Latest Research Trends in Fall Detection and Prevention Using Machine Learning: A Systematic Review.

Falls are unusual actions that cause a significant health risk among older people. The growing percentage of people of old age requires urgent development of fall detection and prevention systems. The emerging technology focuses on developing such systems to improve quality of life, especially for the elderly. A fall prevention system tries to predict and reduce the risk of falls. In contrast, a fall detection system observes the fall and generates a help notification to minimize the consequences of falls. A plethora of technical and review papers exist in the literature with a primary focus on fall detection. Similarly, several studies are relatively old, with a focus on wearables only, and use statistical and threshold-based approaches with a high false alarm rate. Therefore, this paper presents the latest research trends in fall detection and prevention systems using Machine Learning (ML) algorithms. It uses recent studies and analyzes datasets, age groups, ML algorithms, sensors, and location. Additionally, it provides a detailed discussion of the current trends of fall detection and prevention systems with possible future directions. This overview can help researchers understand the current systems and propose new methodologies by improving the highlighted issues.

A Systematic Review of Location Aware Schemes in the Internet of Things.

The rapid development in wireless technologies is positioning the Internet of Things (IoT) as an essential part of our daily lives. Localization is one of the most attractive applications related to IoT. In the past few years, localization has been gaining attention because of its applicability in safety, health monitoring, environment monitoring, and security. As a result, various localization-based wireless frameworks are being presented to improve such applications' performances based on specific key performance indicators (KPIs). Therefore, this paper explores the recently proposed localization schemes in IoT. Initially, this paper explains the major KPIs of localization. After that, a thorough comparison of recently proposed localization schemes based on the KPIs is presented. The comparison includes an overview, architecture, network structure, performance parameters, and target KPIs. At the end, possible future directions are presented for the researchers working in this domain.

Artificial intelligence based real-time microcirculation analysis system for laparoscopic colorectal surgery.

BACKGROUND: Colonic perfusion status can be assessed easily by indocyanine green (ICG) angiography to predict ischemia related anastomotic complications during laparoscopic colorectal surgery. Recently, various parameter-based perfusion analysis have been studied for quantitative evaluation, but the analysis results differ depending on the use of quantitative parameters due to differences in vascular anatomical structure. Therefore, it can help improve the accuracy and consistency by artificial intelligence (AI) based real-time analysis microperfusion (AIRAM). AIM: To evaluate the feasibility of AIRAM to predict the risk of anastomotic complication in the patient with laparoscopic colorectal cancer surgery. METHODS: The ICG curve was extracted from the region of interest (ROI) set in the ICG fluorescence video of the laparoscopic colorectal surgery. Pre-processing was performed to reduce AI performance degradation caused by external environment such as background, light source reflection, and camera shaking using MATLAB 2019 on an I7-8700k Intel central processing unit (CPU) PC. AI learning and evaluation were performed by dividing into a training patient group (n = 50) and a test patient group (n = 15). Training ICG curve data sets were classified and machine learned into 25 ICG curve patterns using a self-organizing map (SOM) network. The predictive reliability of anastomotic complications in a trained SOM network is verified using test set. RESULTS: AI-based risk and the conventional quantitative parameters including T 1/2max , time ratio (TR), and rising slope (RS) were consistent when colonic perfusion was favorable as steep increasing ICG curve pattern. When the ICG graph pattern showed stepped rise, the accuracy of conventional quantitative parameters decreased, but the AI-based classification maintained accuracy consistently. The receiver operating characteristic curves for conventional parameters and AI-based classification were comparable for predicting the anastomotic complication risks. Statistical performance verifications were improved in the AI-based analysis. AI analysis was evaluated as the most accurate parameter to predict the risk of anastomotic complications. The F1 score of the AI-based method increased by 31% for T 1/2max , 8% for TR, and 8% for RS. The processing time of AIRAM was measured as 48.03 s, which was suitable for real-time processing. CONCLUSION: In conclusion, AI-based real-time microcirculation analysis had more accurate and consistent performance than the conventional parameter-based method.

Observation of variable hybridized-band gaps in Eu-intercalated graphene.

We report europium (Eu)-induced changes in the π-band of graphene (G) formed on the 6H-SiC(0001) surface by a combined study of photoemission measurements and density functional theory (DFT) calculations. Our photoemission data reveal that Eu intercalates upon annealing at 120 °C into the region between the graphene and the buffer layer (BL) to form a G/Eu/BL system, where a band gap of 0.29 eV opens at room temperature. This band gap is found to increase further to 0.48 eV upon cooling down to 60 K. Our DFT calculations suggest that the increased band gap originates from the enhanced hybridization of the graphene π-band with the Eu 4f band due to the increased magnetic ordering upon cooling. These Eu atoms continue to intercalate further down below the BL to produce bilayer graphene (G/BL/Eu) upon annealing at 300 °C. The π-band stemming from the BL then exhibits another band gap of 0.37 eV, which appears to be due to the strong hybridization between the π-band of the BL and the Eu 4f band. The Eu-intercalated graphene thus illustrates an example of versatile band gaps formed under different thermal treatments, which may play a critical role for future applications in graphene-based electronics.

Modification of thermal and electronic properties of bilayer graphene by using slow Na+ ions.

Bilayer graphene (BLG) has an extensive list of industrial applications in graphene-based nanodevices such as energy storage devices, flexible displays, and thermoelectric devices. By doping slow Na+ ions on Li-intercalated BLG, we find significantly improved thermal and electronic properties of BLG by using angle-resolved photoemission and high-resolution core level spectroscopy (HRCLS) with synchrotron photons. Our HRCLS data reveal that the adsorbed Na+ ions on a BLG produced by Li-intercalation through single layer graphene (SLG) spontaneously intercalate below the BLG, and substitute Li atoms to form Na-Si bonds at the SiC interface while preserving the same phase of BLG. This is in sharp contrast with no intercalation of Na+ ions on SLG though neutral Na atoms intercalate. The Na+-induced BLG is found to be stable upon heating up to T = 400 °C, but returns to SLG when heated at T d = 500 °C. The evolution of the π-bands upon doping the Na+ ions followed by thermal annealing shows that the carrier concentration of the π-band may be artificially controlled without damaging the Dirac nature of the π-electrons. The doubled desorption temperature from that (T d = 250 °C) of the Na-intercalated SLG together with the electronic stability of the Na+-intercalated BLG may find more practical and effective applications in advancing graphene-based thermoelectric devices and anode materials for rechargeable batteries.

Band gap engineering for single-layer graphene by using slow Li(+) ions.

In order to utilize the superb electronic properties of graphene in future electronic nano-devices, a dependable means of controlling the transport properties of its Dirac electrons has to be devised by forming a tunable band gap. We report on the ion-induced modification of the electronic properties of single-layer graphene (SLG) grown on a SiC(0001) substrate by doping low-energy (5 eV) Li(+) ions. We find the opening of a sizable and tunable band gap up to 0.85 eV, which depends on the Li(+) ion dose as well as the following thermal treatment, and is the largest band gap in the π-band of SLG by any means reported so far. Our Li 1s core-level data together with the valence band suggest that Li(+) ions do not intercalate below the topmost graphene layer, but cause a significant charge asymmetry between the carbon sublattices of SLG to drive the opening of the band gap. We thus provide a route to producing a tunable graphene band gap by doping Li(+) ions, which may play a pivotal role in the utilization of graphene in future graphene-based electronic nano-devices.

Topological modification of the electronic structure by Bi-bilayers lying deep inside bulk Bi2Se3.

We observe the modified surface states of an epitaxial thin film of a homologous series of (Bi2)m(Bi2Se3)n, as a topological insulator (TI), by angle-resolved photoemission spectroscopy measurements. A thin film with m : n = 1 : 3 (Bi8Se9) has been grown with Bi2 bilayers embedded every other three quintuple layers (QLs) of Bi2Se3. Despite the reduced dimension of continuous QLs due to the Bi2 heterolayers, we find that the topological surface states stem from the inverted Bi and Se states and the topologically nontrivial structures are mainly based on the prototype of 3D TI Bi2Se3 without affecting the overall topological order.

Proximity Effect Induced Electronic Properties of Graphene on Bi2Te2Se.

We report that the π-electrons of graphene can be spin-polarized to create a phase with a significant spin-orbit gap at the Dirac point (DP) using a graphene-interfaced topological insulator hybrid material. We have grown epitaxial Bi2Te2Se (BTS) films on a chemical vapor deposition (CVD) graphene. We observe two linear surface bands from both the CVD graphene notably flattened and BTS coexisting with their DPs separated by 0.53 eV in the photoemission data measured with synchrotron photons. We further demonstrate that the separation between the two DPs, Δ(D-D), can be artificially fine-tuned by adjusting the amount of Cs atoms adsorbed on the graphene to a value as small as Δ(D-D) = 0.12 eV to find any proximity effect induced by the DPs. Our density functional theory calculation shows the opening of a spin-orbit gap of ∼20 meV in the π-band, enhanced by 3 orders of magnitude from that of a pristine graphene, and a concomitant phase transition from a semimetallic to a quantum spin Hall phase when Δ(D-D) ≤ 0.20 eV. We thus present a practical means of spin-polarizing the π-band of graphene, which can be pivotal to advance graphene-based spintronics.

Bright field microscopic cells counting method for BEVS using nonlinear convergence index sliding band filter.

BACKGROUND: The Baculovirus Expression Vector System (BEVS) is a very popular expression vector system in gene engineering. An effective host cell line cultivation protocol can facilitate the baculovirus preparation and following experiments. However, the counting of the number of host cells in the protocol is usually performed by manual observation with microscopy, which is time consuming and labor intensive work, and prone to errors for one person or between different individuals. This study aims at giving a bright field insect cells counting protocol to help improve the efficient of BEVS. METHOD: To develop a reliable and accurate counting method for the host cells in the bright field, such as Sf9 insect cells, a novel method based on a nonlinear Transformed Sliding Band Filter (TSBF) was proposed. And 3 collaborators counted cells at the same time to produce the ground truth for evaluation. The performance of TSBF method was evaluated with the image datasets of Sf9 insect cells according to the different periods of cell cultivation on the cell density, error rate and growth curve. RESULTS: The average error rate of our TSBF method is 2.21% on average, ranging from 0.89% to 3.97%, which exhibited an excellent performance with its high accuracy in lower error rate compared with traditional methods and manual counting. And the growth curve was much the manual method well. CONCLUSION: Results suggest the proposed TSBF method can detect insect cells with low error rate, and it is suitable for the counting task in BEVS to take the place of manual counting by humans. Growth curve results can reflect the cells' growth manner, which was generated by our proposed TSBF method in this paper can reflected the similar manner with it's from the manual method. All of these proven that the proposed insect cell counting method can clearly improve the efficiency of BEVS.

In vitro apoptotic effects of methanol extracts of Dianthus chinensis and Acalypha australis L. targeting specificity protein 1 in human oral cancer cells.

BACKGROUND: The aims of this study were to evaluate the apoptotic activities and molecular mechanisms of methanol extracts of Dianthus chinensis (MEDC) and Acalypha australis L. (MEAL) in human oral cancer cells. METHODS: The apoptotic effects and related molecular mechanisms of MEDC and MEAL on oral cancer cells were evaluated using MTS assay, DAPI staining, immunostaining, Western blotting, and reverse transcriptase-polymerase chain reaction. RESULTS: Sp1 was overexpressed in oral tumor tissues compared with normal oral mucosa. Downregulation of Sp1 inhibited the growth of SCC-15 and YD-15 oral cancer cells. MEDC and MEAL inhibited cell growth and induced apoptosis in both cell lines by decreasing the expression of Sp1. In addition, treatment of cells with MEDC and MEAL decreased Mcl-1 expression, which is a downstream target of Sp1. CONCLUSION: Our results indicate that MEDC and MEAL are bioactive natural products that can potentially induce apoptosis of tumor cells that overexpress the Sp1 protein.