Hasil untuk "Industries. Land use. Labor"

Xinyi Zhu, Sheng Zhou, Fucong Xu et al.

Abstract The widespread integration of photovoltaic (PV) power, energy storage systems, and other demand‐side resources highlights the importance of optimal dispatching for the PV‐storage‐load virtual power plant (VPP). However, the fluctuation of the PV power generation and the uncertainty of the electricity prices exacerbate the economic operation risks of the VPP. To address these challenges, an optimal dispatching strategy for the PV‐storage‐load VPP is proposed, with due consideration given to the dual uncertainties of electricity prices and PV power output. Firstly, the conditional value‐at‐risk theory is employed to quantify the uncertainty risk of VPP revenue caused by electricity price fluctuations. Secondly, in view of the asymmetric fluctuation intervals of PV power output, a quantification method for PV uncertainty and dispatch robustness is developed using the confidence gap decision theory. Furthermore, by combining the regulation reserve model of multi‐type flexible resources, a robust optimization model for the PV‐storage‐load VPP is constructed with the objective of maximizing comprehensive operational revenue, which includes the provision of upward and downward reserve services. Finally, case studies based on a PV‐storage‐load VPP in a Chinese province are conducted to validate the effectiveness and superiority of the proposed model. The simulation results indicate that the proposed robust optimization strategy effectively reflects the relationship between the uncertainty of PV power output and the risk preference of decision‐maker, mitigates the fluctuation risks of electricity prices to ensure the stability of the power system, and enhances the economic efficiency and flexibility of the PV‐storage‐load VPP operation.

Sanjit Dandapanthula, Margaret Johnson, Madeleine Pascolini-Campbell et al.

Accurate and high-resolution estimation of land surface temperature (LST) is crucial in estimating evapotranspiration, a measure of plant water use and a central quantity in agricultural applications. In this work, we develop a novel statistical method for downscaling LST data obtained from NASA's ECOSTRESS mission, using high-resolution data from the Landsat 8 mission as a proxy for modeling agricultural field structure. Using the Landsat data, we identify the boundaries of agricultural fields through edge detection techniques, allowing us to capture the inherent block structure present in the spatial domain. We propose a block-diagonal Gaussian process (BDGP) model that captures the spatial structure of the agricultural fields, leverages independence of LST across fields for computational tractability, and accounts for the change of support present in ECOSTRESS observations. We use the resulting BDGP model to perform Gaussian process regression and obtain high-resolution estimates of LST from ECOSTRESS data, along with uncertainty quantification. Our results demonstrate the practicality of the proposed method in producing reliable high-resolution LST estimates, with potential applications in agriculture, urban planning, and climate studies.

Christine Böttcher, Debra McDavid, Angelica M. Jermakow et al.

Conclusions: This study has demonstrated that a relatively simple and robust protoplast isolation, DNA-free protoplast transfection and plant regeneration workflow can be used to efficiently produce nontransgenic, diploid, edited clones with desired phenotypes of four elite winegrape cultivars, including the highly recalcitrant Cabernet Sauvignon.

Khoo Terh Jing, Chin Yee Ha , Mohd Wira Mohd Shafiei et al.

This research explores the current green site management practices (GSMPs) commonly practiced by contractors. While the construction industry has contributed significantly to Malaysia’s development, it has also raised various environmental concerns. GSMPs are gaining attention as a solution to these issues. Nevertheless, their implementation faces various challenges, such as financial concerns and a lack of knowledge. A qualitative approach was adopted to focus on contractors’ experiences with green practices. Five contractors were randomly selected from the construction sites in Malaysia using the convenience sampling method. These respondents, all at the management level, were well-positioned to provide insights. Data collection continued until no new issues emerged from the interviews. The findings revealed that GSMPs are becoming a current trend within the industry. Contractors are beginning to integrate green practices in their construction activities, focusing on construction site waste management, workforce management, best regulatory practices, site establishment and administration, and site facilities. However, there is a notable lack of awareness and knowledge about these green practices among contractors. The study offers practical implications for the future of GSMPs, highlighting the need for increased understanding and adoption. By elaborating on available practices and their implications, this study aims to encourage broader implementation of GSMPs in construction sites.

Jiayu Cao, Yuhui Yang, Xi Liu et al.

Abstract Background The advancement of nanotechnology underscores the imperative need for establishing in silico predictive models to assess safety, particularly in the context of chronic respiratory afflictions such as lung fibrosis, a pathogenic transformation that is irreversible. While the compilation of predictive descriptors is pivotal for in silico model development, key features specifically tailored for predicting lung fibrosis remain elusive. This study aimed to uncover the essential predictive descriptors governing nanoparticle-induced pulmonary fibrosis. Methods We conducted a comprehensive analysis of the trajectory of metal oxide nanoparticles (MeONPs) within pulmonary systems. Two biological media (simulated lung fluid and phagolysosomal simulated fluid) and two cell lines (macrophages and epithelial cells) were meticulously chosen to scrutinize MeONP behaviors. Their interactions with MeONPs, also referred to as nano-bio interactions, can lead to alterations in the properties of the MeONPs as well as specific cellular responses. Physicochemical properties of MeONPs were assessed in biological media. The impact of MeONPs on cell membranes, lysosomes, mitochondria, and cytoplasmic components was evaluated using fluorescent probes, colorimetric enzyme substrates, and ELISA. The fibrogenic potential of MeONPs in mouse lungs was assessed by examining collagen deposition and growth factor release. Random forest classification was employed for analyzing in chemico, in vitro and in vivo data to identify predictive descriptors. Results The nano-bio interactions induced diverse changes in the 4 characteristics of MeONPs and had variable effects on the 14 cellular functions, which were quantitatively evaluated in chemico and in vitro. Among these 18 quantitative features, seven features were found to play key roles in predicting the pro-fibrogenic potential of MeONPs. Notably, IL-1β was identified as the most important feature, contributing 27.8% to the model’s prediction. Mitochondrial activity (specifically NADH levels) in macrophages followed closely with a contribution of 17.6%. The remaining five key features include TGF-β1 release and NADH levels in epithelial cells, dissolution in lysosomal simulated fluids, zeta potential, and the hydrodynamic size of MeONPs. Conclusions The pro-fibrogenic potential of MeONPs can be predicted by combination of key features at nano-bio interfaces, simulating their behavior and interactions within the lung environment. Among the 18 quantitative features, a combination of seven in chemico and in vitro descriptors could be leveraged to predict lung fibrosis in animals. Our findings offer crucial insights for developing in silico predictive models for nano-induced pulmonary fibrosis.

Hannah Hopewell, Matthew Wakelin

This design proposal extends and intensifies the powerfully present geological conditions of Te Whanganui-a-Tara–Wellington’s harbour. Originally conceived for an international design competition it contextualises the magnitude of geologic time and imagines an embodied experience of suspension, a quality of being between worlds yet in the felt immediacy of nothing but kenetic change. The design emerges from and is given meaning by Te Ātiawa pūrākau of taniwha Ngake and Whātaitai. Together these taniwha give the why and how of Pōneke Wellington’s land and seascape; they contextualise geomorphology in deep time and express the entangled alliance between mana whenua and the specificity of place, a quality defining Pōneke Wellington. With the design we touch into multiple relational intersections made possible by the forever mercurial space where the sea and the land meet, yet do so in such a way to unsettle settler colonial schemas of landscape-seascape experience.

O. N. Arunkumar, D. Divya, Chandan

Unlocking the power of sustainable growth, Environmental, Social, and Governance (ESG) principles are redefining the future of responsible investment and corporate excellence. ESG regulations ensure that organizations maintain sustainable development and improve non-monetary metrics, such as stakeholders’ engagement, customer satisfaction, market acceptability, societal ethics, and values. Higher ESG scores demonstrate commitment towards responsible business practices and indicate higher market value for companies, which are valid for all sectors, including IT. However, existing literature reveals that IT sector companies pay less attention to planning their operations to make them more sustainable. Therefore, IT firms must identify methods and practices to maintain high ESG scores to achieve sustainable growth. The current study leads the readers into a new area of ESG through the help of an advanced method, DEA. DEA (Data Envelopment Analysis) methodology has been used to identify the decision units’ relative efficiency scores and helps identify peers and followers based on ESG scores. The study reveals that among the selected IT firms using the output-oriented strategy, 56.25% experience increasing returns to scale, 18.75 per cent experience decreasing returns to scale, and the remaining 25.00 per cent report constant returns to scale. This indicates that most IT industry firms can generate greater output change in proportion to the input change.

Fanze Kong, Jiayi Sun, Shuangquan Xie

One of the most significant findings in the study of spatial Solow-Swan models is the emergence of economic agglomeration, in which economic activities concentrate in specific regions. Such agglomeration provides a fundamental mechanism driving the spatial patterns of urbanization, labor migration, productivity growth, and resource allocation. In this paper, we consider the one-dimensional spatial Solow-Swan model with capital-induced labor migration, which captures the dynamic interaction between labor and capital through migration and accumulation. Focusing on the regime of sufficiently small capital diffusivity, we first construct an interior spike (spiky economic agglomeration) quasi-equilibrium. Next, we perform the linear stability of the corresponding spike equilibrium by using a hybrid asymptotic and numerical method. We show that a single interior spike remains stable for small reaction-time constants but undergoes a Hopf bifurcation when the constant is sufficiently large, leading to oscillations in spike height (economic fluctuation). Finally, we derive a differential-algebraic system to capture the slow drift motion of quasi-equilibrium (core-periphery shift). Numerical simulations are carried out to support our theoretical studies and reveal some intriguing yet unexplained dynamics.

Marc Hoyois, Markus Land

We construct a non-$\mathbb{A}^1$-invariant motivic ring spectrum $\mathrm{KO}$ over $\mathrm{Spec}(\mathbb{Z})$, whose associated cohomology theory on qcqs derived schemes is the Grothendieck-Witt theory of classical symmetric forms (as opposed to homotopy symmetric forms). In particular, we show that this theory satisfies Nisnevich descent, smooth blowup excision, a projective bundle formula, and is locally left Kan extended from smooth $\mathbb{Z}$-schemes up to Bass delooping. More generally, our construction produces $\mathrm{KO}$-modules representing localizing invariants of two different families of Poincaré structures on derived schemes, which we call "classical" and "genuine"; the latter Poincaré structures are defined for spectral schemes with involution, but the former only for derived schemes. We then establish basic properties of these motivic spectra. As in $\mathbb{A}^1$-homotopy theory, the fracture square of $\mathrm{KO}$ with respect to the Hopf element recovers the fundamental cartesian square relating GW-theory, L-theory, and K-theory. A new phenomenon when $2$ is not a unit is that $\mathrm{KO}$ is not Bott-periodic, and the left and right Bott periodizations of $\mathrm{KO}$ represent the Grothendieck-Witt theories of homotopy symmetric and homotopy quadratic forms, respectively. We also construct the expected metalinear $\mathrm{E}_\infty$-orientation of $\mathrm{KO}$. Finally, we show that the $\mathbb{A}^1$-localization of $\mathrm{KO}$ recovers the motivic spectrum recently constructed by Calmès, Harpaz, and Nardin.

Huong Pham, Samuel Cheng, Tao Hu et al.

Given the growing environmental challenges, accurate monitoring and prediction of changes in water bodies are essential for sustainable management and conservation. The Continuous Monitoring of Land Disturbance (COLD) algorithm provides a valuable tool for real-time analysis of land changes, such as deforestation, urban expansion, agricultural activities, and natural disasters. This capability enables timely interventions and more informed decision-making. This paper assesses the effectiveness of the algorithm to estimate water bodies and track pixel-level water trends over time. Our findings indicate that COLD-derived data can reliably estimate estimate water frequency during stable periods and delineate water bodies. Furthermore, it enables the evaluation of trends in water areas after disturbances, allowing for the determination of whether water frequency increases, decreases, or remains constant.

Haldun Özgür Bayındır, Markus Land

We study the classification of $\mathbb{Z}$-DGAs with polynomial homology $\mathbb{F}_p[x]$ with $\lvert x \rvert >0$, motivated by computations in algebraic $K$-theory. This classification problem was left open in work of Dwyer, Greenlees, and Iyengar. We prove that there are infinitely many such DGAs for even $\lvert x \rvert$ and that for $\lvert x \rvert \geq 2p-2$ any such DGA is formal as a ring spectrum. Through this, we obtain examples of triangulated categories with infinitely many DG-enhancements and a classification of prime DG-division rings. Combining our results with earlier work of the second author and Tamme, we obtain new (relative) algebraic $K$-theory computations for rings such as the mixed characteristic coordinate axes $\mathbb{Z}[x]/px$ and the group ring $\mathbb{Z}[C_{p^n}]$.

Sina Kalweit, Elisabeth Zeyen, Marta Victoria

Road transport is responsible for about a quarter of Europe's greenhouse gas emissions, making its transformation a crucial part of Europe's overall decarbonization goals. Current European policies promote decarbonizing the transport sector and passenger car sales show an increased adoption of electric vehicles. Full electrification of land transport will significantly increase the average electricity demand but the use of smart charging and vehicle-to-grid could provide additional flexibility to balance wind and solar generation. In this study, we find cost-optimal transition pathways of the European land transport sector embedded in the sector-coupled open energy model PyPSA-Eur. We consider fossil-fueled, hydrogen-fueled, and electric cars using a 3-hour time resolution for a full year and covering 33 interconnected European countries. We analyze a transition path from 2025 to 2050 under different carbon budgets corresponding to a 1.7°C and 2°C temperature increase. Our results show that rapid electrification of road transport reduces the total system cost, even in the absence of climate targets. We see a clear preference for rapidly decommissioning internal combustion engine vehicles and using electric vehicles in all countries and under all carbon budgets. Allowing smart charging of electric vehicles decreases the total system cost by 1.6% because it reduces the need to install stationary batteries by almost 40%.

Sri Marfuati, Hikmah Fitriani, Mustika Weni et al.

Background: With 10 million cases around the world, pulmonary tuberculosis (TB) has been classified as a highly contagious disease and mostly affecting low and middle countries. Having the second highest incident cases in West Java of Indonesia, Cirebon becomes a challenging city in order to reduce the number of TB cases in the country. Aims: This study aims to identify the patients’ knowledge and treatment phases, and how the two factors encourage patients to comply with their medication. Methods :  This cross-sectional observational study was conducted among 91 new pulmonary tuberculosis patients at the Cirebon City Community Lung Health Centre, selected using random sampling. Not only respondent characteristics, but also data on the patients' knowledge levels, treatment phases, and medication adherence were collected using a questionnaire and medical records. To assess the relationship between these variables, the collected data was then analyzed using the Spearman Correlation test. Ethical clearance was obtained from the Health Research Ethics Commission, and informed consent was gathered from all participants. Results: This study reveals the most updated characteristics of the Tuberculosis patients at the Cirebon City Community Lung Health Center aged 15-64 years old with treatment duration ranged 1-6 months. The majority have insufficient knowledge about tuberculosis (45.1%), and 75.8% of patients adhered to their prescribed medication regimen, regardless of their knowledge level. The data indicates a significant positive correlation between knowledge level and medication adherence (p = 0.015), with 95% of patients with good knowledge adhering to treatment compared to only 34% with poor knowledge. Furthermore, there is a significant relationship between adherence and treatment duration (p = 0.002), as 85% of patients who adhered to treatment did so for more than two months. Conclusion: The study shows that patients with better knowledge of tuberculosis are more likely to stick to their medication, which also leads to longer treatment durations. Given the high incidence of TB in the region, these findings suggest the need for targeted educational programs to enhance patients' understanding of TB, thereby improving adherence to treatment protocols. Received: 20 May 2024, Reviewed: 09 June 2024, Revised: 26 August 2024, Accepted: 30 August 2024.

Fateme Maleki, saeed abbasi, Zahra Maleki

There are various methods to prevent river bank erosion, one of them is groin. These structures are used to control the natural movement of the bed and reduce the movement of sediments by reducing the power of the water flow. Groins are divided into permeable and impermeable types, the permeable form of which is used in rivers where the amount of suspended load is high. By reducing the speed of the flow in the groin field, the sedimentary materials are quickly deposited. This process and then creating a thick sedimentary layer, keeping the erosion flow away from the groin area and it provides the stable conditions necessary to protect the banks. In this study, the performance of parallel permeable groin and single permeable groin has been compared. For this purpose, a flume with a length of 5 meters, width of 30 cm and height of 30 cm has been used. Experiments have been conducted with 60 and 40% permeability rates in clear water conditions, and the observations from the experiments have been made in relation to the maximum scour depth and the changes in the topography of the flume bed have been compared.

Richard Orozco, Philipp Grundmann

Sander Land, Max Bartolo

The disconnect between tokenizer creation and model training in language models allows for specific inputs, such as the infamous SolidGoldMagikarp token, to induce unwanted model behaviour. Although such `glitch tokens', tokens present in the tokenizer vocabulary but that are nearly or entirely absent during model training, have been observed across various models, a reliable method to identify and address them has been missing. We present a comprehensive analysis of Large Language Model tokenizers, specifically targeting this issue of detecting under-trained tokens. Through a combination of tokenizer analysis, model weight-based indicators, and prompting techniques, we develop novel and effective methods for automatically detecting these problematic tokens. Our findings demonstrate the prevalence of such tokens across a diverse set of models and provide insights into improving the efficiency and safety of language models.

Zhuoning Guo, Hao Liu, Le Zhang et al.

Labor market forecasting on talent demand and supply is essential for business management and economic development. With accurate and timely forecasts, employers can adapt their recruitment strategies to align with the evolving labor market, and employees can have proactive career path planning according to future demand and supply. However, previous studies ignore the interconnection between demand-supply sequences among different companies and positions for predicting variations. Moreover, companies are reluctant to share their private human resource data for global labor market analysis due to concerns over jeopardizing competitive advantage, security threats, and potential ethical or legal violations. To this end, in this paper, we formulate the Federated Labor Market Forecasting (FedLMF) problem and propose a Meta-personalized Convergence-aware Clustered Federated Learning (MPCAC-FL) framework to provide accurate and timely collaborative talent demand and supply prediction in a privacy-preserving way. First, we design a graph-based sequential model to capture the inherent correlation between demand and supply sequences and company-position pairs. Second, we adopt meta-learning techniques to learn effective initial model parameters that can be shared across companies, allowing personalized models to be optimized for forecasting company-specific demand and supply, even when companies have heterogeneous data. Third, we devise a Convergence-aware Clustering algorithm to dynamically divide companies into groups according to model similarity and apply federated aggregation in each group. The heterogeneity can be alleviated for more stable convergence and better performance. Extensive experiments demonstrate that MPCAC-FL outperforms compared baselines on three real-world datasets and achieves over 97% of the state-of-the-art model, i.e., DH-GEM, without exposing private company data.

Oksana Buhaichuk, Vitalina Nikitenko, Valentyna Voronkova

The relevance of the study is that the digital challenge is important and stimulating, requiring the formation of digital education in the digital economy. The purpose of the article is to develop a model of digital education as a factor of improving the efficiency of digital competencies that contribute to the development of the digital economy. The object of research is the formation of a digital education model as a factor in the implementation of digital literacy. The subject of the study is the impact of the digital education model on the development of the digital economy. The methodology for researching digital education, which cultivates a smart economy, smart governance and smart people, is represented by the Agile methodology (flexible adaptive), based on the use of the values of artificial intelligence and deep learning, which can create effective tools for education, increasing their effectiveness through rapid change. The results of the study: 1) analyzes the formation of digital competencies in the context of the European educational paradigm that contribute to the development of the digital economy; 2) identifies the directions of implementation of digital competencies in the context of the European educational paradigm; 3) reveals digital tools and educational platforms that contribute to the formation of digital education; 4) formulates the concept of quality, inclusive, accessible digital education as a factor in improving digital competencies and adapting education to the digital age; 5) traces the impact of digital education and digital competencies on the development of the digital economy. The concept of digital education contains both its potential and its risks, which can have serious consequences for the future of the educational process if digital literacy is not developed. The combination of four factors – cultural change, technological innovation, national policy leadership and internal development of the digital education system – stimulates the digital transformation of society.

Cassio F. Dantas, Diego Marcos, Dino Ienco

Counterfactual explanations are an emerging tool to enhance interpretability of deep learning models. Given a sample, these methods seek to find and display to the user similar samples across the decision boundary. In this paper, we propose a generative adversarial counterfactual approach for satellite image time series in a multi-class setting for the land cover classification task. One of the distinctive features of the proposed approach is the lack of prior assumption on the targeted class for a given counterfactual explanation. This inherent flexibility allows for the discovery of interesting information on the relationship between land cover classes. The other feature consists of encouraging the counterfactual to differ from the original sample only in a small and compact temporal segment. These time-contiguous perturbations allow for a much sparser and, thus, interpretable solution. Furthermore, plausibility/realism of the generated counterfactual explanations is enforced via the proposed adversarial learning strategy.

Iryna Shavkun, Yana Dybchinska

In the conditions of the knowledge economy, the most important source of competitive advantages of the organization is human capital. The human capital development is based on the relevant management culture to provide proper material and technical prerequisites for motivating both high activity culture standards and productive performance. Hence efficient organizational culture of modern business environment affects almost all aspects of the organization activities and is a must for human capital formation. The need to understand the role of organizational culture as an effective strategic tool in the management of organizational processes in the modern business environment actualizes the topic of this study and determines the goal - to analyze the significance of organizational culture in transforming human potential into human capital as a social resource that makes efficient production and innovation possible. The specified goal presupposes the setting of a number of tasks to identify the essence of such concepts as "corporate culture", "human capital" and "human potential" and to analyze their interdependence in the process of functioning and development of modern business organizations. Methodology. General scientific methods are used to substantiate the theoretical positions and reasoning of the conclusions. The system method allows to consider the nature and instrumental role of organizational culture for the manager to transform the human potential into the human capital of the organization. The results of the study indicate the complex nature of the organizational culture phenomenon as a factor in the developing and managing human capital: on the one hand, it is a tool for transforming human potential into the human capital of the organization, on the other hand, it is an integral attribute of human capital itself.