Miracle-dz/UniFAR-FTUs huggingface dataset:
Graphene-Sandwiched Van der Waals Heterostructures for Photodetectors

Next generation photodetectors are often considered to be constructed by two-dimensional (2D) materials. Preparing Van der Waals (vdW) heterostructures with stacked 2D material layers are becoming a novel method for the creation of new type of photodetectors. In this work, we designed and studied trilayer vdW heterostructures black-phosphorus (BP)/graphene/MoS2 and blue-phosphorus (BlueP)/graphene/MoS2, in which graphene is sandwiched in between two different type of 2D materials. Using the first-principles calculation, the structural, electronic, and optical characteristic of the above heterostructures were investigated in detail. It is found that these two trilayer heterostructures are dynamically stable, suggesting that they can be practically prepared. Most monolayers in these heterostructures maintain their own good characteristics, which is beneficial to optimize the responsivity of 2D photodetectors. BP/graphene/MoS2 opens the graphene bandgap effectively to be ~ 0.051 eV, appearing much larger than usual heterostructures and associated probably with build-in electric fields, and suggesting more application prospects in photodetectors. The semiconductor-characteristic quantum-well-like band alignment varies to match different application requirements. Together with the improved light absorption in the range of long-wave to short-wave infrared band, visible to ultraviolet band and solar-blind violet band, the proposed trilayer vdW heterostructures may serve as more prospective candidates for graphene-based 2D photodetectors.
Strategic Issues: A Systematic Review of the Literature

Although the strategic issue (SI) construct has been used since the 1970s as a theoretical framework to investigate and explain several phenomena in organizational and strategic management, few reviews on SIs have been carried out so far. This study aims to fill this gap. A systematic review of the literature was conducted, comprising 77 empirical and theoretical papers published in peer-reviewed academic journals since 1975. The analysis of this sample of papers led to the identification of five themes or perspective on SIs, which are discussed. This review contributes to the organizational and strategic management literature by identifying lines of inquiry, convergences
Metabolite Variations in the Hippocampus and Corpus Callosum of Patients with Mild Cognitive Impairment Using Magnetic Resonance Spectroscopy with Three-Dimensional Chemical Shift Images

This study compared the metabolites in the brain regions of hippocampus and corpus callosum between patients with mild cognitive impairment (MCI) and healthy controls using no-radiation and high-sensitivity magnetic resonance spectroscopy (MRS) with three-dimensional chemical shift images (3D-CSI). Twenty volunteers (seven patients with MCI and 13 healthy controls) aged 50–71 years were recruited for this prospective study. MRS with 3D-CSI images of a variety of metabolites was collected from the hippocampus and corpus callosum. Sex and weight showed no significant differences between the two groups. The metabolite levels in the hippocampus and corpus callosum of the MCI group were generally lower than in those of the healthy group, especially for creatine (p < 0.001 in the hippocampus and p = 0.020 in the corpus callosum) and N-acetyl aspartate/creatine (p < 0.001 in the hippocampus and p = 0.020 in the corpus callosum); however, choline/creatine showed a significant difference (p < 0.001) only in the hippocampus, and myo-inositol/creatine showed a significant difference (p < 0.001) only in the corpus callosum. Our study demonstrated that MRS with 3D-CSI can be used to measure these metabolite levels to determine the differences between patients with MCI and healthy individuals. This would aid early diagnosis of MCI in clinical practice, and patients could receive prompt intervention to improve their quality of life.
NeSDeepNet: A Fusion Framework for Multi-step Forecasting of Near-surface Air Pollutants

Air pollution is a global issue that poses significant threats to human health and the environment due to industrial development. Forecasting the concentrations of major pollutants such as NO2 and CO can provide early warnings of harmful substances, minimizing health risks and losses. Recent deep learning models have shown promise in air quality prediction, but they have limitations such as insufficient feature representation, high computational costs, and poor generalization. This paper proposes a near-surface deep network (NeSDeepNet) to overcome these limitations. The NeSDeepNet integrates multiple deep learning models and a shallow model to form a hybrid forecasting system. The proposed framework consists of three modules: a preliminary extraction module, a deep extraction module, and a fusion module. The feature extraction module uses a multi-layer network to extract features from air pollutant and meteorological data, and each of which predicts air pollutants for different forecasting horizons. The fusion module combines the outputs of the deep learning module and the shallow models to produce the final prediction results. The proposed framework is evaluated on a real-world dataset, and the experimental results demonstrate that NeSDeepNet achieves optimal RMSE value of 9.59 for NO2 and 274.0 for CO, MAE value of 2.64 for NO2 and 13.75 for CO, and R2 values 0.89 for NO2 and 0.93 for CO, respectively, outperforming cutting-edge deep learning models. Therefore, NeSDeepNet can be a valuable tool for air quality forecasting and miti-gating the adverse effects of air pollution on human health and the environment. The source code for our proposed NeSDeepNet and comparative models is available on GitHub repository: https://github.com/Prasanjit-Dey/NeSDeepNet.
3D Porous VOx/N-Doped Carbon Nanosheet Hybrids Derived from Cross-Linked Dicyandiamide–Chitosan Hydrogels for Superior Supercapacitor Electrode Materials

Three-dimensional porous carbon materials with moderate heteroatom-doping have been extensively investigated as promising electrode materials for energy storage. In this study, we fabricated a 3D cross-linked chitosan-dicyandiamide-VOSO4 hydrogel using a polymerization process. After pyrolysis at high temperature, 3D porous VOx/N-doped carbon nanosheet hybrids (3D VNCN) were obtained. The unique 3D porous skeleton, abundant doping elements, and presence of VOx 3D VNCN pyrolyzed at 800 °C (3D VNCN-800) ensured excellent electrochemical performance. The 3D VNCN-800 electrode exhibits a maximum specific capacitance of 408.1 F·g−1 at 1 A·g−1 current density and an admirable cycling stability with 96.8% capacitance retention after 5000 cycles. Moreover, an assembled symmetrical supercapacitor based on the 3D VNCN-800 electrode delivers a maximum energy density of 15.6 Wh·Kg−1 at a power density of 600 W·Kg−1. Our study demonstrates a potential guideline for the fabrication of porous carbon materials with 3D structure and abundant heteroatom-doping.
Characterization of Impact Breccias from the Chicxulub Impact Basin: Implications for Excavation and Ejecta Emplacement

Figure 1. The three wells discussed here are shown as U5, U6, and U7. See [1] for other details. CHARACTERIZATION OF IMPACT BRECCIAS FROM THE CHICXULUB IMPACT BASIN: IMPLICATIONS FOR EXCAVATION AND EJECTA EMPLACEMENT. V. L. Sharpton, C. M. Corrigan, L. E. Marín, J. Urrutia-Fucugauchi, and T. A. Vogel, Geophysical Institute and Department of Geology and Geophysics, University of Alaska, 903 Koyukuk Dr., Fairbanks, AK, 99775-7320 (buck.sharpton@gi.alaska.edu) Affiliation (include full mailing address and e-mail address if desired) for first author, Department of Geological Sciences, Michigan State University, East Lansing, MI 48824, Instituto de Geofísica, Universidad Nacional Autónoma de Mexico, Ciudad Universitaria, Mexico City 04510, Mexico.
Multi-Track Music Generation Network Based on a Hybrid Learning Module

Generative models has been widely used for symbolic music generation. However, the quality of the music produced is hindered by the inadequate modeling of the harmonic and rhythmic relationships among various instruments, as well as the neglect of musical principles. To address the aforementioned challenges, we propose an attention-based hybrid learning multitrack music generation(HL-MTMG) model that incorporates regularization of musical rules and simultaneous exploitation between intra-track and inter-track information, resulting in the generation of high-quality symbolic music that conforms to established musical principles. Through extensive examinations of musical structures, specifically rhythm, and assessments by domain experts, our study demonstrates that HL-MTMG exhibits significant improvements in musical integrity and fluency in comparison to symbolic music generation models, such as, MuseGAN and HRNN.
Estimates of carbon stored in harvested wood products from United States Forest Service's Sierra Nevada Bio-Regional Assessment Area of the Pacific Southwest Region, 1909-2012

Global forests capture and store significant amounts of carbon through photosynthesis. When carbon is removed from forests through harvest, a portion of the harvested carbon is stored in wood products, often for many decades. The United States Forest Service (USFS) and other agencies are interested in accurately accounting for carbon flux associated with harvested wood products (HWP) to meet greenhouse gas monitoring commitments and climate change adaptation and mitigation objectives. National-level forest carbon accounting has been in place for over a decade, but there is an increasing need for accounting for smaller scale administrative units, including USFS National Forest System regions and individual National Forests. This paper uses the Intergovernmental Panel on Climate Change (IPCC) production accounting approach to estimate HWP carbon storage from 1909 to 2012 for the USFS Sierra Nevada Bio-Regional Assessment Area (Assessment Area) of the Pacific Southwest Region. For the Assessment Area as a whole, carbon stocks in the HWP pool were increasing at just above 0.5 million megagrams of carbon (MgC) per year beginning in the late 1940's until the early 1990's, with peak cumulative storage to date of 32 million MgC occurring in 1999. Net positive flux into the HWP pool over this period is primarily attributable to high harvest levels during the 1960's through 1980's. In the years between the late 1960s and 1990 timber harvest were at high but volatile levels, with high harvests of over 2.5 million ccf (1.8 million MgC) occurring six times during this period, harvest levels from National Forests have since declined to less than 0.7 million ccf (0.5 million MgC) per year, resulting in less carbon entering the HWP pool. Since 2000, emissions from HWP at solid waste disposal sites exceeded additions from harvesting, resulting in a decline in the total amount of carbon stored in the HWP pool. The Assessment Area's HWP pool is now in a period of negative net annual stock change because the decay of products harvested between 1909 and 2012 exceeds additions of carbon to the HWP pool through harvest. Together with estimates of ecosystem carbon, which are also being developed through the Forest Management Carbon Framework (ForCaMF), Regional level estimates of HWP carbon flux can be used to inform management decisions and guide climate change adaptation and mitigation efforts by the agency. Though our emphasis is on the Assessment Area, this accounting method can be applied more broadly at smaller land management units, such as National Forests.
Comparison of the Three-Dimensional Accuracy of Guided Apicoectomy Performed with a Drill or a Trephine: An In Vitro Study

Guided apicoectomy performed with 3D-printed polymer-based static surgical guides is an emerging trend in endodontic surgery. Static-guided apicoectomy is carried out with either a drill or a trephine. The aim of this in vitro study was to assess the accuracy of osteotomy and apicoectomy performed through a polymer guide, with both drill and trephine, and to compare the accuracy achievable with the two instruments. Six plaster models of a maxilla master model with extracted human maxillary teeth in polymethyl-methacrylate resin were used. The modeled osteotomies were performed in these. The master model was CBCT-scanned, and digital surgical plans were prepared, based on which the surgical guides were printed. The plans contained both drill and trephine apicoectomies. Digital three-dimensional position analysis was performed with dedicated algorithms. A total of 39 drill and 47 trephine osteotomies were analyzed. A statistically significant difference between the two instruments was found only in the global deviation of the distal endpoint, indicating lower deviation for the trephine procedure (1.53 mm vs. 1.31 mm, p = 0.038). Nevertheless, from a clinical perspective, this distinction is inconsequential. The results suggest that, for all practical purposes, the two approaches to apicoectomy allow the same level of accuracy.
A Real-Time Chinese Food Auto Billing System Based on Instance Segmentation

Recently, food segmentation has been a hot topic in both academia and industry. Solutions for western food segmentation have been proposed and the performance is promising which meets the requirements in scenarios like diet managements and calorie estimation. Inspired by these achievements, we decide to design a Chinese food Price automatic billing system based on instance segmentation methods. However, how to segment Chinese food remains a challenge due to the wide variety of ingredients and cook styles. It's impossible to collect sufficient images to train a segmentation model to detect all kinds of potential Chinese food. To overcome these issues, rather than detecting each singular dish, we reformulate the task by segmenting a set of selected plates with Chinese food, we propose a FoodSyn module, which synthesis images by cropping food part in UECFoodPIX and pasting them on plates images. The generated images are then fed into encoder-decoder network to segment instances for training. Extensive experiments show that our proposed approach performs well in practical scenarios with mIoU over 950/0. The fps is over 20 when deployed on OnePlus 9 Pro. Codes will be released once the paper is accepted.
Using a Paper Carrier to Screen Print on Polyurethane Film and Fabricate ECG Probes

Flexible, printed and skin-mounted sensors are increasingly used to support long-term monitoring of vital signs. One challenge in printing on-skin devices is in identifying substrates which both match the properties of the epidermis and can tolerate the temperatures required to cure ink. This work describes a method for screen printing on polyurethane (PU) films using a paper ‘carrier’ during the curing process. We printed Ag/AgCI on $\mathbf{Tegaderm}^{\mathbf{TM}}$ film (3M), demonstrating that carrier bound prints do not shrink or lose conductivity when exposed to temperatures as high as $220\ ^{\circ}\mathrm{C}$. We also printed capacitive electrocardiography sensors on Tegaderm, where the electrode connects to the skin with a layer of adhesive which acts as a dielectric. These probes were able to acquire signals with discernible waveform features (P-Q-R-S-T), exhibiting an signal-to-noise ratio of 32.8 dB and a skin-contact impedance at 30 Hz of 648 $\pm 278\Omega$ (standard deviation).
Poster: Conceptual Design for FPGA Based Artifical Intelligence Model for HIL Applications

Hardware-in-the-Loop (HIL) simulators play a critical role in the automotive industry by providing extensive testing and validation capabilities for electronic control units (ECUs). One of the main challenges faced by HIL simulators involves the task of constructing a virtual environment that accurately replicate the behavior of the actual system. Artificial intelligence (AI) algorithms can be useful in generating precise virtual environments for HIL simulations of complex systems. Moreover, minimal latency is essential for establishing a reliable virtual environment. FPGA (Field Programmable Gate Array) can effectively reduce latency in HIL simulations by providing high-performance computing resources. This paper aims to address these challenge by introducing a machine learning-driven HIL simulator implemented on FPGA. The proposed architecture employs FPGA technology to enhance the computational speed of a temporal convolutional neural network (TCN).
Modification of the SLAM Algorithm Based on Comparative Analysis of the Algorithm for Extracting Features From Images

The SLAM algorithms used on inexpensive compact boards in many standalone mobile devices are not fast enough. To solve this problem, we modified the RTAB-Map method in order to increase its speed without losing the quality of the algorithm itself. One way to improve the speed of the algorithm is to replace the SIFT keypoint extraction algorithm. In this article, we made a comparative analysis of methods for identifying local features from images.
Role of Bacteria-Derived Flavins in Plant Growth Promotion and Phytochemical Accumulation in Leafy Vegetables

Sinorhizobium meliloti 1021 bacteria secretes a considerable amount of flavins (FLs) and can form a nitrogen-fixing symbiosis with legumes. This strain is also associated with non-legume plants. However, its role in plant growth promotion (PGP) of non-legumes is not well understood. The present study evaluated the growth and development of lettuce (Lactuca sativa) and kale (Brassica oleracea var. acephala) plants inoculated with S. meliloti 1021 (FL+) and its mutant 1021ΔribBA, with a limited ability to secrete FLs (FL−). The results from this study indicated that inoculation with 1021 significantly (p < 0.05) increased the lengths and surface areas of the roots and hypocotyls of the seedlings compared to 1021ΔribBA. The kale and lettuce seedlings recorded 19% and 14% increases in total root length, respectively, following inoculation with 1021 compared to 1021ΔribBA. A greenhouse study showed that plant growth, photosynthetic rate, and yield were improved by 1021 inoculation. Moreover, chlorophylls a and b, and total carotenoids were more significantly (p < 0.05) increased in kale plants associated with 1021 than non-inoculated plants. In kale, total phenolics and flavonoids were significantly (p < 0.05) increased by 6% and 23%, respectively, and in lettuce, the increments were 102% and 57%, respectively, following 1021 inoculation. Overall, bacterial-derived FLs enhanced kale and lettuce plant growth, physiological indices, and yield. Future investigation will use proteomic approaches combined with plant physiological responses to better understand host-plant responses to bacteria-derived FLs.
Application of the Apriori Algorithm for Traffic Crash Analysis in Thailand

Accidents pose significant obstacles to economic progress and quality of life, especially in developing countries. Thailand faces such challenges and this research seeks to assess the frequency and most common causes of road accidents that lead to fatalities. This study employed the Apriori algorithm to examine the interrelationships among factors contributing to accidents in order to inform policymaking for reducing accident rates, minimizing economic and human losses, and enhancing the effectiveness of the healthcare system. By analyzing road accident data from 2015 to 2020 in Thailand (167,820 accidents causing THB 1.13 billion in damages), this article specifically focuses on the drivers responsible for fatal highway accidents. The findings reveal several interconnected variables that heighten the likelihood of fatalities, such as male gender, exceeding speed limits, riding a motorbike, traveling on straight roads, encountering dry surface conditions, and clear weather. An association rule analysis underscores the increased risk of injury or death in traffic accidents.
Application of Selenium Can Alleviate the Stress of Cadmium on Rapeseed at Different Growth Stages in Soil

Cadmium (Cd) stress greatly limits the growth of rapeseed, and selenium is a micronutrient that is essential for rapeseed growth, but whether and how selenium application alleviates Cd-induced inhibition remains poorly understood. The present study investigated the alleviating effects of exogenous selenium on rapeseed growth under cadmium (Cd) stress based on the aspects of agronomic traits and soil bacterial community structure and diversity. The results show that low-selenium treatment increased the rapeseed yield by 20.92% by increasing the number of pods per plant under Cd stress, but such effects were not prominent when the selenium application rate was high. Meanwhile, selenium application significantly reduced the cadmium content by 4.74–26.89% in different organs of rapeseed. Further analysis suggested that the benefits of selenium in alleviating Cd stress might be induced by changes in soil bacterial community structure and diversity. In addition, in the functional metabolism spectrum of rapeseed microorganisms treated with selenium, there were 5 primary metabolic pathways with significant differences, and there were 32 and 169 pathways for secondary and tertiary metabolic pathways, respectively. Therefore, selenium treatment in rapeseed soil can alter the composition and metabolic function spectrum of soil microbial communities, ultimately affecting plant growth and Cd tolerance.
Real-Time APT Detection Technologies: A Literature Review

Recently, the usage of advanced persistent threats (APT) increased rapidly in the context of cyberwar. To perform countermeasures against such attacks, an efficient APT detection is necessary. Detecting these attacks in real-time reduces the resulting damage since countermeasures can be applied more quickly. However, not every detection method is applicable in real-time. This paper presents a literature review of technologies used for real-time APT detection based on 26 research articles. The identified technologies are machine learning algorithms, graph inferences, statistical metrics, and rule-based systems.
Discrimination Between Child and Adult Forms Using Radar Frequency Signature Analysis

Abstract : In this thesis we develop a method to discriminate between adult and child radar signatures. In particular, we examine radar data measured from behind a wall, which introduces radar signal attenuation and multipath effects. To investigate the child/adult discrimination problem in a through-wall, multipath scenario, a previously developed free-space human scattering model was expanded to incorporate multiple paths, and the effects of transmission through, and reflections from, walls and ground. The ground was modeled as a perfectly reflecting surface, while the walls were modeled as homogeneous concrete slabs. Twenty-five reflection paths were identified, involving the direct paths, as well as reflected paths between the ground and an adjacent wall. All paths included two-way transmission through an obstructing wall.
Industrial Fire Detector Using Colour Detection and Tracking

Conventional fire detection systems use physical sensors to detect fire. Chemical properties of particles in the air are acquired by sensors and are used by conventional fire detection systems to raise an alarm. However, this can also cause false alarms; for example, a person smoking in a room may trigger a typical fire alarm system. In order to manage false alarms of conventional fire detection systems, a computer vision-based fire detection algorithm is proposed in this paper. The proposed fire detection algorithm consists of two main parts: fire color modeling and motion detection. The algorithm can be used in parallel with conventional fire detection systems to reduce false alarms. It can also be deployed as a stand-alone system to detect fire by using video frames acquired through a video acquisition system.