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Under the impact of Covid-19 pandemic, people worldwide are returning to core tourism values and finding travel safety. Currently, tourists have been paying more attention to living ethically responsibly with natural resources and creating values.

Automated detection of surface defects on three-dimensional (3D) parts is vital for ensuring product quality and safety in manufacturing. However, three key challenges hinder reliable detection: geometric context ambiguity across complex part shapes, domain mismatch between generic pretrained features and industrial scans (with their unique noise and reflectivity), and the scarcity of diverse defect examples for training. To overcome these issues, we propose a novel single-forward-pass framework for point cloud anomaly detection, comprising three new modules: (1) Spatial Context Aggregation, which grounds each local patch in a set of learned global prototypes via an optimal-transport alignment to resolve context ambiguity; (2) Feature Adaptor, a lightweight two-layer multilayer perceptron (MLP) that fine-tunes self-supervised Point-MAE embeddings to the specific characteristics of industrial scans; and (3) Selective Anomalous Feature Generator, which synthesizes realistic hard negatives by corrupting random subsets of feature tokens, thus mitigating the need for extensive defect labels. An attention-based discriminator trained with patch-wise supervision learns to distinguish these hard negatives from genuine defect-free patterns. At inference, our pipeline delivers dense per-point anomaly scores in a single pass at up to 13.5 frames per second (FPS). On the Real3D-AD benchmark, we observe point-level improvements of 2.8% in area under the receiver operating characteristic curve (AUROC) and 5.7% in area under the precision-recall curve (AUPR), with object-level gains of 3.0% (AUROC) and 3.5% (AUPR). Evaluated on our newly released Industrial3D-AD dataset, which captures realistic sensor noise and reflective materials, we see similar enhancements (2.9%/5.3% point-level, 2.8%/3.3% object-level).

Abstract Unsupervised industrial anomaly detection trains models solely on anomaly-free images to detect unseen defects. While embedding-based methods have recently achieved state-of-the-art results, their use of memory banks substantially increases memory usage and inference times, limiting their practicality in industrial settings. In this work, we propose a lightweight and efficient framework for anomaly detection and localization using paired well-lit and low-light images. Our network learns to reconstruct well-lit features from low-light features on nominal (anomaly-free) samples, detecting anomalies by identifying inconsistencies between the reconstructed and extracted features. Experimental results demonstrate that our method outperforms existing state-of-the-art approaches across multiple industrial datasets. Specifically, our model achieves an Image-level Area Under the Receiver Operating Characteristic (I-AUROC) of 0.854 and rea Under the Per-Region Overlap (AUPRO) of 0.823 on low-light industrial anomaly detection (LL-IAD), significantly surpassing existing methods. Furthermore, it attains I-AUROC scores of 0.864 and 0.858 on the Insulator and Clutch datasets, respectively, outperforming all prior approaches in these industrial settings. Notably, even when well-lit images are unavailable, our model maintains high performance using Retinexformer-enhanced low-light images, demonstrating its adaptability to real-world low-light scenarios. Additionally, we introduce a new industrial anomaly detection dataset featuring paired well-lit and low-light images. To our knowledge, this is the first dataset for LL-IAD dataset. Graphical Abstract Graphical Abstract Industrial anomaly detection using paired well-lit and low-light images.

Diabetes mellitus remains a major global public health challenge. Mobile applications and digital platforms offer innovative opportunities to enhance diabetes self-management. This study aimed to evaluate the impact of diaB, a digitally delivered diabetes self-management education and support (DSMES) program, on clinical outcomes and diabetes-related knowledge in individuals with type 2 diabetes. This quasi-experimental, multicenter, open-label study involved 163 adults with type 2 diabetes, allocated to either a DSMES intervention group (n = 81) or a usual care control group (n = 82). The intervention group participated in a ten-week educational program delivered via the diaB mobile app and other digital platforms, based on the DSMES framework recommended by the American Diabetes Association. The program included online group video sessions led by doctors, nutritionists, and health coaches. Clinical outcomes—including fasting blood glucose (FBG), HbA1c, lipid profiles, weight, BMI, waist circumference, and diabetes knowledge (assessed using the Michigan Diabetes Knowledge Test, MDKT)—were measured at baseline and after the intervention. The intervention group showed significant improvements in clinical outcomes. FBG decreased from 7.5 to 6.7 mmol/L (P < 0.001), with a greater reduction than the control group (P = 0.004). HbA1c declined from 7.8 to 7.3% (P < 0.001), with no significant change in the control group. In multivariable linear regression adjusted for the disease risk score (DRS), the intervention was independently associated with greater HbA1c reduction (β = –0.72; 95% CI –1.14 to –0.31; P = 0.001), while higher DRS predicted less improvement (β = +0.57; 95% CI 0.12 to 1.01; P = 0.013). The intervention group also demonstrated greater gains in MDKT scores (+ 3 vs. –1; P < 0.001), more favorable changes in lipid profiles (P < 0.001), and greater weight loss among participants with baseline BMI ≥ 25 kg/m 2 (–1.5 kg vs. 0 kg; P = 0.02). DiaB, a digitally delivered DSMES program, significantly improved glycemic control, diabetes knowledge, and metabolic outcomes. Integrating such digital interventions into routine diabetes care may enhance both clinical management and patient education in resource-limited settings.

Background Non-invasive multi-cancer early detection (MCED) tests have shown promise in enhancing early cancer detection. However, their clinical utility across diverse populations remains underexplored, limiting their routine implementation. This study aims to validate the clinical utility of a multimodal non-invasive circulating tumor DNA (ctDNA)-based MCED test, SPOT-MAS (Screening for the Presence Of Tumor by DNA Methylation And Size). Methods We conducted a multicenter prospective study, K-DETEK (ClinicalTrials.gov identifier: NCT05227261), involving 9057 asymptomatic individuals aged 40 years or older across 75 major hospitals and one research institute in Vietnam. Participants were followed for 12 months. Results Of the 9024 eligible participants, 43 (0.48%) tested positive for ctDNA. Among these, 17 were confirmed with malignant lesions in various primary organs through standard-of-care (SOC) imaging and biopsy, with 9 cases matching our tissue of origin (TOO) predictions. This resulted in a positive predictive value of 39.53% (95%CI 26.37–54.42) and a TOO accuracy of 52.94% (95%CI 30.96–73.83). Among the 8981 participants (99.52%) who tested negative, 8974 were confirmed cancer-free during a 12-month period after testing, yielding a negative predictive value of 99.92% (95% CI 99.84–99.96). The test demonstrated an overall sensitivity of 70.83% (95%CI 50.83–85.09) and a specificity of 99.71% (95% CI 99.58–99.80) for detecting various cancer types, including those without SOC screening options. Conclusions This study presents a prospective validation of a multi-cancer early detection (MCED) test conducted in a lower middle-income country, demonstrating the potential of SPOT-MAS for early cancer detection. Our findings indicate that MCED tests could be valuable additions to national cancer screening programs, particularly in regions where such initiatives are currently limited. Trial registration ClinicalTrials.gov ID: NCT05227261. Date of registration: 07/02/2022.

Trials of fluoxetine for recovery after stroke report conflicting results. The Assessment oF FluoxetINe In sTroke recoverY (AFFINITY) trial aimed to show if daily oral fluoxetine for 6 months after stroke improves functional outcome in an ethnically diverse population. AFFINITY was a randomised, parallel-group, double-blind, placebo-controlled trial done in 43 hospital stroke units in Australia (n=29), New Zealand (four), and Vietnam (ten). Eligible patients were adults (aged ≥18 years) with a clinical diagnosis of acute stroke in the previous 2–15 days, brain imaging consistent with ischaemic or haemorrhagic stroke, and a persisting neurological deficit that produced a modified Rankin Scale (mRS) score of 1 or more. Patients were randomly assigned 1:1 via a web-based system using a minimisation algorithm to once daily, oral fluoxetine 20 mg capsules or matching placebo for 6 months. Patients, carers, investigators, and outcome assessors were masked to the treatment allocation. The primary outcome was functional status, measured by the mRS, at 6 months. The primary analysis was an ordinal logistic regression of the mRS at 6 months, adjusted for minimisation variables. Primary and safety analyses were done according to the patient's treatment allocation. The trial is registered with the Australian New Zealand Clinical Trials Registry, ACTRN12611000774921. Between Jan 11, 2013, and June 30, 2019, 1280 patients were recruited in Australia (n=532), New Zealand (n=42), and Vietnam (n=706), of whom 642 were randomly assigned to fluoxetine and 638 were randomly assigned to placebo. Mean duration of trial treatment was 167 days (SD 48·1). At 6 months, mRS data were available in 624 (97%) patients in the fluoxetine group and 632 (99%) in the placebo group. The distribution of mRS categories was similar in the fluoxetine and placebo groups (adjusted common odds ratio 0·94, 95% CI 0·76–1·15; p=0·53). Compared with patients in the placebo group, patients in the fluoxetine group had more falls (20 [3%] vs seven [1%]; p=0·018), bone fractures (19 [3%] vs six [1%]; p=0·014), and epileptic seizures (ten [2%] vs two [<1%]; p=0·038) at 6 months. Oral fluoxetine 20 mg daily for 6 months after acute stroke did not improve functional outcome and increased the risk of falls, bone fractures, and epileptic seizures. These results do not support the use of fluoxetine to improve functional outcome after stroke. National Health and Medical Research Council of Australia.

Grasp generation is a crucial task in robotics, especially in unstructured environments, where robots must identify suitable grasp locations on objects and determine the grasp configuration. Recent advances in deep learning have led to the development of end-to-end models for 6-DOF grasp generation that can learn to directly map from input point clouds to grasp configurations without intermediate processing steps. However, these models often treat all points in a scene equally, leading to suboptimal results in cluttered contexts where meaningfulness distributions are disparate due to occlusion. While attention mechanisms have shown promise in improving the accuracy and efficiency of various tasks in occluded scenes, their effectiveness in improving grasp generation performance is still an active area of research. Inspired by this potential, we explore the power of attention mechanisms in improving grasp generation from 3D point clouds. Building upon the previous work with VoteGrasp 2022, we integrate a wide range of attention modules and compare their effects and characteristics to identify the most successful combination for enhancing grasp generation performance. We also extend VoteGrasp by adding a semantic object classification loss to the loss function, making our method more flexible than existing approaches. Based on the detailed experiments and analysis, our research provides valuable insights into the use of attention mechanisms for 3D point cloud grasp generation, highlighting their potential to improve the accuracy and efficiency of robotic systems.

With the goal of finding low-cost materials that can completely substitute the precious- and noble-containing catalysts and be active toward the oxidation of CO at low temperatures (around 100 °C), transition metal oxide-doped cryptomelane (e.g., K-OMS-2, MeO x /K-OMS-2, and CuO x -MeO x /K-OMS-2; Me = Ni, Fe, Cd, Sn, Mn, Cr, Co, Zn, and Al) were obtained by the impregnation method with different synthesis conditions. The synthesized samples were then tested for CO conversion and characterized by means of powder X-ray diffraction, elemental mapping and composition, as well as scanning electron microscopy. It was demonstrated that the K-OMS-2 support doped by a composite of CuO x -NiO x (promoter) and calcined at 400 °C gave higher catalytic activity than the other samples, with a 90% CO conversion at around 120 °C. Other factors that could be controlled for increasing CuO x -NiO x /K-OMS-2_400 activity were to set the mass percentage of dopants and the atomic ratio of Cu:Ni to about 15 mass% and 1:1, respectively. Under different catalytic reaction conditions such as various inlet CO concentrations ( C CO , 750–4000 ppm), amounts of catalyst ( m cat , 0.25–1.25 g), and gas-air flow rates ( F gas , 0.5–1.25 L/min), the catalyst activity was found to be enhanced when the C CO and F gas were reduced and the m cat raised. Moreover, the CuO x -NiO x /K-OMS-2_400 catalyst exhibited high stability and durability. The calculated average size of K-OMS-2 crystallites was approximately 12 nm, while the composite phase was in its amorphous state. This sample showed a morphology of nanofiber-like shapes with different lengths. Graphical Abstract

In this paper, the supercontinuum generation with all-normal and anomalous dispersion based on Ga 8 Sb 32 S 60 -photonic crystal fibers with an octagonal lattice is investigated in detail. We propose two types of photonic crystal fibers with the same lattice structure but different numbers of air holes in the cladding (O-PCF 1 with eight rings of air holes arranged parallel to the axis of the core and O-PCF 2 with the air holes in the innermost layer near the core removed) to evaluate the supercontinuum generation performance. By adjusting the structural parameters, the characteristic quantities such as dispersion, effective mode area, nonlinear coefficients, confinement loss can be optimized. The O-PCF 2 s exhibits flatter and near-zero dispersion, higher nonlinear coefficients with values of 1197.526 W −1  km −1 and 695.739 W −1  km −1 , low confinement losses of about 10 −6  dB/m at a pump wavelength of 4.5 µm enabling spectra spanning from 1.262 to 9.531 µm and from 1.863 to 18.0 µm corresponding to bandwidths of 6.82 µm and 12.41 µm at 30 dB in the long-wave infrared region with peak powers of 12 kW and 20 kW using all-normal and anomalous dispersions profiles, respectively. Supercontinuum laser sources using the proposed fibers are promising for many potential infrared applications involving optical coherence tomography, fingerprinting, and molecular spectroscopy.

Ganoderma lucidum is traditionally used to prevent and treat some diseases such as liver disorders, hypertension, insomnia, diabetes, and cancer. G. lucidum spore extracts are also reported to share similar bioactivities as extracts from its other parts. However, there is no systematic review that elucidates its pharmacological effect. Our aim is to comprehensively summarise current evidence of G. lucidum spore extracts to clarify its benefits to be applied in further studies. We searched five primary databases: PubMed, Virtual Health Library (VHL), Global Health Library (GHL), System for Information on Grey Literature in Europe (SIGLE), and Google Scholar on September 13, 2021. Articles were selected according to inclusion and exclusion criteria. A manual search was applied to find more relevant articles. Ninety studies that reported the pharmacological effects and/or safety of G. lucidum spores were included in this review. The review found that G. lucidum spore extracts showed quite similar effects as other parts of this medicinal plant including anti-tumor, anti-inflammatory, antioxidant effects, and immunomodulation. G. lucidum sporoderm-broken extract demonstrated higher efficiency than unbroken spore extract. G. lucidum extracts also showed their effects on some genes responsible for the body's metabolism, which implied the benefits in metabolic diseases. The safety of G. lucidum should be investigated in depth as high doses of the extract could increase levels of cancer antigen (CA)72-4, despite no harmful effect shown on body organs. Generally, there is a lot of potential in the studies of compounds with pharmacological effects and new treatments. Sporoderm breaking technique could contribute to the production of extracts with more effective prevention and treatment of diseases. High doses of G. lucidum spore extract should be used with caution as there was a concern about the increase in CA.