Miracle-dz/UniFAR-FTUs huggingface dataset:
Effect of Antibiotic Treatment on Attenuated Salmonella typhimurium VNP20009 Mediated Schwannoma Growth Control

Background/Aim: This study evaluated the effect of enrofloxacin antibiotic treatment on the ability of an attenuated Salmonella typhimurium (S. typhimurium) strain VNP20009 to control schwannoma growth in a preclinical mouse schwannoma tumor model. Materials and Methods: The antitumor efficacy of VNP20009 intratumoral (i.t.) injection was assessed in a syngeneic mouse-NF2 schwannoma model, with and without subcutaneous (s.c.) injection of enrofloxacin beginning on day-1 or day-8 post-VNP20009 injection. S. typhimurium colonization was assessed in excised tumor samples following antibiotic treatment. Results: I.t. injection of the VNP20009 significantly decreased the growth of schwannoma tumors in mice compared to PBS-treated controls. Treatment of mice with enrofloxacin on day-1 post-VNP20009 injection resulted in abrogation of VNP20009-mediated tumor growth control. In contrast, tumor growth in i.t. VNP20009-injected mice infused with enrofloxacin beginning on day 8 was significantly decreased compared to i.t. PBS-injected controls. Enrofloxacin significantly reduced the number of viable VNP20009 bacteria in excised tumor samples within one day of antibiotic infusion. Viable bacteria were either few or essentially eliminated at the end of the experiment in antibiotic-treated animals compared to VNP20009-only. Conclusion: Viable VNP20009 can persist for as long as 2.5 weeks following intratumoral injection of schwannoma, during which time tumor growth is retarded. Antibiotic treatment starting 1-day following i.t. VNP20009 abrogated bacterial tumor growth control, whereas initiation of antibiotics 8-days following i.t. VNP20009 was associated with control of tumor growth, albeit less than seen in animals unexposed to antibiotics.
Bone Health Care Pathway for Non-metastatic Prostate Cancer Patients on Radiation and Androgen Deprivation Therapy

Background/Aim: Survival rates of prostate cancer (PCa) patients have improved considerably as a result of earlier diagnosis and therapies, including radiotherapy (RT) and androgen deprivation therapy (ADT). Patients on ADT develop cancer treatment-induced bone loss (CTIBL) and a high risk of fragility fractures. Bone health (BH) assessment is strongly recommended, together with timely initiation of treatments, to counteract CTIBL and preserve bone strength. Therefore, we decided to develop an interdisciplinary pathway of care (IPC) dedicated to non-metastatic PCa patients on long-term ADT and RT. Patients and Methods: An interdisciplinary team allocated resources to support an IPC to manage patients’ CTIBL and prevent fragility fractures. The team provided a diagnostic and therapeutic workflow according to patients’ and professional perspectives, consistent with recommendations and healthcare policies. The hospital’s quality department certified the IPC, the Ethical Committee approved procedures over the workflow. The Fracture Liaison Service (FLS) standards inspired services and professionals’ activities and interactions. Results: Preliminary data support the feasibility of the IPC from professionals’ and patients’ perspectives. Median age of the enrolled patients was 75 years, more than a half (58.9%) had low grade osteopenia or normal BMD (T-score ≥−1.5 standard deviation, SD), while 23.5% and 17.6% had osteoporosis and osteopenia, respectively. The IPC meets the requirements of a FLS concerning crucial indicators. Conclusion: Our IPC was a suitable approach to assure timely identification, assessment, initiation, and monitoring of adherence to anti-fracture treatments among non-metastatic PCa patients on long-term ADT and RT. Further data are required to show its effectiveness on fragility fracture prevention.
CTHRC1 Is Associated With Immune Escape and Poor Prognosis in Gastric Cancer

Background/Aim: Collagen triple helix repeat containing-1 (CTHRC1) promotes tumor progression by regulating the immunosuppression of the tumor microenvironment. However, the function of CTHRC1 in gastric cancer (GC) and its relationship with tumor-infiltrating immune cells remains unclear. Patients and Methods: Data were downloaded from The Cancer Genome Atlas (TCGA) database. The difference in expression of CTHRC1 in GC and adjacent non-tumor tissues was analyzed by R software and verified by the online database Oncomine. A Kaplan–Meier survival analysis was selected for evaluating the impact of CTHRC1 expression on the survival of GC and verified by the Kaplan–Meier plotter. The relationship between CTHRC1 expression and clinicopathological parameters was assessed by univariate and multivariate Cox regression. The correlation with tumor-infiltrating immune cells was analyzed by Tumor Immune Estimation Resource (TIMER). A gene set enrichment analysis (GSEA) was used to screen the signaling pathways between low and high CTHRC1 expression datasets. Results: The expression of CTHRC1 in GC tissue was higher than that in adjacent non-tumor tissues. The Kaplan–Meier curve showed that patients with higher CTHRC1 expression had a worse prognosis. The univariate and multivariate Cox analyses showed that high expression of CTHRC1 was an important independent predictor of poor overall survival in GC. The TIMER database analysis revealed that CTHRC1 was associated with five tumor immunosuppressive cells in GC. The GSEA indicated that 10 signaling pathways were enriched in samples with a high CTHRC1 expression phenotype. Conclusion: CTHRC1 might be a new prognostic biomarker for CG and might be a potential target for treatment of GC.
A Resilient UAV Swarm Networking Model Considering Communication Bandwidth

The resilience of unmanned aerial vehicle (UAV) swarm systems describes the capability to adjust its behavior to maintain its essential functions when malfunction or attack occurs. Recent studies have modeled the UAV swarm as a network to analyze its resilience, where each UAV is represented as a node, and each link denotes information exchange between UAVs. However, the current networking method is vulnerable to malicious attacks and does not consider the individual load of one UAV is limited. In this paper, we proposed a resilient UAV swarm networking model by incorporating the effect of the limitation of the bandwidth. Under continuous malicious attacks, this proposed model could enhance the resilience of the swarm systems by rewiring the nodes. Moreover, a case study is conducted in which a UAV swarm is to perform a search and rescue task. Experimental results show that the proposed model is more resilient to malicious attacks than some existing models, e.g., the BA model, ER model, and the balanced individual communication load.
IoT System for Household Electrical Appliance Monitoring and Control

The use of electrical energy is currently still less effective because much electronic equipment consumes electricity excessively. Many users often leave their electronic equipment turned on, increasing electricity bills. Using communication, computer, and information technology to monitor and control electrical energy use will increase efficiency and reduce household electricity consumption costs. Therefore, we created an IoT-based monitoring and control system for household electrical energy consumption for savings. This IoT system consists of NodeMCU ESP32, PZEM004T sensor, RTC DS1302 module, relay, and blynk application. The monitoring system uses the PZEM-004T sensor as a current, voltage, and power reader. The results of this reading will be processed by the NodeMCU ESP32, which will later be displayed on the blynk application. Then the relay is to turn off or turn on electrical equipment, and the RTC module functions as a timer to determine the on/off hours of electrical equipment. With the help of the NodeMCU ESP32 and the blynk application, this system will be able to monitor electricity usage in real-time and control electricity consumption at home anytime and anywhere remotely. Based on tests conducted in boarding houses by comparing before and after using the system for household appliances such as lights, fans, tv, and rice cookers for 1 week, a savings of 1.53 kWh was achieved.
Investigation of Pb/PbS a positive Schottky junction formed on conductive glass in contact with alkaline solution

PbS on Pb thin film (Pb/PbS) was deposited on conductive glass (CG) substrate (CG/Pb/PbS) using electrochemical techniques to investigate the Schottky junction in solution. The CG/Pb/PbS electrode was investigated by different techniques based on thermionic emission theory. Electrochemical cell consisted of the CG/Pb/PbS as a working electrode in an alkaline solution showed character of the Schottky junction. Reduction and oxidation of H2O on the CG/Pb/PbS electrode were enhanced under illumination. Water photo splitting is the main process. Study of linear sweep voltammetry at around open circuit potential (OCP) allowed proposing a model based on the Schottky junction which is charged at potentials more positive than OCP and discharged at potentials more negative than OCP. Electrochemical impedance spectroscopy confirmed the capacitive nature of the Schottky junction.
Computational and Predictive Trust Game Model for Switching Process in fMRI

Game theory paradigms allow analyzing experimentally through simulating the cognitive processes required for social cognition, such as perception, learning and decision-making, and actions. In this paper, the trust game paradigm, which is an extensively used task, is studied for measuring the trust. First, the trust game is designed as an fMRI task and then the trust data is acquired by running the task with n=80 subjects. Additionally, a computational mathematical model is proposed to model the subject-specific decision-making behavior by learning the previous game trials and then computing the difference between the actual and predicted gain, i.e., the prediction error (PE). The model is evaluated on two groups: depressed participants and healthy controls. The performance of the model is evaluated with the root mean square errors (RMSE) metric. The evaluation results show that even if the decision behavior of the participants exhibits different patterns, the proposed model can converge the actual gain fairly well with a low RMSE value.
An Auto-tuning method for aeration control in activated sludge wastewater treatment processes

In this paper, a relay-feedback PID auto-tuning method and applications related to conventional Wastewater Treatment Plants are presented. The developed method has two steps: identification of the process model parameters and PID controller tuning. First, to identify two points on the process Nyquist curve, a relay-feedback based experiment is proposed. A linear model of second order is obtained and this model is further used for the tuning of the PID controller. The performances of the control algorithms are illustrated by simulation results. Both the reference tracking and the disturbance rejection capability have been tested and analyzed. The impact of changing a design parameter in the closed loop system behavior has also been explored.
The Asymptotic Mean Squared Error of Multistep Prediction from the Regression Model with Autoregressive Errors

Abstract The asymptotic prediction mean squared error is derived for the regression model with autoregressive errors. The results cover the cases where future values of the exogenous variables are known, where some exogenous variables are predicted from autoregressive processes, and where lagged endogenous variables are included in the model. A result is also obtained for the predictor that ignores autocorrelation of the errors and is based on ordinary least squares (OLS) estimates of the regression parameters.
Air Quality Monitoring and Forecasting System using IoT and Machine Learning Techniques

Air pollution has been a growing concern in the twenty-first century, affecting the surrounding environment and public health. The previous studies have recently undertaken significant research on air pollution and air quality monitoring. Unfortunately, this area continues to be challenged by unresolved issues. This paper proposes an IoT-based Air Quality Monitoring and Forecasting System to monitor and predict air pollution for a specific area based on various pollution factors. Using Arduino UNO R3 and various low-cost sensors, our IoT system can collect and monitor pollutants, such as PM2.5, CO2, CO, as well as temperature and humidity. The air quality data was collected for several months. To overcome the problems of instability of low-cost devices in monitoring, machine learning (ML) algorithms, such as K-Nearest-Neighbour (KNN), Expectation-Maximization (EM), Multiple Imputation by Chained Equations (MICE), and Autoregressive-Moving-Average (ARMA), are applied to address missing data and outliers due to technical issues. The KNN model outperformed all others in terms of RMSE, MSE, MAE, R-squared, and execution time. Then, Autoregressive Integrated Moving Average (ARIMA) and Long Short-Term Memory (LSTM) algorithms are applied to predict future air quality. The result shows that our system can predict the air quality factors over the next hour with the highest accuracy at 96 %. Finally, a web interface was created to monitor and forecast air quality in real-time.
A Coupled Model of Eddy Current Sensor and Sensitivity Optimization

The thickness is a key parameter of the metal film, which can apparently influence the properties of metal films, such as the conductivity and magnetic. The eddy current technique is a useful method of in-situ metal film thickness measurement due to the crucial properties of non-contact and fast response. Generally, an eddy current sensor consists of a coil and a detection circuit, both of which have direct effects on the measurement performance. And the coupling of the two parts should be paid more attention to application research. For the metal film thickness measurement of nano accuracy, we establish a complete model of eddy current sensor by coupling an electromagnetic field and a circuit field. Using the coupled model, the effects of the coil parameters and excitation signal frequency on the thickness sensitivity are revealed firstly, and it is found there is a big difference between the effects of some parameters on the coil inductance and the output voltage. Based on the effects of the excitation signal frequency, a frequency modulation method is proposed, which provides a theoretical basis for expanding the measurement range with high accuracy and linearity.
Lightweight Intrusion Detection in MQTT Based Sensor Network

The rapid development of IoT devices improves the quality of life and efficiency of industries. However, it also brings the attention of cybercriminals. Many IoT devices do not have enough resources to deploy a standard intrusion detection system and thus there is a need to design an IDS that is lightweight and has a narrower focus on the types of attacks that are specific to IoT devices. Previous studies have suggested a few ways to improve the efficiency of the IDS. However, most of them use datasets that only contain general network traffic but not IoT protocol-related traffic. Our proposed approach focuses on improving the efficiency and performance of a machine learning-based intrusion detection system using a support vector machine that targets attacks within an IoT system that uses Message Queuing Telemetry Transport (MQTT) protocol. We use feature selection techniques to reduce the complexity of the model and evaluate the result using multiple metrics.
A Comparison Of Relational, NoSQL and NewSQL Database Management Systems For The Persistence Of Time Series Data

Time series data is created in a variety of application areas such as sensors in cars, smartwatches or loT devices. This kind of data is often characterized by high resource demand due to the frequency the information is measured, with data points once a day, hour and even down to milliseconds. While real-time processing of such data is often sufficient, there are also many use cases, where batch processing and consequently the storage and managed access of measurements is required. For this reason, this work evaluates different database management systems in the context of storing time related data using different data models such as classical relational models, non-relational models using NoSQL database systems and the recently upcoming group of NewSQL databases. The evaluation shows that a highly optimized time series databases such as InftuxDB is able to outperform the other tested systems regarding write-throughput and RAM as well as disk utilization in a single server setup.
Analytical Technique for Computation of the Back EMF and Electromagnetic Torque for IPM Motors

Interior permanent magnet motors are usually used in electric vehicles, because of the excellent advantages of high torque and efficiency performances. Thus, in this paper, an analytical technique is presented to compute and analyze the electromotive force and electromagnetic torque for V-shaped rotors of interior permanent magnet motors. Therefore, a lumped parameter magnetic circuit model is considered to investigate the saturation and nonlinearity of the bridge. So as to point out the influence of magnetic field distribution on inductance, the considered winding is investigated for accurately computation of the armature reaction due to magnetic fields at the air gap and d- and q-axis inductances. In addition, in order to evaluate the torque density, torque ripple and output power, two-layered rotor structure with the types of double V and VI magnets of the interior permanent magnet motor is presented to analyze a suitable choice for manufacture in the mass production. The proposed method is checked by a finite element method to illustrate the validation.
Quantum Enabled Radar Sensing

We present a quantum enabled networked radar system that incorporates an optical atomic clock to provide the radar systems with both an ultra-low noise oscillator and time synchronization. Characterization of the photonic microwave generator against traditional oscillators currently used in the radar systems is presented. Near-future developments of the radar network are discussed.
Implementation of Smart Vehicle Accident Detection using Raspberry PI in Smart Cities

This article shows that when an accident occurs, the time it takes for an emergency medical facility to be established and put into operation has a significant influence on the survival of the victim. Reducing accident scene time is considered by medical professionals to reduce mortality. Emergency responders can be alerted to disasters using the Raspberry Pi-based accident identification system. This helps to shorten response times. The vibration sensor detects the error and then sends the prepared message to the right person. It is important to know what happened and who was involved in an accident in order to send appropriate information to emergency responders. It is possible to get precise latitude and longitude positions for satellites if GPS is first used in this way. In order for the GSM device to start tracking the vehicle, need to send a message to it. The Raspberry Pi controller's vibration sensor can also be used to identify faults.
Influence of Wing Kinematics on Lateral Dynamic Stability of Bioinspired Flapping Wing Micro Air Vehicle involving Multibody Dynamics

Tailless bioinspired flapping wings micro air vehicle (FWMAV) dependent systems have gained much importance in current years because of their exceptional traits such as agility, superior maneuverability, and compactness particularly in confined spaces. However, these traits have imposed various challenges in their design and development involving aerodynamics, structure, multibody dynamics, wings flexibility, fluid-structure interaction, control, and insect-scale hardware. Previous studies have mostly dealt with longitudinal stability and have ignored multibody dynamics effects. The current study compares the effect of measured hawkmoth and simplified sinusoidal (more practical for control implementation) wing kinematics on lateral motion stability characteristics of a hawkmoth-like multibody FWMAV in both hovering and forward flight. A multibody dynamics (MBD) simulation environment is employed that incorporates the time dependent inertia effects of all the system parts and flapping wings. The multibody dynamics environment is attached to a modified quasi-steady (QS) aerodynamic model for analyzing the hovering and forward flight lateral dynamics characteristics. A gradient-dependent trim searching algorithm that involves system dynamics-aerodynamics coupling is implemented to get precise trim conditions at various flight speeds by solving fully coupled nonlinear six degrees of freedom (6-DOF) equations of motion. For obtaining stability derivatives at each flight speed, trim conditions are searched numerically to obtain accurate trim flight trajectories using the gradient of control effectiveness matrices. Lateral dynamics characteristics of the FWMAV in hovering along with forward flight are analyzed using eigenmode analysis for both the measured hawkmoth and simplified sinusoidal wing kinematics. For measured hawkmoth wing kinematics, there exists a stable fast subsidence mode, a stable slow subsidence mode, and a stable oscillatory mode. However, for simplified sinusoidal wing kinematics, there exists a stable fast subsidence mode, an unstable slow divergence mode, and a stable oscillatory mode having speed dependent varying characteristics. This study is crucial for tailless FWMAV's stability and control investigators and developers to analyze the influence of more practical simplified sinusoidal wing kinematics on a multibody FWMAV for designing efficient flight controllers in both hovering and forward flight.
Toward UAV Communication Network Self-Organization: A Game-Theoretic Distributed Control Approach

One crucial advantage of UAV nodes is their flexible mobility and relocation capabilities, which can be used to improve the communication topology of UAV network by adjusting node locations on the basis of network and formation planning. Although different approaches have been proposed in similar issues, they either require centralized control or are not robust to dynamic environment and unstable wireless links. This paper proposes a self-organization approach that based on game theory and distributed control according to local information. Firstly, a global utility accounting for both UAV gateway loads and topology connectivity is defined. In order to optimize UAV gateway location control in an autonomous manner, we decouple the global optimization problem into local optimization problems under a game framework, which can be easily solved in individual UAV gateways. We prove that the game possesses at least one Nash equilibrium (NE) and the optimal NE is exactly the globally optimal solution. Finally, a distributed UAV gateway location control algorithm only using local wireless link quality and topology information is proposed and theoretically proved to converge to the optimal solutions. Simulation results show that the proposed framework and distribution control algorithm are effective and practical.