Miracle-dz/UniFAR-FTUs huggingface dataset:
Indoor Scene Recognition Mechanism Based on Direction-Driven Convolutional Neural Networks

Indoor location-based services constitute an important part of our daily lives, providing position and direction information about people or objects in indoor spaces. These systems can be useful in security and monitoring applications that target specific areas such as rooms. Vision-based scene recognition is the task of accurately identifying a room category from a given image. Despite years of research in this field, scene recognition remains an open problem due to the different and complex places in the real world. Indoor environments are relatively complicated because of layout variability, object and decoration complexity, and multiscale and viewpoint changes. In this paper, we propose a room-level indoor localization system based on deep learning and built-in smartphone sensors combining visual information with smartphone magnetic heading. The user can be room-level localized while simply capturing an image with a smartphone. The presented indoor scene recognition system is based on direction-driven convolutional neural networks (CNNs) and therefore contains multiple CNNs, each tailored for a particular range of indoor orientations. We present particular weighted fusion strategies that improve system performance by properly combining the outputs from different CNN models. To meet users’ needs and overcome smartphone limitations, we propose a hybrid computing strategy based on mobile computation offloading compatible with the proposed system architecture. The implementation of the scene recognition system is split between the user’s smartphone and a server, which aids in meeting the computational requirements of CNNs. Several experimental analysis were conducted, including to assess performance and provide a stability analysis. The results obtained on a real dataset show the relevance of the proposed approach for localization, as well as the interest in model partitioning in hybrid mobile computation offloading. Our extensive evaluation demonstrates an increase in accuracy compared to traditional CNN scene recognition, indicating the effectiveness and robustness of our approach.
Biological Preparation of Chitosan-Loaded Silver Nanoparticles: Study of Methylene Blue Adsorption as Well as Antibacterial Properties under Light

Human beings have made significant progress in the medical field since antibiotics were widely used. However, the consequences caused by antibiotics abuse have gradually shown their negative effects. Antibacterial photodynamic therapy (aPDT) has the ability to resist drug-resistant bacteria without antibiotics, and as it is increasingly recognized that nanoparticles can effectively solve the deficiency problem of singlet oxygen produced by photosensitizers, the application performance and scope of aPDT are gradually being expanded. In this study, we used a biological template method to reduce Ag+ to silver atoms in situ with bovine serum albumin (BSA) rich in various functional groups in a 50 °C water bath. The aggregation of nanomaterials was inhibited by the protein’s multistage structure so that the formed nanomaterials have good dispersion and stability. It is unexpected that we used chitosan microspheres (CMs) loaded with silver nanoparticles (AgNPs) to adsorb methylene blue (MB), which is both a pollutant and photosensitive substance. The Langmuir adsorption isothermal curve was used to fit the adsorption capacity. The exceptional multi-bond angle chelating forceps of chitosan make it have a powerful physical adsorption capacity, and dehydrogenated functional groups of proteins with negative charge can also bond to positively charged MB to form a certain amount of ionic bonds. Compared with single bacteriostatic materials, the bacteriostatic capacity of the composite materials adsorbing MB under light was significantly improved. This composite material not only has a strong inhibitory effect on Gram-negative bacteria but also has a good inhibitory effect on the growth of Gram-positive bacteria poorly affected by conventional bacteriostatic agents. In conclusion, the CMs loaded with MB and AgNPs have some possible applications in the purification or treatment of wastewater in the future.
Multi-modal medical image classification using deep residual network and genetic algorithm

Artificial intelligence (AI) development across the health sector has recently been the most crucial. Early medical information, identification, diagnosis, classification, then analysis, along with viable remedies, are always beneficial developments. Precise and consistent image classification has critical in diagnosing and tactical decisions for healthcare. The core issue with image classification has become the semantic gap. Conventional machine learning algorithms for classification rely mainly on low-level but rather high-level characteristics, employ some handmade features to close the gap, but force intense feature extraction as well as classification approaches. Deep learning is a powerful tool with considerable advances in recent years, with deep convolution neural networks (CNNs) succeeding in image classification. The main goal is to bridge the semantic gap and enhance the classification performance of multi-modal medical images based on the deep learning-based model ResNet50. The data set included 28378 multi-modal medical images to train and validate the model. Overall accuracy, precision, recall, and F1-score evaluation parameters have been calculated. The proposed model classifies medical images more accurately than other state-of-the-art methods. The intended research experiment attained an accuracy level of 98.61%. The suggested study directly benefits the health service.
Double free flaps for complex oromandibular defects with two teams of head and neck reconstructive surgical oncologists

Abstract From December 2019 to July 2021, double free flaps were used by two teams of head-and-neck reconstructive surgical oncologists in 10 patients with large composite mandibulofacial defects following malignant tumor (n = 8) and osteoradionecrosis (n = 2) ablation. Our report included 10 patients. All of our patients were reconstructed by two free flaps, a combination of an anterolateral thigh flap (n = 8) or a radial forearm flap (n = 2) with an osteocutaneous fibula flap. The survival rate of these flaps was 100%. The mean operative time was 597 ± 41.7 min (range 545–660 min). There were no patients with major complications. Most of our patients had accepted functional and cosmetic results of both recipient site and donor site after a median follow-up of 22.5 months. Two teams of reconstructive surgical oncologists may shorten the operative time and reduce the rate of major complications. Main Points: Huge complex oromandibular defects may require the use of double free flap reconstruction. Double free flaps were used by two teams of head-and-neck reconstructive surgical oncologists. This approach may shorten the operative time and reduce the rate of major complications.
Barriers and facilitators associated with the use of mental health services among immigrant students in high-income countries: A systematic scoping review

Background Immigrant students face various challenges in high-income countries that can contribute to the decline of their mental well-being upon arrival in their host country. Despite the growing population of these students in several high-income countries, there is inadequate attention given to their mental health needs and their access to mental health services. Thus, this systematic scoping review aimed to identify gaps in existing research relating to the barriers and facilitators associated with access to and use of mental health services in high-income countries. Methods Following the PRISMA-ScR checklist as guidance we systematically searched Ovid Medline, APA PsycInfo, Education Source, CINAHL, Web of Science databases for peer reviewed articles related to barriers and facilitators of mental health service use among immigrant students. We conducted a narrative evidence synthesis to highlight barriers and facilitators to the use of mental health services. Results Out of the 2407 articles initially found, 47 studies met the inclusion criteria and were considered for this review. The increasing attention towards the mental health concerns of immigrant students and their access to mental health services is evident. However, various barriers like stigma, insufficient knowledge, or adherence to traditional gender roles (such as masculinity) hinder their utilization of these services. On the other hand, factors such as being a woman, having a strong sense of cultural adaptation, or possessing adequate mental health literacy serve as facilitators for accessing mental health services. Conclusion These students have unique experiences, and their needs are often unmet. To improve their mental health and use of mental health services, it is important to consider the barriers they face and their unique experience in their specific life context and to develop tailored prevention and intervention programs.
Sensor-Based Classification of Primary and Secondary Car Driver Activities Using Convolutional Neural Networks

To drive safely, the driver must be aware of the surroundings, pay attention to the road traffic, and be ready to adapt to new circumstances. Most studies on driving safety focus on detecting anomalies in driver behavior and monitoring cognitive capabilities in drivers. In our study, we proposed a classifier for basic activities in driving a car, based on a similar approach that could be applied to the recognition of basic activities in daily life, that is, using electrooculographic (EOG) signals and a one-dimensional convolutional neural network (1D CNN). Our classifier achieved an accuracy of 80% for the 16 primary and secondary activities. The accuracy related to activities in driving, including crossroad, parking, roundabout, and secondary activities, was 97.9%, 96.8%, 97.4%, and 99.5%, respectively. The F1 score for secondary driving actions (0.99) was higher than for primary driving activities (0.93–0.94). Furthermore, using the same algorithm, it was possible to distinguish four activities related to activities of daily life that were secondary activities when driving a car.
Fault Diagnosis of Rotating Machinery: A Highly Efficient and Lightweight Framework Based on a Temporal Convolutional Network and Broad Learning System

Efficient fault diagnosis of rotating machinery is essential for the safe operation of equipment in the manufacturing industry. In this study, a robust and lightweight framework consisting of two lightweight temporal convolutional network (LTCN) backbones and a broad learning system with incremental learning (IBLS) classifier called LTCN-IBLS is proposed for the fault diagnosis of rotating machinery. The two LTCN backbones extract the fault’s time–frequency and temporal features with strict time constraints. The features are fused to obtain more comprehensive and advanced fault information and input into the IBLS classifier. The IBLS classifier is employed to identify the faults and exhibits a strong nonlinear mapping ability. The contributions of the framework’s components are analyzed by ablation experiments. The framework’s performance is verified by comparing it with other state-of-the-art models using four evaluation metrics (accuracy, macro-recall (MR), macro-precision (MP), and macro-F1 score (MF)) and the number of trainable parameters on three datasets. Gaussian white noise is introduced into the datasets to evaluate the robustness of the LTCN-IBLS. The results show that our framework provides the highest mean values of the evaluation metrics (accuracy ≥ 0.9158, MP ≥ 0.9235, MR ≥ 0.9158, and MF ≥ 0.9148) and the lowest number of trainable parameters (≤0.0165 Mage), indicating its high effectiveness and strong robustness for fault diagnosis.
An Adaptive Parameterized Domain Mapping Method and Its Application in Wheel–Rail Coupled Fault Diagnosis for Rail Vehicles

The rapid development of cities in recent years has increased the operational pressure of rail vehicles, and due to the characteristics of rail vehicles, including harsh operating environment, frequent starting and braking, resulting in rails and wheels being prone to rail corrugation, polygons, flat scars and other faults. These faults are coupled in actual operation, leading to the deterioration of the wheel–rail contact relationship and causing harm to driving safety. Hence, the accurate detection of wheel–rail coupled faults will improve the safety of rail vehicles’ operation. The dynamic modeling of rail vehicles is carried out to establish the character models of wheel–rail faults including rail corrugation, polygonization and flat scars to explore the coupling relationship and characteristics under variable speed conditions and to obtain the vertical acceleration of the axle box. An APDM time–frequency analysis method is proposed in this paper based on the PDMF adopting Rényi entropy as the evaluation index and employing a WOA to optimize the parameter set. The number of iterations of the WOA adopted in this paper is decreased by 26% and 23%, respectively, compared with PSO and SSA, which means that the WOA performs at faster convergence speed and with a more accurate Rényi entropy value. Additionally, TFR obtained using APDM realizes the localization and extraction of the coupled fault characteristics under rail vehicles’ variable speed working conditions with higher energy concentration and stronger noise resistance corresponding to prominent ability of fault diagnosis. Finally, the effectiveness of the proposed method is verified using simulation and experimental results that prove the engineering application value of the proposed method.
Prospective observational studies on nutrition intake and the incidence of cognitive impairment in middle-aged and older adults: A protocol for systematic review and meta-analysis

Introduction According to several studies, a specific dietary pattern can reduce the risk of dementia and cognitive impairment. However, the robustness of these results has not been tested. The study intends to investigate the association between nutrition intake and cognitive impairment in middle-aged and older adults (≥45-years) and provide reliable, evidence-based references for healthcare decision-makers, researchers, and policymakers. Review question Are the dietary characteristics of community-dwelling adults (≥45-years) associated with the occurrence of cognitive impairment? Objectives The primary objective of this protocol is to synthesize the longitudinal observational evidence on the relationship between nutrition intake patterns and the incidence of cognitive impairment in middle-aged and older adults (≥45-years), and to provide detailed dietary recommendations for the prevention of cognitive impairment in this population. Methods and analysis Cohort studies conducted among adults (≥45-years) will be included. The following electronic databases will be searched for relevant records published by July 2023, with a restriction on language to English: Pubmed, Medline, Embase, Web of science, Cochrane Library. The studies will be selected, the data will be extracted, and the bias risk will be assessed by two independent investigators. The Meta-analysis of Observational Studies in Epidemiology guidelines will be followed to summarize observational studies, and the protocol will adhere to the recommendations from the Preferred Reporting Items for Systematic Review and Meta-Analysis Protocols 2015 statement. Endnote X9 will be used to manage data screening. We will use Review Manager 5.4 and Stata 16.0 to conduct data analysis, and a random-effects model will be applied to pool clinically homogenous studies. The results will be presented based on the form of nutrition intake. For assessing publication bias, Egger’s test and visual inspection of funnel plots will be utilized. Ethics and dissemination As this study does not involve primary data, ethical approval is not required. The final report will be published in a peer-reviewed journal. Prospero registration number A registration number of DOI 10.17605/OSF.IO/NAKC3 was assigned to it on October 15, 2022 on Prospero.
World beliefs, character strengths, and hope for the future

Research in recent years has revealed the rate of premature and avoidable deaths from suicide and drug/alcohol misuse is rising in the United States. These are sometimes referred to as deaths of despair based on evidence that they are concentrated in relatively poor communities with less access to social resources and low labor force participation. The pattern was first noted in middle-aged White men but seems to be gradually spreading to other ethnic groups. As a first step in establishing a psychological response to this public health issue, the present article summarizes two studies that compared psychological variables to demographics as predictors of hopefulness. A number of intriguing findings emerged. Despite concerns about American despair and conflict, U.S. residents proved the most hopeful among residents of eight countries. Low-income Americans are particularly hopeful except for low-income Whites. Positive character traits and primal beliefs about the world generally proved to be better predictors of hope than ethnicity, financial status, or their interaction. A number of relationships were found between psychological variables and community demographics. The findings as a group suggest hopefulness is driven more by psychological variables than by life circumstances. It is suggested that psychologists could play an important role in the study of this topic by implementing programs intended to enhance hopefulness in impoverished populations, and by encouraging an intentional communal focus on the importance of enhancing well-being.
Using Deep Learning Architectures for Detection and Classification of Diabetic Retinopathy

Diabetic retinopathy (DR) is a common complication of long-term diabetes, affecting the human eye and potentially leading to permanent blindness. The early detection of DR is crucial for effective treatment, as symptoms often manifest in later stages. The manual grading of retinal images is time-consuming, prone to errors, and lacks patient-friendliness. In this study, we propose two deep learning (DL) architectures, a hybrid network combining VGG16 and XGBoost Classifier, and the DenseNet 121 network, for DR detection and classification. To evaluate the two DL models, we preprocessed a collection of retinal images obtained from the APTOS 2019 Blindness Detection Kaggle Dataset. This dataset exhibits an imbalanced image class distribution, which we addressed through appropriate balancing techniques. The performance of the considered models was assessed in terms of accuracy. The results showed that the hybrid network achieved an accuracy of 79.50%, while the DenseNet 121 model achieved an accuracy of 97.30%. Furthermore, a comparative analysis with existing methods utilizing the same dataset revealed the superior performance of the DenseNet 121 network. The findings of this study demonstrate the potential of DL architectures for the early detection and classification of DR. The superior performance of the DenseNet 121 model highlights its effectiveness in this domain. The implementation of such automated methods can significantly improve the efficiency and accuracy of DR diagnosis, benefiting both healthcare providers and patients.
Recent Progress in Lithium-Ion Battery Safety Monitoring Based on Fiber Bragg Grating Sensors

Lithium-ion batteries are widely used in a variety of fields due to their high energy density, high power density, long service life, and environmental friendliness. However, safety accidents with lithium-ion batteries occur frequently. The real-time safety monitoring of lithium-ion batteries is particularly important during their use. The fiber Bragg grating (FBG) sensors have some additional advantages over conventional electrochemical sensors, such as low invasiveness, electromagnetic anti-interference, and insulating properties. This paper reviews lithium-ion battery safety monitoring based on FBG sensors. The principles and sensing performance of FBG sensors are described. The single-parameter monitoring and dual-parameter monitoring of lithium-ion batteries based on FBG sensors are reviewed. The current application state of the monitored data in lithium-ion batteries is summarized. We also present a brief overview of the recent developments in FBG sensors used in lithium-ion batteries. Finally, we discuss future trends in lithium-ion battery safety monitoring based on FBG sensors.
Variable Rate Point Cloud Geometry Compression Method

With the development of 3D sensors technology, 3D point cloud is widely used in industrial scenes due to their high accuracy, which promotes the development of point cloud compression technology. Learned point cloud compression has attracted much attention for its excellent rate distortion performance. However, there is a one-to-one correspondence between the model and the compression rate in these methods. To achieve compression at different rates, a large number of models need to be trained, which increases the training time and storage space. To address this problem, a variable rate point cloud compression method is proposed, which enables the adjustment of the compression rate by the hyperparameter in a single model. To address the narrow rate range problem that occurs when the traditional rate distortion loss is jointly optimized for variable rate models, a rate expansion method based on contrastive learning is proposed to expands the bit rate range of the model. To improve the visualization effect of the reconstructed point cloud, a boundary learning method is introduced to improve the classification ability of the boundary points through boundary optimization and enhance the overall model performance. The experimental results show that the proposed method achieves variable rate compression with a large bit rate range while ensuring the model performance. The proposed method outperforms G-PCC, achieving more than 70% BD-Rate against G-PCC, and performs about, as well as the learned methods at high bit rates.
Studies of Excited $D$ mesons in $B$ meson decays

This thesis documents the studies of several three-body B + meson decays, each with a charged charmed meson in the final state. All analyses presented use a data sample recorded by the LHCb detector in 2011 and 2012, corresponding to an integrated luminosity of 3.0 $fb^{-1}$ of $pp$ collision data. The $B^{+} \to D^{-}K^{+}\pi^{+}$ and $B^{+} \to D^{+}K^{+}\pi^{-}$ decay modes are observed for the first time. The branching fraction of the favoured $B^{+} \to D^{-}K^{+}\pi^{+}$ decay mode is measured relative to the topologically similar $B^{+} \to D^{-}\pi^{+}\pi^{+}$ decay and the $B^{+} \to D^{-}K^{+}\pi^{+}$ final state is used as a normalisation channel for the suppressed $B^{+} \to D^{+}K^{+}\pi^{-}$ decay branching fraction measurement. Searches are performed for the quasi-two-body decays $B^{+} \to D^{+}K^{*}(892)^{0}$ and $B^{+} \to D_{2}^{*}(2460)^{0}K^{+}$, using the sample of $B^{+} \to D^{+}K^{+}\pi^{-}$ candidate decays. No significant signals are observed for either decay mode and upper limits are set on their branching fractions. Excited charmed mesons decaying to $D^{-}\pi^{+}$ are studied by performing fits to the Dalitz plots of $B^{+} \to D^{-}K^{+}\pi^{+}$ and $B^{+} \to D^{-}\pi^{+}\pi^{+}$ candidates. A structure at $m(D^{-}\pi^{+}) \sim 2.78$ GeV/$c^{2}$ in the $D^{-}K^{+}\pi^{+}$ data sample is determined to have spin-1 for the first time. Evidence from the $B^{+} \to D^{-}\pi^{+}\pi^{+}$ Dalitz plot suggests that the states at $m(D^{-}\pi^{+}) \sim 2.76$ GeV/$c^{2}$ and 3.00 GeV/$c^{2}$, have spin-3 and spin-2, respectively. The mass and width parameters of these states are measured precisely, as are those of the well known $D^{*}_{2}(2460)^{0}$ resonance. Fit fractions and product branching fractions are calculated for all contributions in the $B^{+} \to D^{-}K^{+}\pi^{+}$ and $B^{+} \to D^{-}\pi^{+}\pi^{+}$ amplitude models.