Hasil untuk "Industries. Land use. Labor"

Ashley Klein, Edward Raff, Marcia DesJardin

The meta-analysis's utility is dependent on previous studies having accurately captured the variables of interest, but in medical studies, a key decision variable that impacts a physician's decisions was not captured. This results in an unknown effect size and unreliable conclusions. A Bayesian approach may allow analysis to determine if the claim of a positive effect is still warranted, and we build a Bayesian approach to this common medical scenario. To demonstrate its utility, we assist professional OBGYNs in evaluating Trial of Labor After a Cesarean-section (TOLAC) situations where few interventions are available for patients and find the support needed for physicians to advance patient care.

Akhil Gupta Chigullapally, Sharvan Vittala, Razin Farhan Hussian et al.

The fast pace of modern AI is rapidly transforming traditional industrial systems into vast, intelligent and potentially unmanned autonomous operational environments driven by AI-based solutions. These solutions leverage various forms of machine learning, reinforcement learning, and generative AI. The introduction of such smart capabilities has pushed the envelope in multiple industrial domains, enabling predictive maintenance, optimized performance, and streamlined workflows. These solutions are often deployed across the Industrial Internet of Things (IIoT) and supported by the Edge-Fog-Cloud computing continuum to enable urgent (i.e., real-time or near real-time) decision-making. Despite the current trend of aggressively adopting these smart industrial solutions to increase profit, quality, and efficiency, large-scale integration and deployment also bring serious hazards that if ignored can undermine the benefits of smart industries. These hazards include unforeseen interoperability side-effects and heightened vulnerability to cyber threats, particularly in environments operating with a plethora of heterogeneous IIoT systems. The goal of this study is to shed light on the potential consequences of industrial smartness, with a particular focus on security implications, including vulnerabilities, side effects, and cyber threats. We distinguish software-level downsides stemming from both traditional AI solutions and generative AI from those originating in the infrastructure layer, namely IIoT and the Edge-Cloud continuum. At each level, we investigate potential vulnerabilities, cyber threats, and unintended side effects. As industries continue to become smarter, understanding and addressing these downsides will be crucial to ensure secure and sustainable development of smart industrial systems.

Fabian Muny

Many programs evaluated in observational studies incorporate a sequential structure, where individuals may be assigned to various programs over time. While this complexity is often simplified by analyzing programs at single points in time, this paper reviews, explains, and applies methods for program evaluation within a sequential framework. It outlines the assumptions required for identification under dynamic confounding and demonstrates how extending sequential estimands to dynamic policies enables the construction of more realistic counterfactuals. Furthermore, the paper explores recently developed methods for estimating effects across multiple treatments and time periods, utilizing Double Machine Learning (DML), a flexible estimator that avoids parametric assumptions while preserving desirable statistical properties. Using Swiss administrative data, the methods are demonstrated through an empirical application assessing the participation of unemployed individuals in active labor market policies, where assignment decisions by caseworkers can be reconsidered between two periods. The analysis identifies a temporary wage subsidy as the most effective intervention, on average, even after adjusting for its extended duration compared to other programs. Overall, DML-based analysis of dynamic policies proves to be a useful approach within the program evaluation toolkit.

Claudio Pirrone, Stefano Fricano, Gioacchino Fazio

The foundation model industry exhibits unprecedented concentration in critical inputs: semiconductors, energy infrastructure, elite talent, capital, and training data. Despite extensive sectoral analyses, no comprehensive framework exists for assessing overall industrial vulnerability. We develop the Artificial Intelligence Industrial Vulnerability Index (AIIVI) grounded in O-Ring production theory, recognizing that foundation model production requires simultaneous availability of non-substitutable inputs. Given extreme data opacity and rapid technological evolution, we implement a validated human-in-the-loop methodology using large language models to systematically extract indicators from dispersed grey literature, with complete human verification of all outputs. Applied to six state-of-the-art foundation model developers, AIIVI equals 0.82, indicating extreme vulnerability driven by compute infrastructure (0.85) and energy systems (0.90). While industrial policy currently emphasizes semiconductor capacity, energy infrastructure represents the emerging binding constraint. This methodology proves applicable to other fast-evolving, opaque industries where traditional data sources are inadequate.

Debopom Sutradhar, Arefin Ittesafun Abian, Mohaimenul Azam Khan Raiaan et al.

Accurate land cover classification from satellite imagery is crucial in environmental monitoring and sustainable resource management. However, it remains challenging due to the complexity of natural landscapes, the visual similarity between classes, and the significant class imbalance in the available datasets. To address these issues, we propose a dual encoder architecture that independently extracts modality-specific features from optical and Synthetic Aperture Radar (SAR) imagery, which are then fused using a cross-modality attention-fusion module named Cross-modality Land cover segmentation with Attention and Imbalance-aware Reasoning-Enhanced Explanations (CLAIRE). This fusion mechanism highlights complementary spatial and textural features, enabling the network to better capture detailed and diverse land cover patterns. We incorporate a hybrid loss function that utilizes Weighted Focal Loss and Tversky Loss named RIFT (Rare-Instance Focal-Tversky) to address class imbalance and improve segmentation performance across underrepresented categories. Our model achieves competitive performance across multiple benchmarks: a mean Intersection over Union (mIoU) of 56.02% and Overall Accuracy (OA) of 84.56% on the WHU-OPT-SAR dataset; strong generalization with a mIoU of 59.89% and OA of 73.92% on the OpenEarthMap-SAR dataset; and remarkable robustness under cloud-obstructed conditions, achieving an mIoU of 86.86% and OA of 94.58% on the PIE-RGB-SAR dataset. Additionally, we introduce a metric-driven reasoning module generated by a Small Language Model (Phi-3), which generates expert-level, sample-specific justifications for model predictions, thereby enhancing transparency and interpretability.

Mohammad Nazaripour, Babak Zakizadeh

Neurotransmitters are the chemical messengers nerve cells use to transmit signals to target cells. Neurotransmitters affect the behavioral aspects of investors' performance. Therefore, this study investigated neurotransmitters' effect on individual investors' behavior by using structural equation modeling. To achieve this goal, exploratory and confirmatory factor analysis has been used. The study is applied research and utilizes a survey methodology. The required data were collected through the distribution of questionnaires among 315 individual investors. This study was done in the Tehran stock exchange's second and third seasons of 2023. According to the research, neurotransmitters through eight components of adrenaline or epinephrine, noradrenaline or norepinephrine, dopamine, serotonin, GABA (gamma-aminobutyric add), acetylcholine, glutamate, and endorphins affect the behavior of individual investors. Based on the second-order confirmatory factor analysis, the GABA component (0.39) has the highest impact, and adrenaline or epinephrine (0.25) has the least effect on the behavior of individual investors. The findings indicate the necessity of redefining rationality and considering its effects on investors' decisions and behavior.

Stéphane Vannitsem, David Docquier, Sarah Wauthy et al.

The low-frequency variability of the $δ^{18}$O recorded in ice cores (FK17 and TIR18) recently drilled at two different locations in Dronning Maud Land (Antarctica), is investigated using multi-taper spectral method and singular spectrum analysis. Multiple dominant peaks emerge in these records with periods between 3 and 20 years. The two sites show distinct spectral signatures, despite their relative proximity in space (about 100 km apart), suggesting that different processes are involved in generating the variability at these two sites. In order to clarify which processes are acting on $δ^{18}$O at these two locations, the impact of several climate indices as well as sea ice area is investigated using a causal method, known as the Liang-Kleeman rate of information transfer. The analysis of the origin of this low-frequency variability from external sources reveals that El Niño-Southern Oscillation (ENSO), the Pacific Decadal Oscillation (PDO), the Southern Annular Mode (SAM), the Dipole Mode Index (DMI) and the sea ice area display important causal influences on $δ^{18}$O at FK17. For TIR18, the main influences are from ENSO, PDO, DMI, the sea ice area, and the Atlantic Multidecadal Oscillation (AMO), revealing the complexity of the interactions in Dronning Maud Land. The two locations share several drivers, but also show local specificities potentially linked to ocean proximity and differences in air mass trajectories. The implication of these findings on the low-frequency variability in the two ice cores is discussed.

Justin Zhao, Timothy Wang, Wael Abid et al.

Low Rank Adaptation (LoRA) has emerged as one of the most widely adopted methods for Parameter Efficient Fine-Tuning (PEFT) of Large Language Models (LLMs). LoRA reduces the number of trainable parameters and memory usage while achieving comparable performance to full fine-tuning. We aim to assess the viability of training and serving LLMs fine-tuned with LoRA in real-world applications. First, we measure the quality of LLMs fine-tuned with quantized low rank adapters across 10 base models and 31 tasks for a total of 310 models. We find that 4-bit LoRA fine-tuned models outperform base models by 34 points and GPT-4 by 10 points on average. Second, we investigate the most effective base models for fine-tuning and assess the correlative and predictive capacities of task complexity heuristics in forecasting the outcomes of fine-tuning. Finally, we evaluate the latency and concurrency capabilities of LoRAX, an open-source Multi-LoRA inference server that facilitates the deployment of multiple LoRA fine-tuned models on a single GPU using shared base model weights and dynamic adapter loading. LoRAX powers LoRA Land, a web application that hosts 25 LoRA fine-tuned Mistral-7B LLMs on a single NVIDIA A100 GPU with 80GB memory. LoRA Land highlights the quality and cost-effectiveness of employing multiple specialized LLMs over a single, general-purpose LLM.

Bryan Habas, Bo Cheng

Inverted landing is a routine behavior among a number of animal fliers. However, mastering this feat poses a considerable challenge for robotic fliers, especially to perform dynamic perching with rapid body rotations (or flips) and landing against gravity. Inverted landing in flies have suggested that optical flow senses are closely linked to the precise triggering and control of body flips that lead to a variety of successful landing behaviors. Building upon this knowledge, we aimed to replicate the flies' landing behaviors in small quadcopters by developing a control policy general to arbitrary ceiling-approach conditions. First, we employed reinforcement learning in simulation to optimize discrete sensory-motor pairs across a broad spectrum of ceiling-approach velocities and directions. Next, we converted the sensory-motor pairs to a two-stage control policy in a continuous augmented-optical flow space. The control policy consists of a first-stage Flip-Trigger Policy, which employs a one-class support vector machine, and a second-stage Flip-Action Policy, implemented as a feed-forward neural network. To transfer the inverted-landing policy to physical systems, we utilized domain randomization and system identification techniques for a zero-shot sim-to-real transfer. As a result, we successfully achieved a range of robust inverted-landing behaviors in small quadcopters, emulating those observed in flies.

Viktor I. Karagodin

When planning the operation of land transport and transport technology facilities, their technical condition is not sufficiently taken into account. This can lead to unplanned failure of the machines and failure of the planned work by the remaining machines. The proposed methods of distributing machines by objects and types of work are based on the theory of aging of machines, but unlike the performance potential of machines, which reflects the technical condition of an average machine, they are focused on a specific machine, the probability of failure of which is determined using technical diagnostic methods. The results of the study of the dependence of the probability of failure of the car on the value of the inter-control period, the patterns of change in the probability of failure during the inter-control period and with an increase in the mileage of the car are presented. The goal is to increase the efficiency of the use of ground transportation and transportation technology facilities. Method and methodology. The theory of aging of machines, mathematical modeling, statistical methods of analysis. Results. New dependences of the probability of a car failure on the value of the inter-control period, the regularity of the change in the probability of failure during the inter-control period and with an increase in the mileage of the car are obtained and justified. The field of application of the results is the operation of ground transportation and transportation technology facilities.

Ashish Gupta, Prashant Das, Dongshin Kim

We examine the localized nature of COVID-19 pandemic spread in India. Through Gamma curve-fitting we observe that the infection patterns exhibit substantial variation across locations. Areas with larger male population and higher economic activity witness more cases. Economically-deprived districts experience higher mortality after controlling for the infections. Mobility in spatially contiguous locations is a significant determinant of new infections. Our study emphasizes the role of socioeconomic factors in explaining the variation across districts. The findings support the need for locally-specific policy, better medical infrastructure in socio-economically vulnerable localities and social-distancing measures in controlling the spread.

Aris Agus Styawan

Abstract: The issue of land disputes in urban areas during the New Order era intensified due to the massive use of land for housing needs and to support various development projects. Especially in the late 1970s, land issues had become a major concern in cities at the municipal level. This paper analyzed the causes of land disputes involving the Pawirorejo family in the city of Surakarta. This historical research used archival sources such as trial documents from the Surakarta Court Office, regional statistical data, and newspapers. The study's results suggest that the state-controlled lands in Surakarta, which did not receive full attention, triggered the Pawirorejo land dispute; consequently, the city government's weak control also played a role. Furthermore, land as a commodity with significant value in the city of Surakarta is vulnerable to conflicts, and the adage "land for the people" becomes very difficult to fully realize amidst the strengthening currents of development and the interests of the authorities. Therefore, the use and ownership of land in urban areas, especially state land in Surakarta, require explicit regulation through legal means. Keywords: The Land Disputes, Pawirorejo, Surakarta, New Order

Hanne Meyer-Johansen

The article advocates for a more socially transformative and non-individual-oriented approach to social pedagogy, emphasizing the potential of the empowerment concept in efforts to contribute to individuals' emancipation and the transformation of their life conditions. The significance of this pedagogical aim is examined based on an exploration of the historical development of social pedagogy as a normative and political foundational idea, inspired by two central pedagogical and learning theorists: Paulo Freire and Oskar Negt. Drawing on these critical social analyses and transformative possibilities, illustrative examples from previous studies of social pedagogical practice are included to uncover the presence or absence of educators' reflections on critical social and transformative perspectives and transgressive orientations. The prevailing societal tendency towards an individualized and organizationally internal perspective is revealed to dominate, with the reduced social pedagogical potential inherent in such a compensatory view of citizens. However, another study indicates that the need for pedagogical encounters across the organizational frameworks of the workplace can highlight the importance of a broader societal perspective, with solidaristic identification and a focus on the empowerment of the citizens involved. This might be a useful way to contribute to a renewed generation of meaning and a transformative dimension of social pedagogical work, as well as its practitioners in this field.

Fritz Brugger, Joschka J. Proksik

Illicit financial flows (IFFs) deprive low-income countries of essential revenues while donors’ willingness to fund aid budgets dwindles. IFFs related to foreign direct investment and trade include transfer mispricing, trade mispricing and profit shifting. Policy options to curb IFFs range from short-term fixes to mid-term measures that adjust legal instruments and improve coordination between countries, to more fundamental structural reforms that require a longer time horizon. Which policies are effective and should be pursued is a highly contested point, slowing down the progress of reform. This is unsurprising as reducing IFFs involves a distributional conflict: more for those deprived of revenues now means less for those who currently benefit. We conduct a Q-methodology study among IFF policy experts. We use Q-methodology to reveal participants’ policy preferences and tease out lines of contestation and areas of agreement to identify the policy space available in which to advance reform. We find tensions existing amid preferences for short-term fixes and for more comprehensive structural reforms; tensions regarding the question of extending legal liability to those facilitating and assisting in the creation of IFFs; and tensions over whether and to what extent host countries should be empowered to curb IFFs using their legislative sovereignty. Policy measures to increase targeted transparency that is directly actionable to tax administrations in host countries are the most likely to garner approval from all stakeholders.

Pratibha Gautam, Rajib Chattopadhyay, Gill Martin et al.

What is the role of soil moisture in maintaining the land ITCZ during the active phase of the monsoon? This question has been addressed in this study by using ERA5 reanalysis datasets, and then we evaluate the question in the CFS model-free run. Like rainfall, soil moisture also show intraseasonal oscillation. Furthermore, the sub-seasonal and seasonal features of soil moisture are different from each other. During the summer monsoon season, the maximum soil moisture is found over western coastal regions, central parts of India, and the northeastern Indian subcontinent. However, during active phases of the monsoon, the maximum positive soil moisture anomaly was found in North West parts of India. soil moisture also play a pre-conditioning role during active phases of the monsoon over the monsoon core zone of India. When it is further divided into two boxes, the north monsoon core zone, and the south monsoon core zone, it is found that the preconditioning depends on that region's soil type and climate classification. Also, we calculate the moist static energy (MSE) budget during the monsoon phases to show how soil moisture feedback affects the boundary layer MSE and rainfall. A similar analysis is applied to the model run, but it cannot show the realistic preconditioning role of soil moisture and its feedback on the rainfall as in observations. We conclude that to get proper feedback between soil moisture and precipitation during the active phase of the monsoon in the model, the pre-conditioning of soil moisture should be realistic.

Scott T. Shipley, John E. Lane, Philip T. Metzger

Tools have been developed to model and simulate the effects of lunar landing vehicles on the lunar environment, mostly addressing the effects of regolith erosion by rocket plumes and the fate of the ejected lunar soil particles. The KSC Granular Mechanics and Regolith Operations Lab tools have now been expanded to address volatile contamination of the lunar surface (Stern, 1999). Landing nearby such a crater will result in the migration of significant exhaust plume gas into the cold trap of the crater, and will also create an unnatural atmosphere over the volatile reservoirs that are to be studied. Our calculations address: 1) the time for the plume-induced local atmosphere above cold traps to decay to normal levels, 2) the efficiency of gas migration into a permanently shadowed crater when the landing is outside it but nearby, and 3) reduction on contamination afforded by moving the landing site further from the crater or by topographically shielding the crater from the direct flux of a lander's ground jet. We also address plume volatiles adsorbed onto and driven inside soil ejecta particles from their residence in the high pressure stagnation region of the engine exhaust plume, and how their mechanical dispersal across the lunar surface contributes to the induced atmosphere. One additional question is whether the collection of soil ejecta along the base of a topographic feature will produce a measurable plume volatile release distinct from the background. We mostly address item 2). Item 3) is obvious from our results excepting that the removal distances may be large, but changes to landing strategy can improve the situation.

Chao Tao, Aoran Hu, Rong Xiao et al.

Data-driven deep learning methods have shown great potential in cropland mapping. However, due to multiple factors such as attributes of cropland (topography, climate, crop type) and imaging conditions (viewing angle, illumination, scale), croplands under different scenes demonstrate a great domain gap. This makes it difficult for models trained in the specific scenes to directly generalize to other scenes. A common way to handle this problem is through the "Pretrain+Fine-tuning" paradigm. Unfortunately, considering the variety of features of cropland that are affected by multiple factors, it is hardly to handle the complex domain gap between pre-trained data and target data using only sparse fine-tuned samples as general constraints. Moreover, as the number of model parameters grows, fine-tuning is no longer an easy and low-cost task. With the emergence of prompt learning via visual foundation models, the "Pretrain+Prompting" paradigm redesigns the optimization target by introducing individual prompts for each single sample. This simplifies the domain adaption from generic to specific scenes during model reasoning processes. Therefore, we introduce the "Pretrain+Prompting" paradigm to interpreting cropland scenes and design the auto-prompting (APT) method based on freely available global land cover product. It can achieve a fine-grained adaptation process from generic scenes to specialized cropland scenes without introducing additional label costs. To our best knowledge, this work pioneers the exploration of the domain adaption problems for cropland mapping under prompt learning perspectives. Our experiments using two sub-meter cropland datasets from southern and northern China demonstrated that the proposed method via visual foundation models outperforms traditional supervised learning and fine-tuning approaches in the field of remote sensing.

Tatiane Meurer, Franciele Beck

RESUMO O estudo busca compreender como os tops managers entendem e praticam o estilo de liderança nas empresas familiares, em que aplicou-se uma metodologia interpretativista, a fenomenografia, guiada por entrevistas semiestruturadas com 15 tops managers. A análise fenomenográfica visa identificar dois grupos de estilos predominantes: i) liderança conciliadora, dinâmico; e ii) liderança regrada, estático. Em particular, no primeiro grupo, têm-se a concatenação de três estilos de liderança: referente, especialista e participativo, que compartilham semelhanças entre si, reverberando um ambiente organizacional afetivo e coletivo, o qual impacta no estilo de liderança adotado pela organização. Enquanto, a liderança autocrática é representada isoladamente no segundo grupo, dado os aspectos organizacionais, ressoando um ambiente metódico e hierarquizado, elucidando práticas de lideranças estáticas. Contudo, compreende-se que há variabilidade no estilo de liderança das empresas familiares, sendo que esse estilo advém do envolvimento da família, em que os valores e a conduta organizacional exposta pelos fundadores reporta os traços de comportamento dos líderes com seus subordinados. As contribuições teóricas revelam o avanço conceitual para a literatura de estilo de liderança, no que concerne à definição de estilo de liderança sob o olhar dos tops managers, lançando luz para a heterogeneidade encontrada nas empresas familiares por meio do estilo de liderança.

Peter Sipka

The next big step in technological development is the expansion of the physical world and the emergence of virtual worlds. The technology is now reaching the right level for this to spread, so it is predicted that the virtual presence of individuals will become more common in the coming years. This will naturally bring with it the emergence of working in virtual worlds, as the virtual presence of firms can provide a clear competitive advantage. However, the question arises as to whether labour law, with its current instruments, is suitable for the legal regulation of work in the virtual world and whether this type of work can be understood at all within the framework of the classical employment relationship. The very notion of work, the contracting parties, the contract's content, the place of performance, etc., can be called into question. In this article, I will examine these issues and consider the challenges facing future legislation.

Hong Chang, Qingyi Ding, Wanzheng Zhao et al.

The digital economy plays a pivotal role in assisting the world in tackling climate change. This paper explores the intrinsic mechanism of the digital economy on carbon emissions intensity. Initially, it scrutinizes the suppressive effect of the digital economy on carbon emissions intensity, as well as the mediating mechanism of industrial structure upgrading, on a theoretical level. Subsequently, it utilizes provincial panel data from China between 2010 and 2019 to investigate the quantitative relationship between the digital economy and carbon emissions intensity empirically. The results revealed that, firstly, the digital economy significantly diminishes carbon emissions intensity; secondly, it confirms the significant mediating role of industrial structure upgrading; thirdly, increased levels of economic development, market openness, human capital, technological advancement, and urbanization all have constructive moderating effects on the carbon emission reduction facilitated by the digital economy; fourthly, the influence of the digital economy on carbon emission intensity has spatial spill-overs. This paper contributes an integrated analytical framework and method for studying the digital economy, industrial structure upgrading, and carbon emissions intensity. Furthermore, it offers valuable insight and suggestions for policy-making concerning the digital economy's contribution to carbon emissions reduction.