Human Demands Research Articles

Stable Deuterium-Tritium plasmas with improved confinement in the presence of energetic-ion instabilities

Providing stable and clean energy sources is a necessity for the increasing demands of humanity. Energy produced by Deuterium (D) and Tritium (T) fusion reactions, in particular in tokamaks, is a promising path towards that goal. However, there is little experience with plasmas formed by D-T mixtures, since most of the experiments are currently performed in pure D. After more than 20 years, the Joint European Torus (JET) has carried out new D-T experiments with the aim of exploring some of the unique characteristics expected in future fusion reactors, such as the presence of highly energetic ions in low plasma rotation conditions. A new stable, high confinement and impurity-free D-T regime, with reduction of energy losses with respect to D, has been found. Multiscale physics mechanisms critically determine the thermal confinement. These crucial achievements importantly contribute to the establishment of fusion energy generation as an alternative to fossil fuels.

ECONOMIC ANALYSIS OF POWER PLANT USING UNIT COMMITMENT SCHEDULING

Unit commitment is the scheduling of disconnected generation of generating units in an electric power system within a certain period, with the aim of meeting load demand. This scheduling is usually determined based on human load demand which always changes over time. This research aims to determine the production costs of thermal plants using the Second Order Gradient method to obtain economical operating costs, to determine fuel costs based on plant loading, and to determine the most economical scheduling using unit commitment. This is to determine the most optimal on/off schedule for generating units to be operated to meet the estimated load to achieve minimum fuel costs in rupiah.

Green innovation for a sustainable environment: Modeling the new green knowledge production function for OECD countries

AbstractThe world community faces serious challenges such as climate change, biological loss, and environmental degradation. Developing ecological technologies is vital for coping with these unreversible crises. This study proposed a modified green knowledge production function to achieve sustainable development goals and generate policies for promoting environmental technologies. It examined the impact of market demands, environmental research and development expenditures, human capital, and environmental protection policies on the production of green environment technologies. Panel data included 16 OECD countries from 1990 to 2019. Data analysis strategies included the Gengenbach, Urbain, and Westerlund panel co‐integration test, fully modified least squares, mean group dynamic least squares, and estimators such as feasible generalized least squares and panel‐corrected standard errors for robust checks. A novel panel causality test via a half‐panel jackknife estimator was also applied for causality analyses. The findings showed that human capital, market size, and environmental policy stringency played significant roles in producing environmental technologies and innovations. Although environmental expenditures made by the government positively affected the supply of green innovation, no statistically significant results were observed. The study highlights the importance of enhancing environmental innovation factors by promoting policies, including human capital development, environmental sanctions, and market demands for a sustainable environment.

Spatial distribution of sandeel (Hyperoplus lanceolatus) and implications for monitoring marine protected sites

Increased human demand on the marine environment and associated biodiversity threatens sustainable delivery of ecosystem goods and services, particularly for shallow shelf-sea habitats. As a result, more attention is being paid to quantifying the geographical range and distribution of seabed habitats and keystone species vulnerable to human pressures. In this study, we develop a workflow based on unsupervised K-Means classification units and Generalized Linear Models built from multi-frequency backscatter analyses (95, 300 kHz), bathymetry and bathymetry derivatives (slope) to predict different levels of sandeel densities in Hempton's Turbot Bank Special Area of Conservation (SAC). For Hyperoplus lanceolatus densities, the performance of single frequency verses multi-frequency models is compared. Relatively high agreement between K-Means clustering outputs (from 95 kHz and multi-frequency models) and ground-truthed sandeel densities is noted. Moreover, Root Mean Squared Error (RMSE) values in this instance demonstrate that single-frequency models are favoured over the multi-frequency model in terms of predictive ability. This is mostly linked to the species strong affinity for sedimentary environments whose variability is better captured by the lower frequency system. Generally, these results provide important information about species-habitat relationships and pinpoint bedform features where sandeels are likely to be found and whose variability is potentially linked to the bathymetry domain. The workflow developed in this study also provides a proof of concept to support the design of a robust species-specific monitoring plan in marine protected areas. Most importantly, we highlight how decisions made during sampling, data handling, analysis could impact the final outputs and interpretation of Species Distribution Models and benthic habitat mapping.

Modeling for sustainable groundwater management: Interdependence and potential complementarity of process-based, data-driven and system dynamics approaches

Groundwater systems are vast natural water reservoirs used to support human water demands and ecosystem services. Various modeling approaches have been developed to help manage these complex highly-dynamic systems. This paper discusses the strengths and limitations of three modeling approaches, namely: process-based, data-driven and system dynamics modeling. For demonstration purposes, the three modeling approaches are applied to the Konya Closed Basin, a large agricultural region with semi-dry climate located in central Turkey. Process-based modeling is grounded in the theory-based representation of the governing processes but is somewhat limited by the computational effort and the difficulty of defining the required input parameters that characterize the heterogeneous aquifer system. Process-based models are shown to be powerful tools for resource management purposes provided climatic and water demand scenarios are accurately defined. Data-driven models are efficient tools for the management of groundwater resources but are highly dependent on the availability of large training data sets encompassing the spectrum of possible system responses. The high efficiency of surrogate modeling approaches makes them ideal tools for incorporation into applications such as real-time decision support systems and digital twin platforms. System dynamics modeling examines the groundwater exploitation problem within a socio-economic context that involves multiple stakeholders and their decision making. It combines groundwater flow models with socio-economics and endogenous decision rules to conduct scenario analysis and support policy development. The analyses and model demonstrations presented in this paper underscore the interconnectedness and complementarity of these three modeling approaches and the need for more integrated use of these modeling approaches for enhanced multi-sectoral management of groundwater systems.

Exploring Cognitive Workload Changes in Teleoperators: Collaboration with On-Site Workers and Robots in Assembly Tasks

Teleoperation is commonly employed to perform industrial tasks in remote or inaccessible areas. However, there is a noticeable gap in evaluating cognitive workload of teleoperators in collaboration environments. This study compared the variation of cognitive workload for teleoperators guiding an on-site participant to complete a wire assembly task in two scenarios: one aided by a robot arm (tRH) and another without any robot assistance (HH). Additionally, the task demands for on-site participants were manipulated to measure its impact on teleoperator’s workload. NASA-TLX and EEG activity were utilized to assess workload. The results indicated that EEG theta activity was significantly higher for the HH group than tRH group, potentially showing lower workload for teleoperators in the scenario with robot assistance. Task difficulty did not affect any of the workload measures. The study highlights the importance of cognitive workload assessment in human-robot collaborations to optimize human cognitive demands in complex settings.

The spatio-temporal evolution of spatial structure and supply-demand relationships of the ecological network in the Yellow River Delta region of China

Rapid urbanization has caused unregulated transformation of land use/land cover, further exacerbating the imbalance in ecological supply-demand relationships. Constructing the ecological supply-demand network by integrating natural ecological processes and human ecological demands into a unified spatio-temporal framework and considering their internal dynamic flow characteristics is an effective approach to address this issue. However, there is currently a lack of evaluation methods for the coordination of ecological network supply-demand relationships, which could provide essential guidance and reference. This article takes Yellow River Delta region (YRDR) as an example and utilizes the integrated traditional ecological network paradigm and ecosystem services theory to simulate the ecological supply-demand network for the years 2000, 2010, and 2020 in the study area. Then, a novel evaluation index system for the ecological network supply-demand relationships was proposed from the perspectives of intensity, capacity, and equilibrium. Based on this, combined with a graph-based network structure evaluation system, it reveals the spatio-temporal evolution process of the spatial structure and supply-demand relationship of the ecological network in the study area. The study results showed that: (1) The quantity of elements in the ecological supply network and the demand network show opposite trends, leading to an exacerbation of the spatial imbalance in the arrangement of ecological supply-demand network elements. (2) Although the structural changes in the ecological supply network were significantly greater than those in the ecological demand network, the overall structure of the ecological supply-demand network gradually improved. (3) However, under the coupled influence of imbalanced change in the spatial distribution and connectivity of ecological elements, the overall coordination of the ecological supply-demand relationships within the YRDR's ecological network is gradually deteriorating. (4) The proposed optimization strategy, "Two zones, Three belts, Four corridors", for the ecological security pattern in YRDR can accommodate the ecological process cycle and human ecological demand. This study can provide scientific references for "harmonious coexistence between humans and nature" ecological protection and sustainable development planning under the background of rapid urbanization.

Resilience in River Corridors: How Much Do We Need?

AbstractResilience in river corridors refers to the ability to absorb disturbance and maintain processes, forms, and functions that support the river ecosystem and provide ecosystem services. Resilience derives from characteristics such as three‐dimensional connectivity, spatial heterogeneity, and physical and ecological integrity. Resilience is important as climate warming and growing human populations and consumptive demands change the disturbance regime affecting river corridors. Consequently, river management increasingly focuses on enhancing natural characteristics that create river resilience via (a) identifying the processes and features that promote and sustain resilience, (b) identifying portions of a river network and/or river corridor that are most resilient or most influential in creating resilience at larger spatial scales, and (c) protecting and restoring features that create resilience. Although the basic conceptual framework for these activities is well established, critical questions remain with respect to how reach‐scale management creates catchment‐scale results.

Identification of Potential Land Use Conflicts in Shandong Province: A New Framework

Land use conflicts (LUCs) have become a significant global issue. Accurately identifying potential LUCs is crucial for mediating these conflicts, optimizing land use structure, and enhancing land use function. The necessary conditions of LUCs are land use multi-suitability (LUMS), land resource scarcity (LRS), and diversity of demands (DD). However, few studies have approached LUC identification from these three dimensions simultaneously. In addition, when assessing the diversity of demand, only human needs are considered and wildlife needs are ignored. In order to address this gap in the research, this paper constructs a novel framework for LUC identification and proposes an induction-oriented governance path. LUMS was evaluated from three aspects: construction suitability, cultivation suitability, and ecological suitability. LRS is measured from three dimensions: construction land, cultivated land, and ecological land scarcity. The DD is expanded into human and wildlife demand diversity. By analyzing the combination of LUMS, LRS, and DD, LUCs are classified using the potential LUC identification Rubik’s cube model, and corresponding governance paths are suggested. In Shandong Province, potential LUCs are relatively high, with strong, medium, and weak conflicts accounting for 27.39%, 57.10%, and 13.06%, respectively. Potential strong conflicts are mainly distributed in the metropolitan suburbs and in the western plain of Shandong Province. Cultivated land is the main potential land use conflict space. The new framework of LUC identification proposed in this paper can effectively identify potential LUCs. Our research provides scientific reference for sustainable land use.

Surface engineering of Al2O3 dielectric layer for all-sputtered-oxide transparent and flexible a-IGZO thin film transistor

Transparent flexible display devices have attracted massive attention in recent years owing to their practical applications that can be compatibly adapted to human demand and technological development. Worldwide researchers have studied flexible transparent thin film transistors (TFTs) to qualify them for the backplane component of future transparent and flexible displays. Simplifying the fabrication process of a TFT can be considered a critical need for reducing consumed cost, time, toxic chemicals. In this study, sputtering was utilized as a major fabrication method for manufacturing a transparent oxide TFT device, which demonstrated good mechanical flexibility. A simple surface treatment process was applied to enhance the susceptibility of the sputtered Al2O3 dielectric through multiple coatings of solution processed Al2O3. It is noteworthy that the transparent oxide TFT device exhibits a high optical transparency of 81.12% and good electrical switchability at low operating voltage (3 V) with a decent mobility of 4.37 cm2 V−1 s−1 and proven mechanical durability.

Advancing human pose estimation with transformer models: An experimental approach

This paper explores the integration of Transformer architectures into human pose estimation, a critical task in computer vision that involves detecting human figures and predicting their poses by identifying body joint positions. With applications ranging from enhancing interactive gaming experiences to advancing biomechanical analyses, human pose estimation demands high accuracy and flexibility, particularly in dynamic and partially occluded scenes. This study hypothesizes that Transformers, renowned for their ability to manage long-range dependencies and focus on relevant data parts through self-attention mechanisms, can significantly outperform existing deep learning methods such as Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs). We introduce the PoseTransformer, a hybrid model that combines the precise feature extraction capabilities of CNNs with the global contextual awareness of Transformers, aiming to set new standards for accuracy and adaptability in pose estimation tasks. The model's effectiveness is demonstrated through rigorous testing on benchmark datasets, showing substantial improvements over traditional approaches, especially in complex scenarios.

GCAM–GLORY v1.0: representing global reservoir water storage in a multi-sector human–Earth system model

Abstract. Reservoirs play a significant role in modifying the spatiotemporal availability of surface water to meet multi-sector human demands, despite representing a relatively small fraction of the global water budget. Yet the integrated modeling frameworks that explore the interactions among climate, land, energy, water, and socioeconomic systems at a global scale often contain limited representations of water storage dynamics that incorporate feedbacks from other systems. In this study, we implement a representation of water storage in the Global Change Analysis Model (GCAM) to enable the exploration of the future role (e.g., expansion) of reservoir water storage globally in meeting demands for, and evolving in response to interactions with, the climate, land, and energy systems. GCAM represents 235 global water basins, operates at 5-year time steps, and uses supply curves to capture economic competition among renewable water (now including reservoirs), non-renewable groundwater, and desalination. Our approach consists of developing the GLObal Reservoir Yield (GLORY) model, which uses a linear programming (LP)-based optimization algorithm and dynamically linking GLORY with GCAM. The new coupled GCAM–GLORY approach improves the representation of reservoir water storage in GCAM in several ways. First, the GLORY model identifies the cost of supplying increasing levels of water supply from reservoir storage by considering regional physical and economic factors, such as evolving monthly reservoir inflows and demands, and the leveled cost of constructing additional reservoir storage capacity. Second, by passing those costs to GCAM, GLORY enables the exploration of future regional reservoir expansion pathways and their response to climate and socioeconomic drivers. To guide the model toward reasonable reservoir expansion pathways, GLORY applies a diverse array of feasibility constraints related to protected land, population, water sources, and cropland. Finally, the GLORY–GCAM feedback loop allows evolving water demands from GCAM to inform GLORY, resulting in an updated supply curve at each time step, thus enabling GCAM to establish a more meaningful economic value of water. This study improves our understanding of the sensitivity of reservoir water supply to multiple physical and economic dimensions, such as sub-annual variations in climate conditions and human water demands, especially for basins experiencing socioeconomic droughts.

Blue valorization of lignin-derived monomers via reprogramming marine bacterium Roseovarius nubinhibens.

Biological valorization of lignin, the second most abundant biopolymer on Earth, is an indispensable sector to build a circular economy and net-zero future. However, lignin is recalcitrant to bioupcycling, demanding innovative solutions. We report here the biological valorization of lignin-derived aromatic carbon to value-added chemicals without requesting extra organic carbon and freshwater via reprogramming the marine Roseobacter clade bacterium Roseovarius nubinhibens. We discovered the unusual advantages of this strain for the oxidation of lignin monomers and implemented a CRISPR interference (CRISPRi) system with the lacI-Ptrc inducible module, nuclease-deactivated Cas9, and programmable gRNAs. This is the first CRISPR-based regulatory system in R. nubinhibens, enabling precise and efficient repression of genes of interest. By deploying the customized CRISPRi, we reprogrammed the carbon flux from a lignin monomer, 4-hydroxybenzoate, to achieve the maximum production of protocatechuate, a pharmaceutical compound with antibacterial, antioxidant, and anticancer properties, with minimal carbon to maintain cell growth and drive biocatalysis. As a result, we achieved a 4.89-fold increase in protocatechuate yield with a dual-targeting CRISPRi system, and the system was demonstrated with real seawater. Our work underscores the power of CRISPRi in exploiting novel microbial chassis and will accelerate the development of marine synthetic biology. Meanwhile, the introduction of a new-to-the-field lineage of marine bacteria unveils the potential of blue biotechnology leveraging resources from the ocean.IMPORTANCEOne often overlooked sector in carbon-conservative biotechnology is the water resource that sustains these enabling technologies. Similar to the "food-versus-fuel" debate, the competition of freshwater between human demands and bioproduction is another controversial issue, especially under global water scarcity. Here, we bring a new-to-the-field lineage of marine bacteria with unusual advantages to the stage of engineering biology for simultaneous carbon and water conservation. We report the valorization of lignin monomers to pharmaceutical compounds without requesting extra organic substrate (e.g., glucose) or freshwater by reprogramming the marine bacterium Roseovarius nubinhibens with a multiplex CRISPR interference system. Beyond the blue lignin valorization, we present a proof-of-principle of leveraging marine bacteria and engineering biology for a sustainable future.

Digital transformation, management efficiency and excess employees − evidence from Chinese listed firms

Purpose The purpose of this study is to explore the relationship between digital transformation and excess employees. This research investigates the questions of when human−machine synergy can be achieved after a firm goes through digital transformation and whether there will be excess employees in the interim. Design/methodology/approach This paper takes A-share listed companies as research object in the period of 2011−2020 and a total of 24,718 samples are obtained. Hypothesis testing and regression analysis are performed in STATA. Findings This paper finds a human−machine mismatch in the short term, as evidenced by an increase in the rate of excess employees; however, with the progress of digital transformation, it can drive the achievement of human−machine synergy in the long term, and management efficiency plays a mediating role in this process. Further research showed that the effects of digital transformation on the number of employees, revenue generation per capita and profit generation per capita varied in both the short and long term. In addition, the characteristics of the company affect the relationship between digital transformation and excess employees. Originality/value This paper contributes to the understanding of the impacts of digital transformation on the human capital structure of companies at a micro level. It also provides insights into how to improve human capital demand structure through digitalization, thus providing insights into labor market changes.

Densification of Genetic Map and Stable Quantitative Trait Locus Analysis for Amino Acid Content of Seed in Soybean (Glycine max L.).

Soybean, a primary vegetable protein source, boasts favorable amino acid profiles; however, its composition still falls short of meeting human nutritional demands. The soybean amino acid content is a quantitative trait controlled by multiple genes. In this study, an F2 population of 186 individual plants derived from the cross between ChangJiangChun2 and JiYu166 served as the mapping population. Based on the previously published genetic map of our lab, we increased the density of the genetic map and constructed a new genetic map containing 518 SSR (simple sequence repeats) markers and 64 InDel (insertion-deletion) markers, with an average distance of 5.27 cm and a total length of 2881.2 cm. The content of eight essential amino acids was evaluated in the F2:5, F2:6, and BLUP (best linear unbiased prediction). A total of 52 QTLs (quantitative trait loci) were identified, and 13 QTL clusters were identified, among which loci02.1 and loci11.1 emerged as stable QTL clusters, exploring candidate genes within these regions. Through GO enrichment and gene annotation, 16 candidate genes associated with soybean essential amino acid content were predicted. This study would lay the foundation for elucidating the regulatory mechanisms of essential amino acid content and contribute to germplasm innovation in soybeans.

Influencing Factors Used for Performance Evaluation of Solar Dryers

Due to the rapid growth in human energy demand, research and development in renewable energy sources have become a top priority. Solar energy, as a renewable energy source, is widely used in the drying industry. This study examines the factors that influence the performance evaluation of solar dryers. The dryer consists of an air collector, a drying chamber, and a chimney. Experiments were conducted at the Solar Energy Laboratory of the Hungarian University of Agriculture and Life Sciences in Hungary (47.59° N, 19.36° E). The drying behaviour of single-pass and double-pass solar dryers was compared under the same environmental conditions. The analysis results indicate that the maximum solar radiation value is 950 W/m2 at 12:00. The maximum temperature difference between the top and bottom channels of the double-pass solar air collector is 9.5 °C. The average humidity in the environment, single-pass, and double-pass drying chambers is 31%, 26.5%, and 24%, respectively. The instantaneous efficiency of the single-pass solar collector reaches a peak of 56%, while the double-pass collector’s instantaneous efficiency peaks at around 60%. The collector performance curves show that the double-pass collector has a more stable performance.

The Costs and Threats of the «Green» Energy Transition

This article explores the challenges facing the modern energy market in the context of the accelerated energy transition. It concludes that neither the objectives nor the preparation for the energy transition have been aligned with the actual needs and demands of humanity. The current goals of the energy transition are ideologically driven and unrealistic, prompting countries and companies to reconsider their previously established timelines for achieving carbon neutrality, while investors are growing increasingly disillusioned with the «green» transition. Despite trillions of dollars in investments, alternative energy sources have failed to replace traditional fuels or ensure reliable energy supply. Concurrently, the demand for electricity will continue to rise with the advancement of artificial intelligence and the expansion of data processing centers.The article highlights that in Europe, the emphasis on the «green» energy transition has led to economic downturns, while in developing countries, the implementation of the energy transition has manifested as «green» neocolonialism, characterized by resource extraction. Lobbying for an accelerated energy transition represents one form of market share competition. Another form includes unlawful sanctions and technological restrictions imposed by the United States on several countries, and the use of the dollar as an economic weapon, which has compelled developing nations to embark on dedollarization processes.The article concludes that energy development must be linked to ensuring energy security, combating poverty, and addressing the interests of the majority. Therefore, hydrocarbons will continue to be a critical component of the energy sector.

Social Justice in Tiv Thought System

It is a given that every human society demands justice which reflect not only in its laws, institutions, but social systems as well, and, it is expected that the major social institutions should distribute fundamental rights and duties and, determine the division of advantages from social cooperation. This expresses the conviction that social justice is the highest of values in the society and imperative in the ordering of societal priorities. This paper appraised the concept of social justice as understood in Tiv traditional thought system. The paper employed qualitative research where data were derived from books, journal articles and the internet. The historical, expository and critical methods were adopted for this paper. The historical method presented the views of various philosophers on the concept of justice as well as the history of Tiv people. The expository method relayed the concept of justice in Tiv traditional thought system while the critical method focused on the appraisal of Tiv notion of social justice. The paper found out that, lack of social justice has bedeviled the Tiv nation. This is evidenced in jealousy, envy, land disputes, poverty, politics of bitterness, decline in value orientation, individualism, sectionalism etc. These indices have thwarted development in Tivland, retarded growth and bred suspicion and scorn. The paper therefore, concluded that if Tiv people revive their traditional value system of social justice encapsulated in Gbaaondo, swem, as well as ya na angbian, the much needed cohesion, integration, unity, growth and development that has eluded Tiv people and Tivland for so long would be realised.

Capturing the drivers of crop water footprints in Africa and its spatial patterns

Improving water efficiency in the agricultural sector is essential to ensure sustainable withdrawals and supply of freshwater in a context of increasing water scarcity and human water demand. The water footprint (WF) is an established metric of resource intensity while the drivers steering WF over time remain under-researched. To advance this line of research, this paper assesses the sign and magnitude of macroeconomic, climatic, and agronomic drivers on the agricultural crop WF in 43 countries of the African continent for the period 2002–2016, using econometric panel data techniques and considering potential spatial patterns. The results reveal a significant spatial dependence in the WF across neighbouring countries. Socioeconomic factors are the most important determinant of water productivity, indicating that economic development facilitates a falling water requirement per unit of production. A negative impact of the temperature variation on the WF is also found, while the share of total land dedicated to agriculture tends to increase the crop WF in the continent. These results support designing adequate agricultural and water management policies to achieve sustainable and resilient food systems capable of adapting to anticipated population growth, climate change and other future threats to human health, prosperity and environmental sustainability in Africa.

Internet of things based smart photovoltaic panel monitoring system

<p>Solar energy has the potential to meet the entire world's energy requirements. Previous study revealed that photovoltaic (PV) panel monitoring methods are far more difficult and exclusive. This study examines and proposes an automated internet of things (IoT)-based PV panel monitoring system that allows autonomous monitoring of solar panel properties such as voltage, temperature, humidity, and sun irradiation from anywhere over the internet. Excessive human demand can be reduced by collecting data from solar panels. Many data sets can be collected frequently; for example, thousands of data points can be collected each minute. Continuous monitoring is achievable by gathering real-time data. Because there is no possibility of human error, highly accurate data can be generated.</p>