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Establishing and maintaining colonies of imported fire ants (IFA) (Hymenoptera: Formicidae) in the laboratory are crucial for research. Dehydration is one of the major mortality factors in IFA, and the ants tend to relocate from dry to moist places. In our laboratory, we developed a moisture differential technique to extract fire ant colonies from mound materials. In this technique, the shoveled mound soil was dried by spreading in trays at room temperature. Standard glass test tubes half filled with water and plugged with cotton were placed in drying trays to provide a moist habitat. The gradual loss of moisture created a differential between the moist cotton in test tubes and drying soil in trays. Once the soil dried out, IFA moved from trays to moist cotton in the test tubes to avoid dehydration. All stages including the queens were successfully extracted using this technique. In a comparative study, this method recovered 52% more colony mass of hybrid fire ants than the standard water dripping method. Post separation colony survival was also significantly higher in this method as compared to the water dripping method. In addition to separating and maintaining IFA colonies, the moisture differential technique may have additional applications, especially in conducting behavioral bioassays where workers with active digging behavior are needed. Maintenance of laboratory colonies consisting of all life stages in plastic bottles using this new method mimics the field populations that are required to conduct behavioral bioassays.

To overcome the difficulty of collecting the working resistance and working trajectory of a wheel loader, this paper constructs a statics model of the bucket working resistance and a kinematics model of the working trajectory in the shoveling process and analyzes the key parameters of measuring the working resistance and working trajectory. Based on this, a working resistance and working trajectory acquisition strategy is proposed. To verify the effectiveness of the acquisition strategy, the in-service operation data of fine sand and loose soil shoveled by the wheel loader are collected and analyzed. Then, the test-fitted working resistance and working trajectory are obtained, and the working trajectory is input into the RecurDyn-EDEM co-simulation model to obtain the simulation-fitted working resistance. Considering the complex working conditions of the wheel loader, it is difficult to obtain accurate working resistance, and the actual working resistance is also a relative value. Therefore, a strong correlation between the two curves indicates that the acquisition strategy of the wheel loader's working trajectory and working resistance proposed in this paper is feasible.

Breast myoblastoma or granular cell tumor involving the breast parenchyma has been described in detail for the first time since Abrikossoff in 1931. The location of this injury to the breast is very rare, accounting for between 5% and 15% of all cases of cancer of the granular cells. We present our experience regarding the identification of two cases because of the relative rarity of this tumor. It is often confused with breast cancer on clinical and radiological, and its diagnosis can then be difficult for physicians, radiologists and pathologists. We report the cases of two young women who came to our attention because of the presence of mass shoveled breast, mobile and accompanied by pain cycle independent. In both cases, mammography and ultrasound revealed the presence of heterogeneous mass and irregular, but in one of two such mass located at the Union of external quadrants of the left breast and was in contact with his serratus anterior and suspicion for malignancy. In both cases the 'histology combined with immunohistochemical study proved to be a granular cell tumor. Although a granular cell tumor of the breast is a rare tumor breast, should be considered in the differential diagnosis of benign and malignant lesions. Surgeons and pathologists should keep in mind when considering a granular cell tumor cells with abundant granular cytoplasm containing materials to avoid misdiagnosing breast cancer, which could lead to unnecessary surgery.

To investigate the effect of temperature stress on responses to static-dynamic work in patients with ischemic heart disease (IHD), 10 men with IHD shoveled gravel for 30 min in a warm (29 degrees C), neutral (24 degrees C), and cold (-8 degrees C) environment (on separate days). A pace of 15 lifts.min-1 was set, and the load per lift approximated 5.5 kg. Heart rate (HR), oxygen consumption (VO2), and systolic (SBP) and diastolic blood pressures (DBP) were evaluated at 5-min intervals. Arrhythmias and ST-segment depression were evaluated by ambulatory electrocardiographic monitoring. At 30 min, VO2, SBP, and DBP were higher (P < 0.05) in the cold environment, and HR was higher (P < 0.05) in the warm environment compared with the neutral environment. HR increased (P < 0.05) from 5 to 30 min in all three conditions. The increase in HR was greater (P < 0.05) in the warm environment. None of the subjects reported angina or demonstrated electrocardiographic ST-segment changes during shoveling in any environment. The results indicate that low-risk patients with stable IHD show modest temperature-induced alterations in hemodynamic and VO2 responses during 30 min of moderate intensity (50-60% of peak VO2) static-dynamic work without adverse electrocardiographic responses or symptomatology.

Updated cardiac rehabilitation (CR) and return-to-work guidelines from the American College of Sports Medicine (ACSM) now include specificity of training for industrial athletes (exercise training that involves the muscle groups, movements, and energy systems that these patients use during occupational tasks). However, many CR facilities do not apply this principle, relying instead on the traditional protocol that consists primarily of aerobic exercise. This study was conducted to measure the metabolic cost of typical farming tasks and to compare 2 methods of calculating training intensities. Metabolic data were collected from 28 participants (23 men and 5 women, aged 18 to 57 years) while they loaded 10 hay bales, dug a fence posthole, filled 8 seed hoppers, and shoveled grain. Mean metabolic equivalent levels during these activities were 5.9 to 7.6 and participants reached 60% to 70% of heart rate reserve (HRR). By comparison, their mean resting heart rate + 30 beats per minute (RHR+30, a traditional CR intensity level) represented only 28% of HRR. Participants in the current study performed farming tasks within the ACSM's recommended range of 40% to 80% of HRR, and the results suggest that training at RHR+30 would have been inadequate for helping a farmer return to work after a cardiac event. Using the study tasks as a basis, we described exercises that would be appropriate for the supervised resistance training of farmers in a CR setting.

The objective of this study was to evaluate the effect of age and coronary artery disease on responses to snow shoveling. Little information is available on the hemodynamic and metabolic responses to snow shoveling. Sixteen men with asymptomatic coronary artery disease and relatively good functional work capacity, 13 older normal men and 12 younger normal men shoveled snow at a self-paced rate. Oxygen consumption, heart rate and blood pressure were determined. In nine men with coronary artery disease left ventricular ejection fraction was evaluated with an ambulatory radionuclide recorder. Oxygen consumption during snow shoveling differed (p < 0.05) among groups; it was lowest (18.5 +/- 0.8 ml/kg per min) in those with coronary artery disease, intermediate (22.2 +/- 0.9 ml/kg/min) in older normal men and highest (25.6 +/- 1.3 ml/kg/min) in younger normal men. Percent peak treadmill oxygen consumption and heart rate with shoveling in the three groups ranged from 60% to 68% and 75% to 78%, respectively. Left ventricular ejection fraction and frequency of arrhythmias during shoveling were similar to those during treadmill testing. During snow shoveling 1) the rate of energy expenditure selected varied in relation to each man's peak oxygen consumption; 2) older and younger normal men and asymptomatic men with coronary artery disease paced themselves at similar relative work intensities; 3) the work intensity selected represented hard work but was within commonly recommended criteria for aerobic exercise training; and 4) arrhythmias and left ventricular ejection fraction were similar to those associated with dynamic exercise.

Automated environment configuration is a critical bottleneck in scaling software engineering (SWE) automation. To provide a reliable evaluation standard for this task, we present Multi-Docker-Eval benchmark. It includes 40 real-world repositories spanning 9 programming languages and measures both success in achieving executable states and efficiency under realistic constraints. Our extensive evaluation of state-of-the-art LLMs and agent frameworks reveals key insights: (1) the overall success rate of current models is low (F2P at most 37.7%), with environment construction being the primary bottleneck; (2) model size and reasoning length are not decisive factors, and open-source models like DeepSeek-V3.1 and Kimi-K2 are competitive in both efficiency and effectiveness; (3) agent framework and programming language also have significantly influence on success rate. These findings provide actionable guidelines for building scalable, fully automated SWE pipelines.

Paired image-text data with subtle variations in-between (e.g., people holding surfboards vs. people holding shovels) hold the promise of producing Vision-Language Models with proper compositional understanding. Synthesizing such training data from generative models is a highly coveted prize due to the reduced cost of data collection. However, synthesizing training images for compositional learning presents three challenges: (1) efficiency in generating large quantities of images, (2) text alignment between the generated image and the caption in the exact place of the subtle change, and (3) image fidelity in ensuring sufficient similarity with the original real images in all other places. We propose SPARCL (Synthetic Perturbations for Advancing Robust Compositional Learning), which integrates image feature injection into a fast text-to-image generative model, followed by an image style transfer step, to meet the three challenges. Further, to cope with any residual issues of text alignment, we propose an adaptive margin loss to filter out potentially incorrect synthetic samples and focus the learning on informative hard samples. Evaluation on four compositional understanding benchma

Construction sites frequently require removing large rocks before excavation or grading can proceed. Human operators typically extract these boulders using only standard digging buckets, avoiding time-consuming tool changes to specialized grippers. This task demands manipulating irregular objects with unknown geometries in harsh outdoor environments where dust, variable lighting, and occlusions hinder perception. The excavator must adapt to varying soil resistance--dragging along hard-packed surfaces or penetrating soft ground--while coordinating multiple hydraulic joints to secure rocks using a shovel. Current autonomous excavation focuses on continuous media (soil, gravel) or uses specialized grippers with detailed geometric planning for discrete objects. These approaches either cannot handle large irregular rocks or require impractical tool changes that interrupt workflow. We train a reinforcement learning policy in simulation using rigid-body dynamics and analytical soil models. The policy processes sparse LiDAR points (just 20 per rock) from vision-based segmentation and proprioceptive feedback to control standard excavator buckets. The learned agent discovers different strate

This paper explores the question of creating and maintaining terrain maps in environments where the terrain changes. The specific example explored is the construction of terrain maps from 3D LiDAR measurements on an electric rope shovel. The approach extends the height grid representation of terrain to include a Hidden Markov Model in each cell, enabling confidence-based mapping of constantly changing terrain. There are inherent difficulties in this problem, including semantic labelling of the LiDAR measurements associated with machinery and determining the pose of the sensor. Solutions to both of these problems are explored. The significance of this work lies in the need for accurate terrain mapping to support autonomous machine operation.

In a post-industrial society, the workplace is dominated primarily by Knowledge Work, which is achieved mostly through human cognitive processing, such as analysis, comprehension, evaluation, and decision-making. Many of these processes have limited support from technology in the same way that physical tasks have been enabled through a host of tools from hammers to shovels and hydraulic lifts. To develop a suite of cognitive tools, we first need to understand which processes humans use to complete work tasks. In the past century several classifications (e.g., Blooms) of cognitive processes have emerged, and we assessed their viability as the basis for designing tools that support cognitive work. This study re-used an existing data set composed of interviews of environmental scientists about their core work. While the classification uncovered many instances of cognitive process, the results showed that the existing cognitive process classifications do not provide a sufficiently comprehensive deconstruction of the human cognitive processes; the work is quite simply too abstract to be operational.

We consider energy-dispersive X-ray Fluorescence (EDXRF) applications where the fundamental parameters method is impractical such as when instrument parameters are unavailable. For example, on a mining shovel or conveyor belt, rocks are constantly moving (leading to varying angles of incidence and distances) and there may be other factors not accounted for (like dust). Neural networks do not require instrument and fundamental parameters but training neural networks requires XRF spectra labelled with elemental composition, which is often limited because of its expense. We develop a neural network model that learns from limited labelled data and also benefits from domain knowledge by learning to invert a forward model. The forward model uses transition energies and probabilities of all elements and parameterized distributions to approximate other fundamental and instrument parameters. We evaluate the model and baseline models on a rock dataset from a lithium mineral exploration project. Our model works particularly well for some low-Z elements (Li, Mg, Al, and K) as well as some high-Z elements (Sn and Pb) despite these elements being outside the suitable range for common spectromete

Beam profile engineering, where a desired optical intensity distribution can be generated by an array of phase shifting (or amplitude changing) elements is a promising approach in laser material processing. For example, a spatial light modulator (SLM) is a dynamic diffractive optical element allowing for experimental implementations of controllable beam profile. Scalar Mathieu beams have elliptical intensity distribution perceivable as optical knives in the transverse plane and scalar Weber beams have a parabolic distribution, which enables us to call them optical shovels. Here, we introduce vector versions of scalar Mathieu and Weber beams and use those vector beams as a basis to construct controllable on-axis phase and amplitude distributions with polarization control. Further, we generate individual components of optical knife and shovel beams experimentally using SLMs as a toy model and report on our achievements in the control over the beam shape, dimensions and polarization along the propagation axis.

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