Hasil untuk "Industries. Land use. Labor"

Bosong Zhang, Timothy M. Merlis

The spatial pattern of sea surface temperature (SST) plays a central role in shaping the climate system, yet the influence of land surface temperature (LST) remains poorly understood. Using a state-of-the-art coupled ocean--land--atmosphere model, we examine the model's response to regional LST perturbations imposed through LST nudging and idealized time-dependent ramp warming simulations. We find that LST warming over South America strengthens the tropical Pacific zonal SST gradient, yielding a more La Niña--like mean state. Enhanced LST increases the zonal contrast in diabatic heating and excites stationary Rossby wave responses, which reinforce alongshore winds and coastal upwelling in the eastern Pacific. This provides a dynamical pathway linking regional land warming to changes in the equatorial Pacific mean state. Similar responses are found for warming over North America, accompanied by North Pacific cooling, and for warming over Central Africa, coupled with tropical Atlantic cooling. In contrast, warming over the Maritime Continent or the Tibetan Plateau does not induce significant SST pattern changes. Historical simulations nudged toward observed LST exhibit cooling in the tropical southeast Pacific, with the tentative implication that uncertainty in LST may contribute to model-simulated SST biases during the historical period.

Ivan Felipe Benavides-Martinez, Justin Guthrie, Jhon Edwin Arias et al.

Geospatial foundation models generate high-dimensional embeddings that achieve strong predictive performance, yet their internal organization remains obscure, limiting their scientific use. Recent interpretability studies relate Google AlphaEarth Foundations (GAEF) embeddings to continuous environmental variables, but it is still unclear whether the embedding space exhibits a functional or hierarchical organization, in which some dimensions act as specialized representations while others encode shared or broader geospatial structure. In this work, we propose a functional interpretability framework that reverse-engineers the role of embedding dimensions by characterizing their contribution to land cover structure from observed classification behavior. The approach combines large-scale experimentation with a structural analysis of embedding-class relationships based on feature importance patterns and progressive ablation. Our results show that embedding dimensions exhibit consistent and non-uniform functional behavior, allowing them to be categorized along a hierarchical functional spectrum: specialist dimensions associated with specific land cover classes, low- and mid-generalist dimensions capturing shared characteristics between classes, and highgeneralist dimensions reflecting broader environmental gradients. Critically, we find that accurate land cover classification (98% of baseline performance) can be achieved using as few as 2 to 12 of the 64 available dimensions, depending on the class. This demonstrates substantial redundancy in the embedding space and offers a pathway toward significant reductions in computational cost. Together, these findings reveal that AlphaEarth embeddings are not only physically informative, but also functionally organized into a hierarchical structure, providing practical guidance for dimension selection in operational classification tasks.

Johannes Leonhardt, Juergen Gall, Ribana Roscher

Large-scale land cover maps generated using deep learning play a critical role across a wide range of Earth science applications. Open in-situ datasets from principled land cover surveys offer a scalable alternative to manual annotation for training such models. However, their sparse spatial coverage often leads to fragmented and noisy predictions when used with existing deep learning-based land cover mapping approaches. A promising direction to address this issue is object-based classification, which assigns labels to semantically coherent image regions rather than individual pixels, thereby imposing a minimum mapping unit. Despite this potential, object-based methods remain underexplored in deep learning-based land cover mapping pipelines, especially in the context of medium-resolution imagery and sparse supervision. To address this gap, we propose LC-SLab, the first deep learning framework for systematically exploring object-based deep learning methods for large-scale land cover classification under sparse supervision. LC-SLab supports both input-level aggregation via graph neural networks, and output-level aggregation by postprocessing results from established semantic segmentation models. Additionally, we incorporate features from a large pre-trained network to improve performance on small datasets. We evaluate the framework on annual Sentinel-2 composites with sparse LUCAS labels, focusing on the tradeoff between accuracy and fragmentation, as well as sensitivity to dataset size. Our results show that object-based methods can match or exceed the accuracy of common pixel-wise models while producing substantially more coherent maps. Input-level aggregation proves more robust on smaller datasets, whereas output-level aggregation performs best with more data. Several configurations of LC-SLab also outperform existing land cover products, highlighting the framework's practical utility.

Bharat Sharma, Jitendra Kumar, Nathan Collier et al.

Human-induced carbon dioxide (CO2) emissions, primarily from fossil fuel combustion and changes in land use and land cover (LULCC), are a key contributor to climate change. As the climate warms, extreme events such as heatwaves, droughts, and wildfires have become more frequent and are projected to intensify throughout the 21st century. These escalating extremes are likely to further disrupt vegetation productivity, known as gross primary production (GPP), and reduce the ecosystem's capacity to absorb carbon. In this study, we employ a global Earth system model to assess how (a) CO2 emissions alone and (b) CO2 combined with LULCC influence the severity, frequency, and duration of GPP extremes. Our results show that negative GPP extremes periods of unexpectedly low carbon uptake are increasing more rapidly than positive extremes, especially under LULCC scenarios. The primary climate factor driving these extremes is soil moisture variability, which is influenced by fluctuations in both precipitation and temperature. The delayed responses of GPP to different climate drivers depend on the specific driver and geographical region. Overall, the highest incidence of GPP extremes arises from the combined influence of water stress, temperature anomalies, and fire-related disturbances.

Zili Wang, Frank Montabon, Kristin Yvonne Rozier

Supply chain networks are complex systems that are challenging to analyze; this problem is exacerbated when there are illicit activities involved in the supply chain, such as counterfeit parts, forced labor, or human trafficking. While machine learning (ML) can find patterns in complex systems like supply chains, traditional ML techniques require large training data sets. However, illicit supply chains are characterized by very sparse data, and the data that is available is often (purposely) corrupted or unreliable in order to hide the nature of the activities. We need to be able to automatically detect new patterns that correlate with such illegal activity over complex, even temporal data, without requiring large training data sets. We explore neurosymbolic methods for identifying instances of illicit activity in supply chains and compare the effectiveness of manual and automated feature extraction from news articles accurately describing illicit activities uncovered by authorities. We propose a question tree approach for querying a large language model (LLM) to identify and quantify the relevance of articles. This enables a systematic evaluation of the differences between human and machine classification of news articles related to forced labor in supply chains.

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.

Xiaoxuan Zhang, Quan Pan, Salvador García

Deep learning (DL)-based sea\textendash land clutter classification for sky-wave over-the-horizon-radar (OTHR) has become a novel research topic. In engineering applications, real-time predictions of sea\textendash land clutter with existing distribution discrepancies are crucial. To solve this problem, this article proposes a novel Multisource Semisupervised Adversarial Domain Generalization Network (MSADGN) for cross-scene sea\textendash land clutter classification. MSADGN can extract domain-invariant and domain-specific features from one labeled source domain and multiple unlabeled source domains, and then generalize these features to an arbitrary unseen target domain for real-time prediction of sea\textendash land clutter. Specifically, MSADGN consists of three modules: domain-related pseudolabeling module, domain-invariant module, and domain-specific module. The first module introduces an improved pseudolabel method called domain-related pseudolabel, which is designed to generate reliable pseudolabels to fully exploit unlabeled source domains. The second module utilizes a generative adversarial network (GAN) with a multidiscriminator to extract domain-invariant features, to enhance the model's transferability in the target domain. The third module employs a parallel multiclassifier branch to extract domain-specific features, to enhance the model's discriminability in the target domain. The effectiveness of our method is validated in twelve domain generalizations (DG) scenarios. Meanwhile, we selected 10 state-of-the-art DG methods for comparison. The experimental results demonstrate the superiority of our method.

Kefan Zhang, Zhili Zhang, Junyang Zhao et al.

Traditional land vehicle gravity measurement heavily rely on high-precision satellite navigation positioning information. However, the operational range of satellite navigation is limited, and it cannot maintain the required level of accuracy in special environments. To address this issue, we propose a novel land vehicle gravity anomaly measurement method based on altimeter-assisted strapdown inertial navigation system (SINS)/dead reckoning (DR) integration. Gravimetric measurement trials demonstrate that after low-pass filtering, the new method achieves a fit accuracy of 2.005 mGal, comparable to that of the traditional SINS/global navigation satellite system (GNSS) integration method. Compared with the SINS/DR integration method, the proposed method improves accuracy by approximately 11%.

Sheikh Zeeshan Basar, Satadal Ghosh

In any spacecraft landing mission, fuel-efficient precision soft landing while avoiding nearby hazardous terrain is of utmost importance. Very few existing literature have attempted addressing both the problems of precision soft landing and terrain avoidance simultaneously. To this end, an optimal terrain avoidance landing guidance (OTALG) was recently developed, which showed promising performance in avoiding the terrain while consuming near-minimum fuel. However, its performance significantly degrades in the face of external disturbances, indicating lack of robustness. To mitigate this problem, in this paper, a near fuel-optimal guidance law is developed to avoid terrain and achieve precision soft landing at the desired landing site. Expanding the OTALG formulation using sliding mode control with multiple sliding surfaces (MSS), the presented guidance law, named `MSS-OTALG', improves precision soft landing accuracy. Further, the sliding parameter is designed to allow the lander to avoid terrain by leaving the trajectory enforced by the sliding mode and eventually returning to it when the terrain avoidance phase is completed. And finally, the robustness of the MSS-OTALG is established by proving practical fixed-time stability. Extensive numerical simulations are also presented to showcase its performance in terms of terrain avoidance, low fuel consumption, and accuracy of precision soft landing under bounded atmospheric perturbations, thrust deviations, and constraints. Comparative studies against existing relevant literature validate a balanced trade-off of all these performance measures achieved by the developed MSS-OTALG.

Ellis Scharfenaker, Bruno Theodosio, Duncan K. Foley

Adam Smith's inquiry into the emergence and stability of the self-organization of the division of labor in commodity production and exchange is considered using statistical equilibrium methods from statistical physics. We develop a statistical equilibrium model of the distribution of independent direct producers in a hub-and-spoke framework that predicts both the center of gravity of producers across lines of production as well as the endogenous fluctuations between lines of production that arise from Smith's concept of "perfect liberty". The ergodic distribution of producers implies a long-run balancing of "advantages to disadvantages" across lines of employment and gravitation of market prices around Smith's natural prices.

Toshisuke Maruyama, Sanshiro Fujii, Hiroshi Takimoto

Evapotranspiration (ET) is a critical concern for water management and hydrological cycle; thus, studies of ET have been performed to aid irrigation and water resource planning. Moreover, global warming-related studies are critical, as sensible heat contributes to warming, while the latent heat flux contributes to cooling. Recently, FLUXNET2015, a large energy flux dataset comprising climatic elements, was updated with a corrected heat balance relationship. In this study, we aim to applicability of the inverse analysis (IA) for estimating farmland ET. Practically, we evaluated the estimated ET (LEest) consistency using IA, which compared common climate data with observed data (LEobs) from US-Ne1 (irrigated), US-Ne2 (irrigated), and US-Ne3 (non-irrigated) land in FLUXNET2015. For an hourly time step, net radiation (Rn) and heat flux into the ground (G) were reasonably allocated into sensible (H) and latent (LE) heat fluxes, and LEobs was reasonably reproduced by LEest. For daily and monthly time steps, LEobs was reproduced well by LEest, with similar accuracies. For a yearly time step, LEobs was reproduced by LEest with an R2 of 0.933. Reasonability of the IA method also confirmed ET in crop growing season by comparing LEobs and LEest. A cooling effect under the canopy was observed on irrigated farmland in eight of the 22 analyzed years, whereas non-irrigated farmland did not exhibit a cooling effect. The maximum cooling effect was 4.26 °C of the monthly average. The results confirm that IA can be applied to non-irrigated and irrigated farmland if a cooling effect is not observed. IA can therefore be used to improve farmland water utilization because of accurate LEest and determining the capacities of irrigation facilities. The findings can be used to evaluate cooling effects on farmland, as well as reasonable allocations of Rn into H and LE, which promote the advancement of global warming issues.

Bandar Ali Al-Rami, Yousef Houssni Zrekat

This paper examines speakers’ systematic errors while speaking English as a foreign language (EFL) among students in Arab countries with the purpose of automatically recognizing and correcting mispronunciations using speech recognition, phonological features, and machine learning. Accordingly, three main steps are implemented towards this purpose: identifying the most frequently wrongly pronounced phonemes by Arab students, analyzing the systematic errors these students make in doing so, and developing a framework that can aid the detection and correction of these pronunciation errors. The proposed automatic detection and correction framework used the collected and labeled data to construct a customized acoustic model to identify and correct incorrect phonemes. Based on the trained data, the language model is then used to recognize the words. The final step includes construction samples of both correct and incorrect pronunciation in the phonemes model and then using machine learning to identify and correct the errors. The results showed that one of the main causes of such errors was the confusion that leads to wrongly utilizing a given sound in place of another. The automatic framework identified and corrected 98.2% of the errors committed by the students using a decision tree classifier. The decision tree classifier achieved the best recognition results compared to the five classifiers used for this purpose.

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.

Ran Chen, Jing Zhao, Xiaomin Luo et al.

To improve the environment of the ecosystem, China has implemented the Green-forGrain Program for two decades, which has resulted in an imbalance among ecology.economy and food. This study focuses on the "ecology-food" imbalance problem.taking Sichuan-Chongqing Region as an example, to set up future scenarios topredicate the distribution of ESs. We first forecast land use/cover change in 2050under four different scenarios: Natural Development Scenarios; Arable LandConservation Scenarios; Ecological Priority Scenarios; Ecology-Arable LandHarmonization Scenarios. Then we assess changes in five ESs: habitat quality ,cropproduction, soil conservation, water yield, and carbon storage from 1990 to 2020 and2050. Finally, we reveal the spacial distribution of ESs. The following conclusions areobtained: (1) From 1990-2020, CS, SC, and HQ reveal an increasing trend with growthrates of 1.68%, 0.08%, and 0.46%: CP reveals a reduce rate of 2.75% . (2) S4 has anincrease in arable land, and CP has increased by 7.56% compared to S1, reversingthe trend of reduced CP under S1. (3) The high-high anomalies area of CP under S4 isoasically the same as that under S2, which proves that S4 is a scenario policy that canbe referred to for future development.

Sara Shoouri, Shayan Jalili, Jiahong Xu et al.

At smaller airports without an instrument approach or advanced equipment, automatic landing of aircraft is a safety-critical task that requires the use of sensors present on the aircraft. In this paper, we study falsification of an automatic landing system for fixed-wing aircraft using a camera as its main sensor. We first present an architecture for vision-based automatic landing, including a vision-based runway distance and orientation estimator and an associated PID controller. We then outline landing specifications that we validate with actual flight data. Using these specifications, we propose the use of the falsification tool Breach to find counterexamples to the specifications in the automatic landing system. Our experiments are implemented using a Beechcraft Baron 58 in the X-Plane flight simulator communicating with MATLAB Simulink.

Helen X.H. Bao, Guy M. Robinson

Sidi Mohammed Chekouri, Abderrahim Chibi, Mohamed Benbouziane

Abstract The Central Bank of Algeria has announced a managed float of the Algerian dinar since 1994. Yet, there are some substantial differences between various de facto classifications of Algeria’s exchange rate regime. This study looks into the exchange rate regime of Algeria, aiming to identify de facto regime. To identify the implicit basket weights for the Algerian dinar, first the OLS rolling window methodology is used to estimate the celebrated Frankel-Wei regression. Then, the wavelet-based methods are applied to study the co-movement patterns of the exchange rates of the Algerian dinar, US dollar, and Euro. In the main, the OLS rolling window results show that the US dollar and the Euro are the currencies with the most influence over the Algerian dinar. Further, from the Wavelet Multiple Correlation (WMC) results, the US dollar is identified as the potential leader in the implicit basket for the Algerian dinar. Additionally, from the Wavelet Local Multiple Correlation (WLMC) results, it is found that the Algerian DZD, US dollar, and Euro are highly correlated, with a correlation value around 0.90 for most of the time scales. Based on the results obtained, we suggest that Algeria’s exchange rate regime could be a crawling peg and band around the US dollar and Euro.

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.