Miracle-dz/UniFAR-FTUs huggingface dataset:
Laser Learning Environment: A new environment for coordination-critical multi-agent tasks

We introduce the Laser Learning Environment (LLE), a collaborative multi-agent reinforcement learning environment in which coordination is central. In LLE, agents depend on each other to make progress ( interdependence ), must jointly take specific sequences of actions to succeed ( perfect coordination ), and accomplishing those joint actions does not yield any intermediate reward ( zero-incentive dynamics ). The challenge of such problems lies in the difficulty of escaping state space bottlenecks caused by interdependence steps since escaping those bottlenecks is not rewarded. We test multiple state-of-the-art value-based MARL algorithms against LLE and show that they consistently fail at the collaborative task because of their inability to escape state space bottlenecks, even though they successfully achieve perfect coordination. We show that Q -learning extensions such as priori-tised experience replay and n -steps return hinder exploration in environments with zero-incentive dynamics, and find that intrinsic curiosity with random network distillation is not sufficient to escape those bottlenecks. We demonstrate the need for novel methods to solve this problem and the relevance of LLE as cooperative MARL benchmark.
The President and the Distribution of Federal Spending

Empirical research on distributive politics emphasizes party and committee leaders in Congress. This paper highlights the president, who most credibly fills the role of the proposer in Baron and Ferejohn’s (1989) seminal model. We analyze a large database that tracks the geographic spending of nearly every domestic program over a 21-year period. In contrast to earlier studies, we find only sporadic evidence that committee chairs, party leaders, and majority party members receive larger shares of federal outlays. Instead, we find consistent and robust evidence that districts represented by legislators in the president’s party receive systematically more spending, as do those where the legislator or the president was narrowly elected.
Procedure-Oriented Torsional Anatomy of the Proximal Arm for Spasticity Injection

&NA; This is the first in a series of papers related to the new concept of “torsional” anatomy. The objective of this article is to provide musculoskeletal ultrasound (MSKUS) anatomy of the upper arm in the position of hemispastic flexion as a reference relevant to needle procedures. Methods The MSKUS images were obtained in a healthy human subject. A pair of MSKUS images was recorded for each level: the first in anatomic neutral and second in hemispastic position. Results At the proximal 1/3 level of the upper arm, the pectoralis major rotated out of view. At the middle of the upper arm, the biceps rotated medially, and the brachialis rotated from far lateral to the middle of the screen. At the distal 1/3 level of the upper arm, the radial nerve rotated more anteriorly. At the distal 1/6 level of the upper arm, the biceps shifted and was replaced by the brachialis and brachioradialis. The radial nerve also rotated more anteriorly and superficially. Discussion With torsion, it is possible that intended muscle targets, such as the pectoralis, are missed, and unintended targets, such as the radial nerve, are accidentally injected in the upper arm. Conclusions It is hoped that this series of images will increase the accuracy and safety of needle placement for spasticity and nerve block injections in the proximal upper arm.
University of Memphis Digital Commons University of Memphis Digital Commons

—The complexity of medical problem solving presents a formidable challenge to current theories of cognition. Building on earlier work, we claim that the systems-level cognitive model LIDA (for “Learning Intelligent Distribution Agent”) offers a number of specific advantages for modeling diagnostic thinking. The LIDA Model employs a consciousness mechanism in an iterative cognitive cycle of understanding, attention, and action, endowing it with the ability to integrate multiple sensory modalities into flexible, dynamic, multimodal representations according to strategies that support specific task demands. These representations enable diverse, asynchronous cognitive processes to be dynamically activated according to rapidly changing contexts, much like in biological cognition. The recent completion of the LIDA Framework, a software API supporting the domain-independent LIDA Model, allows the construction of domain-specific agents that test the Model and/or enhance traditional machine learning algorithms with human-style problem solving. Medical Agent X (MAX) is a medical diagnosis agent under development using the LIDA Model and Framework. We review LIDA’s approach to exploring cognition, assert its appropriateness for problem solving in complex domains such as diagnosis, and outline the design of an initial implementation for MAX.
Estimating surface utilization factors for BIPV applications using pix2pix on street captured façade images

While techniques for assessing solar potential, particularly on roofs, are well-established, estimating solar potential on building facades often requires more work due to the complexity of urban features and the elaborate design of building facades. Existing methods to assess the solar potential of building facades often neglect the characteristics of individual facades. This study presents an image-based method for a more accurate estimation of the PV potential of facades. The proposed method is composed of four steps: (1) data acquisition, preprocessing, and manual labeling, (2) training a pixel-wise semantic image segmentation model based on Generative Adversarial Networks (GANs), (3) color content analysis of segmented images, and (4) estimating the annual solar energy potential. We apply the proposed workflow to several buildings in Zurich, Switzerland, and evaluate segmentation quality and resulting changes in façade surface utilization factors. In a comparative analysis between the widely used web-based solar potential assessment tools, Sonnenfassade, Global Solar Atlas and the simulation software Climate Studio, we demonstrate the downstream impact of surface utilization factors on resulting solar potentials. Considerable deviations in façade availability for BIPV deployments among the various tools as compared to our segmentation approach indicate the potential impact of the proposed method on policy-making and the benefits for BIPV design and planning.
The Optimal Layout Combination of Radar Stations in Multi-station Passive Time Difference Positioning System

This paper investigates the problem of optimizing the selection of radar site in three-dimensional terrain for multi-site passive time-difference positioning system. First, the directness of the radar to the surveillance airspace is investigated. A judgment method based on planar convex closure is given to determine whether a terrain point obscures the radar site to be selected, based on it, a screening algorithm is given to find the region where the received signals are not blocked. Second, the problem of optimal position combination of multiple radar stations is investigated. Taking a convex polygonal surveillance airspace as the object of study, a planning model with the objective of selecting the optimal combination of radar positions is given, which takes into account the constraints on the radar’s directness to the surveillance airspace, as well as the constraints on the far bounds of the detections, and proposes a genetic algorithm for solving the model, which uses an individual coding of the real type and introduces the penalty coefficients, which transforms the constrained planning problem to an unconstrained planning problem.
Indoor environmental quality trade-offs due to summertime natural ventilation in London care homes

We evaluate current and future summertime temperature and indoor air quality (IAQ) in two London care homes, occupied by seniors. We further examine the effect of natural ventilation, aiming to identify strategies that can maintain temperature, CO2 and key pollutants (PM2.5, NO2) within acceptable ranges. Data come from simulations in DesignBuilder. Results show a higher risk of overheating in the newer care home, with 85% of hourly outputs exceeding 26°C. In addition, bedrooms are much warmer than lounges in both homes, with averages expected to reach 32-35°C by 2050. In terms of IAQ, 65% of PM2.5 and NO2 hourly outputs are within range; however, without any ventilation, the high CO2 levels are expected to rise by 70-130ppm in 2050, especially in bedrooms of the newer home. Results further indicate that natural ventilation can substantially reduce temperature and CO2, but at the same time it may increase PM2.5 and NO2 coming from outdoors. Yet, these trade-offs can be reduced through a careful ventilation strategy that considers building-specific characteristics, as well as time of day and duration. Findings suggest a need to focus on the interdependencies among indoor environmental quality outputs and highlight the value of inexpensive and sustainable passive ventilation.
Personal comfort models in long-term monitoring using physiological data from wearable sensors

Personal Comfort Models (PCMs) propose a new approach for human-centric comfort studies overcoming the one-size-fits-all of the conventional models. This research addresses the development of PCMs based on a seven-month long-term monitoring campaign including continuous environmental and physiological data collection through wearables and daily survey submission about subjects’ sensations. To tackle the influence of subjects’ environmental exposure history, time series of environmental data of different durations were used to predict individuals’ perception via Machine Learning models with Support Vector Machine and Random Forest methods. The accuracy and F1-score values of seven different PCMs were confronted for each subject and for the whole group (nine people). The number of datapoints per subject and their answers’ consistency during time affected the models’ accuracy, and the inclusion of physiological signals improved the models’ performance. When considering the whole dataset, the comfort model accuracy decreases supporting that individual subjectivity have an important impact in the environmental perception prediction.
Circular Practices in Construction

The construction sector creates exorbitant amounts of emissions and waste. Adressing a livable future, we argue for a radical shift in the way we think, design, and use buildings. Therefore, we present findings of our ongoing research project Circular Material Systems (CMS) that contributes to an understanding of buildings as circular systems that produce reusable components or biodegradable materials by practices operating across a building’s lifecycle. The main question of this paper is: How to translate the Circular Economy (CE) concept for construction? The findings are based on in-depth case studies of 25 buildings across Europe. We use the notion of ‘practices’ to both enrich the academic debate on circular construction and create a best-practice collection for practitioners. The proposed framework includes strategies and practices that address the construction sector’s urgent need to reduce its environmental impact. We connected these strategies to the 9R framework to illustrate the harmonisation with the CE concept. What we found particularly interesting is that there are repeating patterns of practices applied in the analysed buildings while working with local materials, knowing and redesigning supply chains as well as collaboration models to involve a wide variety of necessary actors are characteristic practices for achieving circularity. To advance the paradigm of circular construction, every stakeholder in the building process is required to look beyond their responsibilities to find new innovative solutions for circularity while further research should focus on implementation pathways as well as legal and enabling factors.
Modeling and analysis for reaction control thruster used for station-keeping maneuvers of a GEO satellite

This paper introduces a theoretical description, force, and torque analysis of a Reaction Control Thruster (RCT) used as an actuator in Attitude Determination and Control Subsystem (ADCS). Mathematical formalization, realization, and implementation are offered to implement the RCT model using Matlab-Simulink. Furthermore, this paper proposes an accurate RCT Simulink model to control the orientation and stabilizes the Geostationary Earth Orbit (GEO) satellite within its orbit. Finally, a comparative analysis is carried out with an existing satellite platform (Eurostar-3000) based on the Root Mean Square Error (RMSE) for the delta velocity (delta-𝒱) parameter. The outcomes of the model simulation validated the effectiveness of the proposed approach.
LCA models in building industry practice - how do practitioners’ assumptions affect LCA results?

This study exemplifies uncertainties in Life Cycle Assessments (LCA) by investigating the results of nine building LCAs made by nine LCA practitioners. The study compares material quantities and Global Warming Potential (GWP) results of the nine LCAs before and after a review process. From the building LCAs before review, we identify several errors that significantly influence material quantities and GWP results. After correcting the errors, we still see notable differences in the material quantities and LCA results from the nine LCAs. The study proves a need for conducting thorough reviews to eliminate the most prominent errors in building LCAs and a need for uncertainty assessment as a standard practice.
Relative and Absolute Sea Level Change Variability in The Palabuhanratu Bay Waters

SLR could affect the waters of Palabuhanratu Bay, which are exposed to the Indian Ocean. Sea levels are rising due to RSL and ASL changes. RSL has increased by 22.86 mm/year based on tidal data for 2013–2022. In contrast, ASL has increased by 4.48 mm/year based on satellite altimetry data for 1992–2022. The research uses the linear regression method to get the SLR value. According to earlier studies, dynamic change elements connected to alterations in the atmosphere and ocean circulation are typically considered when analyzing sea level variations. This work explores sea-level variations, including dynamic and static changes impacted by geological processes like deformation. Dynamic change factors, such as climate anomalies, ENSO, and IOD, affect these changes. Regarding RSL changes, ENSO has a more decisive influence than IOD, and vice versa for ASL changes. In the La Nina phenomenon and negative IOD phase, RSL and ASL trend changes are enhanced, whereas they are lowered in the El Nino and positive IOD phases. These waters’ sea-level variations are only slightly impacted by local processes, such as VLM. It has a more decisive influence on RSL changes than ASL.
Can Explainable Artificial Intelligence Optimize the Data Quality of Machine Learning Model? Taking Meibomian Gland Dysfunction Detections as a Case Study

Data quality plays a crucial role in computer-aided diagnosis (CAD) for ophthalmic disease detection. Various methodologies for data enhancement and preprocessing exist, with varying effectiveness and impact on model performance. However, the process of identifying the most effective approach usually involves time-consuming and resource-intensive experiments to determine optimal parameters. To address this issue, this study introduces a novel guidance framework that utilizes Explainable Artificial Intelligence (XAI) to enhance data quality. This method provides evidence of the significant contribution of XAI in classifying meibomian gland dysfunction (MGD) by aiding in feature selection, improving model transparency, mitigating data biases, providing interpretability, enabling error analysis, and establishing trust in machine learning (ML) models using multi-source meibomian datasets. The experimental results demonstrate substantial performance improvements in ML models when utilizing enhanced datasets compared to original images, as indicated by increased accuracy (0.67 vs. 0.86), recall (0.46 vs. 0.89), F1 score (0.48 vs. 0.84), XAI indicator (0.51 vs. 0.81), and IOU score (0.44 vs. 0.79). These findings highlight the significant potential of XAI in ML model MGD classification, particularly in advancing interpretability, standardization, fairness, domain integration, and clinical adoption. Consequently, the proposed framework not only saves valuable resources but also provides interpretable evidence for decision-making in data enhancement strategies. This study contributes to the understanding of XAI’s role in ML model MGD classification and its potential for driving advancements in key areas such as interpretability, standardization, fairness, domain integration, and clinical adoption.
From near real-time urban data to an explainable city-scale model to help reduce the Urban Heat Island (UHI) effect

Urbanization is associated with increasing the temperature of urban areas and phenomena like the Urban Heat Island (UHI). The latter produces a sensible impact on people’s health, quality of life, urban liveability, and even the mortality rate. As a result, reducing high temperatures in cities will improve the quality of life in cities. Overall, the understanding of the relationship between UHI and urban features is not well captured due to a lack of accessibility to data and cost implications related to long-term monitoring and simulation processes. Machine Learning (ML) offers significant opportunities to develop approaches that lead to high accuracy in discovering the significant features of buildings and urban areas, improving the conditions of the inhabitants. The paper aims to describe how the actual spatial data of Tallinn, Estonia, were assessed and applied to build an Explainable ML-based model. The study outcomes include the ML-based models that help understand the importance of urban features and their value in developing strategies and solutions to mitigate the UHI effect. The study’s findings show that changes in the most important features of urban areas and buildings, such as the built area in the neighborhood zone and the area and orientation of buildings, play the most significant role in transforming an urban area from one with no UHI effect to one that experiences heat waves. The results provide essential information for urban planners who implement built environments.
Brebes Shallot Skin Waste Feed Mixture as a Lowering of Tegal Duck Cholesterol Levels

Onion skin is one of the causes of waste in Brebes Regency. Handling shallot skins can be done by utilizing them as feed additive for ducks, which are also commonly encountered. Onion skin contains active compounds such as flavonoids, terpenoids, and phenols, which can act as an antibacterial and as an alternative herbal feed additive replacement for antibiotics. Shallot skin contains compounds that can reduce cholesterol levels. This study aims to analyze the addition of shallot skin waste flour in feed to see the level of cholesterol (LDL) reduction in the blood of Tegal ducks and their hemoglobin levels. The study was conducted using a completely randomized design with a single factor with three levels of treatment and repetition. The results of the addition of shallot skin waste flour showed a significant reduction in blood cholesterol levels in Tegal ducks. The results showed that the average cholesterol level for the first treatment was 9.8 mg/dl, the second treatment was 3.4 mg/dl, and the third treatment was 1.3 mg/dl. The lowest cholesterol content was found in ducks with the addition of 30% shallot skin flour feed.
Unlocking soil carbon stock potential in conservation areas

Indonesia’s nationally determined contributions (NDC) outline ambitious emission reduction targets, with the forestry and other land use (FOLU) sector expected to play a significant role in achieving these goals. This study estimates soil carbon stock in conservation areas covering mineral and peat soils. A total of 72 sample plots in Bukit Tigapuluh National Park (NP), Kutai NP, and Sebangau NP were established for soil sample collections. This study found that the highest organic content in mineral soil is at 0-5 cm depth, accounting for 3.54% in Bukit Tigapuluh NP and 2.72% in Kutai NP. The carbon stocks of mineral soil in Bukit Tigapuluh NP and Kutai NP at 0-30 cm depth are 43.17 tons C ha-1 and 56.77 tons C ha-1, respectively. Meanwhile, the carbon stock in peat soil in Sebangau NP at 400 m depth is 2,155.63 tons C ha-1, 43-fold more significant than the carbon stock in mineral soil. This study underscores the significance of soil carbon in Indonesia’s climate mitigation. It highlights the opportunity to refine emission estimates from the FOLU sector by adding a soil carbon pool to calculate the emission factor.
Educational patterns of health behaviors and body mass index: A longitudinal multiple correspondence analysis of a middle-aged general population, 2007–2016

Social differences in body mass index and health behaviors are a major public health challenge. The uneven distribution of unhealthy body mass index and of unhealthy behaviors such as smoking, physical inactivity, and harmful alcohol consumption has been shown to mediate social inequalities in chronic diseases. While differential exposures to these health variables have been investigated, the extent to which they vary over the lifetime in the same population and their relationship with level of education is not well understood. This study examines patterns of body mass index and multiple health behaviors (smoking, physical activity and alcohol consumption), and investigates their association with education level among adults living in Northern Norway. It presents findings from a longitudinal multiple correspondence analysis of the Tromsø Study. Longitudinal data from 8,906 adults aged 32–87 in 2007–2008, with repeated measurements in 2015–2016 were retrieved from the survey’s sixth and seventh waves. The findings suggest that most in the study population remained in the same categories of body mass index and the three health behaviors at the follow-up, with a clear educational gradient in healthy patterns. That is, both healthy changes and maintained healthy categories were associated with the highest education levels. Estimating differential exposures to mediators of health inequalities could benefit policy priority setting for tackling inequalities in health.