Hasil untuk "Land use"

B. Turner, E. Lambin, A. Reenberg

P. Gislason, J. Benediktsson, J. Sveinsson

M. Camara, N. Jamil, A. F. Abdullah

Land use changes in urbanization, industrialization, and agricultural processes will continue to have negative impacts on water quality at all scales. The impact of land use changes on water quality is generally studied by analyzing the relationships between land use and water quality indicators. Therefore, the purpose of this research was to review and analyze the main relationships between land use and water quality, as well as to visualize the major sources and processes of water quality pollution in Malaysia. To achieve our goal, we evaluated the significance of both land use and water quality attributes used in the past studies and correlated them to understand their relationship from another angle of view. The results revealed that 87% of the reviewed studies indicated urban land use as a major source of water pollution, while 82% indicated agricultural land use, 77% indicated forest land use, and 44% indicated other land uses. However, the results of correlation analysis showed that agricultural and forest-related activities more affected water quality through their significant positive correlation with physical and chemical indicators of water quality, while urban development activities had a greater impact on water quality through altering hydrological processes such as runoff and erosion. These findings would provide decision-makers with useful information for managing water pollution processes rather than sources only.

Burkhonova M. M., Matyakubov B. Sh., Zakirova S. Kh. et al.

This article provides an overview of ProPlanta software, which is specifically designed to provide recommendations for the rational use of agricultural land. The software is based on more than 50 years of research, including data from more than 80 long-term field trials. Designed for use in the agricultural sector of Uzbekistan, ProPlanta provides farmers with recommendations on the optimal use of key nutrients and fertilizers, including nitrogen, phosphorus, potassium, lime, magnesium, zinc (Zn), copper (Cu), and manganese (Mn). In addition, the software offers recommendations for the cultivation of environmentally beneficial plants, thereby promoting sustainable agricultural practices.

Dian Meithasari, endriani Endriani

Efforts to increase soybean production can be done in various ways, one of which is planting soybean varieties that are resistant to attacks pests and diseases, as well as acidic land conditions.The use of rock phosphate and biochar has been widely used to improve land conditions.The research aims to determine the effect of rock phosphate and biochar on the intensity of pests and diseases of soybean variety Demas-1. The research was carried out at Natar Experimental Garden, Negara Ratu Village, Natar District, South Lampung Regency. Ultisols soil type with medium soil fertility, soil pH 4.8. The research location is at coordinates 05o19'17"South Latitude and 105o10'29" East Longitude, with an altitude of 131.9 m above sea level. The research was carried out from March to June 2019. The research used a two-factor Randomized Block Design, namely Corn cob Biochar (A) and Rock phosphate (B) treatment, with 3 replications using the soybean variety Demas-1. The results showed , the level of rust disease attack for the Demas-1 variety was quite resistant, leaf rust attacks ranged from 3.3% - 7.33%, whereas in the A1B1 treatment without rock phosphate and biochar there were no rust disease attacks. These results indicate that Demas-1 plants are resistant to leaf rust disease. On plants aged 65 HST, armyworm attacks in each treatment were not significantly different, attacks reached 34%, the category of attacks that occurred on soybean plants was included in the "medium" category, ranging between >20-<45%. Application of biochar doses of 6, 8 and 10 tons ha-1 tended to increase plant growth, in the characteristics of plant height, number of branches, number of filled pods and seed weight of the Demas-1 variety soybean planting.

Qi Zhao, M. D. K. Lee, Qin Qichen et al.

We report measurements of the Lande g-factors for the 5d6s $^3$D$_2$ hyperfine levels of $^{176}$Lu$^+$ to a fractional inaccuracy of $5\times 10^{-7}$. Combining these measurements with theoretical calculations allows us to estimate hyperfine-mediated modifications to the quadrupole moments for each state and infer a value of $δΘ= 1.59(34)\times 10^{-4} \,ea_0^2$ for the residual quadrupole moment of the $^1S_0\leftrightarrow{^3}D_2$ hyperfine-averaged clock transition.

Keiller Nogueira, Akram Zaytar, Wanli Ma et al.

The increasing accessibility of remotely sensed data and their potential to support large-scale decision-making have driven the development of deep learning models for many Earth Observation tasks. Traditionally, such models rely on large datasets. However, the common assumption that larger training datasets lead to better performance tends to overlook issues related to data redundancy, noise, and the computational cost of processing massive datasets. Effective solutions must therefore consider not only the quantity but also the quality of data. Towards this, in this paper, we introduce six basic core-set selection approaches -- that rely on imagery only, labels only, or a combination of both -- and investigate whether they can identify high-quality subsets of data capable of maintaining -- or even surpassing -- the performance achieved when using full datasets for remote sensing semantic segmentation. We benchmark such approaches against two traditional baselines on three widely used land-cover classification datasets (DFC2022, Vaihingen, and Potsdam) using two different architectures (SegFormer and U-Net), thus establishing a general baseline for future works. Our experiments show that all proposed methods consistently outperform the baselines across multiple subset sizes, with some approaches even selecting core sets that surpass training on all available data. Notably, on DFC2022, a selected subset comprising only 25% of the training data yields slightly higher SegFormer performance than training with the entire dataset. This result shows the importance and potential of data-centric learning for the remote sensing domain. The code is available at https://github.com/keillernogueira/data-centric-rs-classification/.

Tyler A. Chang, Catherine Arnett, Abdelrahman Eldesokey et al.

To date, there exist almost no culturally-specific evaluation benchmarks for large language models (LLMs) that cover a large number of languages and cultures. In this paper, we present Global PIQA, a participatory commonsense reasoning benchmark for over 100 languages, constructed by hand by 335 researchers from 65 countries around the world. The 116 language varieties in Global PIQA cover five continents, 14 language families, and 23 writing systems. In the non-parallel split of Global PIQA, over 50% of examples reference local foods, customs, traditions, or other culturally-specific elements. We find that state-of-the-art LLMs perform well on Global PIQA in aggregate, but they exhibit weaker performance in lower-resource languages (up to a 37% accuracy gap, despite random chance at 50%). Open models generally perform worse than proprietary models. Global PIQA highlights that in many languages and cultures, everyday knowledge remains an area for improvement, alongside more widely-discussed capabilities such as complex reasoning and expert knowledge. Beyond its uses for LLM evaluation, we hope that Global PIQA provides a glimpse into the wide diversity of cultures in which human language is embedded.

Shengjie Liu, Siqin Wang, Lu Zhang

Many real-world applications rely on land surface temperature (LST) data at high spatiotemporal resolution. In complex urban areas, LST exhibits significant variations, fluctuating dramatically within and across city blocks. Landsat provides high spatial resolution data at 100 meters but is limited by long revisit time, with cloud cover further disrupting data collection. Here, we propose DELAG, a deep ensemble learning method that integrates annual temperature cycles and Gaussian processes, to reconstruct Landsat LST in complex urban areas. Leveraging the cross-track characteristics and dual-satellite operation of Landsat since 2021, we further enhance data availability to 4 scenes every 16 days. We select New York City, London and Hong Kong from three different continents as study areas. Experiments show that DELAG successfully reconstructed LST in the three cities under clear-sky (RMSE = 0.73-0.96 K) and heavily-cloudy (RMSE = 0.84-1.62 K) situations, superior to existing methods. Additionally, DELAG can quantify uncertainty that enhances LST reconstruction reliability. We further tested the reconstructed LST to estimate near-surface air temperature, achieving results (RMSE = 1.48-2.11 K) comparable to those derived from clear-sky LST (RMSE = 1.63-2.02 K). The results demonstrate the successful reconstruction through DELAG and highlight the broader applications of LST reconstruction for estimating accurate air temperature. Our study thus provides a novel and practical method for Landsat LST reconstruction, particularly suited for complex urban areas within Landsat cross-track areas, taking one step toward addressing complex climate events at high spatiotemporal resolution. Code and data are available at https://skrisliu.com/delag

Romuald Ait-Bachir, Carlos Granero-Belinchon, Aurélie Michel et al.

Due to the trade-off between the temporal and spatial resolution of thermal spaceborne sensors, super-resolution methods have been developed to provide fine-scale Land SurfaceTemperature (LST) maps. Most of them are trained at low resolution but applied at fine resolution, and so they require a scale-invariance hypothesis that is not always adapted. Themain contribution of this work is the introduction of a Scale-Invariance-Free approach for training Neural Network (NN) models, and the implementation of two NN models, calledScale-Invariance-Free Convolutional Neural Network for Super-Resolution (SIF-CNN-SR) for the super-resolution of MODIS LST products. The Scale-Invariance-Free approach consists ontraining the models in order to provide LST maps at high spatial resolution that recover the initial LST when they are degraded at low resolution and that contain fine-scale texturesinformed by the high resolution NDVI. The second contribution of this work is the release of a test database with ASTER LST images concomitant with MODIS ones that can be usedfor evaluation of super-resolution algorithms. We compare the two proposed models, SIF-CNN-SR1 and SIF-CNN-SR2, with four state-of-the-art methods, Bicubic, DMS, ATPRK, Tsharp,and a CNN sharing the same architecture as SIF-CNN-SR but trained under the scale-invariance hypothesis. We show that SIF-CNN-SR1 outperforms the state-of-the-art methods and the other two CNN models as evaluated with LPIPS and Fourier space metrics focusing on the analysis of textures. These results and the available ASTER-MODIS database for evaluation are promising for future studies on super-resolution of LST.

Nabil Almalki, Mrim M. Alnfiai, Fahd N. Al-Wesabi et al.

Internet of Things (IoT)-based human action recognition (HAR) has made a significant contribution to scientific studies. Furthermore, hand gesture recognition is a subsection of HAR, and plays a vital role in interacting with deaf people. It is the automatic detection of the actions of one or many subjects using a series of observations. Convolutional neural network structures are often utilized for finding human activities. With this intention, this study presents a new bat optimization algorithm with an ensemble voting classifier for human activity recognition (BOA-EVCHAR) technique to help disabled persons in the IoT environment. The BOA-EVCHAR technique makes use of the ensemble classification concept to recognize human activities proficiently in the IoT environment. In the presented BOA-EVCHAR approach, data preprocessing is generally achieved at the beginning level. For the identification and classification of human activities, an ensemble of two classifiers namely long short-term memory (LSTM) and deep belief network (DBN) models is utilized. Finally, the BOA is used to optimally select the hyperparameter values of the LSTM and DBN models. To elicit the enhanced performances of the BOA-EVCHAR technique, a series of experimentation analyses were performed. The extensive results of the BOA-EVCHAR technique show a superior value of 99.31% on the HAR process.

Freeman M. Mateko

Background: The participation of female labour is essential for promoting industrialisation. North African economies are plagued by low levels of female labour force participation (FLP) and high gender inequality gaps. Low levels of FLP are detrimental to the attainment of the Sustainable Development Goals, such as gender equality, decent work, and economic growth, as well as reduced inequalities. Aim: This research aimed to establish the determinants of FLP in North Africa. Setting: North Africa. Method: The research adopted the Panel Auto Regressive Distributed Lag. Data were sourced from the World Bank for the period 1991–2021. Results: The empirical findings showed that the lack of gender-sensitive policies, limited investment in education and institutional barriers limit the capacity of women to participate in the labour market. Primary research findings depicted that the Human Development Index (HDI), fertility rate and life expectancy had a positive impact on FLP in the long run. Economic growth had a positive effect on FLP in the short run. Conclusion: It was concluded that North African governments need to develop policies that advance the interests of women, as well as the implementation of women empowerment programmes. Contribution: The findings of the study imply that addressing FLP requires collaborative efforts from the governments and other stakeholders and this helps in reducing gender inequality.

Joshua Springer, Gylfi Þór Guðmundsson, Marcel Kyas

A remaining challenge in multirotor drone flight is the autonomous identification of viable landing sites in unstructured environments. One approach to solve this problem is to create lightweight, appearance-based terrain classifiers that can segment a drone's RGB images into safe and unsafe regions. However, such classifiers require data sets of images and masks that can be prohibitively expensive to create. We propose a pipeline to automatically generate synthetic data sets to train these classifiers, leveraging modern drones' ability to survey terrain automatically and the ability to automatically calculate landing safety masks from terrain models derived from such surveys. We then train a U-Net on the synthetic data set, test it on real-world data for validation, and demonstrate it on our drone platform in real-time.

Pierre Damien Ntihinyurwa, W. D. Vries, U. E. Chigbu et al.

Abstract Land fragmentation and land consolidation are two interrelated concepts of land management. The dominant discourse is that fragmented land ownership and land use tend to be ineffective and unwanted, and land consolidation is then a solution to this quandary. Not surprisingly, in countries such as Rwanda, the majority of the governmental strategies highlight the negative effects of fragmentation. However, the effects of land fragmentation have been dual. Its positive side has often been overlooked by policy makers and the research community. Therefore, this study investigates to which degree one can benefit from farmland fragmentation, especially in the context of food security at the household level and of climate change vulnerability. The goal of this article is to expand the current land fragmentation discourse and describe in which context specific types of land fragmentation may be just as sustainable as opting for land consolidation. The guiding hypothesis hereby is that there is a high level of fragmented land ownership yet, that physical (location, use, internal, shape and value) fragmentation acts as a risk management strategy which positively impacts the nutritional balance for food quality and food sustainability as components of food security. Conceptually, land fragmentation can be seen from multiple lenses. It can be seen as a land use concept (emphasizing variation in manner of agricultural production, variety of crops, frequency of harvesting, etc.). It can also be seen as a geodetic concept (emphasizing variation in shape and size of parcels on the one hand, and variation in land ownership on the other hand). Additionally, it can be seen as a spatial planning and intervention concept (emphasizing the urgency and need for order, structure and alignment of space). In our article we look at fragmentation (and the variation thereof) in all three ways. If within an area, the utilization, ownership, leasehold, shape, size and location of parcels and spatial policies vary more than average (as compared to a similar area), then we consider it a fragmented landscape. Once we find a case of such a landscape, then we are able to investigate why and/or under which conditions (and by which drivers) this ‘fragmented’ landscape has emerged and what are the implications. This is the main question under investigation in this research. The research relies on a mixed methods research approach via household surveys with 98 random respondents in Gashora sector, Bugesera District, Eastern province of Rwanda. The data collection included further 7 key informants’ interviews, a focus group discussion, field observations of current plot sizes and land uses, and the review of the existing literature on the topic. The findings indicate that a high level of fragmentation exists, both in terms of land ownership (visible and hidden) and physical landscape. The dominant reasons are that land users perceive this as an effective risks management strategy which would positively affect food quality, food sustainability and food security. Multiple land holdings with different shapes in different locations allow farmers to grow multiple crops with different adaptation capacities in different growing conditions (soil type, slope, microclimate variations, etc.). Furthermore, fragmentation seems to help reduce land ownership and use related conflicts despite its negative impacts on agriculture production efficiency, especially the loss of land through boundaries and the increase in boundaries related conflicts. Unlike previous studies on land fragmentation, we posit that environmental and agricultural policies should take both negative and positive impacts of land fragmentation into account equally as sustainable and resilient solutions, given the right circumstances and contexts, especially for vulnerable and food insecure areas in Rwanda.

Jorge Armando García García, Cesar Alveiro Montoya Agudelo

The document presented below is a reflection on the value that social responsibility represents in the field of guarantees that organizations must have on a subject as relevant as decent work. Under a qualitative methodology, where a search for information was carried out in various sources of information with the purpose of making an analysis and bibliographic review with the firm intention of establishing as a fundamental objective, a reflective analysis of the value that represents social responsibility, management human and decent work. The objective of the document is to address the value that social responsibility represents to later go on to analyze socially responsible human management and culminate with the importance of decent work for human dignity. It is concluded that organizations from the processes of human management are called to guarantee the existence of decent work as a response to their social commitment, regardless of skin color, religious creed, political ideology, origin or sexual preference, since what really should matter is the person only for the fact of being part of a society that should be in search of happiness, peace and equality.

A. V. Kolobov

The paper considers the models of organisational development of multidisciplinary companies and their business units. It is shown that the existing models need to be supplemented with two enlarged managerial competencies – management of incremental (modification) innovations and management of radical innovations. The proposed model of a business unit assumes that their development is structured as a progressive passage of the organisation through the stages of housing and communal services by developing the necessary managerial competencies for the next stage. The developed general models are used to form models of organisational development of the “Severgroup”  multidisciplinary corporation and its business units. The strategic portfolio of business units, its parameters and position within the framework of the matrix of housing and communal services of the industry are determined. The result of the study was the formulation of two strategies – “growth to the core” and “growth to the peak”. Models of organisational development of the corporation (changes in the composition and characteristics of the portfolio of business units) and models of transfer of managerial competencies have been developed for each strategy.

Ting Luo, Lu Xia, Dong Xia et al.

Abstract In order to investigate the characteristics of soil structural stability and the factors influencing it under typical land use types in the karst region of western Hubei, soil samples were collected from five land use types (natural mixed forest [NF], cypress forest [CF], stone dike terrace [ST], stone dike forest [SF], and abandoned land [AL]) in Xialaoxi to analyze the particle size distribution of aggregates, structural stability, and the distribution characteristics of carbon, nitrogen, and extracellular enzyme contents. The results showed that the aggregates of NF, CF, SF, and AL were dominated by >2‐mm aggregates, while ST showed mostly 2–0.25‐mm aggregates. The mean weight diameter (EMWD) and the geometric mean diameter (EGMD) of mechanical aggregates showed a trend of CF > NF > SF > AL > ST, whereas the erodibility factor K showed an opposite trend. The total organic carbon (TOC), labile organic carbon (LOC), and total nitrogen (TN) contents of aggregates of each particle size were significantly higher in NF and CF than in ST and AL under the five land types. The content of available nitrogen (AN) was highest in NF and ST. The content of carbon and nitrogen and extracellular hydrolases (β‐glucosidase [BG], cellulose disaccharide hydrolase [CBH], β‐N‐acetyl‐aminoglucosidase [NAG], and leucine aminopeptidase [LAP]) in soil aggregates were mainly concentrated in <0.25–0.053‐mm aggregates. The variation in carbon and nitrogen abundance of each particle size agglomerate mainly originated from the LAP content of that particle size agglomerate and showed significant correlation. The partial least squares path models (PLS‐PM) showed that the main influences on the stability of soil aggregates in small watersheds were land use types, soil physical properties, carbon and nitrogen abundance, and extracellular enzyme activity. In conclusion, the structural stability of soil aggregates in Xialaoxi watershed is the result of the interaction between human disturbance and soil nutrient self‐cycling. In general, NF and CF have better soil aggregate stability and are conducive to soil carbon and nitrogen accumulation, whereas for later development and management of SF, ST, and AL sample plots, scientific cultivation means should be adopted, and attention should be paid to long‐term carbon and nitrogen accumulation and conservation.

Ruzelan Khalid, Mohd Kamal Mohd Nawawi, Nurhanis Ishak et al.

A speed-density model can be utilised to efficiently flow pedestrians in a network. However, how each model measures and optimises the performance of the network is rarely reported. Thus, this paper analyses and optimises the flow in a topological network using various speed-density models. Each model was first used to obtain the optimal arrival rates to all individual networks. The optimal value of each network was then set as a flow constraint in a network flow model. The network flow model was solved to find the optimal arrival rates to the source networks. The optimal values were then used to measure their effects on the performance of each available network. The performance results of the model were then compared with thatof other speed-density models. The analysis of the results can help decision-makers understand how arrival rates propagate through traffic and determine the level of the network throughputs. (original abstract)

Seungku Ahn, Kwon-Sik Kim, Kwang-Hoon Lee

ABSTRACT: Despite large-scale financial support of the government, there is increasing criticism about the inefficiency of public R&D investment that fails to lead directly to technological innovation of technology-based start-ups. This paper analyzes the factors that influence technological innovation in Korean technology-based start-ups based on the resource-based view (RBV). The empirical analysis combines ordinary least squares and ordered probit analysis of data collected from 248 technology-based start-ups in Korea. The analysis results statistically confirm the effects of technological capabilities and entrepreneurship on technological innovation. First, a start-up’s technological capabilities measured by patents and technological competitiveness have significant positive effects on technological innovation, while the effect of having an in-house R&D department for technological innovation is not significant. Second, entrepreneurship has a significant positive effect on the technological innovation of a start-up, and this positive effect has a moderating effect that further promotes the positive effect of technological competitiveness on technological innovation.

Taimoor Hassan Farooq, Muhammad Adnan Bukhari, Muhammad Shahid Irfan et al.

Abiotic stresses, such as high temperature and drought conditions, greatly influence the development of plants and the quality and quantity of products. Barley (<i>Hordeum vulgare</i> L.) crop production is largely impacted by drought, affecting growth, yield, and ultimately the productivity of the crop in hot arid/semi-arid conditions. The current pot experiment was directed to observe the outcome of nicotinic acid (NA) treatments on barley’s physiological, biochemical, and production attributes at two capacity levels, i.e., 100% normal range and withholding water stress. Randomized complete block design (RCBD) was used during the experimentation with the two-factor factorial arrangement. NA was applied exogenously by two different methods, i.e., foliar and soil application (fertigation). NA solution contained various application levels, such as T1 = control, foliar applications (T2 = 0.7368 gL⁻¹, T3 = 1.477 gL⁻¹, T4 = 2.2159 gL⁻¹), and soil applications (T5 = 0.4924 gL⁻¹, T6 = 0.9848 gL⁻¹, and T7 = 1.4773 gL⁻¹). Results depicted that, overall, foliar treatments showed better effects than control and soil treatments. Plant growth was preeminent under T4 treatment, such as plant height (71.07 cm), relative water content (84.0%), leaf water potential (39.73-MPa), leaf area index (36.53 cm²), biological yield (15.10 kgha⁻¹), grain yield (14.40 kgha⁻¹), harvest index (57.70%), catalase (1.54 mmolg⁻¹FW⁻¹), peroxidase (1.90 g⁻¹FWmin⁻¹), and superoxide dismutase (52.60 µgFW⁻¹) were superior under T4 treatment. Soil plant analysis development (54.13 µgcm⁻²) value was also higher under T4 treatment and lowest under T7 treatment. In conclusion, NA-treated plants were more successful in maintaining growth attributes than non-treated plants; therefore, the NA foliar treatment at the rate of 2.2159 gL⁻¹ is suggested to find economical crop yield under drought conditions. The present study would contribute significantly to improving the drought tolerance potential of barley through exogenous NA supply in water deficit areas.