Miracle-dz/UniFAR-FTUs huggingface dataset:
Maximize Peak-to-Sidelobe Ratio for Real-Time RGB-T Tracking

Different from most existing algorithms that explore the integration of information from RGB and thermal (RGB-T) hierarchical features, we propose a novel adaptive learning of modal information from the decision-level perspective to achieve efficient and robust tracking. In our paradigm, the relative reliability between different modalities is mined by maximizing the peak-to-sidelobe ratio (PSR) model. Synchronously, the learned reliability can also be used to guide the correct update of the target template for each modality. Experiments on widely used large-scale benchmarks demonstrate that our method achieves competitive performance against other state-of-the-art trackers while enabling real-time tracking. Our codes will be available at: https://github.com/Liujunzx/MPT.
An Incept-TextCNN Model for Ship Target Detection in SAR Range-Compressed Domain

Traditionally, synthetic aperture radar (SAR)-based ship target detection is performed in the image domain, where SAR imaging processing has to be applied first. However, SAR imaging processing is complex and time-consuming, especially in the wide-swath working mode. Actually, for open sea scenes, most echoes are sea surface signals with no ship targets, and there is no need for imaging processing in those areas. Therefore, non-image domain ship target detection is studied in this letter, and a novel Incept-text convolutional neural network (TextCNN) model is proposed for ship target detection in the SAR range-compressed domain (RCD). In the proposed method, the SAR echo data are converted into a 1-D range profile signal first by range compression and mean pooling, and then, the Incept-TextCNN model is proposed and applied, and information about existence of ship targets in relevant range cells will be its output. Finally, the effectiveness and efficiency of the proposed method is testified by simulation and real spaceborne SAR data, and the results demonstrate that the proposed model can filter out the invalid range-compressed data of the sea surface area, which can significantly reduce the amount of data for subsequent SAR imaging and ship classification.
Characteristics of Future Models of Integrated Outpatient Care

Replacement of fee-for-service with capitation arrangements, forces physicians and institutions to minimize health care costs, while maintaining high-quality care. In this report we described how patients and their families (or caregivers) can work with members of the medical care team to achieve these twin goals of maintaining—and perhaps improving—high-quality care and minimizing costs. We described how increased self-management enables patients and their families/caregivers to provide electronic patient-reported outcomes (i.e., symptoms, events) (ePROs), as frequently as the patient or the medical care team consider appropriate. These capabilities also allow ongoing assessments of physiological measurements/phenomena (mHealth). Remote surveillance of these communications allows longer intervals between (fewer) patient visits to the medical-care team, when this is appropriate, or earlier interventions, when it is appropriate. Systems are now available that alert medical care providers to situations when interventions might be needed.
Breeding ecology of red jungle fowl (Gallus gallus) in Deva Vatala National Park, Azad Jammu and Kashmir, Pakistan

Red Jungle Fowl (Gallus gallus murghi) was reported for the first time in 2010 from Pakistan in Deva Vatala National Park, Azad Jammu and Kashmir. The present study investigated breeding aspects such as nesting, breeding season, clutch size and hatching success of the species at 10 selected sites in the national park. Nests were mostly found on slopes with dense vegetation cover dominated by Lantana camara and Adhatoda zeylanica. The nesting material consisted of dry leaves, downy feathers, and twigs of plants. The nests were found at sloppy areas as a small depression of 2.5-4 inches in soil made with the help of feet and beak for laying eggs. The breeding season of Red Jungle Fowl expanded from January till July with peak in spring season. Red Jungle Fowl formed harem with dominant male maintaining a territory and reproduced with a group of females throughout the breeding season. Harem formation in Red Jungle Fowl was observed from March to May with average harem size of three females with one male. The average clutch size of Red Jungle Fowl in the study area was 5.5±1.5 eggs per nest. Maximum chicks with hen were observed in June (3.1±0.43) and July (3.3±0.36). The forests in Deva Vatala National Park had thick vegetation cover due to occurrence of heavy rains during Monsoon season, when insects were abundant. The thick cover provided shelter to chicks and insects provide food to them. Our results provide some valuable baseline information on breeding aspects of this least studied species in Pakistan which can be used for its conservation in the park.
Integration of RRBS and RNA-seq unravels the regulatory role of DNMT3A in porcine Sertoli cell proliferation

DNMT3A participates in de novo methylation, yet its impact on the proliferation of testicular Sertoli cells remains unclear. Development-specific methylation has been proven to be associated with cellular development. Therefore, in this study, we simulated DNMT3A expression pattern during testicular development by DNMT3A interference. Then, RRBS and RNA-seq were used to decipher DNMT3A regulatory mechanisms on Sertoli cell proliferation. Immunofluorescence staining revealed the expression of DNMT3A in the Sertoli cells of the prepubertal testis. DNMT3A was demonstrated to inhibit the cell cycle and proliferation of Sertoli cells, while promoting cell apoptosis. After transfected with DNMT3A interference, a total of 560 DEGs and 2,091 DMGs produced by DNMT3A interference were identified between two treated groups, respectively. Integrating the results from RRBS and RNA-seq, the overlapping genes between DMGs and DEGs were found to be enriched in the Gene Ontology (GO) terms related to cellular development and the Apelin signaling pathway. The present study demonstrated the impact of DNMT3A on the proliferation of porcine testicular Sertoli cells, suggesting that DNMT3A primarily acts through the Apelin signaling pathway. These findings provide valuable insights into how DNMT3A influences testicular development and health, offering new perspectives.
European Landscape Dynamics: CORINE Land Cover Data

Four unique pan-European CORINE Land Cover datasetsCLC1990, CLC2000, CLC2006, and CLC2012 and three datasets concerning changes between 1990 and 2012 have presented the first-ever opportunity to observe the European landscape by means of land cover and its change. This book brings together all these datasets to demonstrate the methods of identification, analysis and assessment of the European land cover and its changes that took place during the intervals of 19902000, 20002006, and 20062012. It provides examples in which CLC data plays a role in offering solutions to European environmental problems such as the monitoring of urban dynamics, land fragmentation, ecosystems mapping and assessment, and high nature value farmland characteristics. Existing environmental problems require new approaches, and European Landscape Dynamics: CORINE Land Cover Data indicates a set of outlooks for CLC data generation that produce more detailed levels of analysis and bottom-up approaches while addressing the relationship of CLC data to the Infrastructure for Spatial Information in Europe (INSPIRE). It also discusses the future of CLC data generation. A valuable resource of up-to-date information, it is useful to professionals such as scientists, territorial planners, and environmentalists as well as students of geosciences and all those who are interested in cognition of the European landscape, its changes and development.
Adipokines and their role in acute pancreatitis

Acute pancreatitis (AP) is characterized by an inflammatory response that leads to edema and haemorrhaging of pancreatic tissue. In severe cases, it can even result in the necrosis of pancreatic tissue following activation within the pancreas. Adipokines are biologically active molecules released by adipose tissue that have a wide-ranging impact on health and disease. Adipokines are cytokines produced not only in white adipose tissue but also in the fat surrounding the pancreas, and they play a role in the body's inflammatory response. The presence of increased adipose tissue, often associated with obesity, has been linked to a heightened systemic inflammatory response in cases of AP. According to the literature, there are many adipokines. This article summarizes the role of adipokines in AP. Adipokines could be the promising biomarkers for both diagnostic and new therapeutic treatment strategies in AP. However, a deeper knowledge of signaling pathways of adipokines and its potential therapeutic role in AP is necessary.
Dual-Parameter Fiber Sensors for Salinity and Temperature Measurement Based on a Tapered PMF Incorporated With an FBG in Sagnac Loop

A flexible and stable optical fiber sensor based on the Sagnac loop is proposed and experimentally demonstrated for the measurement of salinity and temperature, simultaneously. The sensing unit consists of a tapered polarization maintaining fiber (tPMF) and a fiber Bragg grating (FBG) connected in series in the Sagnac loop. The temperature response comes from the high birefringence of PMF inside Sagnac loop, and the salinity response is enabled by the high-order modes excited in the tapered area of PMF. Besides, the FBG is for temperature compensation. We have succeeded in implementing dual-parameter measurements with the sensitivities of 0.356 nm/‰ for salinity and 0.616 nm/°C for temperature, respectively. The designed sensor has the potential for long-term monitoring of real ocean states.
Using skeletal position to estimate human error rates in telemanipulator operators

In current telerobotics and telemanipulator applications, operators must perform a wide variety of tasks, often with a high risk associated with failure. A system designed to generate data-based behavioural estimations using observed operator features could be used to reduce risks in industrial teleoperation. This paper describes a non-invasive bio-mechanical feature capture method for teleoperators used to trial novel human-error rate estimators which, in future work, are intended to improve operational safety by providing behavioural and postural feedback to the operator. Operator monitoring studies were conducted in situ using the MASCOT teleoperation system at UKAEA RACE; the operators were given controlled tasks to complete during observation. Building upon existing works for vehicle-driver intention estimation and robotic surgery operator analysis, we used 3D point-cloud data capture using a commercially available depth camera to estimate an operator’s skeletal pose. A total of 14 operators were observed and recorded for a total of approximately 8 h, each completing a baseline task and a task designed to induce detectable but safe collisions. Skeletal pose was estimated, collision statistics were recorded, and questionnaire-based psychological assessments were made, providing a database of qualitative and quantitative data. We then trialled data-driven analysis by using statistical and machine learning regression techniques (SVR) to estimate collision rates. We further perform and present an input variable sensitivity analysis for our selected features.
Adapted temperature calibration for Schottky p-GaN power HEMTs

The determination of the junction temperature in power semiconductor devices is an important but challenging task. For GaN HEMTs, no universal temperature sensitive electrical parameter (TSEP) is available. In this study, different GaN HEMTs with Schottky p-GaN gate were subjected to stress by positive gate bias and temperature. The gate leakage current is one parameter to determine the junction temperature of Schottky p-GaN gate devices. An adapted calibration routine is applied to stabilize the desired TSEP of different GaN HEMTs. The gate current method can be applied to GaN Systems devices. Furthermore, the investigations indicate stability of the on-state resistance of Innoscience devices and threshold voltage of EPC devices, respectively.
Enhancing Indoor Robot Pedestrian Detection Using Improved PIXOR Backbone and Gaussian Heatmap Regression in 3D LiDAR Point Clouds

Accurate and robust pedestrian detection is fundamental for indoor robotic systems to navigate safely and seamlessly alongside humans in spatially constrained, unpredictable indoor environments. This paper presents a novel method, IRBGHR-PIXOR, a detection framework specifically engineered for pedestrian perception in indoor mobile robots. This novel approach employs an enhanced adaptation of the cutting-edge PIXOR model, integrating two pivotal augmentations: a remodeled convolutional backbone leveraging Inverted Residual Blocks (IRB) in unison with Gaussian Heatmap Regression (GHR), as well as a Modified Focal Loss (MFL) function to tackle data imbalance issues. The IRB component notably bolsters the network’s aptitude for processing intricate spatial representations generated from sparse 3D LiDAR scans. Meanwhile, integrating GHR further elevates accuracy by enabling precise localization of pedestrian subjects. This is achieved by modeling the probability distribution and predicting the central location of individuals in the point cloud data. Extensively evaluated on the large-scale JRDB dataset comprising intense scans from 16-beam Velodyne LiDAR sensors, IRBGHR-PIXOR accomplishes exceptional results, attaining 97.17% Average Precision (AP) at the 0.5 IOU threshold. Notably, this level of accuracy is achieved without significantly increasing model complexity. By enhancing algorithms to overcome challenges in confined indoor environments, this research paves the way for safe and effective deployment of autonomous technologies once encumbered by perceptual limitations in human-centered spaces. Nonetheless, evaluating performance in diverse edge cases and integration with complementary sensory cues promise continued progress. The developments contribute towards the vital capacity of reliable dynamic perception for next-generation robotic systems coexisting in human-centric environments.
Embroidery style generation with machine learning

Embroidery is an important intangible cultural heritage in China. The development of digital technology has changed the way of transmission and inheritance of traditional culture. At present, the research on digital simulation of embroidery is still relatively small, and there are some problems such as weak generalization ability and weak three-dimensional sense. According to the characteristics of embroidery art works, this paper proposes an embroidery style generation method combining attention mechanism and cycle-consistent adversarial networks. The attention mechanism module is used to guide the generator and discriminator to control the target area migration of embroidery style images, so as to digitally simulate the embroidery art style. The results show that the proposed method has stronger generalization ability than the traditional embroidery digital simulation method, and has greater optimization in embroidery reality compared with the existing deep learning model.
Climate change and water in the UK: Recent scientific evidence for past and future change

A changing climate is anticipated to alter hydroclimatological and hydroecological processes across the UK and around the world. This paper builds on a series of reports commissioned in 2012 (Water Climate Change Impacts Report Card [WCCRC], 2012) and published in a special issue of Progress in Physical Geography in 2015 that interpreted and synthesised the relevant, peer-reviewed scientific literature of climate change impacts on the UK’s water environment. It aims to provide reliable, clear information about the potential impacts of climate change on hydrology and the water environment. We review new evidence since 2012 for historical and potential future changes in precipitation and evapotranspiration, river flows and groundwater levels, river and groundwater temperature/quality and, finally, aquatic ecosystems. Some new evidence exists for change in most of these hydrological components, typically in support of the spatial and temporal trends reported in WCCRC 2012. However, it remains the case that more research has been conducted on rainfall and river flows than evapotranspiration, groundwater levels, river and groundwater temperature, water quality or freshwater ecosystems. Consequently, there remains a clear disparity of robust evidence for historical and potential future change between the top and bottom of the hydroclimatological–hydroecological process chain. As was the case in WCCRC 2012, this remains a significant barrier to informed climate change adaptation in these components of the water environment.
Assessing Feed-Forward Backpropagation Artificial Neural Networks for Strain-Rate-Sensitive Mechanical Modeling

The manufacturing processes and design of metal and alloy products can be performed over a wide range of strain rates and temperatures. To design and optimize these processes using computational mechanics tools, the selection and calibration of the constitutive models is critical. In the case of hazardous and explosive impact loads, it is not always possible to test material properties. For this purpose, this paper assesses the efficiency and the accuracy of different architectures of ANNs for the identification of the Johnson–Cook material model parameters. The implemented computational tool of an ANN-based parameter identification strategy provides adequate results in a range of strain rates required for general manufacturing and product design applications. Four ANN architectures are studied to find the most suitable configuration for a reduced amount of experimental data, particularly for cases where high-impact testing is constrained. The different ANN structures are evaluated based on the model’s predictive capability, revealing that the perceptron-based network of 66 inputs and one hidden layer of 30 neurons provides the highest prediction accuracy of the effective flow stress–strain behavior of Ti64 alloy and three virtual materials.
Hazards Caused by Uncontrolled Vegetation and Inadequate Maintenance Practice in Earth Dams

Small dams for irrigation use are often managed by landowner communities. Dam owners are responsible for the routine maintenance actions of each dam and their common irrigation system net. Due to various social factors, like population ageing or farming loss, in areas where there has been a notable decline of agriculture, the maintenance activity of the irrigation hydraulic net is defectively conducted. An inadequate practice of maintenance first allows the growth of vegetation which may develop over the dam embankment surface, affecting to the compacted earth structure. Trees and big shrubs that grow on dam slopes and crest may have a negative impact on certain geotechnical aspects. Apart from vegetation, once that owners neglect their maintenance obligations, animal invasion or human wrong uses go with vegetation growth. The paper shows frequently found vegetation impacts observed in Spain, along with animal or human impacts, which may involve geotechnical problems. General conclusions and recommendations have been proposed in order to help owners to maintain their dams and to avoid long term problems.
Surveilans Aedes aegypti Menggunakan Ovitrap di Desa Endemis Demam Berdarah Kabupaten Banyumas

Entomological surveillance is essential to evaluate the abundance and distribution of Aedes aegypti mosquito, the dengue virus’s primary vector. This study investigates the abundance and distribution of Aedes spp collected with ovitraps in Mersi and Ledug, Purwokerto Timur, Banyumas. Both localities are identified as dengue endemic areas by the Banyumas’ Health Service. Ovitrap surveillances were performed every two weeks in Mersi and Ledug, in January – March 2022. For each localitiy, 50 respondent houses were randomly selected and 5 ovitrap surveillance trips were conducted. In each trip, 100 ovitraps were distributed to 50 respondent houses. At every single house, one ovitrap was placed inside the house (indoor) and one outside the house (outdoor). All samples collected were transported to and reared at Zoology laboratory, UMP. Mosquito larvae were identified to species at instar IV and totaled. The OPI (Ovitrap positivity index) for both indoor and outdoor ranged from 38,00% - 59,70%. The mean number of larvae for Mersi outdoor (612,50±109,73) was higher than Mersi indoor (478,80±42,96) p= . 031 and Ledug indoor (347,20±45,04) p= . 000, but not significantly different from Ledug outdoor (507,20±51,07) p= . 109. There was no significant difference for EDI (egg density index) in the four ovitrap placements, F(3,16) = 1.132, p= . 366. However, the MNE (mean number of eggs) showed a significant difference for Mersi and Ledug both indoor and outdoor F(3,16) = 14.528, p= . 000. Conclusion: The OPI showed that the distribution of Aedes spp in Mersi and Ledug was high. Ae. aegypti and Ae. albopictus were found as both outdoor and indoor breeder although Ae. aegypti was found to be the dominant both as indoor and outdoor breeder.