bluuebunny/crossref_metadata_2025 huggingface dataset:
A Network Learning Model for College Information Education Using Scientific Computing
Authors: Chen Xiaoling , Diao Haijun
Published: September 2022

The development of network technology has further promoted the informatization of colleges and universities; the network teaching resources have been continuously enriched; the network teaching management has been constantly standardized; and the development of scientific computing technology also occurred. Network learning has become a major learning method and is accepted by more and more people. In order to build an intelligent network learning model, this paper starts from data mining, index data processing, and evaluation system design from the construction of index data as the main line. Based on the analysis of the existing network learning model index system, this paper proposes a boundary change network learning index system and uses data mining technology to mine the student status and student learning activity information recorded in the web page and the background database. In this way, the key factors which can evaluate students' online learning effect are extracted and quantified. Then, based on the basic principles and algorithms of fuzzy logic, artificial neural network, and fuzzy neural system, artificial neural network and adaptive fuzzy neural system are used for network learning modeling, respectively, and the experimental simulation and performance analysis of the two models are carried out. The experimental results show that the test accuracy rates of the two models based on BP neural network and adaptive fuzzy neural network are 68.6% and 91.4%, respectively. The model based on the adaptive fuzzy neural network is better in the field of network learning, is feasible in theory, and has reliable results in practice, and can be used as an intelligent computing model for developing network learning systems. It has a good promotion value and realizes the intelligence and automation of online learning, thereby further improving the online learning platform and promoting the construction and use of online courses.
Application of Data Warehouse Technology Based on Neural Network in Physical Education Quality Management
Authors: Zhao Bo , Liu Yanjin
Published: September 2022

Data warehouse technology has been created because of China’s technological advancement and the increasing requirements of the educational sector. Physical assessments are treated as tests by many students. Institutions spend plenty of time every year through physical tests, yet the results are rarely shared with students. Teachers are impeded by the size and complexity of physical test data, finding it challenging to support experiments or judge individual students’ development. Students have trouble following up and delivering test-based feedback after instruction. In recent years, various researchers have offered insightful advice on how to build multidimensional database structures for such trouble. However, quality requirements alone are not adequate to guarantee quality in reality. So, this paper presents a novel Hypertuned wide polynet convolutional neural network (HWPCNN) framework in the data warehouse technology to attain the greatest performance in physical education quality management. In this paper, we first apply HWPCNNs for physical education quality management to analyze the accuracy and recall of the model. It is no secret that HWPCNN is now one of the most widely used deep learning techniques. When it comes to managing the quality of physical education, the HWPCNN’s local perception feature in the data warehouse technology allows it to achieve the best possible results. To validate the model’s performance, it is compared to other models and then improved further to increase its accuracy. The physical education resources are gathered as a raw dataset for this inquiry. The raw dataset is cleaned using the Z-score approach to get it ready for further data processing. Then, a sparse matrix approach is employed to build a data cube, while the proposed method is used to index multidimensional databases. To demonstrate that our work is of the best quality in managing physical education, performance metrics of the suggested method are also evaluated and compared with other traditional methods.
The Diagnostic Value of Bedside Echocardiography and Lower Extremity Blood Vessels in Acute Pulmonary Embolism
Authors: Xu Xinxin , Yuan Jing , Pan Xiaojie , Du Guibin , Zhang Jiahui
Published: September 2022

Objective. The study aimed to evaluate the value of bedside echocardiography (TTE) and lower extremity blood vessels in diagnosis and prognosis of acute pulmonary embolism (APE). Methods. A retrospective study was performed on 53 patients with APE diagnosed by CT pulmonary angiography (CTPA) (systemic systolic blood pressure was >90 mmHg at time of consultation, and systemic systolic blood pressure decreased by <40 mmHg compared with basic value in those with hypertension). All patients underwent TTE examination before treatment. The high-risk factors, clinical manifestations, laboratory tests, and prognosis were retrospectively analyzed. Results. The rate of PE-related deterioration (cardiopulmonary resuscitation, tracheal intubation, cardiogenic shock, and death) within 14 days of hospitalization in RVD was 28%, and mortality rate (sudden death) was 20%, compared with non-RVD (both 0%). TTE examination showed that RVD as a predictor of pulmonary embolism-related death had a sensitivity of 100%, a specificity of 58%, a positive predictive value of 20%, and a negative predictive value of 100%. Conclusions. (1) TTE has increasingly shown obvious advantages in diagnosis of APE. It can detect direct or indirect signs of pulmonary embolism, confirm diagnosis or suspected diagnosis, and noninvasively and dynamically observe hemodynamic changes of heart in patients with acute PTE before and after treatment.. (2) The PE-related exacerbation rate (28%) or mortality (20%) of APE patients in normotensive with RVD was higher without RVD (0%). RVD is an independent predictor of poor prognosis in normotensive acute PTE. TTE tests allow people to identify people at risk of early death. The short-term prognosis of patients without RVD was better (14 days).
[Retracted] Mechanism of Anti‐Inflammatory Drugs in the Early Treatment of Oral Gingival Mucosa and Soft Tissue Trauma
Authors: Zhu Yanfeng , Lin Fei , Chen Weihui
Published: September 2022

Traumatic tissue develops an uncontrolled inflammatory response that causes secondary damage to the injured tissue and other parts of the body. Therefore, preventing wound infection, reducing inflammatory response, and reducing secondary tissue damage are the keys to early treatment of tissue trauma. In the treatment of gingival soft tissue trauma, anti‐inflammatory and analgesic drugs are reasonably selected according to the condition, which can effectively reduce inflammation, and they help periodontal tissue regeneration and healing. However, there are few studies on the mechanism of anti‐inflammatory drugs in the early treatment of oral gingival mucosal soft tissue trauma, and the specific mechanism is unknown. Therefore, this paper explored the mechanism of anti‐inflammatory drugs in the early treatment of oral gingival mucosa and soft tissue trauma through experiments, which provided theoretical support for the clinical treatment of gingival mucosa and soft tissue trauma repair. In this paper, two anti‐inflammatory drugs, levofloxacin and metronidazole, were selected to measure their release properties in vitro and in vivo. Then, the white‐eared rabbits were treated with gingival wound treatment experiments, and the physiological characteristics, intratissue pressure, tissue partial pressure of oxygen, IL‐6 content, and PGE2 content were determined at each postinjury period, and the mechanism of action of anti‐inflammatory drugs was determined. Research results have shown that anti‐inflammatory drugs can significantly inhibit the content of IL‐6 and PGE2 in gingival soft tissue after injury, reduce the local inflammatory response, and accelerate tissue healing.
Studying the Applicability of Swarm Intelligence in Designing Optimized PI Controller for DC-DC Zeta Converter: A MATLAB-Based Approach
Authors: Shagor Md. Rafid Kaysar , Sami Samiur Rahman , Nishat Mirza Muntasir , Faisal Fahim , Zaman Md. Safwan , Ahmed Zubayer
Published: September 2022

Studying the stability of power converters and improving the performance is a major concern for researchers in the domain of power electronics. In this context, the DC-DC Zeta converter is studied in this paper and the closed-loop operation is comprehensively investigated by employing swarm intelligence (SI) algorithms with a view to design an optimized proportional-integral (PI) controller. These algorithms have been increasingly used to develop and optimize power converters in recent years. The state-space averaging technique was used to design the converter’s closed-loop transfer function. Hence, the traditional and SI algorithm-based PI controllers are inspected, and comparative analysis is presented. Four objective functions termed as integral absolute error, integral time absolute error, integral square error, integral time squared error, gain values, and different performance parameters such as percentage of overshoot, rise time, settling time, and peak amplitude are tabulated to examine the stability of the system. Furthermore, eigenvalues have been analyzed for determining the stability of the system extensively. Finally, a detailed comparative study is shown to provide a detailed evaluation of the performances where ant colony optimization for continuous domains (ACOR)-based PI controller has shown promising results than other SI-based controllers in terms of percentage of overshoot (2.27%), rise time (1.54 μs), and settling time (0.103 μs). All the simulation results and analysis are obtained using the MATLAB-Simulink.
Multivariable Linear Algebraic Discretization of Nonlinear Parabolic Equations for Computational Analysis
Authors: Zuo Li , Mei Fengtai
Published: September 2022

Since the nonlinear parabolic equation has many variables, its calculation process is mostly an algebraic operation, which makes it difficult to express the discrete process concisely, which makes it difficult to effectively solve the two grid algorithm problems and the convergence problem of reaction diffusion. The extended mixed finite element method is a common method for solving reaction-diffusion equations. By introducing intermediate variables, the discretized algebraic equations have great nonlinearity. To address this issue, the paper proposes a multivariable linear algebraic discretization method for NPEs. First, the NPE is discretized, the algebraic form of the nonlinear equation is transformed into the vector form, and the rough set (RS) and information entropy (IE) are constructed to allocate the weights of different variable attributes. According to the given variable attribute weight, the multiple variables in the equation are discretized by linear algebra. It can effectively solve the two grid algorithm problems and the convergence problem of reaction diffusion and has good adaptability in this field.
Construction and Characterization of n6-Methyladenosine-Related lncRNA Prognostic Signature and Immune Cell Infiltration in Kidney Renal Clear Cell Carcinoma
Authors: Chen Zerong , Huang Zehai , Hui Jialiang , Chen zhuangfei , Zhang Haibo , Jiang Yaodong , Zeng Weisen
Published: September 2022

Background. Kidney renal clear cell carcinoma (KIRC) lacks effective prognostic biomarkers and the role and mechanism of N6-methyladenosine (m6A) modification of long noncoding RNAs (lncRNAs) in KIRC remain unclear. Methods. We extracted standard mRNA-sequencing and clinical data from the TCGA database. The prognostic risk model was obtained by Lasso regression and Cox regression. We randomly divided the samples into training and test sets, each taking half of the cases. Based on Lasso regression and Cox regression for training set, the prognostic risk signature was constructed; risk scores were calculated with the R package “glmnet.” Based on the median value of the prognostic risk score, risk scores were calculated for each patient and we divided all KIRC samples into high-risk and low-risk groups. Then, high- and low-risk subtypes were established and their prognosis, clinical features, and immune infiltration microenvironment were evaluated in test set and the entire sampled data set. The reliability of the prognostic model was confirmed by receiver operating characteristic curve analysis. Results. We found 28 prognostic m6A-related lncRNAs and established a m6A-related lncRNAs prognostic signature.

Risk score
=
A
C
015813.1
∗

0.0086

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E
M
X
2
O
S
∗

−
0.0101

+
L
I
N
C
00173
∗

0.0309

+
P
W
A
R
5
∗

−
0.0146

+
S
N
H
G
1
∗

0.0043

.

The signature showed a better predictive ability than other clinical indicators, including tumor node metastasis classification (TNM), histological, and pathological stages. In the high-risk group, M0 macrophages, CD8+ T cells, and regulatory T cells had significantly higher scores. Contrarily, in the low-risk group, activated dendritic cells, M1 macrophages, mast resting cells, and monocytes had significantly higher scores. In the high-risk group, LSECtin was overexpressed. In the low-risk group, PD-L1 was overexpressed. Moreover, high-risk patients may benefit more from AZ628. Conclusions. In conclusion, prognosis prediction of patients with KIRC and new insights for immunotherapy are provided by the m6A-related lncRNA prognostic signature.
Teaching Management System and Algorithm Implementation Based on Big Data Fuzzy K-Means Clustering
Authors: Liu Shiyu
Published: September 2022

In this paper, the clustering algorithm of big data fuzzy K-means is used to research and analyze the teaching management system. Since the values of the indicator variables of bad units are usually significantly different in smooth areas and discontinuous areas, we cluster the indicators of local areas through K-means clustering, so that only good units or bad units are included in each class. The server is deployed on the campus and is networked with the teaching computers of each classroom. Teachers log in to the class rate management system before or during class, log in to their respective users, select the corresponding class, and make corresponding attendance records according to the attendance of students in the class. The system automatically counts the number of expected arrivals, attendance, late arrivals, leave requests, and absenteeism for the class. Teachers can also fill in information feedback for this class: a list of absenteeism, classroom discipline, student learning status, teaching suggestions, and teaching testimonials. The development environment built by this system is a combination of PHP + MYSQL, which reasonably planned according to the overall needs. The development mode adopts the top-down model, which runs through the whole process from system development to testing to application. The most used floating point number encoding is the decimal floating point number encoding. The relationship of each module of the virtual reality teaching management system is sorted out, the overall framework of the virtual reality teaching management system is established, and the design of the business flow diagram of the management system is completed. This paper also uses Oracle database technology to further explain the design process of designing the system database. Finally, combined with the system design and implementation, the system test is completed from two aspects: system function test and performance test.
Transient Postoperative Hemorrhage from Elbow Arthroscopy Portals following Intra-Articular Pentosan Polysulfate Sodium Injection
Authors: Parker Lindsay A. , Wustefeld-Janssens Brandan , Dundas James M.
Published: September 2022

Four adult, client owned dogs with diagnosed bilateral elbow dysplasia undergoing elbow arthroscopy for removal of fragmented medial coronoid process were identified via a retrospective database search, who also received intra-articular administration of pentosan polysulfate sodium (PPS) (Cartrophen Vet, Biopharm Australia Pty Ltd., Bondi Junction, New South Wales). Dogs had postoperative administration of 5 ml PPS injected into each elbow joint following elbow arthroscopy. Within 1-3 hours of administration, each dog experienced hemorrhage from arthroscopy incisions that was determined to be independent of surgical trauma given lack of hemorrhage intraoperatively. Pressure bandages were placed, and the hemorrhage and elevated coagulation parameters resolved 12-18 hours following intra-articular injection. No further intervention was required, and the dogs were discharged 20-26 hours postoperatively. The purpose of this case series is to describe 4 dogs who experienced transient and focal hemorrhage following off-label intra-articular administration of pentosan polysulfate sodium (PPS). While this case series is limited due to small number of cases, results following bilateral, intra-articular injection of PPS support a transient systemic coagulopathy. Though this report represents administration of PSS via a route and at doses beyond that recommended on the label, results suggest that administration of PSS in the manner described in this report should be avoided.
Multi-Subband Radar Signal Fusion Processing Based on Deep Neural Network in Low Signal-to-Noise Ratio
Authors: Jiang Yilin , Tang Sanqiang , Lu Manjun , Zhang Liting
Published: September 2022

The traditional multi-subband radar signal fusion method based on pole information fits nonlinear signals via linear models. It is very important to accurately estimate the order of the pole model, as the wrong order will lead to errors in the resulting fusion signal. In the case of low signal-to-noise ratio (SNR), it is difficult to obtain accurate pole values. When a noise suppression method is included in the algorithm, the influence of noise on the pole order may be effectively avoided. However, traditional methods have many complex links, and only a few samples can be tested each time. Also, approximating a linear model onto a nonlinear signal will inevitably have errors. Therefore, this paper proposes a method based on the deep neural network (DNN) that applies nonlinear fitting of deep learning to complete the fusion process. The multi-subband distance envelopes are input into the DNN, and the fusion full-band distance envelopes are obtained as the output. Using DNN for subband fusion could improve the radar range resolution and obtain high-resolution one-dimensional range profiles.
Durable Responses to Chemotherapy in Patients with Metastatic Soft-Tissue Sarcoma
Authors: Wessels Tim , Rogiers Aljosja , Schöffski Patrick
Published: September 2022

Introduction: Patients with metastatic soft-tissue sarcoma generally have a poor prognosis. Although chemotherapy is associated with improved overall survival in these patients, long-term benefit is rare. Case Report: We report on 2 patients with metastatic soft-tissue sarcoma who achieved a complete response to chemotherapy, without any local therapy, and had no signs of disease progression more than a decade after discontinuing treatment. Conclusion: These observations highlight the potential benefit of chemotherapy in a disease that is often considered to be poorly responsive to this treatment. These observations support further efforts to identify factors to guide patient selection and treatment optimization.
Effects of Rapamycin on the Expression of Redox Enzymes in Aortic Vascular Smooth Muscle Cells from Marfan Syndrome Mice
Authors: Mathies Marius , Krieg Elisa M. , Mohr Franziska , Zaradzki Marcin , Wagner Andreas H.
Published: September 2022

Activation of the mechanistic target of rapamycin (mTOR) pathway has been implicated in an increasing number of diseases, including Marfan syndrome (MFS), an inherited connective tissue disorder. mTOR-dependent reactive oxygen species (ROS) formation has also been suggested to play a role in aortic aneurysm formation in MFS patients. This study aimed to characterize the effects of mTOR inhibition by rapamycin on key redox enzymes and NADPH oxidases (NOX) in cultured vascular smooth muscle cells of a murine MFS model. Therefore, the influence of 5 and 20 nmol/L rapamycin solved in 0.1% (vol/vol) DMSO on glutathione peroxidases 1 (Gpx1) and 4 (Gpx4), superoxide dismutase 2 (Sod2), and catalase (Cat) mRNA and protein expression was investigated in isolated murine aortic smooth muscle cells. Rapamycin inhibited the mRNA expression of all redox enzymes by 30–50%, except Gpx1. In the same cells, the mRNA expression of the transcription factor NFE2-related factor-2 and peroxisome proliferator-activated receptor-γ, key factors against oxidative stress, and controlling redox gene expression were also inhibited to a comparable extent under these conditions. In addition, Nox1 but not Nox4 mRNA expression was significantly inhibited by up to 40%. DMSO alone increased nearly 2-fold the redox enzyme protein expression, which was reduced considerably to basal levels by rapamycin. Proteasomal inhibition by bortezomib could not reverse the observed decrease of GPx protein content. The rapamycin-mediated decrease in GPx protein abundance was reflected in a reduced total GPx enzymatic activity. Higher rapamycin concentrations did not further decrease but led to a renewed increase in enzymatic activity despite low GPx protein concentrations. Baseline ROS formation was slightly inhibited at 13% with 5 nmol/L rapamycin and returned to baseline levels with the higher 20 nmol/L rapamycin concentration. In conclusion, this study further characterized the mechanism of action of rapamycin. It provided an insight into how rapamycin interferes with the regulation of redox homeostasis essential for ROS-dependent signaling that does not incur cellular damage.
Strategy to reduce solar power fluctuations by using battery energy storage system for UTeM’s grid-connected solar system
Authors: Tee Wei Hown , Yee Yen Hoe , Gan Chin Kim , Baharin Kyairul Azmi , Tan Pi Hua
Published: September 2022

Recent years have witnessed the increasing uptake of solar photovoltaic (PV) installations, ranging from a few kilowatts for residential rooftops to a few megawatts for large-scale solar farms. One of the key challenges for the solar PV systems is its dependency on the solar energy, which is intermittent in nature and highly unpredictable. In this regard, battery energy storage system (BESS) is regarded as the effective solution that can smoothen the output power fluctuation from the solar PV system. Hence, this work utilized BESS that had fast response time with high power and energy density to reduce the solar output fluctuations from a real grid-connected solar system installed at the campus rooftop. The characteristic of the PV power fluctuation and the BESS storage requirement to smooth out the fluctuation within the allowable limit were determined and analyzed. More importantly, actual solar irradiance data with an interval of one minute was utilized in this work. The findings suggest that BESS with 66% of the installed solar capacity and 21% of the average daily solar generation of the installed system are required to smoothen the solar fluctuation that exceeds the ramp rate limit of 10%/min.
Enabling unmanned aerial vehicle to serve ground users in downlink NOMA system
Authors: Nguyen Nhat-Tien , Nguyen Hong-Nhu , Huynh Leminh Thien , Voznak Miroslav
Published: September 2022

The emergence of internet-of-things (IoT) devices in homes and industry, has resulted in the current and future generation of wireless communications facing unique challenges in spectral efficiency, energy efficiency, and massive connectivity issues. Non-orthogonal multiple access (NOMA) has been proposed as a viable solution to address these challenges as it offers low-latency, spectral efficiency, and massive connectivity capabilities, which are key requirements in upcoming next-generation networks. In addition, another technology that has emerged as a solution to spectral efficiency and coverage is an unmanned aerial vehicle (UAV). Therefore, the combination of UAVs with NOMA has great potential to minimize the challenges and maximize the benefits. Specifically, we investigate the outage performance of the NOMA-UAV network over Nakagami-m channel fading. To this end, we derive a closed-form outage performance metric. The formulated framework is validated using simulations to verify the effectiveness of the proposed solution.
Secrecy capacity analysis of bi-static backscatter communication systems
Authors: That Phung Ton , Ha Duy-Hung , Nguyen Hong-Nhu
Published: September 2022

The rapid adoption of battery-free internet-of-things (IoT) sensors in multiple environments ranging from agriculture to wildlife surveillance, has seen increased research interest in backscatter communication (BSC) technology. BSC is viewed as a potential technology to enable the further spread of sustainable battery-free IoT applications in environments and scenarios where bulkier-sized battery-powered IoT devices would be unsuitable. In this study, we investigate the secrecy capacity of a bi-static BSC network in the presence of a malicious eavesdropper. The proposed BSC system consists of a reader, multiple backscatter devices, and an eavesdropper. We derive closed-form strictly positive secrecy capacity (SPSC) expressions and ergodic secrecy capacity (ESC) expressions for the BSC reader. Monte Carlo simulations verify the exact capacity expressions.
Power system contingency classification using machine learning technique
Authors: Gongada Sandhya Rani , Chakravarthy Muktevi , Mangu Bhukya
Published: September 2022

One of the most effective ways for estimating the impact and severity of line failures on the static security of the power system is contingency analysis. The contingency categorization approach uses the overall performance index to measure the system's severity (OPI). The newton raphson (NR) load flow technique is used to extract network variables in a contingency situation for each transmission line failure. Static security is categorised into five categories in this paper: secure (S), critically secure (CS), insecure (IS), highly insecure (HIS), and most insecure (MIS). The K closest neighbor machine learning strategy is presented to categorize these patterns. The proposed machine learning classifiers are trained on the IEEE 30 bus system before being evaluated on the IEEE 14, IEEE 57, and IEEE 118 bus systems. The suggested k-nearest neighbor (KNN) classifier increases the accuracy of power system security assessments categorization. A fuzzy logic approach was also investigated and implemented for the IEEE 14 bus test system to forecast the aforementioned five classifications.
Sensor fault diagnosis technique applied to three-phase induction motor drive
Authors: Nguyen Tien Xuan , Huu Nguyen Minh Chau , Tran Cuong Dinh
Published: September 2022

This paper conducts research on sensor fault diagnosis for a three-phase induction motor drive (IMD) in steady-state operation. An improving diagnostic technique based on the integration algorithm of the sinusoidal current signal is proposed for detecting and locating faulty current sensors in the induction motor drive. The IMD integrated a proposed fault detection-isolation (FDI) system is investigated for operating characteristics when sensor failures occur. The faulty sensor needs to be accurately identified and quickly isolated from the control system. Then the estimated signal will be used to replace the fault signal to retain the IMD stability. MATLAB/Simulink software will be applied to simulate the speed-torque characteristics of the IMD system as well as sensor failures occurring during operation. The performance of the proposed method will be evaluated through the accuracy and timeliness in each fault case corresponding to each sensor.
Hiding text using the least significant bit technique to improve cover image in the steganography system
Authors: Halboos Estabraq Hussein Jasim , Albakry Abbas M.
Published: September 2022

One of the highest priorities in the era of information technology is to achieve an accurate and effective system for hiding security data. One of the goals of steganography is imperceptability to intruder. So this paper work to increase the imperceptibility on image, which has weaknesses in previous studies, as well as to avoid statistical attacks such as chi-square. A method has been proposed that includes calculating the color contrasts in the homogeneous areas of the image and dividing them according to the color contrast and exploiting the data of pixels that have a high impact to embed on the two first and third bits of least significant bit (LSB) to increase the amount of embedded data, impact regions (IR) classify according to selected features extracted in advance by using the support vector machine (SVM) classifier. Work was done on standard images taken from a standard dataset (USC-SIPI) for two types of gray and color images. The results showed the worth of the proposed method through a high peak signal to noise ratio (PSNR) that reached 89.5 dB due to the distribution of data on pixels according to the proposed method
Packet loss compensation over wireless networked using an optimized FOPI-FOPD controller for nonlinear system
Authors: Hasan Muhannad Ali , Oglah Ahmed A. , Marie Mehdi J.
Published: September 2022

Wireless networked control systems (WNCS) consist of an actuator, sensor, and controller communicating over wireless networks in place of traditional point-to-point wired connection. Due to their main advantages, a decrease in maintenance costs, more flexibility, and safety could be achieved. As a result, it attracted a great deal of interest, but packet losses and time delays in the wireless network through transmitting and receiving the data are considered very challenging issues, which impair the output accuracy of the WNCS and can affect the entire system stability. In this study, integer-order proportional integral-proportional derivative (PI-PD) and fractional-order PI-PD (FOPI-FOPD) controllers are proposed to reduce the effect of expected packet loss in a WNCS to improve system performance. At high packet loss percent, the PI controller is introduced to act as a compensator in the feed-forward loop to keep the system stable. MATLAB/Simulink and Truetime simulator are used to simulate the WNCS. The rotary inverted pendulum (RIP) is utilized as the object of the controllers. Grey wolf optimization (GWO) algorithm is used to find the optimal controllers and compensator parameters. The simulation results showed that the FOPI-FOPD is superior to PI-PD in the packet loss compensation.
An IoT-fuzzy based password checker system for wireless video surveillance system
Authors: Jasim Mohammed Ahmed , Atia Tayseer Salman
Published: September 2022

Wireless video surveillance systems (WVSS) are deployed in large environments for use in strategic places such as town centers, public streets, and airports and play an essential role in protecting critical infrastructure. However, WVSSs are vulnerable to unauthorized access due to weak login credentials, which leads to their exploitation to launch cyberattacks on other systems, such as distributed denial-of-service attacks. Hence, it is essential to secure these systems from unauthorized access. This paper proposes the Mamdani fuzzy inference system (FIS)-based password checker algorithm to estimate the password strength ratio (PSR) of internet protocol (IP) cameras and internet of things (IoT) devices. This algorithm composes three stages, the password extraction stage, which evaluates the input parameters of FIS from the real-time streaming protocol (RTSP) protocol using a counter of password characters. Then, the processing stage uses Mamdani FIS to optimize the input parameters to calculate the PSR. Finally, the alarm stage will notify the system administrator about weak IoT nodes. Unlike the existing approaches, this algorithm improves detection accuracy by informing the system administrator about threatened nodes. Extensive experiments are carried out to determine the efficiency of the proposed algorithm. The results confirm the efficiency of the proposed algorithm with high accuracy, which outperforms existing schemes.
Percutaneous Left Atrial Appendage Occlusion in Comparison to Non‐Vitamin K Antagonist Oral Anticoagulant Among Patients With Atrial Fibrillation
Authors: Noseworthy Peter A. , Van Houten Holly K. , Krumholz Harlan M. , Kent David M. , Abraham Neena S. , Graff‐Radford Jonathan , Alkhouli Mohamad , Henk Henry J. , Shah Nilay D. , Gersh Bernard J. , Friedman Paul A. , Holmes David R. , Yao Xiaoxi
Published: September 2022

Background

This study aimed to compare percutaneous left atrial appendage occlusion (LAAO) with non‐vitamin K antagonist oral anticoagulants among patients with atrial fibrillation.

Methods and Results

Using a US administrative database, 562 850 patients with atrial fibrillation were identified, among whom 8397 were treated with LAAO and 554 453 were treated with non‐vitamin K antagonist oral anticoagulants between March 13, 2015 and December 31, 2018. Propensity score overlap weighting was used to balance baseline characteristics. The primary outcome was a composite end point of ischemic stroke or systemic embolism, major bleeding, and all‐cause mortality. The mean age was 76.4±7.6 years; 280 097 (49.8%) were female. Mean follow‐up was 1.5±1.0 years. LAAO was associated with no significant difference in the risk of the primary composite end point (hazard ratio [HR], 0.93 [0.84–1.03]), or the secondary outcomes including ischemic stroke/systemic embolism (HR, 1.07 [0.81–1.41]), and intracranial bleeding (HR, 1.08 [0.72–1.61]). LAAO was associated with a higher risk of major bleeding (HR, 1.22 [1.05–1.42],
P
=0.01) and a lower risk of mortality (HR, 0.73 [0.64–0.84],
P
<0.001). The lower risk of mortality associated with LAAO was most pronounced in patients with a prior history of intracranial bleeding.

Conclusions

In comparison to non‐vitamin K antagonist oral anticoagulants, LAAO was associated with no significant difference in the risk of the composite outcome and a lower risk of mortality, which suggests LAAO might be a reasonable option in select patients with atrial fibrillation. The observation of higher bleeding risk associated with LAAO highlights the need to optimize postprocedural antithrombotic regimens as well as systematic efforts to assess and address bleeding predispositions.
Adherence and Discontinuation of Optimal Heart Failure Therapies According to Age: A Danish Nationwide Study
Authors: Garred Caroline H. , Zahir Deewa , Butt Jawad H. , Ravn Pauline B. , Bruhn Jonas , Gislason Gunnar H. , Fosbøl Emil L. , Torp‐Pedersen Christian , Petrie Mark C. , McMurray John J. V. , Køber Lars , Schou Morten
Published: September 2022

Background

Guideline‐recommended disease‐modifying pharmacological therapies for heart failure (HF) with reduced ejection fraction are underutilized, particularly among elderly patients. We studied the association of age in adherence and discontinuation of angiotensin‐converting enzyme inhibitors/angiotensin‐II receptor blockers (ACEi/ARB), β‐blockers (BB), and mineralocorticoid receptor antagonists.

Methods and Results

Patients with a first heart failure diagnosis who had initiated ACEi/ARB and BB within 120 days of presentation were included from nationwide registries and divided into 3 age groups: <65 years (reference), 65 to 79, and ≥80. One‐year median proportions of daily target doses were calculated. Adherence was estimated by the proportion of days covered. The 5‐year risk of discontinuation was assessed with the Aalen‐Johansen estimator. Discontinuation rates were evaluated using Multivariable Cox regression. Twenty‐nine thousand four hundred eighty‐two patients were included. Advancing age was associated with lower median proportions of daily target doses and adherence (ACEi/ARB 79.1%, 77.5%, and 69.4%; BB 79.1%, 78.6%, and 73.8%), in the <65, 65 to 79, and ≥80 age groups, respectively. Age ≥80 was associated with higher discontinuation rates (cumulative incidence, ACEi/ARB 41%, 44%, and 51%; BB 38%, 35%, and 39%; hazard ratio, ACEi/ARB 1.60 [95% CI, 1.51–1.69]; BB 1.33 [95% CI, 1.25–1.41]). The risk of mineralocorticoid receptor antagonists discontinuation differed little with age (50%, 54%, and 56%), although mineralocorticoid receptor antagonists initiation in the most elderly was less frequent (33%, 33%, and 22%).

Conclusions

In a nationwide cohort of patients with heart failure, advanced age was associated with lower proportions of daily target doses, lower adherence, and higher discontinuation rates of ACEi/ARB and BBs. Focus on treatment adherence and optimal dosages among elderly patients with heart failure could improve outcomes.
The Musical Abilities, Pleiotropy, Language, and Environment (MAPLE) Framework for Understanding Musicality-Language Links Across the Lifespan
Authors: Nayak Srishti , Coleman Peyton L. , Ladányi Enikő , Nitin Rachana , Gustavson Daniel E. , Fisher Simon E. , Magne Cyrille L. , Gordon Reyna L.
Published: September 2022

Using individual differences approaches, a growing body of literature finds positive associations between musicality and language-related abilities, complementing prior findings of links between musical training and language skills. Despite these associations, musicality has been often overlooked in mainstream models of individual differences in language acquisition and development. To better understand the biological basis of these individual differences, we propose the Musical Abilities, Pleiotropy, Language, and Environment (MAPLE) framework. This novel integrative framework posits that musical and language-related abilities likely share some common genetic architecture (i.e., genetic pleiotropy) in addition to some degree of overlapping neural endophenotypes, and genetic influences on musically and linguistically enriched environments. Drawing upon recent advances in genomic methodologies for unraveling pleiotropy, we outline testable predictions for future research on language development and how its underlying neurobiological substrates may be supported by genetic pleiotropy with musicality. In support of the MAPLE framework, we review and discuss findings from over seventy behavioral and neural studies, highlighting that musicality is robustly associated with individual differences in a range of speech-language skills required for communication and development. These include speech perception-in-noise, prosodic perception, morphosyntactic skills, phonological skills, reading skills, and aspects of second/foreign language learning. Overall, the current work provides a clear agenda and framework for studying musicality-language links using individual differences approaches, with an emphasis on leveraging advances in the genomics of complex musicality and language traits.
Immigration Lottery Design: Engineered and Coincidental Consequences
of H-1B Reforms
Authors: Pathak Parag A. , Rees-Jones Alex , Sönmez Tayfun
Published: September 2022

The H-1B Visa Reform Act of 2004 dictates an annual allocation of 85,000 visas with 20,000 reserved for advanced-degree applicants. We represent the main requirements of this legislation as formal axioms and characterize visa allocation rules consistent with the axioms. Despite the precise number reserved, we show that the range of implementations satisfying these axioms can change the allocation of advanced-degree visas by as much as 14,000 in an average year. Of all rules satisfying these axioms, the 2019 rule imposed by executive order is most favorable to advanced-degree holders. However, two earlier modifications resulted in larger changes, possibly unintentionally.
A review of digger wasp genus Harpactus Shuckard, 1837 (Hymenoptera: Crabronidae) of the Indian subcontinent, with description of a new species and rediscovery of Harpactus impudens (Nurse, 1903)
Authors: BINOY C. , KUMAR P. GIRISH , MONKS JOSEPH , SHEIKH ALTAF HUSSAIN
Published: September 2022

The digger wasp genus Harpactus Shuckard (Crabronidae: Bembicinae) of the Indian subcontinent is reviewed, resulting in the discovery of a new species H. pulawskii Binoy & Girish Kumar and the rediscovery of H. impudens (Nurse) after more than a century since its description. All species are illustrated, including the holotypes of H. ornatus F. Smith and H. vividus (Turner) whose illustrations were provided by the Natural History Museum, London (NHMUK). Brief diagnoses and a key to species are also given.
Toward a Coherent Approach to Market Power in the Digital Sector: Complexity, Growth through Acquisition, and Remedies
Authors: Moss Diana L.
Published: September 2022

The Digital Business Ecosystem (DBE) model far surpasses other models and structures in its scope, scale, and complexity. DBEs feature unique economic, technological, business, and growth characteristics that increase their opacity to consumers, competition enforcers, and lawmakers. These include a range of market failures, the role of cloud computing in realizing the DBE value proposition, and growth through acquisition.The United States is making slow progress in addressing competition concerns in the digital sector. Legislative initiatives remain focused on the largest players, and there is little political appetite for a dedicated sector regulator to develop a system of non-discrimination “access” regulation. This article discusses the implications of the widening gap between the complexity and growth of DBEs, and policy responses to the market power problems they raise. The analysis recommends a more coherent approach centered on identifying policy tools—including antitrust, regulation, and privacy law—that are best suited to addressing the unique features of DBEs and that work in a complementary way.
Privacy and Competition: Discord or Harmony?
Authors: Ohlhausen Maureen K. , Rossen Ben
Published: September 2022

Privacy and competition law pursue different goals that do not always align, but they can be harmonized under the right approach. Recent proposals in Congress, however, put privacy and competition values in direct conflict, likely at the expense of consumer privacy. Rather than diminishing consumer privacy and security through competition legislation, Congress should enact a comprehensive consumer privacy law that protects consumers while leveling the playing field for all competitors. This article addresses recent calls to regulate consumer privacy and the commercial use of data through competition law. First, we address the changing landscape of consumer privacy and calls for additional regulation. We then discuss recent proposals to use competition law to address issues stemming from widespread data collection and aggregation. Finally, we describe the collision course of antitrust and privacy in recent legislative proposals and offer an alternative path forward.
Toward Empowered Identities: An Exploration of Female ESL Learners’ Literacy Through Digital Storytelling
Authors: Pandhiani Saima Murtaza
Published: September 2022

This research explores the learner identity of female students learning English as a second language (ESL) at a public university in Pakistan through digital texts of identity (DTIs) created by them and learners’ associations of educational experiences across time and locations which impact their emergent identities. Following the poststructuralist framework on identity negotiation and “thematic” and “dialogic/performative” analysis, and image analysis framework by Kress and van Leeuwen, 33 DTIs were gathered and analyzed from 10 female ESL learners. The function of gender as a fundamental identity marker in educational experiences was investigated in particular, in order to demonstrate how social identity is established through multiple discourses. The study highlights DTIs’ ability to strengthen learners’ identities and promote more empowered identities through diverse learning environments. The main argument of the research is that female learners use digital stories to create an interpersonal space that expresses strong ties between their family and everyday locations, such as university, and the target language population, all of which shape their social identities as women and learners. The participants felt empowered by associating themselves with competent users of English making authority claims, developing their authority in and via their digital texts.
Transversus abdominis plane block in rats: Preliminary cadaveric studies
Authors: Burrows Chris S , Duncan Juliet C , Martinez-Taboada Fernando
Published: September 2022

The transversus abdominis plane (TAP) block is an ultrasound guided regional anaesthetic technique used to provide analgesia to the abdominal wall. Research in humans and cats has demonstrated that TAP blocks reduce pain and post-operative opioid requirements after abdominal surgery. To date TAP blocks have not been described in rats. The optimal technique to employ when performing TAP blocks is controversial with single point injection techniques failing to reliably provide adequate coverage of the cranial abdominal wall. It has been suggested that performing a two point injection may provide more reliable coverage of the cranial abdominal wall.

The objective of this study was to determine the feasibility of performing ultrasound guided TAP blocks in rat cadavers and to evaluate whether performing a two point technique provides greater spread of injectate than a one point technique when administering the same total volume of methylene blue solution.

Twenty-three, four-month-old, female Sprague Dawley rat cadavers weighing 506 ± 78 g were used. TAP blocks were performed using a total of 1 ml/kg of methylene blue solution.

Overall success rates for injections were 21.7% (13.6–32.8%). Single point injection area of spread was 87.8 ± 32 mm2 compared with 102.4 ± 17 mm2 for the two point injection technique.

Due to the low success rate the use of TAP blocks using the current technique cannot be recommended. Two point injection technique appears to provide greater spread; however, additional data is required to draw meaningful conclusions.
From defying to (re-)defining Europe in Viktor Orbán’s discourse about the past
Authors: Sadecki Andrzej
Published: September 2022

One of the salient features of the recent populist turn in Europe has been a redefinition of the European. Traditionally, (far) right-wing parties defined themselves as Eurosceptic and focused on national identity. Increasingly, however, they have referred to pan-European heritage, although against the mainstream conceptualization of it. The article looks at the case of Hungary and the image of Europe constructed by the Hungarian Prime Minister Viktor Orbán, whose rhetoric has become a key reference point for other European populist movements. The past decade his government has witnessed, on the one hand, a tendency to redefine what is European against the dominant and institutionalized interpretations, and, on the other, an attempt to single-out the concept of Central Europe as the locus for maintaining and nurturing European values in contrast to perceived Western decline. This paper examines this discourse through a close reading of major speeches delivered by Orbán on the anniversaries of historical events. Analysis of these speeches reveals the ideological foundations of his political project and presents historical and philosophical interpretations of the European political situation. It seeks to identify how Orbán’s discourse evolved across time, by putting the speeches about the past into the present political context.