Search

Smishing (SMS phishing) is a cybercrime in which criminals send fraudulent messages, including malicious links, to steal the victims’ private data or cause financial losses. The damage caused by smishing has become more severe, particularly with the proliferation of mobile devices. In smishing, a major difficulty faced by victims is discrimination between normal and smishing messages. To resolve this problem, we present an on-device smishing classifier based on a deep-learning model. In real-world scenarios, access to a substantial, authentic dataset is crucial. We trained and evaluated the classifier using real SMS datasets containing approximately 250,000 smishing messages and 950,000 normal messages obtained from victims in Korea. To ensure privacy, the classifier operates solely on mobile devices without externally transmitting any data. It utilizes a lightweight method that does not require significant computing power on mobile devices. We explored several models to determine a suitable model for mobile devices and optimized it using real datasets. Furthermore, our statistical analysis of actual smishing messages revealed that 98% of smishing messages are variants of previously sent messages. To address the prevalence of variant smishing messages, we propose a text evasion attack tool called EVA that is capable of generating pseudo-variant messages from a given message using an adversarial attack approach. We used this tool to evaluate and enhance the robustness of our classifier against various messages. Our classifier exhibited exceptional classification accuracy (0.99) while being lightweight (at 127 kB) and robust against variant smishing messages (attack success rate of 0.41).

In 2015, the U.S National Institute of Standards and Technology (NIST) funded the Center of Excellence for Risk-Based Community Resilience Planning (CoE), a fourteen university-based consortium of almost 100 collaborators, including faculty, students, post-doctoral scholars, and NIST researchers. This paper highlights the scientific theory behind the state-of-the-art cloud platform being developed by the CoE - the Interdisciplinary Networked Community Resilience Modeling Environment (IN-CORE). IN-CORE enables communities, consultants, and researchers to set up complex interdependent models of an entire community consisting of people, businesses, social institutions, buildings, transportation networks, water networks, and electric power networks and to predict their performance and recovery to hazard scenario events, including uncertainty propagation through the chained models. The modeling environment includes a detailed building inventory, hazard scenario models, building and infrastructure damage (fragility) and recovery functions, social science data-driven household and business models, and computable general equilibrium (CGE) models of local economies. An important aspect of IN-CORE is the characterization of uncertainty and its propagation throughout the chained models of the platform.Three illustrative examples of community testbeds are presented that look at hazard impacts and recovery on population, economics, physical services, and social services. An overview of the IN-CORE technology and scientific implementation is described with a focus on four key community stability areas (CSA) that encompass an array of community resilience metrics (CRM) and support community resilience informed decision-making. Each testbed within IN-CORE has been developed by a team of engineers, social scientists, urban planners, and economists. Community models, begin with a community description, i.e., people, businesses, buildings, infrastructure, and progresses to the damage and loss of functions caused by a hazard scenario, i.e., a flood, tornado, hurricane, or earthquake. This process is accomplished through chaining of modular algorithms, as described. The baseline community characteristics and the hazard-induced damage sets are the initial conditions for the recovery models, which have been the least studied area of community resilience but arguably one of the most important. Communities can then test the effect of mitigation and/or policies and compare the effects of “what if” scenarios on physical, social, and economic metrics with the only requirement being that the change much be able to be numerically modeled in IN-CORE.

Climate change has increased variable weather patterns that affect plants. To address these issues, we developed a microbial biocontrol agent against drought stress in kimchi cabbage (Brassica rapa L. subsp. pekinensis). We selected three bacterial strains (Leifsonia sp. CS9, Bacillus toyonensis TSJ7, and Lysinibacillus capsici TT41) because they showed a survival rate of up to 50% and good growth rate when treated with 30% PEG 6000. The three strains were treated with kimchi cabbage to confirm their enhanced drought stress resistance under non-watering conditions. Among the three strains, the TT41 treated group showed a significant increase in various plant parameters compared with the negative control on the 7th day. We performed extensive profiling of primary and secondary metabolites from kimchi cabbage and the TT41 strain. Multivariate and pathway analyses revealed that only the TT41 group clustered with the well-watered group and showed almost the same metabolome on the 7th day. When treated with TT41, lactic acid was identified as an indicator metabolite that significantly improved drought stress tolerance. Furthermore, lactic acid treatment effectively induced drought stress tolerance in kimchi cabbage, similar to that achieved with the TT41 strain.

Biomonitoring of workers is an approach of evaluating workers’ exposure to chemicals and particulate matter by measuring biomarkers of parent chemicals, their metabolites, and reaction products in workers’ biospecimens. Prerequisites for biological monitoring in the workplace include permission to enter the workplace, approval of the study plan from the IRB (Institutional Review Board), and obtaining consent from workers. Because of the complex legal process involved in biomonitoring, few studies have been conducted so far on biomonitoring of workers’ exposures to nanoparticles and other hazards from emerging materials and advanced nanotechnologies. We have developed a cell-based biomonitoring device that can evaluate acute cytotoxicity and various other effect biomakers, such as inflammation, at realistic workplace exposure. This device is based on air–liquid interphase (ALI) and can be used to evaluate cell toxicity and early effect biomarkers along adverse outcome pathways. Following exposure of A549 lung epithelial cells in ALI to workplace air for 1–2 h, the cells were processed to assess the induction of inflammatory and cell damage biomarkers. Initially, we estimated the deposition rate of nanoparticles in the transwell by exposing the cell-free ALI device to silver nanoparticle aerosols (AgNP 20–30 nm) for 2 h in the laboratory. Then A549 lung epithelial cells cultured on the transwell in the ALI device were exposed to AgNP nanoaerosols for 2 h and evaluated for cytotoxicity and induction of mRNAs of pro-inflammatory cytokines IL-1b, IL-6, and TNF-α. Then the cells in the ALI device were exposed to 3-D printer emissions at the workplace and evaluated for the same matched endpoints. The mRNA levels for IL-1b, IL-6, and TNF-α increased significantly at the end of 2-h exposure of A549 cells to the positive control AgNP aerosols. These mRNAs, as well as LDH and microprotein concentrations, increased even more after 24-h post-exposure incubation (p < 0.05). Cytotoxicity evaluation of 3-D printer emissions at 810 and 957 μg/m3, which was more than 80 times higher than the airborne total suspended particulate concentrations in the workplace air (9–12.5 μg/m3), suggested no significant acute cytotoxicity at the end of 2-h exposure to 3-D-printing emission, as well as at 24-h post-exposure incubation. Hyperspectral microscopic observation showed that 3-D printers emitted particles to be attached to A549 cells after 2-h exposure, and many particles were internalized by A549 cells after 24 h of post-exposure incubation. The mRNA expression of pro-inflammatory cytokine IL-1b and IL-6 increased significantly after 2-h exposure to 3-D printer emissions and after 24-h incubation (only IL-6). In contrast, the expression of TNF-α mRNA decreased significantly after 2 h of exposure to 3-D printers and decreased even more after 24-h post-exposure incubation. These results support the use of cell-based ALI devices for direct assessment of airborne hazards in the workplace, for probing toxicological properties of airborne contaminants using adverse molecular pathways, and for guiding study design for workplace biomonitoring. ALI devices can bridge conventional exposure assessment with cellular toxicity testing platforms for hazard and risk assessment.

This study monitored particulates, and volatile organic compounds (VOCs) emitted from 3-D printers using acrylonitrile-butadiene-styrene copolymer (ABS) filaments at a workplace to assess exposure before and after introducing exposure mitigation measures. Air samples were collected in the printing room and adjacent corridor, and real-time measurements of ultrafine and fine particle were also conducted. Extensive physicochemical characterizations of 3-D printer emissions were performed, including real-time (size distribution, number concentration) nanoparticle characterization, size-fractionated mass distribution and concentration, as well as chemical composition for metals by ICP-MS and VOCs by GC-FID, real-time VOC monitors, and proton-transfer-reaction time-of-flight mass spectrometer (PTR-TOF-MS). Air sampling showed low levels of total suspended particulates (TSP, 9–12.5/m3), minimal levels (1.93–4 ppm) of total volatile organic chemicals (TVOC), and formaldehyde (2.5–21.7 ppb). Various harmful gases, such as formaldehyde, acrolein, acetone, hexane, styrene, toluene, and trimethylamine, were detected at concentrations in the 1–100 ppb by PTR-TOF-MS when air sample was collected into the Tedlar bag from the front of the 3-D printer. Ultrafine particles having an average particle size (30 nm count median diameter and 71 nm mass median diameter) increased during the 3-D printing operation. They decreased to the background level after the 3-D printing operation, while fine particles continually increased after the termination of 3-D printing to the next day morning. The exposure to 3-D printer emissions was greatly reduced after isolating 3-D printers in the enclosed space. Particle number concentration measured by real-time particle counters (DMAS and OPC) were greatly reduced after isolating 3-D printers to the isolated place.

This study was undertaken to characterize a lactic acid bacterium 4I1, isolated from the freshwater fish, Zacco koreanus. Morphological, biochemical and molecular characterization of 4I1 revealed it to be Pediococcus pentosaceus 4I1. The cell free supernatant (CFS) of P. pentosaceus 4I1 exhibited significant (p

With the increasing requirement of high accuracy for particulate matter prediction, various attempts have been made to improve prediction accuracy by applying machine learning algorithms. However, the characteristics of particulate matter and the problem of the occurrence rate by concentration make it difficult to train prediction models, resulting in poor prediction. In order to solve this problem, in this paper, we proposed multiple classification models for predicting particulate matter concentrations required for prediction by dividing them into AQI-based classes. We designed multiple classification models using logistic regression, decision tree, SVM and ensemble among the various machine learning algorithms. The comparison results of the performance of the four classification models through error matrices confirmed the f-score of 0.82 or higher for all the models other than the logistic regression model. [ABSTRACT FROM AUTHOR]

This study was undertaken to characterize a lactic acid bacterium 4I1, isolated from the freshwater fish, Zacco koreanus. Morphological, biochemical, and molecular characterization of 4I1 revealed it to be Pediococcus pentosaceus 4I1. The cell free supernatant (CFS) of P. pentosaceus 4I1 exhibited significant (p < 0.05) antibacterial effects (inhibition zone diameters: 16.5-20.4 mm) against tested foodborne pathogenic bacteria with MIC and MBC values of 250-500 and 500-1,000 mg/mL, respectively. Further, antibacterial action of CFS of P. pentosaceus 4I1 against two selected bacteria Staphylococcus aureus KCTC-1621 and Escherichia coli O157:H7 was determined in subsequent assays. The CFS of P. pentosaceus 4I1 revealed its antibacterial action against S. aureus KCTC-1621 and E. coli O157:H7 on membrane integrity as confirmed by a reduction in cell viability, increased potassium ion release (900 and 800 mmol/L), reduced absorption at 260-nm (3.99 and 3.77 OD), and increased relative electrical conductivity (9.9 and 9.7%), respectively. Gas chromatography-mass spectrometry (GC-MS) analysis of the CFS of P. pentosaceus 4I1 resulted in the identification of seven major compounds, which included amino acids, fatty acids and organic acids. Scanning electron microscopic-based morphological analysis further confirmed the antibacterial effect of CFS of P. pentosaceus 4I1 against S. aureus KCTC-1621 and E. coli O157:H7. In addition, the CFS of P. Pentosaceus 4I1 displayed potent inhibitory effects on biofilms formation by S. aureus KCTC-1621 and E. coli O157:H7. The study indicates the CFS of P. pentosaceus 4I1 offers an alternative means of controlling foodborne pathogens. [ABSTRACT FROM AUTHOR]

The explosive growth of online education seems to slow down. Does this trend indicate that some courses are not appropriate for online education? We sought to find an answer to this question regarding graduate business statistics. We assumed that a course is well suited for online education if the students in the online and on-ground courses make overall grades that are not significantly different. We found that overall grades in the online graduate business statistics course were not significantly different from those in the on-ground course, though there was significant difference between the midterm grades of the two versions of courses. We assumed that it was due to the difference of learning speed. Comparison of letter grade distributions of the two modes of learning led us to the same conclusion. The results of this study led us to believe that graduate business statistics is appropriate for online education. We also recommend further research on students' perceptions on the two learning environments to find how to reduce or remove the difference in learning speed. [ABSTRACT FROM AUTHOR]

This paper presents an integrated decision-support framework that helps public agencies identify high-crash locations and develop cost-effective safety improvement projects. State-of-the-art safety concepts, such as safety performance functions, empirical Bayesian method, and potential for safety improvements, are incorporated to identify hazardous highway locations. Cost-effective safety projects are prioritized based on life-cycle safety benefit evaluation and optimal resource allocation models. An efficient solution algorithm to the optimization model is also proposed. The models are implemented in an internet-based, geographic information system (GIS) software tool that builds on state-of-the-art information technologies, graphical interfaces, and customized GIS algorithms such as spatial analysis with buffering operations. [ABSTRACT FROM AUTHOR]

This paper analyzes the economic impact of an earthquake on transportation network that contains more detail for the Midwest states. Two aspects of cost are considered in the paper: final demand loss and transport cost increase. The 1812 New Madrid earthquake is used to develop a scenario for the analysis. The modeling system includes a transportation network loss function, a final demand loss function, and an integrated commodity flow model. After running the earthquake scenario, the analysis identifies the most significant link on the network in an economic sense as well as the link with the greatest physical disruption. The results reveal that the links with greater physical disruption are not always the ones exhibiting the greater economic damage. The resulting outputs can provide information to perform a cost-benefit analysis as well as to support a decision-making process on the optimal retrofit priority of bridges and links on the transportation network. [ABSTRACT FROM AUTHOR]

A novel approach is proposed to mitigate the multipath effect that is considered a major source of error in global navigation satellite system (GNSS) positioning in urban areas. We utilize code pseudorange acceleration measurements as a weight in a least squares estimation process. If GNSS signals are reflected off a surrounding surface, they cause large variations in the recorded pseudorange measurement. Accelerations computed at each epoch with three consecutive pseudoranges exhibit significant fluctuations in a multipath signal. As a result, positioning accuracy improved by 75% horizontally and 79% vertically compared to not applying any weight. Even when multipath errors exist, the range acceleration (RA) value is sometimes low at many epochs. When a threshold value for the signal-to-noise ratio was additionally applied besides RA, the positioning accuracy at two test sites (including a deep urban environment) improved by more than 80% in both horizontal and vertical directions.

Tungsten has been commonly used for fine interconnects due to its good gap-filling characteristics in 3D molds, such as trench patterns. However, tungsten shows high deposition stress. This causes mold distortion because tungsten has low ad-atom mobility, and diffusion-driven relaxation does not occur. To reduce tungsten’s deposition stress, the shape of the nuclei can be controlled, which is an effective way to suppress the mechanical deformation caused by the formation of a grain boundary between free surfaces during the coalescence stage. In this study, elliptical tungsten nuclei with various aspect ratios, which suppress coalescence in the early stage of deposition, were proposed to reduce the deposition stress. Stress was calculated using the finite element method (FEM) in the range of 0.5 to 8 radius ratios of the tungsten nuclei. The bending of the trench mold was calculated due to tungsten stress and additional coalescence between films during the filling process. As a result, the wider the elliptical nucleus was, the lower the film stress, and mold bending between line patterns was also reduced. The defects in the depth and width of the periodic trench influenced the mold bending in the early growth stage and the stage of coalescence between films, respectively.

This paper presents the rotordynamic performance measurements and model predictions of a rotor supported on three-pad gas foil journal bearings (GFJBs) with various mechanical preloads. The rotor with its length of 240 mm, diameter of 40 mm, and weight of 19.6N is supported on two GFJBs and one pair of gas foil thrust bearings (GFTBs), being a permanent magnet rotor of a high speed electric motor. Each bearing pad consisting of a top foil and a bump-strip layer is installed on a lobed bearing housing surface over the arc length of 120 deg along the circumference. Test three-pad GFJBs have four different mechanical preloads, i.e., 0pm, 50 pm, 70pm, 100pm with a common radial nominal clearance of 150 pm. A series of speed-up tests are conducted up to 93 krpm to evaluate the effects of increasing mechanical preloads on the rotordynamic performance. Two sets of orthogonally positioned displacement sensors record the rotor horizontal and vertical motions at the thrust collar and the other end. Test results show that the filtered synchronous amplitudes change little, but the onset speed of subsynchronous motions (OSS) increases dramatically for the increasing mechanical preloads. In addition, test bearings with the 100 pm preload show a higher OSS in load-on-pad (LOP) condition than that in load-between-pads (LBP) condition. A comparison with test results for a one-pad GFJB with a single top foil and bump-strip layer reveals that three-pad GFJB has superior rotordynamic performance to the one-pad one. Finally, the test data benchmark against linear rotordynamic predictions to validate a rotor-GFJB model. In general, predicted natural frequencies of the rotor-bearing system and synchronous rotor motions agree well with test data. However, stability analyses underestimate OSSs recorded during the experimental tests. [ABSTRACT FROM AUTHOR]

Abstract Organic neuromorphic computing/sensing platforms are a promising concept for local monitoring and processing of biological signals in real time. Neuromorphic devices and sensors with low conductance for low power consumption and high conductance for low‐impedance sensing are desired. However, it has been a struggle to find materials and fabrication methods that satisfy both of these properties simultaneously in a single substrate. Here, nanofiber channels with a self‐formed ion‐blocking layer are fabricated to create organic electrochemical transistors (OECTs) that can be tailored to achieve low‐power neuromorphic computing and fast‐response sensing by transferring different amounts of electrospun nanofibers to each device. With their nanofiber architecture, the OECTs exhibit a low switching energy of 113 fJ and operate within a wide bandwidth (cut‐off frequency of 13.5 kHz), opening a new paradigm for energy‐efficient neuromorphic computing/sensing platforms in a biological environment without the leakage of personal information.

Abstract Biodegradable electronics are disposable green devices whose constituents decompose into harmless byproducts, leaving no residual waste and minimally invasive medical implants requiring no removal surgery. Stretchable and flexible form factors are essential in biointegrated electronic applications for conformal integration with soft and expandable skins, tissues, and organs. Here a fully biodegradable MgZnCa metallic glass (MG) film is proposed for intrinsically stretchable electrodes with a high yield limit exploiting the advantages of amorphous phases with no crystalline defects. The irregular dissolution behavior of this amorphous alloy regarding electrical conductivity and morphology is investigated in aqueous solutions with different ion species. The MgZnCa MG nanofilm shows high elastic strain (≈2.6% in the nano‐tensile test) and offers enhanced stretchability (≈115% when combined with serpentine geometry). The fatigue resistance in repeatable stretching also improves owing to the wide range of the elastic strain limit. Electronic components including the capacitor, inductor, diode, and transistor using the MgZnCa MG electrode support its integrability to transient electronic devices. The biodegradable triboelectric nanogenerator of MgZnCa MG operates stably over 50 000 cycles and its fatigue resistant applications in mechanical energy harvesting are verified. In vitro cell toxicity and in vivo inflammation tests demonstrate the biocompatibility in biointegrated use.

Electronic skins (e-skins)--electronic sensors mechanically compliant to human skin--have long been developed as an ideal electronic platform for noninvasive human health monitoring. For reliable physical health monitoring, the interface between the e-skin and human skin must be conformal and intact consistently. However, conventional e-skins cannot perfectly permeate sweat in normal day-to-day activities, resulting in degradation of the intimate interface over time and impeding stable physical sensing. Here, we present a sweat pore-inspired perforated e-skin that can effectively suppress sweat accumulation and allow inorganic sensors to obtain physical health information without malfunctioning. The auxetic dumbbell through-hole patterns in perforated e-skins lead to synergistic effects on physical properties including mechanical reliability, conformability, areal mass density, and adhesion to the skin. The perforated e-skin allows one to laminate onto the skin with consistent homeostasis, enabling multiple inorganic sensors on the skin to reliably monitor the wearer's health over a period of weeks. [ABSTRACT FROM AUTHOR]

We have developed a membrane filter-assisted cell-based biosensing platform by using a polyester membrane as a three-dimensional (3D) cell culture scaffold in which cells can be grown by physical attachment. The membrane was simply treated with ethanol to increase surficial hydrophobicity, inducing the stable settlement of cells via gravity. The 3D membrane scaffold was able to provide a relatively longer cell incubation time (up to 16 days) as compared to a common two-dimensional (2D) cell culture environment. For a practical application, we fabricated a cylindrical cartridge to support the scaffold membranes stacked inside the cartridge, enabling not only the maintenance of a certain volume of culture media but also the simple exchange of media in a flow-through manner. The cartridge-type cell-based analytical system was exemplified for pathogen detection by measuring the quantities of toll-like receptor 1 (TLR1) induced by applying a lysate of P. aeruginosa and live E. coli, respectively, providing a fast, convenient colorimetric TLR1 immunoassay. The color images of membranes were digitized to obtain the response signals. We expect the method to further be applied as an alternative tool to animal testing in various research areas such as cosmetic toxicity and drug efficiency.