Anika Arifin, Duncan DeProfio, Layla Lammers
et al.
Environmental policy evaluation frequently requires thoughtful consideration of space and time in causal inference. We use novel statistical methods to analyze the causal effect of land concessions on deforestation rates in Cambodia. Standard approaches, such as difference-in-differences regression, effectively address spatiotemporally-correlated treatments under some conditions, but they are limited in their ability to account for unobserved confounders affecting both treatment and outcome. Double Spatial Regression (DSR) is an approach that uses double machine learning to address these scenarios. DSR resolves the confounding variables for both treatment and outcome, comparing the residuals to estimate treatment effectiveness. We improve upon DSR by considering time in our analysis of policy interventions with spatial effects. We conduct a large-scale simulation study using Bayesian Additive Regression Trees (BART) with spatial embeddings and find that, under certain conditions, our DSR model outperforms standard approaches for addressing unobserved spatial confounding. We then apply our method to evaluate the policy impacts of land concessions on deforestation in Cambodia.
Chun-Chang Lee, Cheng-Yen Chuang, Wen-Chih Yeh
et al.
In this study, we examined the differences between three house price indexes constructed using hedonic price, mix adjustment, and hierarchical linear growth repeat-sales modeling. The data consisted of housing sales across 13 administrative districts in Kaohsiung City from the third quarter of 2013 to 2022. The predictions were compared using the mean standard error, mean absolute percentage error, mean absolute error, and root-mean-square error. The results revealed that the hedonic price index performed the best; its prediction scores, as reflected by the four aforementioned metrics were 0.072, 1.176, 0.181, and 0.181, respectively. The index with the second best performance was the mix adjustment model, with scores of 0.154, 1.905, 0.293, and 0.293. The worst-performing index was the repeat-sales model, with scores of 0.309, 2.804, 0.439, and 0.439. After comparing the annual prediction errors of the three models, it became apparent that the hedonic price index had the best performance, followed by the mix adjustment index, and then the hierarchical linear growth repeat-sales index.
Sonia Dupuis, Nando Metzger, Konrad Schindler
et al.
Land surface temperature (LST) is an essential climate variable (ECV) crucial for understanding land-atmosphere energy exchange and monitoring climate change, especially in the rapidly warming Arctic. Long-term satellite-based LST records, such as those derived from the Advanced Very High Resolution Radiometer (AVHRR), are essential for detecting climate trends. However, the coarse spatial resolution of AVHRR's global area coverage (GAC) data limit their utility for analyzing fine-scale permafrost dynamics and other surface processes in the Arctic. This paper presents a new 42 years pan-Arctic LST dataset, downscaled from AVHRR GAC to 1 km with a super-resolution algorithm based on a deep anisotropic diffusion model. The model is trained on MODIS LST data, using coarsened inputs and native-resolution outputs, guided by high-resolution land cover, digital elevation, and vegetation height maps. The resulting dataset provides twice-daily, 1 km LST observations for the entire pan-Arctic region over four decades. This enhanced dataset enables improved modelling of permafrost, reconstruction of near-surface air temperature, and assessment of surface mass balance of the Greenland Ice Sheet. Additionally, it supports climate monitoring efforts in the pre-MODIS era and offers a framework adaptable to future satellite missions for thermal infrared observation and climate data record continuity.
A life cycle model of consumption and labor supply describes employment decisions of a collection of individuals during their lifetime. We develop a life cycle model describing a heterogeneous population operating in Finland under a wide variety of employment states and life situations. A rich life cycle model requires a large state space representing the possible states of simulated agents. The results demonstrate that the model reproduces a number of statistics of the Finnish employment market such as the age structures of employment rate and unemployment rate, distributions of observed effective marginal tax rates and participating tax rates, and proportion of part time work. As an application of analysis of a reform, we analyze how the program of Orpo government influences employment and public finances in Finland.
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.
In this project, we attempt to optimize a landing trajectory of a rocket. The goal is to minimize the total fuel consumption during the landing process using different techniques. Once the optimal and feasible trajectory is generated using batch approach, we attempt to follow the path using a Model Predictive Control (MPC) based algorithm, called Trajectory Optimizing Path following Estimation from Demonstration (TOPED), in order to generalize to similar initial states and models, where we introduce a novel cost function for the MPC to solve. We further show that TOPED can follow a demonstration trajectory well in practice under model mismatch and different initial states.
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.
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)
Water is one of the essential natural resources for human beings. However, rising worldwide water demand and a significant decline in availability due to a lack of dynamic management and over-extraction have resulted in a complex scenario in terms of water availability. The current paper examines water resources and their management, methodologies, aims, and scope. Through the perspective of water resources and their management in Pakistan, 93 research publications were critically analyzed using a systematic review technique. The technique includes a systematic review of existing literature on water resource management, with particular emphasis on policy, governance, and environmental challenges. The study results demonstrate gaps and weaknesses in existing laws and regulations, alongside the threats to water resource management due to population expansion, urban development, climate change, and water contamination. To properly address these problems, the current study proposed a comprehensive framework for water resource management. This framework includes a national water policy that argues for sustainability and improves institutional strength. Infrastructure development, climate change adaptation, and examining social and environmental variables are all emphasized as important problems. Furthermore, it is essential to emphasize the importance of education and raising knowledge about water resource management among the general public and relevant stakeholders. By following these recommendations and the proposed OECD key principles on water governance, Pakistan may make significant progress towards achieving sustainable water management, aligning with its development objectives, and ensuring clean and safe water availability for future generations.
River, lake, and water-supply engineering (General), Water supply for domestic and industrial purposes
Abstract Sustainable development efforts, initiated by the SDGs and the Paris Agreement on climate change, are bringing banking to the center of the debate, which calls for, among other things, sustainable banking. In the current academic discussion, sustainable banking is described as a terminological jungle that is subject to change over time. Using Webster and Watson’s conceptual model, this review analyzes the definitions and conceptual descriptions used in academia to present a consolidated result. The definition analysis conducted in this paper shows that definitions used mostly refer to the implementation of social, environmental aspects in the respective business strategies and / or to the offering of sustainably labeled products. This paper also shows that the various forms of the definition have a purely descriptive character and that measurability and comparability are hardly possible due to the lack of a generally accepted sustainability index.
Social responsibility of business, Business ethics
Surface-enhanced Raman spectroscopy (SERS) is widely used as a detection method in scientific research fields. However, the method for creating SERS substrates often requires expensive equipment and involves a complex process. Additionally, preserving and effectively utilizing SERS substrates in the long term poses a challenging problem. In order to address these issues, we propose a new method for creating SERS substrates on various types of paper using a combination of a ballpoint pen and 3D printing. This method ensures a high enhancement factor and maximizes the utilization of the substrate. We achieved an enhancement factor of up to 8.2 × 10<sup>8</sup> for detecting R6G molecules, with a relative standard deviation of 11.13% for the Raman peak at 612 cm<sup>−1</sup> of R6G, demonstrating excellent SERS sensitivity and spectral reproducibility. Furthermore, we successfully detected thiram at a concentration as low as 10<sup>−8</sup>, which is lower than both the Chinese national standard and European standard.
Engineering machinery, tools, and implements, Technological innovations. Automation
Allison Woodruff, Renee Shelby, Patrick Gage Kelley
et al.
Generative AI is expected to have transformative effects in multiple knowledge industries. To better understand how knowledge workers expect generative AI may affect their industries in the future, we conducted participatory research workshops for seven different industries, with a total of 54 participants across three US cities. We describe participants' expectations of generative AI's impact, including a dominant narrative that cut across the groups' discourse: participants largely envision generative AI as a tool to perform menial work, under human review. Participants do not generally anticipate the disruptive changes to knowledge industries currently projected in common media and academic narratives. Participants do however envision generative AI may amplify four social forces currently shaping their industries: deskilling, dehumanization, disconnection, and disinformation. We describe these forces, and then we provide additional detail regarding attitudes in specific knowledge industries. We conclude with a discussion of implications and research challenges for the HCI community.
Vladislav Zubko, Natan Eismont, Konstantin Fedyaev
et al.
A problem of constructing the trajectory of a spacecraft flight to Venus within the framework of a mission including landing of a lander in a given region of the planet's surface is being considered. A new celestial mechanics related method based on the use of gravity assist maneuver near Venus is proposed to transfer the spacecraft to a heliocentric orbit resonant with the orbit of Venus, so that, at the next approach the planet, the given region of the surface becomes attainable for landing. It is shown that the best resonant orbit in terms of the cost of the characteristic velocity is an orbit with a 1:1 ratio of the period to the orbital period of Venus. A procedure for choosing one of possible resonant orbits depending on coordinates of the desired landing point on the surface and the launch date of the mission is described. An example of calculating the flight trajectory that ensures landing in the Vellamo-South region of the Venus surface at launch from the Earth in 2031 is considered.
Precise landing of Unmanned Aerial Vehicles (UAVs) onto moving platforms like Autonomous Surface Vehicles (ASVs) is both important and challenging, especially in GPS-denied environments, for collaborative navigation of heterogeneous vehicles. UAVs need to land within a confined space onboard ASV to get energy replenishment, while ASV is subject to translational and rotational disturbances due to wind and water flow. Current solutions either rely on high-level waypoint navigation, which struggles to robustly land on varied-speed targets, or necessitate laborious manual tuning of controller parameters, and expensive sensors for target localization. Therefore, we propose an adaptive velocity control algorithm that leverages Particle Swarm Optimization (PSO) and Neural Network (NN) to optimize PID parameters across varying flight altitudes and distinct speeds of a moving boat. The cost function of PSO includes the status change rates of UAV and proximity to the target. The NN further interpolates the PSO-founded PID parameters. The proposed method implemented on a water strider hexacopter design, not only ensures accuracy but also increases robustness. Moreover, this NN-PSO can be readily adapted to suit various mission requirements. Its ability to achieve precise landings extends its applicability to scenarios, including but not limited to rescue missions, package deliveries, and workspace inspections.
مطالعه حاضر با هدف تحلیل ویژگیهای درگاه کسبوکار الکترونیکیB2B در میان باغداران دشت مغان در سال 1398 انجام شد. دادههای مورد نیاز مطالعه از طریق تکمیل پرسشنامه محققساخته جمع آوری شد که پایایی آن با استفاده از آلفای کرونباخ (بالاتر از مقدار 0/7) و روایی آن با استفاده از نظر کارشناسان مورد تأیید قرار گرفت. جامعه آماری پژوهش مشتمل بر تمامی باغداران دشت مغان (1250=N) بود که 267 نفر از آنها بر مبنای فرمول کوکران و با استفاده از تکنیک نمونهگیری طبقهای با انتساب متناسب مورد مطالعه قرار گرفتند. مطالعه حاضر در چارچوب 7Cs جاورسکی (شامل ویژگیهای زمینه، ارتباطات، محتوا، سفارشیسازی، تجارت، جامعه و اتصالات) با استفاده از تکنیک تحلیل مؤلفه سلسهمراتبی به روش حداقل مربعات جزیی (PLS) صورت گرفت. نتایج نشان داد که از میان هفت ویژگی مورد مطالعه، تجارت در یک درگاه الکترونیکی از اهمیت بالاتری برخوردار است و پس از آن ویژگیهایی همچون محتوا، جامعه، سفارشیسازی، ارتباطات، زمینه و اتصالات قرار دارند. بهطور کلی، باغداران در وهله اول بر خدماتی که در حین شکلگیری یک معامله ضرورت دارد، تأکید مینمایند. این خدمات میتواند شامل اعتماد بین طرفین برای فروش محصولات، وجود پشتیبانی ارسال محصول، امکان رهگیری محصولات، خدمات مربوط به امنیت فروش محصولات و مواردی از این قبیل باشند.
Michael James Horry, Subrata Chakraborty, Biswajeet Pradhan
et al.
The ever-growing volume of satellite imagery data presents a challenge for industry and governments making data-driven decisions based on the timely analysis of very large data sets. Commonly used deep learning algorithms for automatic classification of satellite images are time and resource-intensive to train. The cost of retraining in the context of Big Data presents a practical challenge when new image data and/or classes are added to a training corpus. Recognizing the need for an adaptable, accurate, and scalable satellite image chip classification scheme, in this research we present an ensemble of: i) a slow to train but high accuracy vision transformer; and ii) a fast to train, low-parameter convolutional neural network. The vision transformer model provides a scalable and accurate foundation model. The high-speed CNN provides an efficient means of incorporating newly labelled data into analysis, at the expense of lower accuracy. To simulate incremental data, the very large (~400,000 images) So2Sat LCZ42 satellite image chip dataset is divided into four intervals, with the high-speed CNN retrained every interval and the vision transformer trained every half interval. This experimental setup mimics an increase in data volume and diversity over time. For the task of automated land-cover/land-use classification, the ensemble models for each data increment outperform each of the component models, with best accuracy of 65% against a holdout test partition of the So2Sat dataset. The proposed ensemble and staggered training schedule provide a scalable and cost-effective satellite image classification scheme that is optimized to process very large volumes of satellite data.
Computational guidance is an emerging and accelerating trend in aerospace guidance and control. Combining machine learning and convex optimization, this paper presents a real-time computational guidance method for the 6-degrees-of-freedom powered landing guidance problem. The powered landing guidance problem is formulated as an optimal control problem, which is then transformed into a convex optimization problem. Instead of brutally using the neural networks as the controller, we use neural networks to improve the state-of-the-art sequential convex programming (SCP) algorithm. Based on the deep neural network, an initial trajectory generator is designed to provide a satisfactory initial guess for the SCP algorithm. Benefitting from designing the initial trajectory generator as a sequence model predictor, the proposed data-driven SCP architecture is capable of improving the performance of any state-of-the-art SCP algorithm in various applications, not just powered landing guidance. The simulation results show that the proposed method can precisely guide the vehicle to the landing site. Moreover, through Monte Carlo tests, the proposed method can averagely save 40.8% of the computation time compared with the SCP method, while ensuring higher terminal states accuracy. The proposed computational guidance scheme is suitable for real-time applications.