Amount Of Resources Research Articles

Effective strategies for universities in the new era to curb students' food waste behavior

With the rapid development of economy and the gradual acceleration of urbanization, the phenomenon of food waste has become increasingly common, and the overall scale is very shocking. Under the new situation, the issue of food security has received high attention from the government and society. By reducing consumers' food consumption, it has become the second set of solutions to ensure food security. Moreover, food waste leads to the wastage of a significant amount of food resources in both the food itself and various links in the supply chain, resulting in economic losses and the emission of some greenhouse gases. In the context of the new era, universities are not only temples for knowledge dissemination, but also important places for cultivating students' comprehensive qualities. Faced with the increasingly serious problem of food waste, universities have the responsibility and ability to play an active role in curbing students' food waste behavior. Based on this, this article takes college students as the research object and explores the strategies for universities to stop students' food waste behavior in the new era, hoping to provide corresponding references for universities.

Design and Analysis of Small Fallen Leaf Collection, Crushing, and Recycling Vehicle

Leaf recycling, a crucial component of urban environmental protection, aims to systematically collect and reuse fallen leaves on streets. At present, fallen leaves are usually cleaned manually by sanitation workers, and environmental protection departments then use special cars to transport fallen leaves to treatment stations for incineration. This not only requires a significant amount of labor and material resources but also causes air pollution and wastes fallen leaves. In this study, a collecting, crushing, and recycling vehicle was designed to solve this problem. The proposed device significantly improves the efficiency of collecting fallen leaves and facilitates their secondary utilization. The design process is described in detail, including the working design of the entire device, static analysis, and transmission system design. The entire vehicle model was 3D printed to verify that the initial design concept satisfies the assembly and production requirements of the device.

A Comprehensive Evaluation of Water Resource Carrying Capacity Based on the Optimized Projection Pursuit Regression Model: A Case Study from China

The Han River Ecological Economic Belt (HREEB) has a substantial amount of water resources; however, its distribution is uneven, and issues such as seasonal and engineering water shortages are prevalent. This necessitates a thorough assessment of the current water resource situation and trends in water resource carrying capacity (WRCC) to provide scientific support for the rational allocation of water resources. This study employed the RAGA-PP model to establish a WRCC evaluation index system composed of four subsystems: water resources, economy, society, and the ecological environment. The WRCC of the 17 major cities in the HREEB was evaluated from 2008 to 2022. The differentiation method was introduced to compare the reliability of the RAGA-PP model with three evaluation methods: the entropy weight TOPSIS method, the rank sum ratio method, and the principal component analysis method. In addition, an obstacle degree model was introduced to analyze the factors influencing WRCC enhancement. The results indicated the following. (1) In the differentiation test of the four models, the RAGA-PP model was found to have the highest differentiation value, and the results showed that it was more reliable in the WRCC evaluation of HREEB. (2) WRCC in the HREEB underwent significant changes between 2008 and 2022. (3) The WRCC in Shiyan and Wuhan, which are located in the eastern part of the HREEB, were high in Hubei, low in four cities in Henan, and satisfactory in three cities in Shaanxi. (4) The carrying capacity of the subsystems of the cities in the HREEB exhibited fluctuating changes with obvious internal variations. (5) The problems in the WRCC guideline layer were consistent across all cities in the HREEB, with limited per capita water resources being the primary issue in the indicator layer. Assessing WRCC is essential for achieving sustainable water resource use and high-quality regional development.

Pantograph fault prediction of urban rail transit vehicles based on edge feature extraction and detection

Nowadays, as an indispensable part of urban infrastructure, urban rail transit (URT) vehicles have also developed rapidly. A large amount of manpower, material resources, and financial resources need to be invested in the construction process of URT. For URT vehicles, research on more accurate fault prediction methods can save a lot of maintenance costs and improve the reliability of URT construction. As an important electrical equipment for urban rail transit vehicles to obtain electric energy from the catenary, the operation of rail transit vehicles puts forward higher performance requirements for the pantograph. For solving the problems of low accuracy of fault prediction, over reliance on practical experience and high cost of fault prediction in the application of traditional URT vehicle pantograph fault prediction model. Combining sensor network and artificial intelligence algorithm, this paper analyzed the traditional rail transit vehicle pantograph fault prediction model, and verified it through comparative experiments. Through the comparative analysis of the experimental results, this paper can draw a conclusion that compared with the traditional rail transit vehicle pantograph fault prediction model, the rail transit vehicle pantograph fault prediction model has higher fault prediction accuracy, less model response time, lower risk of pantograph failure, higher model application satisfaction, and the accuracy of fault prediction increased by about 6.6%. The rail transit vehicle pantograph fault prediction model can effectively improve the accuracy of vehicle pantograph fault prediction, which can greatly promote the safety of URT and promote the intelligent process of URT.

Transactional pathways: Institutional possibilities for status and wealth under racial/caste capitalism

I introduce the concept of transactional pathways, coined by anthropologist Jane Guyer, to trace the institutional processes that contribute to uneven and unjust patterns of wealth and status distribution under systems of colonialism and racial/caste capitalism. Transactional pathways employ historical perspectives to reveal how divergent economic institutions and sources of valuation relate to social hierarchies. This approach links everyday economic transactions with patterns of wealth accumulation that are based not only on class, but intersectional social groups including race, caste, and/or ethnicity. Transactional pathways account for the fact that not only do different groups receive different amounts of valuable resources because of uneven distribution, but they have variable access to the symbolic power required to define which resources are deemed valuable in the first place and to monopolize them accordingly. The article briefly surveys foundational concepts from the economic anthropology literature before detailing transactional pathways. It introduces the case of attempted formalization of waste collection and recycling in Delhi, India, using the related concepts of thresholds and conversions to analyze key moments when wealth and status accumulation becomes especially possible in institutionally plural contexts.

Using the Single-Term Haar Wavelet to Solve the State Variable Models of nth-Order Circuits

This study is dedicated to investigating the utilization of the single-term Haar wavelet approach for solving a state variable model of nth-order circuits. We provide the matrix calculations in a simplified manner to facilitate comprehension. Furthermore, a numerical mathematical illustration is presented, demonstrating the application of this technique to solving two first-order differential equations with both constant and variable coefficients. The study’s findings unequivocally demonstrate that the suggested approach surpasses previous methods, providing greater levels of accuracy and precision. Moreover, the single-term Haar wavelet approach is exceptionally user-friendly and efficient, necessitating only a minimal amount of processing resources.

Organizational Action Style Focused on the Performance of the Quintana Roo Employee Based on the Work Behavior Clinic

This work is a report of the research on clinical work performance developed from October 2022 to August 2023, in three Quintana Roo organizations, one of private initiative, another of a parastatal nature and the third was a public institution. It is based on the problem that represents the low level of results, despite the strategies of administrative enrichment implemented, where a good amount of financial resources are invested, and that according to managers it is largely the human factor that determines that the results do not occur as expected. The objective was to identify the style of action of the employees during their working hours. The models of Organizational Development, Management by Objectives, Strategic Planning and Total Warmth were analyzed to identify their essence; finding important invariants in them, for their implementation. Epistemology was interpretive with an ethnographic paradigm. As a result, four particular prototypes of value attitudes were identified, corresponding to four particular types of people, which were called by the authors of the study as: Type I. Circumstantial worker; Type II. Tormented worker; Type III. Resentful worker; Type IV. Passionate worker; distributed equally within the three organizations investigated. Concluding that value attitudes have greater influence the higher the level of authority that a person has within the administrative structure, recommending that, in order to make real changes within organizations, it is necessary to start from the eradication of institutional vices.

Popularity-Aware Service Provisioning Framework in Cloud Environment

To balance the tradeoff between quality of service (QoS) and operating expenditure (OPEX), the service provider should request the appropriate amount of resources to the cloud operator based on the estimated variation of service requests. This paper proposes a popularity-aware service provisioning framework (PASPF), which leverages the network data analytics function (NWDAF) to obtain analytics on service popularity variations. These analytics estimate the congestion level and list of top services contributing most of the traffic change. Based on the analytics, PASPF enables the service provider to request the appropriate amount of resources for each service for the next time period to the cloud operator. To minimize the OPEX of the service provider while keeping the average response time of the services below their requirements, we formulate a constrained Markov decision process (CMDP) problem. The optimal stochastic policy can be obtained by converting the CMDP model into a linear programming (LP) model. Evaluation results demonstrate that the PASPF can achieve less than 50% OPEX of the service provider compared to a popularity-non-aware scheme while keeping the average response time of the services below the requirement.

Augmentation of Reclaimed Water with Excess Urban Stormwater for Direct Potable Use

Groundwater and surface water have been the primary sources of our public water supply around the world. However, rapid population and economic growth, as well as global climate change, are posing major threats to the quality and quantity of these water resources. Treated wastewater (reclaimed water) and stormwater are becoming more important water resources. Use/reuse of these unconventional water resources can enable a truly sustainable, closed-loop, circular water system. However, these two sources are not usually mixed with each other. In this study, we propose the use of combined excess urban stormwater and reclaimed water as a source of potable water supply. One of the most pronounced benefits of this proposed scheme is the possible elimination of costly and energy-intensive processes like reverse osmosis. Reclaimed water tends to have high concentrations of dissolved solids (>500 mg/L) and nitrate-N (>10 mg/L), which can be lowered by blending with stormwater or rainwater. Despite technical and engineering challenges, this approach can benefit various communities—small, medium, large, upstream, downstream, urban, and rural—in diverse climates. Our study suggests that this new holistic approach is feasible, enabling the combined water to be directly used as a sustainable drinking water source.

Evidence for the Beneficial Effect of Reward on Working Memory: A Meta-Analytic Study.

Rewards act as external motivators and can improve performance in various cognitive tasks. However, previous research demonstrated mixed findings regarding the effect of reward on working memory (WM) performance, and the question of whether reward enhances WM performance is arguable. It remains unclear how the effect of reward on WM can be influenced by various factors, such as types of reward and experimental paradigms. In this meta-analytic study, we systematically investigated the effect of reward on WM by analyzing data from 51 eligible studies involving a total of 1767 participants. Our results showed that reward robustly enhanced WM performance, with non-monetary rewards inducing more benefits than monetary rewards. This may be because, while both types of reward could induce extrinsic motivation, non-monetary rewards enhanced intrinsic motivation while monetary rewards reduced it. Notably, all three reward methods-reward binding, reward expectation, and subliminal reward-effectively improved WM performance, with the reward binding paradigm exhibiting the greatest effects. This finding suggests that the reward effect can be attributed to both increasing the total amount of WM resources and improving the flexibility of resource reallocation. Moreover, the type of WM, the experimental paradigms, and the outcome measures are three moderators that should be jointly considered when assessing the reward effects on WM. Overall, this meta-analytic study provides solid evidence that reward improves WM performance and reveals possible mechanisms underlying these improvements.

An Efficient Green AI Approach to Time Series Forecasting Based on Deep Learning

Time series forecasting is undoubtedly a key area in machine learning due to the numerous fields where it is crucial to estimate future data points of sequences based on a set of previously observed values. Deep learning has been successfully applied to this area. On the other hand, growing concerns about the steady increase in the amount of resources required by deep learning-based tools have made Green AI gain traction as a move towards making machine learning more sustainable. In this paper, we present a deep learning-based time series forecasting methodology called GreeNNTSF, which aims to reduce the size of the resulting model, thereby diminishing the associated computational and energetic costs without giving up adequate forecasting performance. The methodology, based on the ODF2NNA algorithm, produces models that outperform state-of-the-art techniques not only in terms of prediction accuracy but also in terms of computational costs and memory footprint. To prove this claim, after presenting the main state-of-the-art methods that utilize deep learning for time series forecasting and introducing our methodology we test GreeNNTSF on a selection of real-world forecasting problems that are commonly used as benchmarks, such as SARS-CoV-2 and PhysioNet (medicine), Brazilian Weather (climate), WTI and Electricity (economics), and Traffic (smart cities). The results of each experiment conducted objectively demonstrate, rigorously following the experimentation presented in the original papers that addressed these problems, that our method is more competitive than other state-of-the-art approaches, producing more accurate and efficient models.

Mechanisms determining the multi-diversity of carrion visiting species along a gradient of carrion body mass

Resource availability and habitat heterogeneity are essential drivers of biodiversity, but their individual roles often remain unclear since both factors are often correlated. Here, we tested the more-individuals hypothesis (MIH) and the habitat-heterogeneity hypothesis (HHH) for bacteria, fungi, dipterans, coleopterans, birds, and mammals on 100 experimentally exposed carcasses ranging by three orders of magnitude in body mass. At the level of each carcass we found marginal or significant support for the MIH for bacteria, fungi, and beetles in spring and significant support for fungi, dipterans, and mammals in summer. The HHH was supported only for bacteria in spring, while it was supported for all groups except mammals in summer. Overall multidiversity always increased with body mass, with a steeper increase in summer. Abundance based rarefaction-extrapolation curves for three classes of body mass showed the highest species richness for medium-sized carcasses, particular for dipterans and microbes, supporting the HHH also among carcasses. These findings complement existing necromass studies of deadwood, showing there are more niches associated with larger resource amounts and an increasing habitat heterogeneity between carcasses most pronounced for medium-sized species. Higher resource amount led to increased diversity of carrion-consuming organisms in summer, particularly due to the increasing number of niches with increasing size. Our findings underline the importance of distributed large carrion as well as medium-sized carrion in ecosystems supporting overall biodiversity of carrion-consumers. Furthermore, the different responses in spring and summer may inform strategies of carrion enrichment management schemes throughout the year.

The Impact of Fractures on Shale Oil and Gas Enrichment and Mobility: A Case Study of the Qingshankou Formation in the Gulong Depression of the Songliao Basin, NE China

To study the impact of faults on the enrichment and mobility of shale oil in the Gulong area, representative rock samples were selected in this paper. Based on geochemical data and chemical kinetics methods, coupled with shale oil enrichment and mobility analysis techniques, the shale oil generation quantity and in situ oil content were evaluated from the perspectives of shale oil generation and micro migration, and the mobility of shale oil was revealed. At the same time, the hydrocarbon expulsion efficiency (HEE) of shale was qualitatively and quantitatively characterized, combined with the development of faults. The research results indicate that the study area mainly develops organic-rich felsic (ORF)/organic-containing felsic (OCF) shale, their proportion in both wells exceeds 65%, and the resource amount is the largest in this type of lithofacies. The development of a fault controls the enrichment of shale oil, and the in situ oil content and oil saturation index (OSI) of the shale in well Y58, which is close to the fault, are significantly worse than those in well S2. Well Y58 has 9.52 mg/g and 424.83 mg/g TOC respectively, while well S2 has 11.34 mg/g and 488.73 mg/g TOC respectively. The fault enhanced the migration of shale oil, increasing the efficiency of oil expulsion. As a result, the components with weak polarity or small molecules, such as saturated hydrocarbons and low carbon number n-alkanes, are prone to migration, reducing the mobility of shale oil.

Correlation of the solubility of isoniazid in some aqueous cosolvent mixtures using different mathematical models

AbstractSolubility is one of the most important physicochemical properties, because it is related to some industrial processes such as: formulation, preformulation, purification and quantification. The experimental determination of solubility requires rigorous processes that involve a significant amount of resources. In this context, mathematical models allow estimating solubility under conditions different from the experimental ones from a limited number of data. The objective of this research was to evaluate the pertinence of 10 mathematical models (Extended Hildebrand, van’t Hoff, Two-parameter Weibull, Buchowski–Ksiazczak $$\lambda h$$ λ h , van’t Hoff-Yaws, Apelblat, Wilson, NRTL, Modified Wilson and van’t Hoff-Modified Wilson) in the calculation of the solubility of isoniazid in PEG 200 (1) + Water (2) cosolvent mixtures, the parameters of each model were calculated using Python, Pandas and the NumPy and SciPy library. Once each model was evaluated, two models were defined as the best alternatives based on their predictive power and mathematical simplicity. Thus, the van’t Hoff and Modified Wilson models were combined to obtain an equation that allows the calculation of solubility as a function of temperature and cosolvent composition, obtaining MRD% less than 3.0. In conclusion, mathematical models represent a good prediction tool being a potential alternative in relation to the optimization of some industrial processes related to solubility.

Agent-Based Modelling for the Cost-Benefit Analysis of Adaptation Strategies: A Case Study from Inuit Nunangat

This paper presents a case study of how agent-based modelling can be utilized to conduct a cost-benefit analysis of two differing adaptational strategies to resource insecurity. Using Inuit Nunangat (the Canadian Arctic) as the setting, models are developed to represent two adaptational strategies in response to the onset of the Little Ice Age: exchange with other communities via long-distance trade and intensification of local resource procurement. After determining the average kilograms of resources acquired through a model of local resource procurements, two models were then developed to determine under what scenarios long-distance journeys to procure perishable food goods would be more productive than hunting locally. Ultimately, the results showed that while there are scenarios where undertaking a trading journey would result in a higher average amount of resources acquired, those scenarios would not have been realistic for most Thule communities, leaving hunting locally as the more beneficial adaptational strategy on an economic basis.

High fidelity adaptive mirror simulations with reduced order models

In the design process of large adaptive mirrors numerical simulations represent the first step to evaluate the system design compliance in terms of performance, stability and robustness. For the next generation of Extremely Large Telescopes increased system dimensions and bandwidths lead to the need of modeling not only the deformable mirror alone, but also all the system supporting structure or even the full telescope. The capability to perform the simulations with an acceptable amount of time and computational resources is highly dependent on finding appropriate methods to reduce the size of the resulting dynamic models. In this paper we present a framework developed together with the company Microgate to create a reduced order structural model of a large adaptive mirror as a preprocessing step to the control system simulations. The reduced dynamic model is then combined with the remaining system components allowing to simulate the full adaptive mirror in a computationally efficient way. We analyze the feasibility of our reduced models for Microgate’s prototype of the adaptive mirror of the Giant Magellan Telescope.

Energy-Efficient Algorithm for Data Path Selection in High-Density Wireless Sensor Networks

Relevance. Increasing the number of sensor devices per unit area consequently reduces the physical distance between the devices in the sensor network. Such networks are usually deployed over a large area and the sensor device that wants to transmit a data packet is located far away from the base station. In such a case, the source device is challenged to choose a transmission path that consumes the least amount of energy resources and satisfies the delivery time requirements. The objective of this study is to develop and validate the effectiveness of an empirical algorithm for selecting a transmission path that reduces the energy consumption of high-density wireless sensor networks. Methods of system analysis, analytical modeling, geometry and probability theories are used. Solution. It is assumed that the sensor network is deployed in a limited area and is a set of devices that are connected to each other informationally and energetically. When building data transmission routes, any sensor devices can be used as repeaters. At the same time, the increase in the number of repeaters leads to an increase in the time of data delivery. Novelty. It is assumed that the sensor network is deployed in a limited area and is a set of devices that are connected to each other informationally and energetically. Any sensor devices may be used as repeaters when constructing data transmission routes.Significance (theoretical). Dependences of power consumption level on various system parameters affecting the processes of functioning of high-density wireless sensor networks have been obtained. Significance (practical). The proposed empirical algorithm for selecting a rational data transmission route in a wireless sensor network allows us to determine, among all alternatives, the route to the coordinator that requires the least power. The effectiveness of the proposed empirical power-saving algorithm is confirmed by simulation modeling.

TinyWolf — Efficient on-device TinyML training for IoT using enhanced Grey Wolf Optimization

Training a deep learning model generally requires a huge amount of memory and processing power. Once trained, the learned model can make predictions very fast with very little resource consumption. The learned weights can be fitted into a microcontroller to build affordable embedded intelligence systems which is also known as TinyML. Although few attempts have been made, the limits of the state-of-the-art training of a deep learning model within a microcontroller can be pushed further. Generally deep learning models are trained with gradient optimizers which predict with high accuracy but require a very high amount of resources. On the other hand, nature-inspired meta-heuristic optimizers can be used to build a fast approximation of the model’s optimal solution with low resources. After a rigorous test, we have found that Grey Wolf Optimizer can be modified for enhanced uses of main memory, paging and swap space among α,β,δ and ω wolves. This modification saved up to 71% memory requirements compared to gradient optimizers. We have used this modification to train the TinyML model within a microcontroller of 256KB RAM. The performances of the proposed framework have been meticulously benchmarked on 13 open-sourced datasets.

Assessment of REY resource potential in deep-sea sediments with Fe–Mn (oxyhydr)oxides in the Pacific Ocean

Deep-sea sediments with an abundance bioapatites and Fe–Mn (oxyhydr)oxides in the Pacific Ocean have been considered potential reservoirs of rare earth elements and yttrium (REY). However, comprehensive assessment of the resource potential of REY in deep-sea sediments with Fe–Mn (oxyhydr)oxides throughout the Pacific Ocean is limited due to difficulties in accurately predicting the distribution of extensive Fe–Mn (oxyhydr)oxides and the associated REY. In this study, we predicted the prospective area and resource potential of REY-rich sediments with Fe–Mn (oxyhydr)oxides by considering multiple factors that control REY enrichment based on data from International Ocean Discovery Program (IODP) samples and previous research. According to the distribution map inferred by comprehensively evaluating lithology (clay sediment), hydrothermal fluid influence (δ3He), and water depth (bathymetry), deep-sea sediments with Fe–Mn (oxyhydr)oxides, which have higher than 1000 ppm REY concentration, are distributed in the vicinity of the East Pacific Ridge within a water depth range of 4000–4600 m, and their distribution area is estimated to be approximately 1.1 million km2. If the sedimentation rate (<1.5 m/Myr) is considered, which is a crucial factor influencing REY enrichment, we can achieve a more precise assessment of their distribution area. Assuming a recovery depth of only 1 m, the REY resource amount was estimated to be approximately >450 million tons of REY oxide. Even without accounting for REY resource amount associated with bioapatite, the minimum REY resource amount estimated in this study exceed the world's current land reserves. Furthermore, these sediments contain a significant abundance of industrially important heavy REY, accounting for 53 % of REY resources. This implies that the deep-sea sediments with Fe–Mn (oxyhydr)oxides in the Pacific Ocean are a promising resource of REY. Our findings will serve as essential information for the technological progress required in the exploration and development of REY resources in deep-sea sediments in the future.

Hybrid reward-punishment in feedback-evolving game for common resource governance.

How to maintain the sustainability of common resources is a persistent challenge, as overexploiters often undermine collective efforts by prioritizing personal gain. To mitigate the overexploitation of resources by violators, previous theoretical studies have revealed that the introduction of additional incentives, whether to reward rule-abiding cooperators or to punish those who overexploit, can be beneficial for the sustainability of common resources when the resource growth rate is not particularly low. However, these studies have typically considered rewarding and punishing in isolation, thus overlooking the role of their combination in common resource governance. Here, we introduce a hybrid incentive strategy based on reward and punishment within a feedback-evolving game, in which there is a complex interaction between human decision making and resource quantity. Our coevolutionary dynamics reveal that resources will be depleted entirely, even with cooperative strategies for prudent exploitation, when resource growth is slow. When the rate of resource growth is not particularly low, we find that the coupled system can generate a state where resource sustainability and cooperation can be maintained. Furthermore, when the rate of resource growth is moderate, we find that achieving this state cannot simply allocate all incentive budgets to reward. In addition, the increase in per capita incentives significantly promotes the stability of this state.