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Myocastor coypus (coypu or nutria) is considered one of the 100 worst invasive species in the world due to its risk to local wildlife, such as waterfowl, zoonotic risks, and environmental damage, such as riverbank erosion, arising from its habit of constructing burrows along the edge of water bodies. The presence of M. coypus is already known locally in the municipality of Londrina based on records at Igapó Lake. This paper presents the first record of M. coypus in Arthur Thomas Municipal Park, a Conservation Unit of Integral Protection located in the urban area of Londrina. The records were obtained through direct observation of one live individual and one carcass during campaigns to monitor medium and large mammals. Subsequently, ten records were obtained using camera traps, of unknown gender, located near waterbodies. We emphasize the need for continuous fauna monitoring in conservation units to detect and verify potential increases in invasive alien species populations that can result in environmental damage.

A espécie Myocastor coypus (ratão-do-banhado) é considerada uma das 100 piores espécies invasoras do mundo por representar risco para a fauna local, como aves aquáticas, e risco de zoonoses, além de causar danos ambientais, como desbarrancamento de encostas de rios, devido ao seu hábito de construção de tocas nas margens de corpos d'água. A presença de M. coypus já é conhecida localmente no município de Londrina a partir de registros no Lago Igapó. Assim, este estudo apresenta o primeiro registro de M. coypus no Parque Municipal Arthur Thomas, uma Unidade de Conservação de Proteção Integral localizada na zona urbana de Londrina. Os registros foram obtidos através da observação direta de um indivíduo vivo e uma carcaça durante campanhas de monitoramento de mamíferos de médio e grande porte. Posteriormente foram obtidos dez registros por armadilha fotográfica de indivíduos de sexo desconhecido, próximos a corpos d'água. Enfatiza-se a necessidade do monitoramento contínuo da fauna em áreas de conservação para a detecção do potencial aumento na população de espécies exóticas e invasoras que podem causar severos danos ambientais.

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Ecological interactions are a key component of biodiversity, essential for understanding ecosystem services and functioning. Recording and quantifying ecological interactions is challenging, frequently requiring complex logistics and substantial effort in the field. Camera traps are routinely used in ecology for various applications, and have proven to be an excellent method for passive and non-invasive sampling of plant-animal interactions. We implemented a standardized camera trap protocol to document vertebrate frugivores-fleshy fruited plants interactions in Doñana National Park, SW Spain, with the central objective of inventorying the diversity of plant-animal ecological interactions providing seed dispersal services. From 2018 to 2023 we recorded pairwise interactions from which we obtained qualitative (presence-absence) and quantitative (frequency of visits) information. Each record in the dataset contains information of a visit by an individual animal to an individual plant, resulting in any form of fleshy-fruit use and provides information on visitation phenology, visit length, and feeding behavior. The dataset presented here includes 10,659 frugivory interaction events for 59 vertebrate species (46 birds, 13 mammals) recorded on 339 plant individuals from 13 different plant species which dominate the fleshy-fruited plant assemblage in the Doñana National Park. The most recorded animal species consuming fruits and playing a legitimate seed dispersal role was Curruca melanocephala (1678 records) among birds and Vulpes vulpes among mammals (751 records). Cervus elaphus, a fruit consumer with a marginal role as legitimate seed disperser, was the most recorded mammal species (1508 records). Avian frugivores, particularly those from the Sylviidae and Turdidae families, are widespread in the region and play a crucial role in maintaining the dispersal service for the fleshy-fruited plant populations in the area. The dataset offers highly versatile quantitative information that can be used to investigate frugivory from the highest resolution scale, the interaction event between pairs of individuals. In addition, other information that can be extracted includes the timing of interactions of animals and plants (their phenological couplings), activity periods of the animals, behavior during the events and preferences for individual plants within populations. There are no copyright restrictions on the data. When using the data from this data paper in publications, we kindly request that you cite the paper accordingly. Additionally, we encourage researchers and educators to inform us about how they are using this data, as we value feedback and would like to be aware of its various applications.

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OBJECTIVE: This clinical study aimed to evaluate the practical value of integrating an AI diagnostic model into clinical practice for caries detection using intraoral images. METHODS: In this prospective study, 4,361 teeth from 191 consecutive patients visiting an endodontics clinic were examined using an intraoral camera. The AI model, combining MobileNet-v3 and U-net architectures, was used for caries detection. The diagnostic performance of the AI model was assessed using sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy, with the clinical diagnosis by endodontic specialists as the reference standard. RESULTS: The overall accuracy of the AI-assisted caries detection was 93.40%. The sensitivity and specificity were 81.31% (95% CI 78.22%-84.06%) and 95.65% (95% CI 94.94%-96.26%), respectively. The NPV and PPV were 96.49% (95% CI 95.84%-97.04%) and 77.68% (95% CI 74.49%-80.58%), respectively. The diagnostic accuracy varied depending on tooth position and caries type, with the highest accuracy in anterior teeth (96.04%) and the lowest sensitivity for interproximal caries in anterior teeth and buccal caries in premolars (approximately 10%). CONCLUSION: The AI-assisted caries detection tool demonstrated potential for clinical application, with high overall accuracy and specificity. However, the sensitivity varied considerably depending on tooth position and caries type, suggesting the need for further improvement. Integration of multimodal data and development of more advanced AI models may enhance the performance of AI-assisted caries detection in clinical practice.

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Can training police officers on how to best interact with the public actually improve their interactions with community members? This has been a challenging question to answer. Interpersonal aspects of policing are consequential but largely invisible in administrative records commonly used for evaluation. In this study, we offer a solution: body-worn camera footage captures police-community interactions and how they might change as a function of training. Using this footage-as-data approach, we consider changes in officers' communication following procedural justice training in Oakland, CA, USA, one module of which sought to increase officer-communicated respect during traffic stops. We applied natural language processing tools and expert annotations of traffic stop recordings to detect whether officers enacted the five behaviors recommended in this module. Compared with recordings of stops that occurred prior to the training, we find that officers employed more of these techniques in posttraining stops; officers were more likely to express concern for drivers' safety, offer reassurance, and provide explicit reasons for the stop. These methods demonstrate the promise of a footage-as-data approach to capture and affect change in police-community interactions.

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Diligent application and implementation of biosecurity measures stand as the most effective measures to prevent disease transmission through direct or indirect interactions between poultry and free-ranging animals. Among these, free-ranging mammals can be hosts or disseminators of several pathogens relevant to poultry and of public health concern. Moreover, evidence of susceptibility to avian influenza virus infection in non-human mammals has raised questions about their potential role in the virus' epidemiology at the domestic animal-wildlife interface. Given this background, this study aimed to identify mammal species occurring near laying-hen houses and characterize the spatiotemporal patterns of these visits. Seven camera traps were deployed for a year-long period in three commercial poultry farms in a densely populated poultry area in Northern Italy. Various methods, including time series analysis and generalized linear models, were employed to analyze daily mammal visits. A total of 1,867 camera trap nights yielded 567 videos of seven species of wild mammals, and 1,866 videos showed domestic pet species (cats and dogs). Coypus (Myocastor coypus) and cats were the two mammals more frequently observed near poultry houses. For wild mammals, visits significantly increased at night, and slightly decreased during the spring season. Overall, the data hereby provided lay the groundwork for designing novel surveillance and intervention strategies to prevent cross-species disease transmission. Moreover, the utilization of visual evidence depicting free-ranging animals approaching poultry houses could assist health authorities in educating and raising awareness among stakeholders about potential risks of pathogen spillover.

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In Dickie et al. (2024), we contrasted the effects of climate and habitat alteration on white-tailed deer density, recognizing the role of both these factors. Barnas et al.'s (2024) critique raised concerns about data transformations, model overfitting, and inference methods, but our analysis demonstrates that these criticisms are either unfounded or align with our original conclusions. We reaffirm that while both climate and habitat alteration contribute to deer densities, management decisions cannot ignore the strong role of climate, which is only predicted to increase in coming decades.

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Outdoor recreation has experienced a boom in recent years and continues to grow. While outdoor recreation provides wide-ranging benefits to human well-being, there are growing concerns about the sustainability of recreation with the increased pressures placed on ecological systems and visitor experiences. These concerns emphasize the need for managers to access accurate and timely recreation data at scales that match the growing extent of the recreation footprint. Here, we compare spatial and temporal patterns of winter and summer recreation using traditional (trail cameras, infrared counters, aerial surveys, participatory mapping) and application-based tools (Strava Metro, Strava Global Heatmap, Wikiloc) across the Columbia and Canadian Rocky Mountains of western Canada. We demonstrate how recreation use can be estimated using traditional and application-based tools, although their accuracy and utility varies across space, season and activity type. We found that trail cameras and infrared counters captured similar broad-scale patterns in count estimates of pedestrians and all recreation activities. Aerial surveys captured areas with low recreation intensity and participatory mapping captured coarser information on the intensity and extent of recreation across large spatial and temporal scales. Application-based data provided detailed spatiotemporal information on recreation use, but datasets were biased towards specific activities. Strava Metro data was more suited for capturing broad-scale spatial patterns in biking than pedestrian recreation. Application-based data should be supplemented with data from traditional tools to identify biases in data and fill in data gaps. We provide a comparison of each tool for measuring recreation use, highlight each tools' strengths and limitations and applications to address real-world monitoring and management scenarios. Our research contributes towards a better understanding of which tool, or combinations of tools, to use that can expand the rigor and scope of recreation research. These findings support decision-making to mitigate pressures on wildlife and their habitats while allowing for high-quality recreation experiences.

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In vivo imaging of bacterial infection models enables noninvasive and temporal analysis of individuals, enhancing our understanding of infectious disease pathogenesis. Conventional in vivo imaging methods for bacterial infection models involve the insertion of the bacterial luciferase LuxCDABE into the bacterial genome, followed by imaging using an expensive ultrasensitive charge-coupled device (CCD) camera. However, issues such as limited light penetration into the body and lack of versatility have been encountered. We focused on near-infrared (NIR) light, which penetrates the body effectively, and attempted to establish an in vivo imaging method to evaluate the number of lung-colonizing bacteria during the course of bacterial pneumonia. This was achieved by employing a novel versatile system that combines plasmid-expressing firefly luciferase bacteria, NIR substrate, and an inexpensive, scientific complementary metal-oxide semiconductor (sCMOS) camera. The D-luciferin derivative "TokeOni," capable of emitting NIR bioluminescence, was utilized in a mouse lung infection model of Acinetobacter baumannii, an opportunistic pathogen that causes pneumonia and is a concern due to drug resistance. TokeOni exhibited the highest sensitivity in detecting bacteria colonizing the mouse lungs compared with other detection systems such as LuxCDABE, enabling the monitoring of changes in bacterial numbers over time and the assessment of antimicrobial agent efficacy. Additionally, it was effective in detecting A. baumannii clinical isolates and Klebsiella pneumoniae. The results of this study are expected to be used in the analysis of animal models of infectious diseases for assessing the efficacy of therapeutic agents and understanding disease pathogenesis. IMPORTANCE: Conventional animal models of infectious diseases have traditionally relied upon average assessments involving numerous individuals, meaning they do not directly reflect changes in the pathology of an individual. Moreover, in recent years, ethical concerns have resulted in the demand to reduce the number of animals used in such models. Although in vivo imaging offers an effective approach for longitudinally evaluating the pathogenesis of infectious diseases in individual animals, a standardized method has not yet been established. To our knowledge, this study is the first to develop a highly versatile in vivo pulmonary bacterial quantification system utilizing near-infrared luminescence, plasmid-mediated expression of firefly luciferase in bacteria, and a scientific complementary metal-oxide semiconductor camera. Our research holds promise as a useful tool for assessing the efficacy of therapeutic drugs and pathogenesis of infectious diseases.

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Laser iridotomy (LI) is an effective treatment for patients with pupillary block mechanisms. Here, we report a case of LI performed on a patient with primary angle closure (PAC) and elevated intraocular pressure (IOP), who was unsuitable for other treatments due to specific social circumstances. The patient, a 97-year-old female residing in a private nursing home, had a medical history notable only for mild dementia and was wheelchair-bound. She had not undergone ophthalmologic evaluation for over 50 years. The patient presented with intermittent tenderness and redness in the left eye. Therefore, an ophthalmologist visited the nursing home. Examination revealed visual acuity of 20/200 in the right eye and 20/100 in the left eye, IOP of 24 mmHg in the right eye and 26 mmHg in the left eye, no conjunctival hyperemia, shallow anterior chambers, and nuclear sclerosis grade 4 cataracts in both eyes. Fundus examination was challenging due to lens opacity, and both optic nerve papillae appeared pale. Given her history of episodic eye pain and hyperemia, PAC was diagnosed. Treatment options, including eye drops and cataract surgery, were discussed. However, the patient opted for LI due to her advanced age and inability to attend frequent follow-up visits. LI was successfully performed on both eyes during her visit to the clinic. One week post-procedure, IOP decreased to 12 mmHg bilaterally, with no complications. This case demonstrates that LI can be a viable option for managing PAC and high IOP in patients who are not candidates for surgery or eye drops due to social constraints.

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Objective: The study aimed to investigate the subjective method of estimating linear breast dimensions in comparison to the objective method. Methods: The reproducibility and accuracy of the subjective method of estimating linear breast dimensions during a simplified breast shape analysis were examined. Four linear breast dimensions including the distance from the sternal notch to the nipple, distance from the nipple to the inframammary fold, distance from the nipple to the midline and under-breast width were evaluated based on subjective estimates. Images from 100 women with natural breasts and without any history of breast surgery were reviewed by two examiners three times each. The cases were obtained from a large database of breast images captured using the Vectra Camera System (Canfield Scientific Inc., USA). The subjective data were then compared with the objective linear data from the Vectra Camera System in the automated analysis. Statistical evaluation was conducted between the three repeated estimates of each examiner, between the two examiners and between the objective and subjective data. Results: The intra-individual variations of the three subjective estimates were significantly greater in one examiner than in the other. This trend was consistent across all eight parameters in the majority of the comparisons of the standard deviations and variation coefficients, and the differences were significant in 14 out of 16 comparisons (p<0.05). Conversely, in the comparison between the subjective and objective data, the estimates were closer to the measurements in one examiner than the other. In contrast to the reproducibility observed, the assessment of the accuracy revealed that the examiner who previously presented with less reproducibility of the estimated data overall showed better accuracy in comparison to the objective data. The overall differences were inconsistent, with some being positive and others being negative. Regarding the distances from the sternal notch to the nipple and breast width, both examiners underestimated the values. However, the deviations were at different levels, particularly when considering the objective data from the Vectra Camera System as the gold standard data for comparison. Regarding the distance from the nipple to the inframammary fold, one examiner underestimated the distance, while the other overestimated it. An opposite trend was noted for the distance from the nipple to the midline. There were no differences in the estimates between the right and left sides of the breasts. The correlations between the measured and estimated distances were positive: as the objective distances increased, the subjective distances also increased. In all cases, the correlations were significant. However, the correlation for the breast width was notably weaker than that for the other distances. Conclusions: The error assessment of the subjective method reveals that it varies significantly and unsystematically between examiners. This is true when assessing the reproducibility as well as the accuracy of the method in comparison to the objective data obtained with an automated system.

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Carnivores play key roles in maintaining ecosystem structure and function as well as ecological processes. Understanding how sympatric species coexist in natural ecosystems is a central research topic in community ecology and biodiversity conservation. In this study, we explored intra- and interspecific niche partitioning along spatial, temporal, and dietary niche partitioning between apex carnivores (wolf Canis lupus, snow leopard Panthera uncia, Eurasian lynx Lynx lynx) and mesocarnivores (Pallas's cat Otocolobus manul, red fox Vulpes vulpes, Tibetan fox Vulpes ferrilata) in Qilian Mountain National Park, China, using camera trapping data and DNA metabarcoding sequencing data. Our study showed that apex carnivore species had more overlap temporally (coefficients of interspecific overlap ranging from 0.661 to 0.900) or trophically (Pianka's index ranging from 0.458 to 0.892), mesocarnivore species had high dietary overlap with each other (Pianka's index ranging from 0.945 to 0.997), and apex carnivore and mesocarnivore species had high temporal overlap (coefficients of interspecific overlap ranging from 0.497 to 0.855). Large dietary overlap was observed between wolf and snow leopard (Pianka's index = 0.892) and Pallas's cat and Tibetan fox (Pianka's index = 0.997), suggesting the potential for increased resource competition for these species pairs. We concluded that spatial niche partitioning is likely to key driver in facilitating the coexistence of apex carnivore species, while spatial and temporal niche partitioning likely facilitate the coexistence of mesocarnivore species, and spatial and dietary niche partitioning facilitate the coexistence between apex and mesocarnivore species. Our findings consider partitioning across temporal, spatial, and dietary dimensions while examining diverse coexistence patterns of carnivore species in Qilian Mountain National Park, China. These findings will contribute substantially to current understanding of carnivore guilds and effective conservation management in fragile alpine ecosystems.

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Driver Monitoring Systems (DMSs) play a key role in preventing hazardous events (e.g., road accidents) by providing prompt assistance when anomalies are detected while driving. Different factors, such as traffic and road conditions, might alter the psycho-physiological status of a driver by increasing stress and workload levels. This motivates the development of advanced monitoring architectures taking into account psycho-physiological aspects. In this work, we propose a novel in-vehicle Internet of Things (IoT)-oriented monitoring system to assess the stress status of the driver. In detail, the system leverages heterogeneous components and techniques to collect driver (and, possibly, vehicle) data, aiming at estimating the driver's arousal level, i.e., their psycho-physiological response to driving tasks. In particular, a wearable sensorized bodice and a thermal camera are employed to extract physiological parameters of interest (namely, the heart rate and skin temperature of the subject), which are processed and analyzed with innovative algorithms. Finally, experimental results are obtained both in simulated and real driving scenarios, demonstrating the adaptability and efficacy of the proposed system.

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A THz-to-IR converter can be an effective solution for the detection of low-IR-signature targets by combining the advantages of mature IR detection mechanisms with high atmospheric transmittance in the THz region. A metallic metasurface (MS)-based absorber with linear polarization dependence based on a split-ring resonator (SRR) unit cell has been previously studied as a preliminary example of a THz-to-IR converter structure in the literature. In this simulation-based study, a new cross-shaped unit cell-based metallic MS absorber structure sensitive to dual polarization is designed to eliminate linear polarization dependency, thereby allowing for incoherent detection of THz radiation. A model is developed to calculate the temperature difference and the response time for this new cross-shaped absorber structure, and its performance is compared to the SRR structure for both coherent and incoherent illumination. This model allows for understanding the efficiency of these structures by considering all loss mechanisms which previously had not been considered. It is found that both structures show similar performance under linearly polarized coherent illumination. However, under incoherent illumination, the IR emittance efficiency as gauged by the temperature difference for the cross-shaped structure is found to be twice as high as compared to the SRR structure. The results also imply that calculated temperature differences for both structures under coherent and incoherent illumination are well above the limit of the minimum resolvable temperature difference of the state-of-the-art IR cameras. Therefore, dual-polarized or multi-polarization-sensitive MS absorber structures can be crucial for developing cost-effective THz-to-IR converters and be implemented in THz imaging solutions.

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Geoffroy's spider monkeys, an endangered, fast-moving arboreal primate species with a large home range and a high degree of fission-fusion dynamics, are challenging to survey in their natural habitats. Our objective was to evaluate how different flight parameters affect the detectability of spider monkeys in videos recorded by a drone equipped with a thermal infrared camera and examine the level of agreement between coders. We used generalized linear mixed models to evaluate the impact of flight speed (2, 4, 6 m/s), flight height (40, 50 m above ground level), and camera angle (-45°, -90°) on spider monkey counts in a closed-canopy forest in the Yucatan Peninsula, Mexico. Our results indicate that none of the three flight parameters affected the number of detected spider monkeys. Agreement between coders was "substantial" (Fleiss' kappa coefficient = 0.61-0.80) in most cases for high thermal-contrast zones. Our study contributes to the development of standardized flight protocols, which are essential to obtain accurate data on the presence and abundance of wild populations. Based on our results, we recommend performing drone surveys for spider monkeys and other medium-sized arboreal mammals with a small commercial drone at a 4 m/s speed, 15 m above canopy height, and with a -90° camera angle. However, these recommendations may vary depending on the size and noise level produced by the drone model.

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In this work, a method for measuring tow angles in carbon fiber components, based on the use of a polarized camera, is analyzed from a metrological point of view. Carbon fibers alter the direction of the reflected light's electrical field, so that in each point of the surface of a composite piece, the angle of polarization of reflected light matches the fiber orientation. A statistical analysis of the angle of linear polarization (AoLP) in each pixel of each examined area allows to evaluate the average winding angle. An evaluation of the measurement uncertainty of the method on a cylinder obtained by a filament winding process is carried out, and the result appears adequate for the study of the distribution of angles along the surface of the piece, in order to optimize the process.

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Fusing data from many sources helps to achieve improved analysis and results. In this work, we present a new algorithm to fuse data from multiple cameras with data from multiple lidars. This algorithm was developed to increase the sensitivity and specificity of autonomous vehicle perception systems, where the most accurate sensors measuring the vehicle's surroundings are cameras and lidar devices. Perception systems based on data from one type of sensor do not use complete information and have lower quality. The camera provides two-dimensional images; lidar produces three-dimensional point clouds. We developed a method for matching pixels on a pair of stereoscopic images using dynamic programming inspired by an algorithm to match sequences of amino acids used in bioinformatics. We improve the quality of the basic algorithm using additional data from edge detectors. Furthermore, we also improve the algorithm performance by reducing the size of matched pixels determined by available car speeds. We perform point cloud densification in the final step of our method, fusing lidar output data with stereo vision output. We implemented our algorithm in C++ with Python API, and we provided the open-source library named Stereo PCD. This library very efficiently fuses data from multiple cameras and multiple lidars. In the article, we present the results of our approach to benchmark databases in terms of quality and performance. We compare our algorithm with other popular methods.

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In this paper, a new light event acquisition chain in a three-gamma liquid xenon prototype for medical nuclear imaging is presented. The prototype implements the Multi-Time-Over-Threshold (MTOT) method. This method surpasses the Single-Time-Over-Threshold (STOT) by precisely determining both the number of vacuum ultraviolet (VUV) photons detected by each photomultiplier tube (PMT) and their arrival times for light signal measurement. Based on both the experimental and simulated results, the MTOT method achieved a 70% improvement in reconstructing photoelectrons (PEs) and enhanced the precision of the arrival time estimation by 20-30% compared with STOT. These results will enable an upgrade of the XEMIS2 (Xenon Medical Imaging System) camera, improving its performance as the imaged activity increases.

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Body temperature is one of the most important physiological parameters of a human being used to assess his basic vital functions. In medical practice, various types of measuring instruments are used to measure temperature, such as liquid thermometers, electronic thermometers, non-contact ear thermometers, and non-contact forehead thermometers. Such body temperature measurement techniques require the connection of appropriate sensors to a person, and non-contact thermometers operate over short distances and force a specific position of the person during the measurement. As a result, using the above methods, it is practically impossible to perform body temperature measurements of a moving human being. A thermal imaging camera can be used effectively for the purpose of the temperature measurement of moving objects, but the remote measurement of a human body temperature using a thermal imaging camera is affected by many factors that are difficult to control. Accurate remote measurement of human body temperature requires a measurement system that implements a specialized temperature determination algorithm. This article presents a model of a measurement system that facilitates the development of a highly accurate temperature measurement method. For the model, its parameters were determined on the calibration stand. The correct operation of the developed method and the effectiveness of temperature measurement have been confirmed by tests on a test stand using reference radiation sources.

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Current state-of-the-art (SOTA) LiDAR-only detectors perform well for 3D object detection tasks, but point cloud data are typically sparse and lacks semantic information. Detailed semantic information obtained from camera images can be added with existing LiDAR-based detectors to create a robust 3D detection pipeline. With two different data types, a major challenge in developing multi-modal sensor fusion networks is to achieve effective data fusion while managing computational resources. With separate 2D and 3D feature extraction backbones, feature fusion can become more challenging as these modes generate different gradients, leading to gradient conflicts and suboptimal convergence during network optimization. To this end, we propose a 3D object detection method, Attention-Enabled Point Fusion (AEPF). AEPF uses images and voxelized point cloud data as inputs and estimates the 3D bounding boxes of object locations as outputs. An attention mechanism is introduced to an existing feature fusion strategy to improve 3D detection accuracy and two variants are proposed. These two variants, AEPF-Small and AEPF-Large, address different needs. AEPF-Small, with a lightweight attention module and fewer parameters, offers fast inference. AEPF-Large, with a more complex attention module and increased parameters, provides higher accuracy than baseline models. Experimental results on the KITTI validation set show that AEPF-Small maintains SOTA 3D detection accuracy while inferencing at higher speeds. AEPF-Large achieves mean average precision scores of 91.13, 79.06, and 76.15 for the car class's easy, medium, and hard targets, respectively, in the KITTI validation set. Results from ablation experiments are also presented to support the choice of model architecture.

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Infectious diseases in livestock not only cause significant economic losses but also affect food security. Although wildlife may be involved in these infectious diseases by serving as reservoirs, research has primarily focused on livestock and related species. Moreover, while these species represent a potential threat in wildlife-borne infectious diseases, comprehensive surveys of the presence of various species are limited. In this study, we aimed to quantitatively investigate the occurrence of various mammals and birds at three pig farms in Japan. We conducted camera trap surveys from October 15, 2020, to March 24, 2022, and determined the relative abundance index inside and outside the sanitary control zone. Wild boar (Sus scrofa), sika deer (Cervus nippon), and Japanese serow (Capricornis crispus) were photographed only outside the sanitary control zone. In contrast, small and medium-sized mammals, such as feral cats (Felis catus), raccoons (Procyon lotor), and rodent species (Muridae), and birds, such as the rufous oriental turtle dove (Streptopelia orientalis) and crows of the genus Corvus, were photographed both inside and outside the sanitary control zone. This comprehensive quantitative evidence suggests that various mammals and birds may be in indirect contact with livestock, highlighting the need to improve biosecurity at livestock farms. Moreover, while fences are effective against large mammals, they are inefficient against small to medium-sized mammals and birds. Therefore, the results of this study provide important insights into enhancing biosecurity on livestock farms by strengthening physical fencing, disinfecting clothing and equipment, and complying with standards of hygiene management.

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