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by
Konstantinos Roditis, Paraskevi Tsiantoula, Nikolaos-Nektarios Giannakopoulos, Afroditi Antoniou, Vasileios Papaioannou, Sofia Tzamtzidou, Dimitra Manou, Konstantinos G. Seretis, Theofanis T. Papas and Nikolaos Bessias
Background/Objectives: this systematic review aims to explore the efficacy and safety of the laparoscopic ligation of the inferior mesenteric artery (IMA) as an emerging trend for addressing a type II endoleak following endovascular aortic aneurysm repair (EVAR). Methods: A comprehensive literature
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Background/Objectives: this systematic review aims to explore the efficacy and safety of the laparoscopic ligation of the inferior mesenteric artery (IMA) as an emerging trend for addressing a type II endoleak following endovascular aortic aneurysm repair (EVAR). Methods: A comprehensive literature search was conducted across several databases including Medline, Scopus, and the Cochrane Central Register of Controlled Trials, adhering to the PRISMA guidelines. The search focused on articles reporting on the laparoscopic ligation of the IMA for the treatment of a type II endoleak post-EVAR. Data were extracted regarding study characteristics, patient demographics, technical success rates, postoperative outcomes, and follow-up results. Results: Our analysis included ten case studies and two retrospective cohort studies, comprising a total of 26 patients who underwent a laparoscopic ligation of the IMA between 2000 and 2023. The mean age of the cohort was 72.3 years, with a male predominance (92.3%). The mean AAA diameter at the time of intervention was 69.7 mm. The technique demonstrated a high technical success rate of 92.3%, with a mean procedure time of 118.4 min and minimal blood loss. The average follow-up duration was 19.9 months, with 73% of patients experiencing regression of the aneurysmal sac, and no reports of an IMA-related type II endoleak during the follow-up period. Conclusions: The laparoscopic ligation of the IMA for a type II endoleak following EVAR presents a promising, minimally invasive alternative with high technical success rates and favorable postoperative outcomes. Despite its potential advantages, including reduced contrast agent use and radiation exposure, its application remains limited to specialized centers. The findings suggest the need for further research in larger prospective studies to validate the effectiveness of this procedure and potentially broaden its clinical adoption.
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This paper presents the methodology of measuring chip temperature in the cutting zone in the rough milling of magnesium alloys. Infrared measurements are taken to determine the effect of variable cutting speed, feed per tooth, and depth of cut on the maximum temperature
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This paper presents the methodology of measuring chip temperature in the cutting zone in the rough milling of magnesium alloys. Infrared measurements are taken to determine the effect of variable cutting speed, feed per tooth, and depth of cut on the maximum temperature of chips. Thermal images of chip temperature for a generated collective frame and corresponding histograms are presented. Chip temperatures are presented in numerical terms as median and average values; maximum and minimum values; range; and standard deviation. Box plots are also shown for selected machining conditions. The problems arising during signal recording with a mean emissivity coefficient ε = 0.13, a value which is dedicated during machining magnesium alloys, are discussed in detail. Chip temperatures obtained in the tests do not exceed approx. 420 °C. Therefore, the dry rough milling process carried out with carbide tools with different blade geometries can be considered safe for a wide range of machining parameters. The proposed methodology of chip temperature measurement and result processing is a new and effective approach to safety assessment in the dry milling of magnesium alloys.
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The role of rice genomics in breeding progress is becoming increasingly important. Deeper research into the rice genome will contribute to the identification and utilization of outstanding functional genes, enriching the diversity and genetic basis of breeding materials and meeting the diverse demands
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The role of rice genomics in breeding progress is becoming increasingly important. Deeper research into the rice genome will contribute to the identification and utilization of outstanding functional genes, enriching the diversity and genetic basis of breeding materials and meeting the diverse demands for various improvements. Here, we review the significant contributions of rice genomics research to breeding progress over the last 25 years, discussing the profound impact of genomics on rice-genome sequencing, functional-gene exploration, and novel breeding methods, and we provide valuable insights for future research and breeding practices.
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In this paper, we present a novel approach referred to as the audio-based virtual landmark-based HoloSLAM. This innovative method leverages a single sound source and microphone arrays to estimate the voice-printed speaker’s direction. The system allows an autonomous robot equipped with a single
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In this paper, we present a novel approach referred to as the audio-based virtual landmark-based HoloSLAM. This innovative method leverages a single sound source and microphone arrays to estimate the voice-printed speaker’s direction. The system allows an autonomous robot equipped with a single microphone array to navigate within indoor environments, interact with specific sound sources, and simultaneously determine its own location while mapping the environment. The proposed method does not require multiple audio sources in the environment nor sensor fusion to extract pertinent information and make accurate sound source estimations. Furthermore, the approach incorporates Robotic Mixed Reality using Microsoft HoloLens to superimpose landmarks, effectively mitigating the audio landmark-related issues of conventional audio-based landmark SLAM, particularly in situations where audio landmarks cannot be discerned, are limited in number, or are completely missing. The paper also evaluates an active speaker detection method, demonstrating its ability to achieve high accuracy in scenarios where audio data are the sole input. Real-time experiments validate the effectiveness of this method, emphasizing its precision and comprehensive mapping capabilities. The results of these experiments showcase the accuracy and efficiency of the proposed system, surpassing the constraints associated with traditional audio-based SLAM techniques, ultimately leading to a more detailed and precise mapping of the robot’s surroundings.
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High-pressure pipelines are critical for transporting hazardous materials over long distances, but they face threats from third-party interference activities. Preventive measures are implemented, but interference accidents can still occur, making the need for high-quality detection strategies vital. This paper proposes an end-to-end Artificial
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High-pressure pipelines are critical for transporting hazardous materials over long distances, but they face threats from third-party interference activities. Preventive measures are implemented, but interference accidents can still occur, making the need for high-quality detection strategies vital. This paper proposes an end-to-end Artificial Intelligence of Things (AIoT) solution to detect potential interference threats in real time. The solution involves developing a smart visual sensor capable of processing images using state-of-the-art computer vision algorithms and transmitting alerts to pipeline operators in real time. The system’s core is based on the object-detection model (e.g., You Only Look Once version 4 (YOLOv4) and DETR with Improved deNoising anchOr boxes (DINO)), trained on a custom Pipeline Visual Threat Assessment (Pipe-VisTA) dataset. Among the trained models, DINO was able to achieve the best Mean Average Precision (mAP) of 71.2% for the unseen test dataset. However, for the deployment on a limited computational-ability edge computer (i.e., the NVIDIA Jetson Nano), the simpler and TensorRT-optimized YOLOv4 model was used, which achieved a mAP of 61.8% for the test dataset. The developed AIoT device captures the image using a camera, processes on the edge using the trained YOLOv4 model to detect the potential threat, transmits the threat alert to a Fleet Portal via LoRaWAN, and hosts the alert on a dashboard via a satellite network. The device has been fully tested in the field to ensure its functionality prior to deployment for the SEA Gas use-case. The AIoT smart solution has been deployed across the 10km stretch of the SEA Gas pipeline across the Murray Bridge section. In total, 48 AIoT devices and three Fleet Portals are installed to ensure the line-of-sight communication between the devices and portals.
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Pressure ulcers carry a significant risk in clinical practice. This paper proposes a practical and interpretable approach to estimate the risk levels of pressure ulcers using decision tree models. In order to address the common problem of imbalanced learning in nursing classification datasets,
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Pressure ulcers carry a significant risk in clinical practice. This paper proposes a practical and interpretable approach to estimate the risk levels of pressure ulcers using decision tree models. In order to address the common problem of imbalanced learning in nursing classification datasets, various oversampling configurations are analyzed to improve the data quality prior to modeling. The decision trees built are based on three easily identifiable and clinically relevant pressure ulcer risk indicators: mobility, activity, and skin moisture. Additionally, this research introduces a novel tabular visualization method to enhance the usability of the decision trees in clinical practice. Thus, the primary aim of this approach is to provide nursing professionals with valuable insights for assessing the potential risk levels of pressure ulcers, which could support their decision-making and allow, for example, the application of suitable preventive measures tailored to each patient’s requirements. The interpretability of the models proposed and their performance, evaluated through stratified cross-validation, make them a helpful tool for nursing care in estimating the pressure ulcer risk level.
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The generation of co-speech gestures for digital humans is an emerging area in the field of virtual human creation. Prior research has progressed by using acoustic and semantic information as input and adopting a classification method to identify the person’s ID and emotion
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The generation of co-speech gestures for digital humans is an emerging area in the field of virtual human creation. Prior research has progressed by using acoustic and semantic information as input and adopting a classification method to identify the person’s ID and emotion for driving co-speech gesture generation. However, this endeavor still faces significant challenges. These challenges go beyond the intricate interplay among co-speech gestures, speech acoustic, and semantics; they also encompass the complexities associated with personality, emotion, and other obscure but important factors. This paper introduces “DiT-Gestures”, a speech-conditional diffusion-based and non-autoregressive transformer-based generative model with the WavLM pre-trained model and a dynamic mask attention network (DMAN). It can produce individual and stylized full-body co-speech gestures by only using raw speech audio, eliminating the need for complex multimodal processing and manual annotation. Firstly, considering that speech audio contains acoustic and semantic features and conveys personality traits, emotions, and more subtle information related to accompanying gestures, we pioneer the adaptation of WavLM, a large-scale pre-trained model, to extract the style from raw audio information. Secondly, we replace the causal mask by introducing a learnable dynamic mask for better local modeling in the neighborhood of the target frames. Extensive subjective evaluation experiments are conducted on the Trinity, ZEGGS, and BEAT datasets to confirm WavLM’s and the model’s ability to synthesize natural co-speech gestures with various styles.
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In this paper, we address the challenge of ensuring stability in bipedal walking robots and exoskeletons. We explore the feasibility of real-time implementation for the Predicted Step Viability algorithm (PSV), a complex multi-step optimization criterion for planning future steps in bipedal gait. To
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In this paper, we address the challenge of ensuring stability in bipedal walking robots and exoskeletons. We explore the feasibility of real-time implementation for the Predicted Step Viability algorithm (PSV), a complex multi-step optimization criterion for planning future steps in bipedal gait. To overcome the high computational cost of the PSV algorithm, we performed an analysis using 11 classification algorithms and a stacking strategy to predict if a step will be stable or not. We generated three datasets of increasing complexity through PSV simulations to evaluate the classification performance. Among the classifiers, k Nearest Neighbors, Support Vector Machine with Radial Basis Function Kernel, Decision Tree, and Random Forest exhibited superior performance. Multi-Layer Perceptron also consistently performed well, while linear-based algorithms showed lower performance. Importantly, the use of stacking did not significantly improve performance. Our results suggest that the feature vector applied with this approach is applicable across various robotic models and datasets, provided that training data is balanced and sufficient points are used. Notably, by leveraging classifiers, we achieved rapid computation of results in less than 1 ms, with minimal computational cost.
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Coaxial magnetic gears have piqued the interest of researchers due to their numerous benefits over mechanical gears. These include reduced noise and vibration, enhanced efficiency, lower maintenance costs, and improved backdrivability. However, their adoption in industry has been limited by drawbacks like lower
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Coaxial magnetic gears have piqued the interest of researchers due to their numerous benefits over mechanical gears. These include reduced noise and vibration, enhanced efficiency, lower maintenance costs, and improved backdrivability. However, their adoption in industry has been limited by drawbacks like lower torque density and slippage at high torque levels. This work presents an analytical 2D model to compute the magnetic potential in Halbach array coaxial magnetic gears for every rotational angle, geometry configuration, and magnet specifications. This model calculates the induced torques and torque ripple in both rotors using the Maxwell Stress Tensor. The results were confirmed through Finite Element Analysis (FEA). Unlike FEA, this analytical model directly produces harmonics values, leading to faster computational times as it avoids torque calculations at each time step. In a case study, a standard coaxial magnetic gear was compared to one with a Halbach array, revealing a 14.3% improvement in torque density and a minor reduction in harmonics that cause torque ripple. Additionally, a case study was conducted to examine slippage in both standard and Halbach array gears during transient operations. The Halbach array coaxial magnetic gear demonstrated a 13.5% lower transmission error than its standard counterpart.
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The purpose of this work is to investigate the controllability of non-instantaneous impulsive (NII) Hilfer fractional (HF) neutral stochastic evolution equations with a non-dense domain. We construct a new set of adequate assumptions for the existence of mild solutions using fractional calculus, semigroup
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The purpose of this work is to investigate the controllability of non-instantaneous impulsive (NII) Hilfer fractional (HF) neutral stochastic evolution equations with a non-dense domain. We construct a new set of adequate assumptions for the existence of mild solutions using fractional calculus, semigroup theory, stochastic analysis, and the fixed point theorem. Then, the discussion is driven by some suitable assumptions, including the Hille–Yosida condition without the compactness of the semigroup of the linear part. Finally, we provide examples to illustrate our main result.
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To enhance the storage time of cucumbers, this research investigated the impact of chitosan (CS) and hyperbranched poly-L-lysine (HBPL) on the quality and nutritional attributes of cucumbers when stored at a temperature of 25 °C. The results demonstrated that sensory evaluation scores for
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To enhance the storage time of cucumbers, this research investigated the impact of chitosan (CS) and hyperbranched poly-L-lysine (HBPL) on the quality and nutritional attributes of cucumbers when stored at a temperature of 25 °C. The results demonstrated that sensory evaluation scores for cucumbers treated with a CS–HBPL combination were significantly higher than the control (CK), CS, and HBPL groups. On the 18th day of storage, cucumbers in the CK group exhibited significant decay and softening; however, there was a decrease in hardness observed in the CS–HBPL group and no decay or noticeable sour taste was detected. Furthermore, compared to the CK group, treatment with CS–HBPL effectively delayed cucumber decay and weight loss rate while significantly inhibiting decreases in cucumber hardness and growth of surface microorganisms. Additionally, it substantially reduced losses of soluble protein content as well as vitamin C (Vc), reducing sugars, and total phenolic compounds within cucumbers, which were 4.7 mg/g, 4.7 mg/g, 0.94 mg/g, and 0.52 mg/kg, respectively. Moreover, compared to the CK group, combined treatment with CS–HBPL significantly inhibited malondialdehyde (MDA) accumulation and reducing relative electrolyte permeability within cucumbers, which were 1.45 μmol·g−1FW and 29.82%. Furthermore, it notably enhanced activities of superoxide dismutase (SOD) and catalase (CAT), while exerting a significant inhibitory effect on polyphenol oxidase (PPO). In summary, the combined CS–HBPL treatment successfully prolonged cucumber shelf life at room temperature, enabling new possibilities for extending cucumber shelf life.
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Previous studies showed that NaIO3 can induce oxidative stress-mediated retinal pigment epithelium (RPE) damage to simulate age-related macular degeneration (AMD). Lemon peel is rich in antioxidants and components that can penetrate the blood–retinal barrier, but their role in retinal oxidative damage remains
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Previous studies showed that NaIO3 can induce oxidative stress-mediated retinal pigment epithelium (RPE) damage to simulate age-related macular degeneration (AMD). Lemon peel is rich in antioxidants and components that can penetrate the blood–retinal barrier, but their role in retinal oxidative damage remains unexplored. Here, we explore the protection of lemon peel ultrasonic-assisted water extract (LUWE), containing large amounts of flavonoids and polyphenols, against NaIO3-induced retinal degeneration. We initially demonstrated that LUWE, orally administered, prevented retinal distortion and thinning on the inner and outer nuclei layers, downregulating cleaved caspase-3 protein expression in RPE cells in NaIO3-induced mice. The effect of LUWE was achieved through the suppression of apoptosis and the associated proteins, such as cleaved PARP and cleaved caspase-3, as suggested by NaIO3-induced ARPE-19 cell models. This is because LUWE reduced reactive oxygen species-mediated mitochondrial fission via regulating p-Drp-1 and Fis1 expression. We further confirmed that LUWE suppresses the expression of p-MEK-1/2 and p-ERK-1/2 in NaIO3-induced ARPE-19 cells, thereby providing the protection described above, which was confirmed using PD98059 and U0126. These results indicated that LUWE prevents mitochondrial oxidative stress-mediated RPE damage via the MEK/ERK pathway. Elucidation of the molecular mechanism may provide a new protective strategy against retinal degeneration.
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Solar filaments are good tracers of space weather and magnetic flux ropes in the corona. Identifying and detecting filaments helps to forecast space weather and explore the solar magnetic field. Many automatic detection methods have been proposed to process the large number of
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Solar filaments are good tracers of space weather and magnetic flux ropes in the corona. Identifying and detecting filaments helps to forecast space weather and explore the solar magnetic field. Many automatic detection methods have been proposed to process the large number of observed images. Current methods face issues of unreliable dataset annotations and poor anti-interference capability. First, to address the issue of unreliable dataset annotations, we built a solar filament dataset using a manual annotation method. Second, we introduced Transformer into Convolutional Neural Networks. Transformer, with the ability to extract more global features, can help counter interference. In addition, there is large disparity in the size of solar filaments. Therefore, a multi-scale residual block is designed to extract features across various scales. Deformable large kernel attention and a res path are used to better integrate encoder and decoder information. Results show that this method outperforms the existing solar filament detection methods (improved U-Net and improved V-Net), achieving an F1 score of 91.19%. In particular, our results show lower interference by sunspots and background noise than existing methods. The ability to counter interference is improved.
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The study of bio-inspired structures and their reproduction has always fascinated humans. The advent of soft robotics, thanks to soft materials, has enabled considerable progress in this field. Over the years, polyps, worms, cockroaches, jellyfish, and multiple anthropomorphic structures such as hands or
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The study of bio-inspired structures and their reproduction has always fascinated humans. The advent of soft robotics, thanks to soft materials, has enabled considerable progress in this field. Over the years, polyps, worms, cockroaches, jellyfish, and multiple anthropomorphic structures such as hands or limbs have been reproduced. These structures have often been used for gripping and handling delicate objects or those with complex unknown a priori shapes. Several studies have also been conducted on grippers inspired by the seahorse tail. In this paper, a novel biomimetic soft pneumatic actuator inspired by the tail of the seahorse Hippocampus reidi is presented. The actuator has been developed to make a leg to sustain a multi-legged robot. The prototyping of the actuator was possible by combining a 3D-printed reinforcement in thermoplastic polyurethane, mimicking the skeletal apparatus, within a silicone rubber structure, replicating the functions of the external epithelial tissue. The latter has an internal channel for pneumatic actuation that acts as the inner muscle. The study on the anatomy and kinematic behaviour of the seahorse tail suggested the mechanical design of the actuator. Through a test campaign, the actuator prototype was characterized by isotonic tests with an external null load, isometric tests, and activation/deactivation times. Specifically, the full actuator distension of 154.5 mm occurs at 1.8 bar, exerting a maximum force of 11.9 N, with an activation and deactivation time of 74.9 and 94.5 ms, respectively.
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The demand for high-precision CRISPR/Cas9 systems in biomedicine is experiencing a notable upsurge. The editing system fdCas9 employs a dual-sgRNA strategy to enhance editing accuracy. However, the application of fdCas9 is constrained by the stringent requirement for two protospacer adjacent motifs (PAMs) of
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The demand for high-precision CRISPR/Cas9 systems in biomedicine is experiencing a notable upsurge. The editing system fdCas9 employs a dual-sgRNA strategy to enhance editing accuracy. However, the application of fdCas9 is constrained by the stringent requirement for two protospacer adjacent motifs (PAMs) of Cas9. Here, we devised an optimized editor, fRYdCas9, by merging FokI with the nearly PAM-less RYdCas9 variant, and two fRYdCas9 systems formed a dimer in a proper spacer length to accomplish DNA cleavage. In comparison to fdCas9, fRYdCas9 demonstrates a substantial increase in the number of editable genomic sites, approximately 330-fold, while maintaining a comparable level of editing efficiency. Through meticulous experimental validation, we determined that the optimal spacer length between two FokI guided by RYdCas9 is 16 base pairs. Moreover, fRYdCas9 exhibits a near PAM-less feature, along with no on-target motif preference via the library screening. Meanwhile, fRYdCas9 effectively addresses the potential risks of off-targets, as analyzed through whole genome sequencing (WGS). Mouse embryonic editing shows fRYdCas9 has robust editing capabilities. This study introduces a potentially beneficial alternative for accurate gene editing in therapeutic applications and fundamental research.
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Ring Rolling is a near-net manufacturing process with some measurable dimensional inaccuracies in its products. This fact is exaggerated even more under the scope of high-precision manufacturing, where these imprecisions render such products unfitting for the strict dimensional requirements of high-precision applications (e.g.,
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Ring Rolling is a near-net manufacturing process with some measurable dimensional inaccuracies in its products. This fact is exaggerated even more under the scope of high-precision manufacturing, where these imprecisions render such products unfitting for the strict dimensional requirements of high-precision applications (e.g., bearings, casings for turbojets, etc.). In order to remedy some of the dimensional inaccuracies of Ring Rolling, the novel approach of Reverse Ring Rolling is proposed and investigated in the current analysis. The conducted research was based on a numerical simulation of a flat Ring Rolling process, previously presented by the authors. Since the final dimensions of the ring from the authors’ previous work diverged from those initially expected, the simulation of a subsequent Reverse Ring Rolling process was performed to reach the target dimensions. The calculated deformational results revealed a great agreement in at least two of the three crucial dimensions. Additionally, the evaluation of the calculated stress, strain, temperature and load results revealed key aspects of the mechanisms that occur during the proposed process. Overall, it was concluded that Reverse Ring Rolling can effectively function as a corrective process, which can increase the dimensional accuracy of a seamless ring product with little additional post-processing and cost.
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Bifidobacterium infantis are the primary colonizers of the infant gut, yet scientific research addressing the transmission of the genus Bifidobacterium to infants remains incomplete. This review examines microbial reservoirs of infant-type Bifidobacterium that potentially contribute to infant gut colonization. Accordingly, strain inheritance from
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Bifidobacterium infantis are the primary colonizers of the infant gut, yet scientific research addressing the transmission of the genus Bifidobacterium to infants remains incomplete. This review examines microbial reservoirs of infant-type Bifidobacterium that potentially contribute to infant gut colonization. Accordingly, strain inheritance from mother to infant via the fecal-oral route is likely contingent on the bifidobacterial strain and phenotype, whereas transmission via the vaginal microbiota may be restricted to Bifidobacterium breve. Additional reservoirs include breastmilk, horizontal transfer from the environment, and potentially in utero transfer. Given that diet is a strong predictor of Bifidobacterium colonization in early life and the absence of Bifidobacterium is observed regardless of breastfeeding, it is likely that additional factors are responsible for bifidobacterial colonization early in life.
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A field’s historical performance data are used for management zone delineation in precision agriculture, but including abnormal data leads to inappropriate zones. This paper introduces a framework incorporating historical performance data and a new Zoning Dissimilarity Metric () to
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A field’s historical performance data are used for management zone delineation in precision agriculture, but including abnormal data leads to inappropriate zones. This paper introduces a framework incorporating historical performance data and a new Zoning Dissimilarity Metric () to detect abnormal zoning data automatically. The methodology identifies abnormal zoning data among the field’s performance indicators extracted from satellite images to enhance the accuracy of the delineated zones. We experimented with our framework using Sentinel-2 images on 39 fields across Canada. Our experimental results, which involve both real and synthetic data, clearly demonstrate the importance of in effectively excluding abnormal data during the zone delineation process.
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Background. EcoHIV is a chimeric HIV that replicates in mice in CD4+ T cells, macrophages, and microglia (but not in neurons), causing lasting neurocognitive impairment resembling neurocognitive disease in people living with HIV. The present study was designed to develop EcoHIV-susceptible primary mouse
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Background. EcoHIV is a chimeric HIV that replicates in mice in CD4+ T cells, macrophages, and microglia (but not in neurons), causing lasting neurocognitive impairment resembling neurocognitive disease in people living with HIV. The present study was designed to develop EcoHIV-susceptible primary mouse brain cultures to investigate the indirect effects of HIV infection on neuronal integrity. Results. We used two EcoHIV clones encoding EGFP and mouse bone marrow-derived macrophages (BMM), mixed mouse brain cells, or enriched mouse glial cells from two wild-type mouse strains to test EcoHIV replication efficiency, the identity of productively infected cells, and neuronal apoptosis and integrity. EcoHIV replicated efficiently in BMM. In mixed brain cell cultures, EcoHIV targeted microglia but did not cause neuronal apoptosis. Instead, the productive infection of the microglia activated them and impaired synaptophysin expression, dendritic density, and axonal structure in the neurons. EcoHIV replication in the microglia and neuronal structural changes during infection were prevented by culture with an antiretroviral. Conclusions. In murine brain cell cultures, EcoHIV replication in the microglia is largely responsible for the aspects of neuronal dysfunction relevant to cognitive disease in infected mice and people living with HIV. These cultures provide a tool for further study of HIV neuropathogenesis and its control.
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In this paper, we evaluate the uniqueness of a hypothetical iris recognition system that relies upon a nonlinear mapping of iris data into a space of Gaussian codewords with independent components. Given the new data representation, we develop and apply a sphere packing
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In this paper, we evaluate the uniqueness of a hypothetical iris recognition system that relies upon a nonlinear mapping of iris data into a space of Gaussian codewords with independent components. Given the new data representation, we develop and apply a sphere packing bound for Gaussian codewords and a bound similar to Daugman’s to characterize the maximum iris population as a function of the relative entropy between Gaussian codewords of distinct iris classes. As a potential theoretical approach leading toward the realization of the hypothetical mapping, we work with the auto-regressive model fitted into iris data, after some data manipulation and preprocessing. The distance between a pair of codewords is measured in terms of the relative entropy (log-likelihood ratio statistic is an alternative) between distributions of codewords, which is also interpreted as a measure of iris quality. The new approach to iris uniqueness is illustrated using two toy examples involving two small datasets of iris images. For both datasets, the maximum sustainable population is presented as a function of image quality expressed in terms of relative entropy. Although the auto-regressive model may not be the best model for iris data, it lays the theoretical framework for the development of a high-performance iris recognition system utilizing a nonlinear mapping from the space of iris data to the space of Gaussian codewords with independent components.
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An artificial wetland is used to treat gray, waste, storm or industrial water. This is an engineering system that uses natural functions of vegetation, soil and organisms to provide secondary treatment to gray water. In the physical design of each artificial wetland, there
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An artificial wetland is used to treat gray, waste, storm or industrial water. This is an engineering system that uses natural functions of vegetation, soil and organisms to provide secondary treatment to gray water. In the physical design of each artificial wetland, there are various action factors that must meet certain characteristics so that the level of gray-water pollution is reduced. In this sense, several design methodologies have been developed and reported in the literature, but some are customized designs and often do not meet the required decontamination objectives. This challenge increases as the complexity of the task in its structure also increases. Particularly in this work, a multi-objective evolutionary algorithm is used to optimize the physical design of a horizontal flow subsurface wetland (HFSW) for gray-water treatment. The study aims to achieve two objectives: first, to minimize the physical volume, and second, to maximize the contaminant removal efficiency. The defined objective functions depend on six design variables called hydraulic retention time, width, length, water depth inside the wetland, substrate depth and slope. Three constraint functions are also defined: removal efficiency greater than 95%, physical volume below 500 m and compliance with a length–width ratio is 3:1, varying the population size and number of generations equal to 200, 400, and 600. The set of solutions according to the number of generations as well as the Pareto front corresponds to the best solution that complies with the constraints of the problem of oversizing the HFSW, and the Pareto front shows the interaction between the objectives and their behavior, reflecting the problem’s nature as minimization–maximization.
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This paper showcases the integration of the Interfacing Toolbox for Robotic Arms (ITRA) with our newly developed hybrid Visual Servoing (VS) methods to automate the disassembly of electric vehicle batteries, thereby advancing sustainability and fostering a circular economy. ITRA enhances collaboration between industrial
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This paper showcases the integration of the Interfacing Toolbox for Robotic Arms (ITRA) with our newly developed hybrid Visual Servoing (VS) methods to automate the disassembly of electric vehicle batteries, thereby advancing sustainability and fostering a circular economy. ITRA enhances collaboration between industrial robotic arms, server computers, sensors, and actuators, meeting the intricate demands of robotic disassembly, including the essential real-time tracking of components and robotic arms. We demonstrate the effectiveness of our hybrid VS approach, combined with ITRA, in the context of Electric Vehicle (EV) battery disassembly across two robotic testbeds. The first employs a KUKA KR10 robot for precision tasks, while the second utilizes a KUKA KR500 for operations needing higher payload capacity. Conducted in T1 (Manual Reduced Velocity) mode, our experiments underscore a swift communication protocol that links low-level and high-level control systems, thus enabling rapid object detection and tracking. This allows for the efficient completion of disassembly tasks, such as removing the EV battery’s top case in 27 s and disassembling a stack of modules in 32 s. The demonstrated success of our framework highlights its extensive applicability in robotic manufacturing sectors that demand precision and adaptability, including medical robotics, extreme environments, aerospace, and construction.
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Green roofs have become a popular sustainable solution in urban areas, and in recent years, shipping containers have gained popularity as a sustainable alternative for housing. A promising proposal is to combine these two solutions. This research aims to analyze the thermal behavior
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Green roofs have become a popular sustainable solution in urban areas, and in recent years, shipping containers have gained popularity as a sustainable alternative for housing. A promising proposal is to combine these two solutions. This research aims to analyze the thermal behavior of experimental modules of scale constructions. Four modules were constructed with different substrate thicknesses (4, 6, 8, and 12 cm) to verify the impact on thermal behavior and provide guidance for this technology. Additionally, another module was built without a green roof for control purposes. The indoor and outdoor air temperatures and humidities, soil moistures, and temperatures between green roof layers were recorded in a tropical climate in summer. The behavior was similar between the different thicknesses for the whole period but with significant differences in the indoor temperature amplitudes (13.8 °C for the thinner substrate, 9.7 °C for the thicker one, and 38.7 °C for the bare roof). This study also revealed considerable heat conduction between the side walls and the slab, which resulted in an upward heat flow to the substrate during a day with a clear sky, which is the opposite of what is observed in conventional roofs. During the night and rainy periods, temperatures tend to become closer between the roof’s layers when the substrate dissipates the energy absorbed throughout the day.
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