Future Demand Research Articles

Enhanced radiation use efficiency and grain filling rate as the main drivers of grain yield genetic gains in the CIMMYT elite spring wheat yield trial

Common wheat (Triticum aestivum L.) is a major staple food crop, providing a fifth of food calories and proteins to the world’s human population. Despite the impressive growth in global wheat production in recent decades, further increases in grain yield are required to meet future demands. Here we estimated genetic gain and genotype stability for grain yield (GY) and determined the trait associations that contributed uniquely or in combination to increased GY, through a retrospective analysis of top-performing genotypes selected from the elite spring wheat yield trial (ESWYT) evaluated internationally during a 14-year period (2003 to 2016). Fifty-six ESWYT genotypes and four checks were sown under optimally irrigated conditions in three phenotyping trials during three consecutive growing seasons (2018–2019 to 2020–2021) at Norman E. Borlaug Research Station, Ciudad Obregon, Mexico. The mean GY rose from 6.75 (24th ESWYT) to 7.87 t ha−1 (37th ESWYT), representing a cumulative increase of 1.12 t ha−1. The annual genetic gain for GY was estimated at 0.96% (65 kg ha−1 year−1) accompanied by a positive trend in genotype stability over time. The GY progress was mainly associated with increases in biomass (BM), grain filling rate (GFR), total radiation use efficiency (RUE_total), grain weight per spike (GWS), and reduction in days to heading (DTH), which together explained 95.5% of the GY variation. Regression lines over the years showed significant increases of 0.015 kg m−2 year−1 (p < 0.01), 0.074 g m−2 year−1 (p < 0.05), and 0.017 g MJ−1 year−1 (p < 0.001) for BM, GFR, and RUE_total, respectively. Grain weight per spike exhibited a positive but no significant trend (0.014 g year−1, p = 0.07), whereas a negative tendency for DTH was observed (− 0.43 days year−1, p < 0.001). Analysis of the top ten highest-yielding genotypes revealed differential GY-associated trait contributions, demonstrating that improved GY can be attained through different mechanisms and indicating that no single trait criterion is adopted by CIMMYT breeders for developing new superior lines. We conclude that CIMMYT’s Bread Wheat Breeding Program has continued to deliver adapted and more productive wheat genotypes to National partners worldwide, mainly driven by enhancing RUE_total and GFR and that future yield increases could be achieved by intercrossing genetically diverse top performer genotypes.

PODE: privacy-enhanced distributed federated learning approach for origin-destination estimation

The statewide consumer transportation demand model analyzes consumers’ transportation needs and preferences within a particular state. It involves collecting and analyzing data on travel behavior, such as trip purpose, mode choice, and travel patterns, and using this information to create models that predict future travel demand. Naturalistic research, crash databases, and driving simulations have all contributed to our knowledge of how modifications to vehicle design affect road safety. This study proposes an approach named PODE that utilizes federated learning (FL) to train the deep neural network to predict the truck destination state, and in the context of origin-destination (OD) estimation, sensitive individual location information is preserved as the model is trained locally on each device. FL allows the training of our DL model across decentralized devices or servers without exchanging raw data. The primary components of this study are a customized deep neural network based on federated learning, with two clients and a server, and the key preprocessing procedures. We reduce the number of target labels from 51 to 11 for efficient learning. The proposed methodology employs two clients and one-server architecture, where the two clients train their local models using their respective data and send the model updates to the server. The server aggregates the updates and returns the global model to the clients. This architecture helps reduce the server’s computational burden and allows for distributed training. Results reveal that the PODE achieves an accuracy of 93.20% on the server side.

Effect on combustion performance and emission behavior of diesel engine fueled with hybrid biodiesel produced from a novel ternary oil mixture incorporated with nano additive

ABSTRACT Global energy demand has been above 80% fossil fuels for decades, creating a greater and more complicated energy crisis. Biodiesel is a great alternate energy source for future energy demand. Producing biodiesel from a mixture of waste and non-edible oils may be a practical solution to address the growing global need of biodiesel. In this study, hybrid biodiesel is produced from a ternary oil mixture of Waste cooking oil, Ricinus communis oil, and Melia azedarach oil. The hybrid biodiesel blends are prepared by 10% blending (HB10), 20% blending (HB20) and 30% blending (HB30) and 30ppm multiwall carbon nanotube has been added in the blended fuel through sonicating process. At steady 1500rmp and variable load (0–100%), the engine behavior has been examined. The results show that the hybrid biodiesel blend (HB10) with 30ppm MWCNT has shown highest cylinder pressure of 70.9 bar, maximum mean gas temperature 1467.56°C. The engine shows 34.08% rise in net heat release and 9.13% rise in exhaust gas temperature as compare to diesel. The maximum rise in break thermal efficiency of 19.74% and minimum reduction in break specific fuel consumption of 13.79% is noticed in comparison to B00. The highest reduction of HC emission is found as 20.83% for HB20MWCNT30 and 19.34% for HB30MWCNT30 as compare to B00. The inclusion of multiwall carbon nanotube in hybrid biodiesel reduces the HC, CO, NOx and smoke emission and increases CO2 emission as compare to respective hybrid biodiesel blend and diesel. The hybrid biodiesel-diesel blend, with multiwall carbon nanotube can be utilized efficiently in diesel engine with improved overall engine behavior.

Does an aptitude for surgery exist and can we predict it? an experimental study.

The selection and allocation of surgeons with a greater potential for high surgical performance are essential aspects of improving the quality, safety and effectiveness of surgical procedures. Objective of this trial was to determine the existence of basic skills and traits that would predict better performance in surgery, and those predictive factors that constitute a driving force in different stages of training. The randomized crossover training trial took place from January 2021 to December 2021 and was conducted at an educational training center for minimally invasive surgery. A total of n=87 physicians (residents and experts) from surgical disciplines and n=239 fifth-year medical students were studied. The participants underwent extensive neuropsychological testing and surgical training, which was performed with conventional as well as robot-assisted laparoscopy by way of identical brief tasks conducted six times in a randomized crossover setting. Main Outcome was the latent factor structure of "psychomotor skills", "personality" and "motivation" based on structural equation modeling. The training performance of both students and physicians was significantly explained by the interaction of the three factors (explained variance: 8.2% for students, 23.8% for physicians). In students, motivation (explained variance 8.4%) and personality (explained variance 4.5%) revealed the highest contribution to surgical training performance (explained variance through psychomotor skills 1.1%). In physicians, psychomotor skills (explained variance 27.4%) made the greatest contribution to surgical training performance (explained variance through motivation 2.3%; explained variance through personality 10.5%). The study showed that surgical performance is sensitive to, and fragile in regard of non-surgery-related general individual traits. This aligns with the notion that early selection of surgeons with prospects of high surgical performance is possible, and perhaps even necessary in order to keep up with future demands on the medical system.

Towards food security: Exploring the spatio-temporal dynamics of soybean in India

India is the world’s largest edible oil importer, and soybean oil accounts for a major portion of those imports, with implications for the Indian economy. Despite being the 4th largest globally in terms of harvested soybean area and 5th largest in terms of production, India is still heavily dependent on imports to meet the vegetable oil requirement for its population. It is therefore imperative to understand the dynamics and trends in India’s soybean production to help the country achieve self-sufficiency in edible oils. This study provides the first spatially explicit analysis of soybean in India, using long-term spatial and temporal statistics at national and subnational levels, using spatial and temporal statistical analysis models to examine the historical trends and its future prospects. Our analysis details the overall soybean expansion across the country and the increase in production but we also note that the annual growth rate has declined in each consecutive decade even though the area continues to expand. The average national yield has been stagnant at around 1 T/Ha but for some of the low-producing districts, a higher yield of more than 3 T/ha is reported. For most major producing districts, soybean yields are below 1.5 T/Ha. The state of Madhya Pradesh which was the major soybean producer is now matched by the state of Maharashtra in terms of production, however, Madhya Pradesh still has the largest area under soybean. We analyzed soybean hotspot expansion in India and found that the mean center of the soybean area and production has shifted approximately 93 km towards the south and 24 km to the west as the crop is rapidly being adopted in the southern and western parts of India expanding the hotspot in these parts. District-level analysis showed that the total number of districts constituting hotspots of soybean cultivation in India has increased from 29 to 42 in three decades. Furthermore, analysis of soybean oil and meal consumption with respect to the national population, import, export, domestic production, GDP per capita, and price of soybean oil and meal suggests that soybean oil and meal are highly correlated with GDP per capita and population, indicating that consumption of soybean oil and meal is likely to increase as GDP per capita increases, and future demand is expected to rise with the anticipated growth in the Indian population. Increased soybean production can play a significant role in increasing national food security for India and reducing dependence on foreign oil imports and also help the economy with soy meal exports. Understanding the spatiotemporal variability in area and yield will help target interventions to increase production. Given the overall low yields with high variability in production, particularly in recent years primarily due to successive extreme rains and droughts in major producing districts and the overall need to increase production to meet the country’s demand, there is a pressing need for government policies and research aimed at narrowing the yield gap and developing soybean varieties that are more productive and resilient to climate change.

Techno-economic analysis of a hybrid photovoltaic-wind-biomass-battery system for off-grid power in rural Guatemala

Guatemala has made significant progress in improving its electrical infrastructure in recent years. However, most studies and efforts have focused on developing policies that directly benefit the national electrical market, which may cause a lack of attention toward solutions that cater to low-consumption cases, such as residential and rural communities. Therefore, it is vital to consider the needs of these communities while developing policies and solutions to ensure that they also have access to reliable and affordable sources of electricity. This study analyzes the cost-effectiveness and technical performance of a hybrid renewable energy system (HRES) that can meet the power needs of low electricity-consuming households in a rural region of Guatemala. The proposed HRES comprises a hybrid photovoltaic-wind turbine-bio generator coupled to battery storage, which caters to the energy needs of a typical household in Alta Verapaz, a rural area in Guatemala with limited electricity access (64.61%). The research considers three scenarios: I) basic electricity needs for the household, II) increased electricity needs for cooking and water heating, and III) future electricity demand in 2050, considering the role of the renewable energy market. Based on Scenario I, the cost-effective solution is a PV system with a capacity of 5.39 kW and 29 kWh battery capacity, with a cost of energy (COE) of 0.893 $/kWh. In Scenario II, a hybrid solution consisting of a 2.46 kW PV system, a 2.20 kW bio-generator, and 16 kWh battery capacity o, results in a COE of 0.605 $/kWh. Scenario III suggests a hybrid system, including 7.90 kW of PV, 3.30 kW bio-generator, and 14 kWh battery to meet the expected energy demand in 2050. COE for this solution is estimated to be 0.297 $/kWh. Considering the declining costs of renewable energy technologies by 2050, the findings highlight that the proposed HRES can be an affordable solution for low-consumption scenarios such as off-grid areas in Guatemala.

MnO/Mn2O3 Aerogels as Effective Materials for Supercapacitor Applications

Mixed-oxide transition-metal aerogels (AGLs), particularly manganese-based AGLs, have attracted considerable interest over the past decade owing to their extraordinary properties, including high porosity, good surface area, and ultralow density. To develop easy and lightweight materials for the ever-increasing energy storage demands of the near future, we designed a novel Mn-based electrode material to meet these rising requirements. MnO/Mn2O3 AGLs were synthesized using a novel borohydride hydrolysis method and then annealed at 200, 400, and 550 °C. The as-synthesized AGLs yielded flower-like network structures, but their porosity increased with increasing temperatures, to a high temperature of 400 °C. This increased porosity and network structure facilitate a high capacitance. A supercapacitor (SC) constructed with the three-electrode material yielded 230 F/g for the MnAGL@400 sample, followed by yields from the MnAGL@200 and MnAGL@550 electrodes. Furthermore, the device constructed with MnAGL@400 exhibited an energy density of 9.8 Wh/kg and a power density of ~16,500 W/kg at a current density of 20 A/g. The real-time applicability of the AGL was demonstrated by engineering a two-electrode device employing MnAGL@400 as the positive electrode, which exhibited 97% capacity retention and 109% Coulombic efficiency over 20,000 cycles.

Spatiotemporal variation in rice water requirements and area in the cold rice cultivation region of China: Past and Future

Studying rice water requirements (ETc) and area is crucial for developing irrigation schedules and adjusting crop structures. The cold rice cultivation region is China’s main high-quality rice production base and has the lowest temperature, highest latitude, and greatest sensitivity to climate. However, little research has focused on its past rice distributions and ETc, and its future variations are not yet clear. Past ETc was calculated by the Penman–Monteith and crop-corrected coefficient method, and past rice distribution patterns were analyzed based on the annual paddy rice planting area datasets. Future reference crop evapotranspiration (ET0) was projected by the statistical downscaling model (SDSM) based on the CMIP6 ensemble of daily predictor variables. The food and agriculture projections to 2050 datasets were used to project future rice areas, and the patch-generating land use simulation (PLUS) model simulated rice distribution. Finally, we projected future rice water supply demand. The results showed that ETc showed a decreasing trend, and the total rice area and production increased by 4.52 and 5.49 times from 1990 to 2020. Both in the past and future, rice was densely distributed in and expanded into the Sanjiang and Songnen Plains, and the spatial characteristic of ETc was as follows: the west was larger than the east, the north was larger than the south, and the central was in between. In the four future periods, the total ETc demonstrated an upward trend, increasing by 7.44–21.04, 15.64–34.74, 24.74–45.84, and 16.14–41.14 mm under the SSP126, SSP245, SSP370, and SSP585 scenarios, respectively, compared to the historical period. And it is generally ranked as SSP370 > SSP245 > SSP126. Finally, we projected future rice water supply meet the demand caused by rice area expansion.

Energy Load Forecasting Techniques in Smart Grids: A Cross-Country Comparative Analysis

Energy management systems allow the Smart Grids industry to track, improve, and regulate energy use. Particularly, demand-side management is regarded as a crucial component of the entire Smart Grids system. Therefore, by aligning utility offers with customer demand, anticipating future energy demands is essential for regulating consumption. An updated examination of several forecasting techniques for projecting energy short-term load forecasts is provided in this article. Each class of algorithms, including statistical techniques, Machine Learning, Deep Learning, and hybrid combinations, are comparatively evaluated and critically analyzed, based on three real consumption datasets from Spain, Germany, and the United States of America. To increase the size of tiny training datasets, this paper also proposes a data augmentation technique based on Generative Adversarial Networks. The results show that the Deep Learning-hybrid model is more accurate than traditional statistical methods and basic Machine Learning procedures. In the same direction, it is demonstrated that more comprehensive datasets assisted by complementary data, such as energy generation and weather, may significantly boost the accuracy of the models. Additionally, it is also demonstrated that Generative Adversarial Networks-based data augmentation may greatly improve algorithm accuracy.

Assessment of Ghana's current energy types to meet tomorrow's needs

Ghana is currently facing challenges in aligning its energy options with future energy demands and reducing its dependency on fossil fuels. This study assessed Ghana's current energy types and their potential to meet future energy needs. A structured questionnaire with a cross-sectional survey and random sampling technique was employed to gather information on energy choices, drivers and challenges from 868 respondents. A multiple linear regression model was used to evaluate the impact of energy drivers on preferences in Ghana. Results showed that 82 % of Ghanaians are ready to transition to cleaner energy sources, with preferences for hydropower/grid electricity (45.70 %) and natural gas/LPG (32.90 %) and biofuels (12.00 %). Economic (16.20%) and population (15.50%) growth are the main drivers of energy transitions, while challenges include high initial costs (11.20%) and limited awareness (4.90%). Strategies such as financial support, education, renewable energy promotion, technological advancement and international collaboration should be promoted to actualise Ghana's transition to future renewable energy usage.

Seasonal Data Cleaning for Sales with Chase Demand Strategy

Seasonal Data Cleaning for Sales with Chase Demand Strategy

The Role of Forecasts in Planning for Energy Infrastructure: A Historical Look at Past Futures in Postwar Quebec

The Role of Forecasts in Planning for Energy Infrastructure: A Historical Look at Past Futures in Postwar Quebec

Towards sustainable, direct printed, organic transistors with biocompatible copolymer gate dielectrics

Towards sustainable, direct printed, organic transistors with biocompatible copolymer gate dielectrics

Sales Order Backlog and Credit Ratings

This study examines the association between sales order backlog and credit ratings. We posit that credit rating agencies consider order backlog as a positive signal about strong future demand and incorporate that into their rating decisions and provide higher ratings to firms with substantial order backlogs. However, being a non-GAAP, unaudited metric, order backlog could also reduce financial reporting quality and hence, credit ratings of firms. Using a sample of US firms from 1980 to 2017, we find a positive and significant association between order backlog and credit ratings, suggesting that order backlog serves as a valuable measure in credit rating assessment by providing positive signals about future earnings to rating agencies.

A-LLTO Nanoparticles Embedded Composite Solid Polymer Electrolyte for Room Temperature Operational Li-metal Batteries.

Solid-state batteries (SSBs) have the potential to revolutionize the current energy storage sector. A significant portion of the current development of electric vehicles and the electrification of various appliances relies on Lithium (Li)-ion batteries. However, future energy demands will require the development of stronger and more reliable batteries. This report presents a novel solid state electrolyte (SSE) composed of a self-healing composite solid polymer electrolyte (CSPE) matrix and aluminum-doped (Li0.33La0.56)1.005Ti0.99Al0.01O3 (A-LLTO) nanofillers. The CSPE contains Jeffamine ED-2003 monomer, Benzene-1,3,5-tricarbaldehyde (BTC)crosslinker dissolved in a 1:1 ratio of Dimethylformamide (DMF) to LiPF6, and a certain amount (x) of A-LLTO nanofillers (x=5, 7.5, 10, 12.5%). A CSPE containing x-amount of A-LLTO fillers (referred to as CAL-x%) demonstrates excellent ion-conducting properties and stable battery performance. The CAL-10% demonstrates 1.1×10-3Scm-1 of ionic conductivity at room temperature (RT). A-LLTO nanofillers dispersed uniformly within the polymer matrix form a percolation network, which is believed to improve ionic conductivity and the diffusion of Li+ ions. The CR-2032 cell, consisting of LiFePO4 (LFP)║CAL-10%║Li, at RT offers an initial discharge capacity of ≈165mAhg-1 at 0.1C rate for 120 cycles with 98.85% coulombic efficiency (C.E.).

Development of world class ski performance: Similarities and differences in pathways to expertise for freeskiers and cross-country skiers

The overall purpose of the present study was to investigate similarities and differences in pathways to expertise for freeskiers and cross-country skiers. Specifically, 1) Entrance to the main sport, 2) Amount of training hours and organization of training, and 3) Variability in activities and content in training. Sampling athletes had to be on the highest level in their sport by competing in, e.g., the World Cup, World Championship, Olympic games, freeride World Tour. In total, 18 world-class skiers participated in the study. Eight freeskiers (age range 19-37) and ten cross-country skiers (age range 22-32 years). They answered a digital questionnaire designed to collect retrospective data describing their pathways to expertise. The first section elicits biographical information, age for entering sport and practice. The second section focused on sport-specific training, accumulated hours of training, and the distribution of organized versus self-organized training. The third section recalled the variability of activities they participated in and how they perceived the relevance of the content of different types of training for their sport-specific development. The results showed similarities in athletes’ entrance to the main sport, specialization age and total amount of training. At the same time, differences were observed in their training history regarding the organization of training. In contrast to cross-country skiers, freeskiers seem to be more self-organized and more involved in additional activities besides their main sport. Interestingly, the two groups of world-class athletes representing sports with distinctive demands share several common variables in their paths to expertise, amplified by perceived specificity. Practitioners and academics should substantiate patience in expertise development, driven by a multi-disciplinary understanding of distinct and individual characteristics or conditions that may be beneficial in fulfilling varied future demands. Hence, research should explore the rationale behind and potential learning effects between sports and training content.

The impact of climate policy on oil and gas investment: Evidence from firm-level data

Using a text-based firm-level measure of climate policy exposure, we show that climate policies have led to a global decline of 6.5 percent in investment among publicly traded oil and gas companies between 2015 and 2019, with European companies experiencing the most significant impact. Similarly, climate policy uncertainty has also had a negative impact. Our empirical results support the Neoclassical investment model. According to this model, firms pre-emptively cut investment in response to downward shifts in future demand. These findings contrast with the Green Paradox theory, which predicts an increase in current investment by oil and gas firms aimed at shifting extraction toward the present.

Near-, medium- and long-term impacts of climate change on the thermal energy consumption of buildings in Finland under RCP climate scenarios

The building sector is critical in climate change mitigation by reducing its energy consumption. At the same time, warming climate requires adaptation measures from buildings, making it necessary to study the future thermal energy demand of buildings. This paper studies the impact of climate change on the thermal energy demand of buildings in a heating dominated climate of Finland, under “representative concentration pathway” (RCP) climate scenarios in near- (2030), medium- (2050) and long-term (2080) horizon. Additionally, the impact of the thermal performance level of the buildings is investigated. The thermal energy demand calculation is based on overarching standards from the European “energy performance of buildings” directive. The weather scenarios present both typical and extreme weather years, applied through morphing and future weather data from the Finnish Meteorological Institute. The results show that thermal energy demand in Finland in 2080 can decrease up to 74.6kWh/m2. In addition, the decrease in heating demand will be higher than the increase in cooling demand. Cooling can become the dominant peak in long-term evaluations, especially in the South (Vantaa) with passive buildings under RCP8.5 climate scenario. Renovation is the most efficient action to decrease energy intensity, the earliest it is done the better.

Reinforcement Learning for Efficient Power Systems Planning: A Review of Operational and Expansion Strategies

The efficient planning of electric power systems is essential to meet both the current and future energy demands. In this context, reinforcement learning (RL) has emerged as a promising tool for control problems modeled as Markov decision processes (MDPs). Recently, its application has been extended to the planning and operation of power systems. This study provides a systematic review of advances in the application of RL and deep reinforcement learning (DRL) in this field. The problems are classified into two main categories: Operation planning including optimal power flow (OPF), economic dispatch (ED), and unit commitment (UC) and expansion planning, focusing on transmission network expansion planning (TNEP) and distribution network expansion planning (DNEP). The theoretical foundations of RL and DRL are explored, followed by a detailed analysis of their implementation in each planning area. This includes the identification of learning algorithms, function approximators, action policies, agent types, performance metrics, reward functions, and pertinent case studies. Our review reveals that RL and DRL algorithms outperform conventional methods, especially in terms of efficiency in computational time. These results highlight the transformative potential of RL and DRL in addressing complex challenges within power systems.

Experimental and numerical investigations on synergistic coupling of metal hydride hydrogen storage systems with low-temperature proton exchange membrane fuel-cell

Hydrogen has emerged as a promising alternative for various energy needs, and metal hydride hydrogen systems offer significant potential due to their near-ambient working conditions and safe storage. This study presents experimental and numerical investigations on a lab-scale metal hydride fuel cell system to showcase its viability in addressing future energy demands. The experimental setup includes a 41-tube Metal Hydride (MH) reactor with an outer cooling jacket, containing 3.75 kg of La0.7Ce0.1Ca0.3Ni5. The output of the reactor is coupled to a 1 kW fuel cell, with controlled hydrogen discharge at temperatures ranging from 30 to 50 °C through numerical modelling. The study found that maintaining the reactor at 40 °C was sufficient to sustain the required bed pressure for a 1 kW fuel cell. Numerical simulations revealed that a 1 kW fuel cell operating at a current density of 0.8 A/cm2 and 50 °C rejected approximately 1.3 kW of waste heat. Moreover, 3.75 kg MH reactor could discharge hydrogen at a constant rate of 13 l/min for 36.3 min with just 125–225 W of heat input, highlighting the thermal coupling potential between these systems. The energy efficiency of the coupled MH-PEMFC system increased by 1.8 times to reach 49 % at 50 °C. Additionally, the waste heat generated proved sufficient to provide the required endothermic heat to multiple MH reactors, with a single reactor utilizing only 16.3 % of the total waste heat. This integrated system demonstrates promising efficiency and sustainability for future energy applications.