5071C network analyzer from 0.3 MHz to 650 MHz and then converted to a relative dielectric constant and tangent loss via closed form capacitance model and lift-off calibration process. Measurement error of dielectric constant, Δε_r is less than 2.5 % from 1 MHz to 400 MHz and the resolution of loss tangent, tan δ measurement is capable of achieving 3×10^-3.

This study evaluated the effect of low-level laser irradiation induces by diode laser on the speed of orthodontic tooth movement of canines submitted to initial retraction. Twenty-four mandibular canines were retracted by using NiTi spring (force of 150 g/side). Thirteen of those were irradiated with a diode laser 980-nm diode laser (Wiser Laser Doctor Smile, Lambda) operating at an 810-nm wavelength (1 W of output power, continuous wave of 66.7 J/cm2) that was equipped with a 0.6-mm optical fiber in continuous-wave mode. The canine retraction was accomplished by using prefabricated coil springs. The right of the mandible was chosen to be irradiated with the laser, whereas the left side was considered the control without laser irradiation. The laser was applied with 0-, 3-, 7-, and 14-day intervals. The amount of canine retraction was measured with a digital electronic caliper while the pain level was prompted by a patient questionnaire. The speed of tooth movement was significantly greater in the laser group than in the control group. The pain intensity was also at a lower level in the laser group. Our findings suggest that diode laser therapy can highly accelerate tooth movement during orthodontic treatment and can also effectively reduce pain level.

The effect of filling velocity on positive macrosegregations in large size steel ingots was studied. Macrosegregation and macro-/micro-structure were characterized on the hot-tops and a portion of the upper section of two ingots. The measurements revealed that segregation features in the two ingots varied as a function of the alloying elements, and that the severity of positive macrosegregation in the casting body was reduced when the filling rate was increased. It was also found that at the higher filling rate, grain morphologies in the first solidified zones of the ingot changed from columnar to equiaxe, and secondary dendrite arm spacing (SDAS) became slightly smaller in the intermediate and final solidified zones. The experimental findings were analyzed in the framework of diffusion and convection controlled solidification, as well as liquid metal flow theories. The solute dependence of segregation features was related to the difference in solid-liquid partition coefficient and diffusion capability of each element in the liquid iron. Calculation of Reynolds numbers (Re) during the filling process, for both ingots, showed that higher filling velocity caused more instable movement of the liquid metal in the initial solidification stage, resulting in the modification of grain morphology, as well as accelerated solidification rate.

Integrated ultra-low-loss waveguides are highly desired for integrated photonics to enable applications that require long delay lines, high-Q resonators, narrow filters, etc. Here we present an ultra-low-loss silicon waveguide on 500 nm thick SOI platform. Meter-scale delay lines, million-Q resonators and tens of picometer bandwidth grating filters are experimentally demonstrated. We design a low-loss low-reflection taper to seamlessly integrate the ultra-low-loss waveguide with standard heterogeneous Si/III-V integrated photonics platform to allow realization of high-performance photonic devices such as ultra-low-noise lasers and optical gyroscopes.

Infertility affects approximately 15% of couples worldwide, an estimated 30% of which is related to male factor infertility. Application of low level laser therapy (LLLT) to improve fertility status is a rapidly growing discipline in medicine. Laser therapy triggers a variety of biological processes through interaction with primary cellular photoacceptors. The present review aims at evaluating the literature available in the MEDLINE/ PubMed on cellular and molecular mechanisms of photobiomodulation in the domains of reproductive and veterinary medicine. We primarily focused on the clinical application of laser treatment on seminal quality, in particular its role in promoting spermatozoa motility, as well as the role of phototherapy in modulating assisted reproduction (ART). Additionally, we investigated the strength of evidence in support of the positive impact of photobiomodulation on facilitating ART.

Containing hundreds of spectral bands (features), hyperspectral images (HSIs) have high ability in discrimination of land cover classes. Traditional HSIs data processing methods consider the same importance for all bands in the original feature space (OFS), while different spectral bands play different roles in identification of samples of different classes. In order to explore the relative importance of each feature, we learn a weighting matrix and obtain the relative weighted feature space (RWFS) as an enriched feature space for HSIs data analysis in this paper. To overcome the difficulty of limited labeled samples which is common case in HSIs data analysis, we extend our method to semisupervised framework. To transfer available knowledge to unlabeled samples, we employ graph based clustering where low rank representation (LRR) is used to define the similarity function for graph. After construction the RWFS, any arbitrary dimension reduction method and classification algorithm can be employed in RWFS. The experimental results on two well-known HSIs data set show that some dimension reduction algorithms have better performance in the new weighted feature space.

With firm commitment to energy labelling, it is not a question of if but only of when the European Union will introduce an energy label for electric cars. To support such policy efforts, we conducted a scoping analysis of energy consumption and efficiency trade-offs for 342 electric car models sold in Europe. The results suggest that certified and real-world energy consumption average 19 ± 4 kWh/100 km and 21 ± 4 kWh/100 km, translating into drive ranges of 440 ± 120 km and 380 ± 110 km, respectively. Energy consumption is correlated with vehicle mass, frontal area, and battery capacity, but less so with rated power and vehicle price. Each 100 kg of vehicle mass and 0.1 m2 of frontal area tend to increase energy consumption by 0.20 ± 0.06 kWh/100 km and 0.72 ± 0.05 kWh/100 km. Raising battery capacity by 10 kWh increases vehicle mass by 145 kg, energy consumption by 0.6 kWh/100 km, and drive range by 45 km. Efficient vehicles are available at any price but drive range has a cost. Our findings point to considerable efficiency trade-offs that could be tapped by a dedicated energy label. We propose several options for categorizing vehicles on an efficiency scale from A to G, with and without additional utility parameters. Our analysis provides rationale for energy labelling of electric cars in Europe and could inspire similar analyses for other vehicle categories such as e-scooters, lightweight three- and four-wheelers, e-busses, e-trucks, and electric non-road machinery.

This article proposes a particular strategy to proceed with a progressive electrification of public transport systems in cities. Starting from a bus operation model, the possible electrification of two routes is analysed, one urban and another extra-urban in the city of Pisa. An estimate is made of the energy uses associated with certain operating modes. The maximum level of consumption is estimated at approximately 280 kWh per day per bus for the urban route and excluding some special days, less than 215 kWh per day for the extra-urban route, for which a hybrid bus is proposed. Starting from an estimate of the daily consumption for the management of the two routes, the sizing of a photovoltaic system distributed on some modular shelters which serves to power the same routes is carried out. The resulting system has a power of the order of 190-200 kW. The modular solution is also outlined, and an installation is proposed. The analyzed case lends itself to being easily replicated.

To stabilize the Earth’s climate a large-scale transition is needed to non-carbon emitting renewable energy technologies like wind and solar energy. Although these renewable energy sources are now lower-cost than fossil fuels, their inherent intermittency make them unable to supply a constant load without storage. To address these challenges rechargeable electric batteries are currently the most promising option; however, their high capital costs limit current deployment velocities. To both reduce the cost as well as improve performance 3D printing technology has emerged as a promising solution. This literature review provides the state-of-the-art enhancements of battery properties with 3D printing including efficiency, mechanical stability, energy and power density, customizability and sizing, production process efficiency, material conservation and environmental sustainability as well as the progress in solid-state batteries. The principles, advantages, limitations, and recent advancements associated with the most common types of 3D printing are reviewed focusing on their contributions to the battery field. 3D printing batteries offer design flexibility, material flexibility, reduces pack weight, minimizes material waste, increases the range of applications and has the potential to reduce costs. As 3D printing technologies become more accessible, the prospect of cost-effective production for customized batteries is extremely promising.

Energy communities are key actors in the energy transition since they optimally interconnect renewable energy capacities with the consumers. Despite versatile objectives, they usually aim at improving the self-consumption of renewable electricity within low voltage grids to maximize revenues. In addition, energy communities are an excellent opportunity to supply renewable electricity to regional and national energy systems. However, effective price signals have to be designed to coordinate the needs of the energy infrastructure with the interests of these local stakeholders. The aim of this paper is to demonstrate the integration of energy communities at the national level with a bottom-up approach. District energy systems with a building scale resolution are modelled in a mixed integer linear programming problem. The Dantzig-Wolfe decomposition is applied to reduce the computational time. The methodology lies within the framework of renewable energy hub, characterized by a high share of photovoltaic capacities. Both investments into equipment and its operation are considered. The model is applied on a set of five typical districts and weather locations representative of the Swiss building stock. The extrapolation to the national scale revealed a heterogeneous photovoltaic potential throughout the country. Present electricity tariffs promote a maximal investment into photovoltaic panels in every region, reaching an installed capacity of 67.2 GW and generating 80 TWh per year. Since the national PV capacity needed is forecast at 15.4 GW peak, coordinated investments between local and national actors are needed to prevent dispensable expenses. An uncoordinated design is expected to increase the total costs for residential energy systems by 43.2% and curtails 48.2% of local renewable electricity.

The prohibitive costs of small-scale solar photovoltaic (PV) racks decreases PV adoption velocity. To overcome these costs challenges, an open hardware design method is used to develop two novel variable tilt racking designs. These are the first stilt mounted racking designs that allow manual change of tilt angle from zero to 90 degrees by varying the length of cables. The racks are designed using the calculated dead, wind and snow loads for Canada as conservative design for most of the rest of the world. Structural capacities of the wooden members are then ascertained and resisting bending moment, shear force, tensile force, and compressive force for them is calculated. A structural and truss analysis is performed to ensure that racking design with-stands the applicable forces. Moreover, implications of changing the tilt angle on the wooden members/cables used to build the system are also determined. The systems offer significant economic savings ranging from 1/3rd to 2/3rd the capital expenses of the commercially available alternatives. In addition, the racking designs are easy-to-build and require minimal manufacturing operations, which increases their accessibility. The stilt-mounted designs can be employed for agrivoltaic settings while allowing farm workers shaded ergonomic access to perform planting, weeding, and harvesting.

The transition of the energy system to renewable sources means the cheap energy storage in fossil fuels must be replaced by other options. A large part of the demand for useful energy is heat and cold, often produced from electric energy by a resistance heater, compression heat pump or cooler. The question is then to store the initial electric energy by electric energy storage (EES) or the useful energy by thermal energy storage (TES). In a desktop study both options were compared, by the choice made in existing applications, and also generally analyzing current technology data. For the latter, cost, round-trip efficiencies, life cycles and life time of EES, specifically for batteries, and of TES, specifically for hot and cold water, ice and other PCM were collected. Applications studied are heating and cooling in buildings and in industry. Application-typical conversion efficiencies were also collected and taken into account. The results show that in many existing installations TES, incl. by PCM, is already preferred, and that TES is advantageous in most investigated applications economically, in addition to technical advantages. Thus, TES has a large potential in the transition of the energy system to stabilize the electricity grid by demand side management.

This paper focused on the finite element analysis of structural system in extreme loading condition. Two different stud shape and thicknesses were analyzed under blast. The stud thickness such as 1.19 mm and 1.5 mm were modelled and analyzed using ABAQUS 6.14. FEM is a tool which predicts the engineering physics of the real structure. To validate the finite element modelling performed by authors, a reference work published by earlier researchers on cold-formed steel stud wall is considered and examined in the present study. The novelty of this study was web corrugation and influence of flange width on stud. The models mimic like an air bag in a car to delay the pressure timing inside the stud wall. The mass of explosive used as 1.56 kg at a standard scaled distance. Time versus displacement was captured out at four locations in the stud wall. One of the objectives is to develop mathematical model to validate the deformation of stud under blast loading. Two mathematical models were validated using Artificial Neural Network (ANN). The results captured in ANN model was error histogram, regression plot, best performance fit and training data. The models were capable of resisting the moderate blast load. The response surface methodology (RSM) was employed to evaluate model performance Regression equations are useful for predicting future trends and outcomes, which is crucial for planning and decision-making. The primary goal of this work is to evaluate cold-formed steel stud walls with varying stud sizes under blast loading using finite element analysis and validated by ANN and RSM.

In this study, a novel two-layer control scheme aimed at enhancing the dynamic performance of a nonlinear vehicle through the utilization of an optimal control approach grounded in predictive control principles is presented. The first layer of our control system focuses on the computation of an optimized rotational torque that ensures lateral dynamic stability. This torque is subsequently translated into differential forces acting on the wheels, employing the inverse tire model to derive the desired longitudinal slips. These slip values are then transmitted to the second layer of our control system. In the second layer, the electric motors associated with the vehicle's wheels dynamically adjust the input torque to accurately track the desired slip, thereby ensuring the overall stability of the electric vehicle. In view of the paramount importance of energy consumption in electric vehicles, we adopt optimal control strategies to substantially minimize battery utilization. To this end, careful selection of appropriate weighting coefficients in the control laws enables us to maintain the electric motors within their permissible operational range while simultaneously minimizing battery energy consumption for desired slip tracking. Extensive simulation results validate the effectiveness of our proposed control system in proficiently managing nonlinear effects and safeguarding the vehicle's stability.

Proteins in biological systems function at the interface of single molecule and bulk chemistry and thus provide novel insights into the basic physical chemistry of scaling and emergent phenomena. For example, a binary mechanical model based on the chemistry of muscle contraction unifies molecular mechanics and thermodynamics and provides an explicit solution to the Gibbs paradox. Using the same model system, here I show that chemical activities of molecular states have no effect on chemical kinetics or energetics. Specifically, while the concentration or number of molecules in a molecular state is widely thought to contribute to reaction free energies, here I show that it is not the physical presence of molecules that pushes a reaction forward but the number of microstates, , accessible in a chemical state that pulls a reaction toward equilibrium with an entropic force down an entropic funnel. With the derivation of an entropic contribution to chemical kinetics, I develop a novel chemical kinetic formulation that fully describes the chemical thermodynamics of both equilibrium and non-equilibrium reactions in terms of an a priori system reaction energy landscape.

This paper addresses the optimal stochastic allocation of Distributed Energy Resources in distribution Networks. Typically, uncertain problems are analyzed in multi-stage formulations including case generation routines, resulting in computationally exhaustive programs. In this article, two probabilistic approaches are proposed resulting in a single-stage, convex, stochastic optimal power flow problem: The Range-Probability Optimization (RPO) and Value-Probability Optimization (VPO). The RPO maximizes probabilities within a range of uncertainty, whilst the VPO optimizes the values of the random variables and maximizes their probabilities. Random variables are modeled with hourly measurements fitted to the logistic distribution. These formulations were tested on two systems and compared against the deterministic case built from expected values. Results indicate that assuming deterministic conditions ends in highly underestimated losses. The RPO showed that by including a ±10 % uncertainty, the losses can be increased up to 40 % with up to −72 % photovoltaic capacity, depending on the system, whereas the VPO resulted in up to 85 % increases in power losses despite PV installations, with 20 % greater probabilities in average. By implementing any of the proposed approaches, it was possible to obtain more probable upper envelopes in the objective, avoiding case generation stages and heuristic methods.

Energy imports and the transition to renewable energy sources are of critical importance in the current geopolitical context, which necessitates concrete actions to tackle the energy crisis at the European Union level. The study aims to explore the impact of imported non-renewable energy resources on the EU-27 economy. It examines the correlations and causal relationships between GDP, GVA, R&D investments, and energy imports from 2000 to 2021. Data normality was assessed using the Shapiro-Wilk test, while Pearson's test identified correlations between variables. Linear and multiple regression analyses were conducted to determine the effects of changes in independent variables on dependent variables. The study found a strong association between natural gas imports and GDP, with increases in GDP leading to a more than fourfold rise in imports. Furthermore, multiple regression analysis indicated that a 1% increase in R&D investments results in a 2.21% decrease in fossil fuel imports in 91.7% of cases. This suggests that R&D investments contribute to improved efficiency and the use of renewable energy sources.

The purpose of this research is to present a thorough evaluation of the energy management system, which consists of hybrid energy storage systems, and its control algorithms, which may be used in electric vehicles. This paper outlines the characteristics of electric vehicles, research methods, analysis of the hybrid energy storage system architecture, converter topology, and energy management techniques. The strength and co-occurrence of keywords over the past ten years are shown in this study using a systematic research framework for performing a literature review and using keyword analysis techniques. The study reveals a pattern of recently and frequently used terms in works of literature. Consequently, their suitability, benefits, and drawbacks are assessed. In this study, the hybrid energy storage system and converter circuit architecture are evaluated and rated. Non-isolate DC-DC converter converter connected to SC is a suitable configuration for the hybrid converter because it is simple to build, reliable, and has minimal loss/weight/cost, which improves vehicle performance. In terms of the application of control strategies, it is shown that deterministic and fuzzy rule-based control techniques are successfully assessed using real-scale vehicle experiments and selected for manufacturing. On the other hand, real-time optimization-based energy management techniques have been effectively shown in lab-scale tests and may be used in a future real-scale vehicle.

The objective of this study is to investigate the effect of the premixed ethanol ratio based on the same total heating value in-cylinder on the equivalence ratio distributions and the injected fuel droplet behavior in the cylinder of the RCCI engine. The spray simulation was conducted by dividing two-part. First, the spray validation simulations progressed to find the spray-influenced factor of the test injector. Next, the engine simulations were performed with the spray-influenced factor obtained from spray validation simulations to investigate the effect of the premixed ethanol ratio based on the same total heating value in-cylinder on the injected fuel atomization and the equivalence ratio distributions. The introduced total heating value was fixed at 595J based on the lower heating value of diesel 14mg. The heating value of the premixed ethanol ratio varied from 0% to 40% based on the same total heating value in-cylinder in steps of 10%. It was revealed that when the premixed ethanol ratio based on the same total heating value in-cylinder was increased, the spray tip penetration value was reduced from the time after 4deg of diesel injection start because of the short injection duration by the small amount of diesel fuel. The SMD value was also more enormous up to 32.58% with increasing the premixed ethanol ratio because of the low kinetic energy of the injected fuel by the short injection duration and the slow evaporation of the injected fuel by the low cylinder temperature.

Coastal wave modeling and forecasting are essential in oceanography, sustainable marine energy, and ocean engineering. Precise forecasting of wave speed and direction are crucial for offshore operations, marine energy, risk management, environmental management, coastal and sustainable maritime management. The coastline of Brunei Darussalam can generate between 15 and 126 Giga Watt of wave energy. In this experimental research, we used two numerical approaches, the finite difference, and spectral element methods, to model and simulate wave speed and direction. We calculated the mean error between numerical and analytical solutions. We proposed a novel, promising, univariate time series forecasting model by combining the Long Short-Term (LSTM) with KerasTuner hyperparameters tuning and optimization techniques. This method helps us to improve the accuracy and efficiency of time-series forecasting.. The experimental data was computed from high-precision Acoustic Doppler Current Profiler (ADCP) sensor data. This research is part of the preliminary feasibility analysis of wave energy production in Brunei Darussalam and net zero commitment for a sustainable environment. Seven independent forecast experiments were performed for wave speed and direction in degree and radian units for 1, 3, 6, 8, 10, 12, and 24 hours. Mean squared error (MSE) was adopted as a metric for both training and testing. The experimental results reveal that the wave speed forecast has the lowest MSE compared to direction, regardless of the unit of measure, but has a longer duration. In addition, the direction forecast in the degree unit has the lowest errors compared to the unit of radians; the latter has a longer running time than the former. The model has delivered optimal results throughout the experiments with minor training and test errors. We conducted a thorough evaluations on two benchmark time series datasets, which include the study dataset and air quality index dataset, to validate the performance of the proposed model with other models. The proposed model outperforms cutting-edge forecasting models, such as the conventional LSTM, ARIMA, and Prophet. The model has the slightest forecast error compared to the existing literature’s result.

Voltage sags can cause the interruption of power supply and can negatively affect operations of customers. In this paper, the authors study the impact of battery energy storage systems (BESS) on voltage sags. A stochastic method of fault positions is used. Faults of various types are simulated and voltages are recorded. Firstly, with the BESS integrated into the network, there are higher residual voltages, fewer voltage sags and less expected critical voltage loss. Secondly, if the BESS converter power factor is reduced, recorded residual voltages are higher, voltage sags are fewer, and the number of expected critical voltage sags is lower. Finally, when three BESS converter control modes, namely constant voltage, constant power factor, and constant reactive power, were assessed, results showed similar voltage sag performances for constant power factor and constant reactive power modes. Furthermore, operating in constant voltage control outperformed the other two modes as it resulted in higher residual voltages, a lower number of voltage sags, and fewer expected critical voltage sags. The paper has demonstrated that the BESS can improve voltage sag performance. In addition, the power factor of the BESS converter and the mode of operation of the converter can influence the magnitude of the voltage sag performance improvement.

Battery electric vehicles provide an opportunity to balance supply and demand in future power systems with high shares of fluctuating renewable energy. Compared to other storage systems such as pumped-storage hydroelectricity, electric vehicle energy demand is highly dependent on charging and connection choices of vehicle users. We present a model framework of a utility-based stock and flow model, a utility-based microsimulation of charging decisions, and an energy system model including respective interfaces to assess how the representation of battery electric vehicle charging affects energy system optimization results. We then apply the framework to a scenario study for controlled charging of nine million electric vehicles in Germany in 2030. Assuming a respective fleet power demand of 27 TWh, we analyze the difference between power-system-based and vehicle user-based charging decisions in two respective scenarios. Our results show that taking into account vehicle users’ charging and connection decisions significantly decreases the load shifting potential of controlled charging. The analysis of marginal values of equations and variables of the optimization problem yields valuable insights on the importance of specific constraints and optimization variables. In particular, state-of-charge assumptions and representing fast charging drive curtailment of renewable energy feed-in and required gas power plant flexibility. A detailed representation of fleet charge connection is less important. Peak load can be significantly reduced by 5% and 3% in both scenarios, respectively. Shifted load is very robust across sensitivity analyses while other model results such as curtailment are more sensitive to factors such as underlying data years. Analyzing the importance of increased BEV fleet battery availability for power systems with different weather and electricity demand characteristics should be further scrutinized.

A new circuit model of the self-powered device for heart rate measurement is presented in this paper. This device consists of piezoelectric energy harvester (PEH), power management circuit (PMC) with energy storage, microcontroller, Photoplethysmography (PPG) sensor, and Wi-Fi module. The PEH is placed under the insole to harvest the pressure energy from human foot-step to generate ac power. In our model, a PEH is represented by sine voltage source, where its parameters were taken from experiments with 20 volunteers. The PMC is simplified by a switch with gain δ placed in series with the main circuit. The model of the main circuit is RC elements in parallel, where C is the capacitance of the storage device, and R is the equivalent parallel resistance of the microcontroller, PPG sensor, and Wi-Fi modules, respectively. The value of R depends on the power and current absorbed by those modules during sleep, deep sleep, sense, and transmit modes which collected from the datasheet. Finally, the proposed circuit model of the self-powered device was built and simulated in SPICE. The simulation results were compared with the laboratory experiment using commercial devices. Based on the results, the proposed model had small gaps compared to the real self-powered devices in terms of average current, voltage, power and efficiency.

Agricultural greenhouse is largely answered in the agricultural sphere, despite the shortcomings it has, including overheating during the day and night cooling which sometimes results in the thermal inversion mainly due to its low inertia. The glasshouse dressed chapel is relatively more efficient than the conventional tunnel greenhouse. Its proliferation on the ground is more or less timid because of its relatively high cost[14-22]. Agricultural greenhouse aims to create a favorable microclimate to the requirements of growth and development of culture, from the surrounding weather conditions, produce according to the cropping calendars fruits, vegetables and flower species out of season and widely available along the year. It is defined by its structural and functional architecture, the quality thermal, mechanical and optical of its wall, with its sealing level and the technical and technological accompanying[12-13]. The greenhouse is a very confined environment, where multiple components are exchanged between key stakeholders and them factors are light, temperature and relative humidity[8]. This state of thermal evolution is the level sealing of the cover of its physical characteristics to be transparent to solar, absorbent and reflective of infrared radiation emitted by the enclosure where the solar radiation trapping effect otherwise called "greenhouse effect" and its technical and technological means of air that accompany. The socio-economic analysis of populations in the world leaves appear especially the last two decades of rapid and profound transformations These changes are accompanied by changes in eating habits, mainly characterized by rising consumption spread along the year[14]. To effectively meet this demand, greenhouse-systems have evolved, particularly towards greater control of production conditions (climate, irrigation, ventilation techniques, CO2 supply, etc ...). Technological progress has allowed the development of greenhouses so that they become increasingly sophisticated and of an industrial nature (heating, air conditioning, control, computer, regulation, etc ...). New climate driving techniques have emerged, including the use of control devices from the classic to the use of artificial intelligence[10-11] such as neural networks and / or fuzzy logic, etc... As a result, the greenhouse growers prefer these new technologies while optimizing the investment in the field to effectively meet the supply and demand of these fresh products cheaply and widely available throughout the year.

The field of solar technology has experienced remarkable advancements, transforming from early photovoltaic discoveries to a diverse array of high-efficiency, cost-effective solutions. This review explores the evolution of solar technology, detailing its development from the initial discovery of the photovoltaic effect to contemporary innovations. The current generation of solar technologies, including silicon photovoltaic cells, thin-film solar cells, perovskite solar cells, bifacial panels, concentrated solar power, and building-integrated photovoltaics, is examined for their efficiencies, applications, and contributions to the renewable energy landscape. Future prospects highlight promising trends such as next-generation photovoltaics, advanced energy storage solutions, agrivoltaics, floating solar farms, and artificial photosynthesis. Despite the progress, challenges related to efficiency, stability, scalability, cost, grid integration, environmental impact, policy support, and public acceptance remain. Addressing these challenges through continued research, supportive policies, and collaborative efforts is crucial for realizing the full potential of solar technology. This review underscores the importance of solar energy in achieving a sustainable future and emphasizes the need for ongoing innovation and commitment to overcome existing barriers.

Hydrokinetic energy constitutes a significant promise as a renewable energy source, yet many regions experience flow velocities insufficient to support conventional turbine installations. According to this, the present work proposes a vertical axis hydrokinetic turbine aimed to environments where conven-tional turbines may not be feasible due to insufficient water velocity. This turbine is based on a biologi-cally inspired design which improves the traditional Savonius turbine. This innovative configuration is inspired by the Fibonacci spiral. The turbine's performance was analyzed through Computational Fluid Dynamics. A notable improvement was found in comparison with the traditional Savonius turbine, providing a 15.1% increment in the power coefficient.

The Meissner effect is a special property of superconductors. In this paper, based on the characteristics of the Meissner effect, a superconducting phase transition model of a superconductor under a changing magnetic field is proposed. A thermodynamic analysis was performed on the model to calculate both the changes in the thermodynamic parameters of the superconductor during the phase transition and the changes in the overall work and energy of the model. For Model 1 in the present study, the thermodynamic energy of the superconductor was not conserved due to the phase change of the superconductor under a changing magnetic field.

As the global demand for clean and sustainable energy sources intensifies, hydrogen emerges as a promising alternative fuel. The widespread adoption of hydrogen, however, is impeded by the lack of efficient systems for storage and transportation. This review aims to summarize the recent advancements and prevailing challenges within the realm of hydrogen storage and transportation, thereby providing guidance and impetus for future research and practical applications in this domain. Through a systematic selection and analysis of the latest literature, this study highlights the strengths, limitations, and technological progress of various hydrogen storage methods, including compressed gaseous hydrogen, cryogenic liquid hydrogen, organic liquids hydrogen, solid materials hydrogen storage, as well as the feasibility, efficiency, and infrastructure requirements of different transportation modes such as pipelines, road and seaborne transportation. The findings reveal that challenges such as low storage density, high costs, and inadequate infrastructure persist despite progress in high-pressure storage and cryogenic liquefaction. The review also underscores the potential of emerging technologies and innovative concepts, including metal-organic frameworks, nanomaterials, and underground storage, along with the potential synergies with renewable energy integration and hydrogen production facilities. In conclusion, interdisciplinary collaboration, policy support, and ongoing research are essential in harnessing hydrogen’s full potential as a clean energy carrier. This review concludes that research in hydrogen storage and transportation is vital to global energy transformation and climate change mitigation.

Solar energy is one of the most important renewable energy sources for many settings, such as households, solar-powered vehicles, power grid support, etc. However, the amount of solar energy produced is directly proportional to the surface area of the solar panel exposed to the sun rays, and so the challenges arise for the space required to install solar panels in order to get maximum area coverage of sun radiation energy absorption in constricted spaces such as vehicles powered by solar, small residential apartments, etc. The challenge with solar energy is that once a plate is installed, the space under it or above it cannot be used for another solar panel installation. This is due to the fact that the shadow cast by the above solar panel or structure prevents sun rays from reaching the below solar panel. The introduction of flexible solar panels that can take on any shape has given us the opportunity to explore the possibility of maximising solar plate surface area using surface manifold techniques. In this optimisation study, the constraint is that we have a space with fixed dimensions of length, width, and height, and the objective is to maximise the surface area of the panel within the given space dimensions to maximise solar energy absorption. Thus, this paper then proposes curving the panel surface and/or protruding the surfaces of the panel using the concept of a topology surface manifold to maximise the surface area: Protruding panel surface with shapes such as pyramidal, cone, hemisphere, sinusoidal wavy curve, etc. are analysed to check if the panel surface increases at all. The geometrical design result shows that the panel surface can increase by more than 20%. Although this paper only presents concept work and details of experimental design are yet to be performed, it should also be noted that curving surfaces have drawbacks, such as some protruding parts of solar panels when the plate is tilted, not facing the sun directly, which may cast shadows on parts of the solar panel, causing hindrance.

A selective permeable membrane between two different solutions can generate osmotic pressure, Donnan potential, and ion/pH gradients based on various factors such as membrane type, solute concentration and temperature. In this study, we introduce a novel method for extracting energy from ion gradients through an innovative cycle, presenting intriguing experimental findings. Additionally, by leveraging Raoult’s Law, which describes the relationship between vapor pressure and solute presence, in conjunction with osmosis within a closed system, we successfully engineer the simplest iteration of Maxwell’s demon device. This pioneering approach to sustainable energy generation harnesses ambient temperature marking a significant advancement in energy research. Finally this renewable energy source is available all the time and everywhere.

Despite achieving over 90% household service coverage, the Latin American and Caribbean region faces energy poverty (EP) challenges, particularly in rural areas. With growing GDP and foreign investments, Panama grapples with significant energy inequality, ranking poorly in clean energy access. Almost 177,000 Panamanians experience energy poverty, impacting their quality of life. This study proposes a Multidimensional Energy Poverty Index for Panama (MEPIP), considering dimensions like lighting, cooking, food refrigeration, thermal comfort, and knowledge/communication. MEPIP, inspired by indicators from Chile, Mexico, and the UK, aims to provide a nuanced understanding of EP. Results from surveyed rural communities, Guásimo, Llano Grande Abajo, Los Aguilares, and La Tranquilla, reveal pervasive energy poverty, emphasizing the urgency of targeted interventions. MEPIP's application identifies critical areas, such as Llano Grande Abajo, highlighting stark inequalities and the need for comprehensive energy policies to address EP in Panama.

LuBO3 crystallizes in the calcite type (CaCO3) structure and is a widely applied inorganic host for luminescent materials and scintillators. Even though many scientific works have been published concerning the optical properties of rare earth doped LuBO3, so far, no study of the emission spectra as function of the excitation energy of such orthoborates has been conducted. Therefore, this work deals with the photoluminescence of RE doped LuBO3 with RE = Ce3+, Eu3+, Gd3+, or Tb3+, while an emphasize is laid on the energy dependence of these four luminescent compounds.

Energy and information are fundamental concepts that play crucial roles in the foundations of science, each contributing to different aspects of our understanding of the natural world. Here we encounter that Fisher' measure is able to link them. We also investigate some interesting relations between Fisher's information, Poisson's distribution, and thermal quantifiers in a grand canonical ensemble. These connections permit one to visualize interesting novel artifacts in statistical mechanics. We clearly see them emerge in discussing the ideal gas. For example, we encounter that the Poisson distribution is intrinsically involved in the physics of the ideal gas, and we are able to give a Fisher-estimation of the difference in informational content between the grand canonical and canonical ensembles.

Welcome to the world of bioethanol revolution, in response to the soaring global energy demand, the production of bioethanol has taken center stage, captivating the attention of governments, academia, and the private sector alike. Over the years, research in this field has witnessed exponential growth, paving the way for sustainable energy production. Ethanol, the front-runner in renewable energy, can be derived from a plethora of sources. In this abstract, we will explore the exciting advancements and emerging trends in the production of bioethanol, shedding light on the promising future of sustainable energy. Bioethanol production presents a cost-effective solution by utilizing the abundance of agricultural resources. These resources serve as the foundation for producing bioethanol from various biomass sources. In this context, we evaluate the raw materials used, including sucrose-based, starchy-based, and perennial grasses. By exploring these options, we can gain a comprehensive understanding of the diverse range of agricultural feedstocks that contribute to the production of bioethanol, this paper delves into the multiple processes involved in the production of bioethanol from agricultural base biomass, unveiling a range of possibilities for harnessing renewable energy. The key steps that were thoroughly examined include pretreatment, which encompasses both physical and chemical methods, hydrolysis, direct fermentation, and saccharification. These steps serve as crucial components in the overall bioethanol production process from agricultural base biomass. By understanding and optimizing each of these stages, we can unlock the full potential of sustainable energy generation. The paper goes beyond exploring the production processes and delves into the different generations of bioethanol, each with its unique components. Additionally, it provides a comprehensive overview of the composition of agricultural and forest residues, shedding light on their potential as valuable resources for bioethanol production. Moreover, it highlights the significance of the agricultural sector in Nigeria's economy, emphasizing the importance of energy for enhancing productivity and efficiency. The integration of bioethanol as an energy source has the potential to propel this sector forward, fostering sustainable development and contributing to the overall growth of the Nigerian economy. By harnessing the power of bioethanol, Nigeria can tap into its energy potential, promoting energy utilization and creating a positive impact on various industries.

In Nigeria, the adoption of sustainable biomass for energy generation is on the rise. One crucial factor that affects the efficiency of Biomass utilization is the moisture content. This study investigates the economic impact of moisture at different stages of the wood biomass distribution chain, considering the entire chain. The methodology employed includes a comprehensive literature review, interviews, and economic calculations. By analyzing these factors, this research aims to provide valuable insights into the economics of moisture in wood biomass, contributing to the sustainable development of the Biomass energy sector in Nigeria. Based on the outcomes of this investigation, it has been discovered that the costs associated with moisture content in Nigeria amount to approximately ₦500,000,00 (Five Hundred Thousand Naira only). Utilizing wood Biomass with a moisture content of 32% weight, as opposed to 18% weight, has proven to be more costly. Transportation contributes to a significant portion of this increase, while the reduction in burning efficiency accounts for the remaining half. To further elaborate on these findings, it is crucial to understand that the transportation costs are impacted by the additional weight and volume of biomass with higher moisture content. This necessitates the use of more fuel and resources during transportation, resulting in increased expenses. Additionally, the decreased burning efficiency associated with higher moisture content poses a challenge. It leads to reduced energy output and increased fuel consumption, ultimately impacting the overall economic viability of wood biomass as an energy source. By identifying these factors, this study aims to provide insights into the economic implications of moisture content in the wood biomass distribution chain in Nigeria. These findings can serve as a basis for developing strategies to optimize the use of biomass, reduce costs, and enhance the sustainability of the energy generation process. One of the most convenient and cost-effective solutions to reduce transportation expenses and improve combustion efficiency is through planned air drying of wood biomass. Large-scale power plants typically prefer utilizing wood biomass that has undergone air drying, resulting in a moisture content ranging from 18% to 36% by weight. By implementing planned air-drying techniques, the moisture content of wood biomass can be significantly reduced, thereby decreasing transportation costs. As the biomass becomes lighter and less bulky, transportation requirements are optimized, leading to enhanced efficiency and reduced expenses. Moreover, air-dried wood biomass offers improved combustion characteristics. The reduced moisture content allows for better heat transfer during the combustion process, resulting in higher energy output and increased fuel efficiency. This not only improves the overall economics of utilizing wood biomass but also contributes to a more sustainable and environmentally friendly energy generation system. The application of planned air drying in the wood biomass distribution chain in Nigeria can serve as a practical solution to address the economic challenges associated with moisture content. By adopting this approach, stakeholders can maximize the potential of wood biomass as a renewable energy resource while minimizing costs and promoting sustainable practices.

"Hydrogen is an extremely promising energy carrier, poised to become a commercially viable product in the coming years. However, there are significant hurdles that need to be addressed. One such challenge is the high energy consumption during the electrolysis process, which is used to produce hydrogen. Furthermore, the co-production of CO and CO2 during steam hydrocarbon reforming adds to the complexity. Finding innovative solutions to these issues is crucial for the widespread adoption of hydrogen as a clean and efficient energy source." Simultaneously, various industries have the capability to generate hydrogen as a by-product, each with differing levels of purity. For instance, sodium and chlorine production plants have the potential to produce high-purity hydrogen. On the other hand, digester and sewage gas can yield low-purity hydrogen. In both instances, there is an economic incentive to utilize this hydrogen for energy production or to inject it into existing natural gas pipelines A brief estimation of the technical potential of utilizing hydrogen from various sources in Nigeria, specifically for fuel cell usage, has been conducted. Moreover, resource potential visualization maps have been generated to help visualize the availability and distribution of these hydrogen sources throughout the country.

Energy market price volatility and an upward trajectory of prices per unit of electricity have sent all industrial sectors and many economies to the brink of recession. Alongside the urgent need for decarbonisation of all industries, achieving a higher level of energy independence across all sectors seems imperative. A multi-disciplinary approach with a proposed system of CO₂ emissions reduction and capture technologies has the potential for short-term emissions reduction to near-zero in absolute terms. The findings of this research showed CO₂ emissions reduction of ~30% from 977t of CO_2e to 684t in one single blast furnace production cycle, by switching the energy provider generating energy exclusively from renewable sources. Replacing coal with biomass (BECCS), resulted in an additional reduction of ~30%, to CO_2e to 479t CO_2e. Installing solar PV panels for energy generation on site resulted in a further ~30% reduction, to 335 tonnes CO_2e. Finally, the installation of CO₂ filters reduced the CO₂ emissions to near-zero. Simultaneously, energy-saving and process improvement measures implementation (up to 60% efficiency increase), excess heat recovery (<30% of energy savings), and retrofitting renewable energy technology resulted in an energy independence of 88%. Short-term engineering solutions, partly subsidised in the UK, are readily available.

In the quest for sustainable energy solutions, predicting electricity prices for renewable energy sources plays a pivotal role in efficient resource allocation and decision-making. This article presents a novel approach to forecasting electricity prices for renewable energy sources using deep learning models, leveraging historical data from the Power System Operator (PSE). The proposed methodology encompasses data collection, preprocessing, feature engineering, model selection, training, and evaluation. By harnessing the power of recurrent neural networks (RNNs) and other advanced deep learning architectures, the model captures intricate temporal relationships, weather patterns, and demand fluctuations that impact renewable energy prices. The study demonstrates the applicability of this approach through empirical analysis, showcasing its potential to enhance energy market predictions and aid in the transition to more sustainable energy systems. The outcomes underscore the importance of accurate renewable energy price predictions in fostering informed decision-making and facilitating the integration of renewable sources into the energy landscape. As governments worldwide prioritize renewable energy adoption, this research contributes to the arsenal of tools driving the evolution towards a cleaner and more resilient energy future.

This paper is the third application-oriented paper based on the "unified theory of forces" and also addresses the shortcomings of previous research in fully explaining the origin and nature of the weak force. The main conclusions of this paper are as follows: (1) It is argued that neutrinos are generated by the gravitational field, and their generation mechanism is similar to the radiation of photons from electron orbital transitions. Particles undergoing orbital transitions in a gravitational field will emit neutrinos. (2) We rigorously derive the energy formula for neutrinos and demonstrate that the energy formula for neutrinos does not comply with the Planck energy formula. Neutrinos with higher energy have longer wavelengths and are more easily observed in experiments.

This paper delves into the intricate interactions of photons in the realms of gravity and antigravity. Photons, being fundamental particles of light, exhibit remarkable behaviors as they traverse through the cosmos. They journey through the vast expanses of the universe until they venture into the enigmatic invisible realm. The photon's encounters with gravity and antigravity are explored in detail. When confronting the gravitational influence of massive objects, such as celestial bodies, photons neither gain nor lose energy, but they exchange momentum with the external gravitational field while steadfastly preserving their intrinsic momentum. Intriguingly, the photon's interaction with the mysterious force of antigravity, propelled by dark energy, presents an irreversible transformation. The consequences of this interaction are profound, as photons undergo a cosmic redshift of a magnitude greater than that induced by gravity or other redshift mechanisms. This distinctive effect manifests as the photon departs from the gravitational embrace of galaxies and embarks on a journey beyond their boundaries, where the domain of zero gravity commences. Furthermore, the paper elucidates the dynamics of external forces exerted by massive objects on photons during their interactions. These forces momentarily carry the photons while they engage with the massive objects. Yet, despite this external assistance, the photons maintain their original momentum. Notably, within a gravitational field, the effective deviation from this transportation remains zero, reaffirming the photon's commitment to its initial trajectory.

In the present work we consider two modified Chevallier-Polarski-Linder (CPL) models in the background of homogeneous and isotropic FLRW space-time, namely (i) the generalized CPL model (Model I) and (ii) the logarithmic form of the the equation of state for the dark energy. From the observational data sets ((Pantheon+)+BAO+HST), we find that at the present epoch (redshift $z=0$), the equation of state for the dark energy converges almost at the same value $\omega_{fld}\approx -0.86$ and the variation of $\omega_{fld}(z)$ with respect to the redshift parameter is very small for both models. we also find that the present value of the Hubble parameter $H_0$ is almost same for both the models. Finally, we compare the models in the light of Akaike, Bayesian Information Criterion (BIC) and Bayesian Evidence. However, we find that Model II is better compared to the Model I from the estimated value of the deceleration parameter.