bluuebunny/crossref_metadata_2025 huggingface dataset:
A remote health monitoring framework for heart disease and diabetes prediction using advanced artificial intelligence model
Authors: Ramesh Adari , Subbaraya Ceeke Kalappagowda , Krishnegowda Ravi Kumar Guralamata
Published: February 2023

Remote health monitoring frameworks gained significant attention due to their real intervention and treatment standards. The proposed work intends to design an artificial intelligence (AI) based remote health monitoring framework for predicting heart disease and diabetes from the given medical datasets. In this framework, the smart devices are used to gather the health information of patients, and the obtained information is integrated together by using different nodes that includes the detecting node, visualization node, and prognostic node. Then, at that point, the health care dataset preprocessing is performed to standardize the characteristics by recognizing the missing qualities and taking out the unessential characteristics. Consequently, the unified levy modeled crow search optimization (ULMCSO) algorithm is employed to select the optimal features based on the global fitness function, which helps increase the accuracy and reduce the training time of the classifier. Finally, the probabilistic guided naïve distribution (PGND) based classification model is utilized for predicting the label as to whether normal or disease affected. During an evaluation, two different datasets, such as PIMA and Hungarian, are used to validate and compare the results of the proposed model by using various performance measures.
Query-based image tagging model using ensemble learning with enhanced artificial bee colony optimization
Authors: Devareddi Ravi Babu , Srikrishna Atluri
Published: February 2023

Digital images make up most multimedia data and are analysed in computer vision applications. Daily uploads of millions of pictures to Internet archives such as satellite image repositories complicate multimedia content and image graphs. As feature vectors, content based image retrieval (CBIR) and image classification models represent high-level image viewpoints. Observing photos recognizes objects and evaluates their significance for image enhancement. To access the visual information of big datasets, efficiently retrieve and query picture graphs. The artificial bee colony (ABC) algorithm is inspired by the foraging behaviour of honeybee swarms. ABC is susceptible to laziness in convergence and local optimums, just like other optimization methods. This study created an enhanced ABC (EABC) model to enhance precision. This study presents query-based image tagging model using ensemble learning with EABC (QbITM-ELEABC) for CBIR for appropriately tagging images based on the query image. We examine a number of convolutional neural network (CNNs) with varying topologies that can be trained on the dataset with varying degrees of similarity. As representations, each network extracts class probability vectors from images. The final image representation is created by combining the ensemble's class probability vectors with image.
A comprehensive survey on deep-learning based gait recognition for humans in the COVID-19 pandemic
Authors: Sayeed Md Shohel , Yusof Ibrahim Bin , Abdullah Mohd Fikri Azli bin , Bari Md Ahsanul , Min Pa Pa
Published: February 2023

Human gait recognition is a biometric technique that has been utilized for security purposes for the last decade. Gait recognition is an appealing biometric modality that aims to identify individuals based on the way they walk. The outbreak of the novel coronavirus (COVID-19), has spread across the world. The number of people infected with COVID-19 is rising rapidly throughout the world. Even though some vaccines for this pandemic have been developed to minimize the effects of COVID-19, deep learning-based gait recognition techniques have shown themselves to be an effective tool for identifying the individuals wearing face mask in COVID-19 pandemic. These techniques play an important part in reducing the rate of COVID-19 spreading throughout the world in the context of the COVID-19 pandemic. Deep learning methods are currently dominating the state-of-the-art in gait recognition and have fostered real-world applications. The main objective of this paper is to provide a comprehensive overview of recent advancements in gait recognition with deep learning, including datasets, test protocols, state-of-the-art solutions, challenges, and future research directions. The purpose of this discussion is to identify current challenges that need to be addressed as well as to suggest some directions for future research that could be explored.
Intelligent malware classification based on network traffic and data augmentation techniques
Authors: Jasim Ammar D. , Farhan Rawaa Ismael
Published: February 2023

To prevent detection, attackers frequently design systems to rearrange and rewrite their malware automatically. The majority of machine learning techniques are not sufficiently resistant to such re-orderings because they develop a classifier based on a manually created feature vector. Deep learning techniques like convolutional neural networks (CNN) have lately proven to perform better than more traditional learning algorithms, especially in applications like picture categorization. As a result of this success, CNN network proposed with data augmentation techniques (to enhance the performance) to classify malware samples. We trained a CNN to classify the photos using converted grayscale images from malware files. Our methodology outperforms other methods with an accuracy of 98.80%, according to experimental results.
Imbalanced dataset classification using fuzzy ARTMAP and computational intelligence techniques
Authors: Kushwaha Anita , Pandey Ravi Shanker
Published: February 2023

Recently, fuzzy adaptive resonance theory mapping (ARTMAP) neural networks are applied to solving complex problems due to their plasticity-stability capability and resonance property. An imbalanced dataset occurs when there is the presence of one class containing a greater number of instances than other classes. It is skewed representation of data. Many standard algorithms have failed in mitigating imbalanced dataset problems. There are four paradigms used-data level, algorithm level, cost-sensitive, and ensemble method in solving imbalanced dataset problems. Here we put forward a method to solve the imbalanced dataset problem by a brain-neuron framework and an ensemble of a special type of artificial neural network (ANN) called fuzzy ARTMAP thereafter we applied a clustering algorithm known as fuzzy C-means clustering to handle missing value and also propose to make fuzzy ARTMAP cost-sensitive. Results indicate 100% accuracy in classification.
Prediction of heart disease outcomes using machine learning classifier
Authors: Adeniyi Kehinde Marvelous , Oladapo Olasunkanmi James , Araoye Timothy Oluwaseun , Adebayo Taiwo Felix , Egoigwe Sochima Vincent , Odo Mathew Chinedu
Published: February 2023

The responsibility of heart organ is to supply blood to every part of the human body. The method of diagnose heart disease in medical hospital is extremely costly and also consume doctors time of operations. This research work applied forward, backward, and enter method for selection of variables in the logistic regression model, sensitivity, specificity, accuracy, and area under characteristic curve (AUC). The logistic regression model, at 5% level of significance with the enter method is used which denotes that the risk variables associated with heart disease gives accuracy of 87.9%. The preferred model of variable selection method used was the model from forward which has 88.6%. Also using the forward method of variables selection, the process produces 10 models with the best accuracy of 88.6%. The specificity and sensitivity of the analysis model was 91.4% and 85.6%. Also, the misclassification rate was also 11.4%, Positive predicted value is 87% and negative predicted value is 90.5%. Finally, the suitable model to predict the heart disease is from the forward method of variables selection and the positive likelihood ratio is 6 i.e the patients are 6 times likely to have the heart disease and the model has AUC value of 1.
Speech scrambling based on multiwavelet and Arnold transformations
Authors: Hasan Zahraa Abdulmuhsin , Hadi Suha Mohammed , Al-Jawher Waleed Ameen Mahmoud
Published: February 2023

For communication applications where secure speech signal transmissions are a key requirement, speech scrambler is taken into consideration. To prevent someone from listening in on private conversations without their knowledge, it can transform clear speech into a signal that is unintelligible. The proposed speech scrambling system involves using two types of frequency transformation techniques: multiwavelet transform and Arnold transform. The effectiveness of the scrambling algorithm was evaluated with the help of three different measurements: the peak signal to noise ratio (PSNR), the estimated time (ET), and the mean square error (MSE). According to the final findings, the outcome of the scrambled speech signal does not have any residual intelligibility, while the quality of the descrambled speech is extremely satisfactory and has a low MSE level.
Classification of electrocardiogram signals based on federated learning and a gaussian multivariate aggregation module
Authors: Meqdad Maytham N. , Hussein Abdullah Hasan , Husain Saif O. , Jawad Alyaa Mohammed
Published: February 2023

Categorization of cardiac abnormalities received from several centers is not possible within the quickest time because of privacy and security restrictions. Today, individuals’ security problem is considered as one of the most important research fields in most research sciences. This study provides a novel approach for detection of cardiac abnormalities based on federated learning (FL). This approach addresses the challenge of accessing data from remote centers and presents the possibility of learning without the need for transferring data from the main center. We present a novel aggregation approach in the FL for addressing the challenge of imbalanced data using the averaging stochastic weights (SWA) optimizer and a multivariate Gaussian in order to make a better and more accurate detection possible. The advantage of the present proposed approach is robust and secure aggregation for unbalanced electrocardiogram (ECG) data from heterogeneous clients. We were able to achieve 87.98% accuracy in testing with the robust VGG19 architecture.
Energy aware optimized dynamic routing mechanism in wireless sensor networks
Authors: Patil Geeta , Bhavikatti Arvind Mallikarjun
Published: February 2023

A trade-off between energy efficiency and optimized routing is massively recommended for transmission efficiency enhancement in wireless sensor networks (WSNs). Therefore, in this paper, graph-based energy optimized dynamic routing (GEODR) mechanism is introduced to set up a balance between energy consumption minimization and throughput enhancement using a dynamic and optimized routing mechanism in WSNs. A clustering scheme is employed based on graph theory, and cluster boundaries are formed using distance vectors. Cluster head (CH) selection is performed based on residual energy, the distance between CHs, and the mobility of the sink node. Each cluster is scattered with multiple tiny nodes, and event monitoring is performed. A model for graph-based dynamic routing to transmit data packets, cluster and cluster boundary formation, and optimization of routing problems is discussed. The performance efficiency of the proposed GEODR mechanism is determined by taking 100 sensor nodes, and 20 nodes are selected as CHs in a sensor network, and several other network parameters are also considered. A massive improvement in energy is observed by using sink node mobility. Experimental results are obtained using the proposed GEODR mechanism in terms of data packet transmission, alive nodes, dead nodes, and residual energy and compared against classical routing mechanisms such as low energy adaptive clustering hierarchy (LEACH) and stable election protocol (SEP).
Hermitan matrices based malicious cognitive radio detection and bayesian method for detecting primary user emulation attack
Authors: Jeyakumar Devasahayam Joseph , Shanmathi Boominathan , Smitha Parappurathu Bahulayan , Vinurajkumar Sekar , Murali Mohanan , Mariselvam Muthuraj
Published: February 2023

Cognitive radio (CR) is a facilitating technology to efficiently deal with the spectrum scarceness, and it will significantly enhance the spectrum deployment of upcoming wireless transmission method. Security is a significant concern, although not well tackle in cognitive radio networks (CRN). In CR networks, this approach regard as a security issue happen from primary user emulation attack (PUEA). A PUEA attacker forwards an emulated primary signal and defraud the CR users to avoid them from accessing spectrum holes. Here, we introduce a Hermitan matrices based malicious cognitive radio (CMCR) detection and Bayesian method for detecting PUEA attack in the CRN. In this approach, the Bayesian method is used for detecting the PUEA attack. The trust analyzer evaluates the CR trust. Here, the node trust value is computed by node activeness and inactiveness, degree of data transmission, and hermitan matrics verification. In addition, the Hermitan Matrices method is used to detect the malicious CR user in the CRN. The simulation outcomes propose that the CMCR leads to improve the performance in terms of better detection ratio, minimized the possibility of miss detection ratio. Furthermore, it minimized the possibility of false alarm in the CRN.
Resource allocation optimization for mitigating multi-jammer in underwater sensor network
Authors: Bagali Sheetal , Sundaraguru Ramakrishnan
Published: February 2023

Wireless underwater sensor networks (UWSNs) are used for coastal area monitoring and military monitoring applications, such as tsunami prevention and target tracking. In UWSNs, jamming is considered to be a serious problem where the intruder affects the lifetime of sensor motes and impacts the performance of the packet transmission. This paper considers that the jammer device is capable of reducing battery life and preventing the trustworthy UWSN mote from communication. Considering the presence of multiple jammers, the existing resource utilization model is not effective. This work presents an efficient resource allocation design to mitigate multiple jammers in UWSNs to overcome research problems. The resource allocation (ERA) model adopts a cross-layer design and can interact in a cooperative manner using direct and hop-based communication to maximize the quality of resource use. Compared to existing resource allocation methodologies, considering the presence of multiple jammer motes, the ERA achieves a much better detection rate, resource utilization, packet drop, and energy efficiency performance.
Development of real-time monitoring BLE-LoRa positioning system based on RSSI for non-line-of-sight condition
Authors: Ja'afar Abd Shukur , Suseenthiran Kavetha , Saipullah Khairul Muzammil , Aziz Mohamad Zoinol Abidin Abd , Khang Adam Wong Yoon , Salleh Azahari
Published: February 2023

Indoor positioning has become popular in this decade and is used to locate users or objects in indoor environments. This is because global positioning system (GPS) is not efficient for indoor use due to the multipath fading effect. This research is about development bluetooth low energy (BLE) indoor positioning system with the aid of long range (LoRa) network and guideline on selection of the BLE beacons. Next, positioning systems are developed consisting of BLE beacons, a transceiver of hybrid BLE-LoRa module, a LoRa receiver and Raspberry Pi as real-time monitoring. The received signal strength indicator (RSSI) and BLE Mac address from BLE beacons received via LoRa network are analyzed using the positioning algorithm designed in MATLAB. The positioning algorithm incorporates distance estimation, filter implementation and trilateration technique. The estimated location is analyzed with the root mean square error (RMSE) and cumulative distribution function (CDF). According to the results, implementing the filter reduces the positioning accuracy error, achieving 90% accuracy of positioning error less than 1.20 meters for the whole testbed. Finally, the algorithm is embedded into Raspberry Pi to view the location via desktop.
Implementation of a secure wireless communication system using true random number generator for internet of things
Authors: Meitei Huirem Bharat , Kumar Manoj
Published: February 2023

This paper describes the design and implementation of an internet of thing (IoT)-based application that uses a true random number generator (TRNG) with an all digital phase locked loop (ADPLL) for secure wireless communication. Field programmable gate array (FPGA) boards were used on the transmitter and receiver sides and were interfaced with Esp8266 chips to wirelessly send and receive encrypted sensor data. The MQ-2 gas sensor and tracking sensor were connected to the FPGA board on the transmitter side, where data from the sensors was encrypted using the exclusive-OR (XOR) function and the TRNG architecture. The system can be controlled by users through a web browser served by the ThingSpeak cloud. The Artix-7 FPGA device is used to implement the proposed wireless communication system, for which design and synthesis were done using the Xilinx Vivado 2015.2 tool. The proposed system uses a low amount of power and is suitable for a standalone, highly secure TRNG-based IoT application. The National Institute of Standard and Testing (NIST SP 800-22) test showed that ADPLL with finite impulse response (FIR) filter-based TRNGs are better for encrypting IoT devices for secure wireless communication.
Peak to average power ratio reduction in spectrally efficient FDM using repeated clipping and filtering
Authors: Oman Buthaina Mosa , Majeed Yamaan Esmaeel , Ahmad Iftekhar
Published: February 2023

Multi-carrier transmission may be considered one of the important developments in wireless communications. Spectrally efficient frequency division multiplexing (SEFDM) is a promising multi-carrier modulation which can significantly improve utilization of spectral. The SEFDM has high peak to average power ratio (PAPR) like any multicarrier system. High PAPR reduces the random forest (RF) transmitter power amplifier efficiency, which minimize the use of this technique in limited power supply transmitters. In this work, a repeated clipping and filtering method is introduced to reduce the PAPR in SEFDM with minimum or no out of band radiation. The results of the simulated approach show that the PAPR of the SEFDM was reduced from 16.264 dB to 7.9146 dB with marginal degradation in system performance when the clipping ratio varied from 4 to 2.
Longitudinal Study of Emotional Intelligence, Well-being, and Burnout of Surgical and Medical Residents
Authors: Wasfie Tarik , Kirkpatrick Heather , Barber Kimberly , Hella Jennifer R. , Anderson Tara , Vogel Mark
Published: February 2023

Introduction

Emotional intelligence (EI) as a concept is becoming increasingly relevant in the healthcare industry. In order to examine the relationship between EI, burnout, and wellness, we administered these measures quarterly in resident physicians and analyzed the variables in each subset to gain insights and understanding of their relationship.

Methods

In 2017 and 2018, all residents entering the training programs in year one (PGY-1) were administered The Emotional Intelligence Questionnaire – Short Form (TEIQue-SF), The Maslach Burnout Inventory (MBI), and The Physician Wellness Inventory (PWI). The questionnaires were completed quarterly. Statistical analysis included ANOVA and ANCOVA.

Results

The overall combined PGY-1 resident year (n = 80) had an EI global trait mean score of 5.47 (SD: 0.59) at the beginning of their first year. The domains of burnout and physician wellness were examined across four different time points during the resident’s first year. Domain scores changed significantly over the four time points during the first year. There was a relative 46% increase in exhaustion ( P < .001), 48% increase in depersonalization ( P < .001), and an 11% decrease in personal achievement ( P < .001). Physician wellness domains also changed significantly between time 1 and the end of the year (time 4). There was a relative 12% decrease in career purpose ( P < .001), a 30% increase in distress ( P < .001), and 6% decrease in cognitive flexibility ( P < .001). Each burnout domain and physician wellness domain were highly correlated with emotional quotient (EQ). Emotional quotient was independently assessed with each domain at baseline and with changes overtime. The lowest EQ group reported their distress increased significantly over time ( P = .003) and a decline in career purpose ( P < .001) and cognitive flexibility ( P = .04). The response rate was 100%.

Conclusion

Emotional intelligence is associated with well-being and burnout in individual residents; therefore, it is important to identify those who require increased support during residency in order to succeed.
Distal Pancreatic Necrosis After Splenic Angioembolization
Authors: Yoo Rachel , Gaziano Michael D. , Cortes Vicente , Chauhan Nikunj , Bamberger Peter K. , Reilly Eugene F. , Geng Thomas , Butts Christopher A. , Ong Adrian
Published: February 2023

Pancreatic ischemia with necrosis is an extremely rare complication of splenic angioembolization (SAE). A 48-year-old male with a grade IV blunt splenic injury underwent angiography which demonstrated no active bleeding or pseudoaneurysm. Proximal SAE was performed. One week later, he developed severe sepsis. Repeat CT imaging showed nonperfusion of the distal pancreas, and laparotomy found necrosis of approximately 40% of the pancreas. Distal pancreatectomy and splenectomy were performed. He endured a prolonged hospital course with multiple complications. Clinicians should have a high index of suspicion for ischemic complications after SAE when sepsis develops.
Surveillance Imaging Associated With Delayed Splenectomy in High-Grade Blunt Splenic Trauma
Authors: Hawley Kristy L. , Dhillon Navpreet K. , DuBose Joseph J. , Kozar Rosemary A. , Scalea Thomas M. , Harfouche Melike N.
Published: February 2023

This retrospective, single-site study at a level I trauma center (2016-2021) sought to determine whether repeat CT had an impact on clinical decision making after splenic angioembolization following blunt splenic trauma (grades II-V). The primary outcome was need for intervention after subsequent imaging (defined as angioembolization and/or splenectomy) by high- or low-grade injury. Of the 400 individuals examined, 78 (19.5%) underwent intervention after repeat CT, from which 17% were in the low-grade group (grades II and III) and 22% were in the high-grade group (grades IV and V). Individuals in the high-grade group were 3.6 times more likely to undergo delayed splenectomy than those in the low-grade group ( P = .006). Delayed intervention after surveillance imaging in blunt splenic injury is driven mostly by the identification of new vascular lesions and leads to greater rates of splenectomy in high-grade injuries. Surveillance imaging should be considered for all AAST injury grades II or higher.
Acute Traumatic Subdural Hematoma in the Elderly and Associated Factors that May Influence Chronicity
Authors: Wasfie Tarik , Young Carly , Naisan Mursal , Senger Barbara , Brimmeier Alexis , Sobell Florence Ann , Stanbaugh Charlotte , Hille Jennifer , Hella Jennifer , Barber Kimberly
Published: February 2023

Traumatic acute subdural hematomas (TASDH) is by far the most common traumatic brain injury in adult patients with blunt trauma, who presented to the Emergency Department (ED). One of the serious sequale of TASDH is the development of Chronic Subdural Hematomas (CSD) with associated deterioration in mental status and convulsion. 1 , 2 Studies to identify the risk factors that favors development of chronicity of TASDH are few and inconclusive. As seen in our prior initial study, there were few factors which were common in those who developed chronicity of their TASDH, and we elected to expand our pool of patients to include those admitted between the years of 2015 and 2021 with ATSDH and identify the common factors associated with development of CSD.
Geospatial Analysis of Prehospital Triage and Early Potential Preventable Traumatic Deaths
Authors: Klutts Garrett N. , Kalkwarf Kyle J. , Yang Yijiong , Gill Joseph P. , Wade Charles E. , Persse David , Wolf Dwayne A. , Deloach Joe P. , Smedley Weston A. , Corbin Seana L. , Schulz Kevin , Tabor Jeff , Bhavaraju Avi , Drake Stacy
Published: February 2023

Severely injured patients often depend on prompt prehospital triage for survival. This study aimed to examine the under-triage of preventable or potentially preventable traumatic deaths. A retrospective review of Harris County, TX, revealed 1848 deaths within 24 hours of injury, with 186 being preventable or potentially preventable (P/PP). The analysis evaluated the geospatial relationship between each death and the receiving hospital. Out of the 186 P/PP deaths, these were more commonly male, minority, and penetrating mechanisms when compared with NP deaths. Of the 186 PP/P, 97 patients were transported to hospital care, 35 (36%) were transported to Level III, IV, or non-designated hospitals. Geospatial analysis revealed an association between the location of initial injury and proximity to receiving Level III, IV, and non-designated centers. Geospatial analysis supports proximity to the nearest hospital as one of the primary reasons for under-triage.
Development of a Pancreatic Fistula Prediction Model After Pancreaticoduodenectomy Based on a Decision Tree and Random Forest Algorithm
Authors: Zheng Jisheng , Lv Xiaoqin , Jiang Lihui , Liu Haiwei , Zhao Xiaomin
Published: February 2023

Background

The incidence of postoperative pancreatic fistula (POPF) after pancreaticoduodenectomy (PD) is high. We sought to develop a POPF prediction model based on a decision tree (DT) and random forest (RF) algorithm after PD and to explore its clinical value.

Methods

The case data of 257 patients who underwent PD in a tertiary general hospital from 2013 to 2021 were retrospectively collected in China. The RF model was used to select features by ranking the importance of variables, and both algorithms were used to build the prediction model after automatic adjustment of parameters by setting the respective hyperparameter intervals and resampling as a 10-fold cross-validation method, etc. The prediction model’s performance was assessed by the receiver operating characteristic curve (ROC) and the area under curve (AUC).

Results

Postoperative pancreatic fistula occurred in 56 cases (56/257, 21.8%). The DT model had an AUC of .743 and an accuracy of .840, while the RF model had an AUC of .977 and an accuracy of .883. The DT plot visualized the process of inferring the risk of pancreatic fistula from the DT model on independent individuals. The top 10 important variables were selected for ranking in the RF variable importance ranking.

Conclusion

This study successfully developed a DT and RF algorithm for the POPF prediction model, which can be used as a reference for clinical health care professionals to optimize treatment strategies to reduce the incidence of POPF.
Differential Effects of Procedural Justice? Examining Heterogeneity in the Perceptions and Effects of Procedural Justice Across First-Time and Recurrent Detainees
Authors: van Hall Matthias , Dirkzwager Anja J. E. , van der Laan Peter H. , Nieuwbeerta Paul
Published: February 2023

Several scholars have suggested that perceptions of procedural justice matter less for legitimacy beliefs and compliant behavior among serious offenders, but it remains unknown to what extent this also holds true for male detainees interacting with multiple criminal justice authorities. Using longitudinal data from the Prison Project on adult detainees entering Dutch pre-trial detention centers, the current study examines whether effects of procedural justice—arising from encounters with police officers, prison staff, and judges—on felt obligation to obey and recidivism operate in a different manner for first-time detainees and recurrent detainees. Our findings support the idea that procedural justice exerts differential effects on beneficial outcomes depending on detainees’ prior detention experience.
Patterns and factors associated with consulting traditional healers on the care pathway of psychiatric patients in the United Arab Emirates
Authors: Adel Shaden , Abdel Aziz Karim , El Tabei Dina , Mahfouz Noha Adel , El-Gabry Dina Aly
Published: February 2023

Background:

Despite the availability of mental health services in the United Arab Emirates (UAE), reluctance in seeking a mental health professional is widely prevalent. In many countries, psychiatric patients consult Traditional Healers (THs) prior to seeing mental health professionals. Data from the UAE on the pattern of consulting THs is limited.

Aims:

To investigate patterns and factors for visiting THs in psychiatric patients in Abu Dhabi, the capital of UAE.

Method:

We conducted a cross-sectional study of patients attending the adult psychiatry clinic, Maudsley Health, Abu Dhabi. We assessed 214 patients for the pattern and possible factors for contact with THs on their pathway to psychiatric care.

Results:

There were 58 males and 156 females. Most (43.5%) had a depressive disorder. Prior to consulting a mental health professional, 28% had seen a TH, of whom 36.7% had only one visit and 60% had seen only one TH. Advice from a friend or family member was the commonest reason for consulting THs (81.7%). Envy was the commonest explanation given by THs for symptoms (26.7%). Female gender and having a high school education or less significantly predicted contact with THs.

Conclusions:

Almost a third of our sample consulted THs prior to seeking psychiatric care. Closer collaboration with THs could help bridge the gap with psychiatrists to avoid delays in patients accessing psychiatric care, however caution is needed to mitigate the possible negative effects of such a collaboration.
Purging the Lingering Shadow of Colonialism? Zimbabwe’s Third Chimurenga and the Struggle Over School Names
Authors: Masakure Clement , Nkomo Lotti
Published: February 2023

Drawing on a range of source material that is in public domain, chiefly newspapers and Parliamentary Debates, this article examines the efforts at renaming government schools in early 2000s in Zimbabwe, the debates surrounding the renaming and the results thereof. The renaming of schools by the Zimbabwe African National Union-Patriotic Front (ZANU-PF) government aimed at replacing names that appeared to celebrate colonialism with those that expressed Zimbabwe’s historical anti-colonial struggles. The article attempts to establish the source of this nationalist zeal—the need to purge the lingering shadow of colonialism, particularly continued British influence on Zimbabwe’s political landscape—and analyze the extent to which it was deployed. The article notes that these efforts at decolonizing school names must be understood within the broader Third Chimurenga revolutionary fervor. In doing so, the article highlights that while substantial efforts were invested in this exercise, the lingering shadow of colonialism remained, as many schools retained their colonial names. By focusing on renaming of schools, this article does not only address a neglected subject, but pulls together in one narrative the connections between the renaming of schools, anti-colonial struggles, decolonization and the post-2000 resurgent nationalism hinged on what is popularly known as the Third Chimurenga.
Impact and post-impact flexural property of 3D five-directional braided carbon/glass hybrid epoxy composites
Authors: Fang Dandan , Yan Jianhua , Gu Bohong , Sun Baozhong
Published: February 2023

The impact property and residual flexural property of three-dimensional (3D) five-directional braided carbon/glass hybrid epoxy composites were studied. Four kinds of arrangement for carbon fiber and glass fiber in the axial yarn were designed. Taking pure carbon fiber composites (C/Ca) as the control, the rest of arrangements were that glass fiber and carbon fiber alternately arranged at the width direction (C/G1), glass fiber and carbon fiber alternately arranged at the thickness direction (C/G2), and only glass fiber appeared at the axial yarn (C/G3). Results showed that the C/G2 composite has higher impact energy absorption capacity, and the C/G1 composite has higher impact stress. The flexural properties of C/Ca composite are the best and that of C/G3 composite are the worst before impacting. This study is helpful for the structural design of composites in specific occasions.
Creep behaviour of ozone treated jute fabric/epoxy composites
Authors: Bhattacharyya Debarati , Baheti Vijay
Published: February 2023

The present work attempted to study the creep behaviour of jute fabric/epoxy composites at different environment temperatures using several creep models (i.e., Burger’s model, Findley’s power law model, and Coupling model). The surface of jute fabrics was modified by ozone gas treatment to remove the non-cellulosic materials and thereby improve their adhesion with epoxy matrix. Further, the performance of ozone surface treatment was compared with traditional alkali treatment based on surface morphology, mechanical properties, hydrophilicity, etc. The ozone surface treatment was found to remove lignin and increase hydrophilicity of jute fibres to greater extent as compared to alkali treatment, however with higher tendency of defibrillation and fibre rupture. Later, the creep resistance of alkali treated jute fabric/epoxy composites was found superior to the ozone treated jute fabric/epoxy composites at lower temperature of 40°C and 70°C. However, the ozone treated jute fabric/epoxy composites showed higher instantaneous elastic deformation and lower viscous deformation at elevated temperatures of 70°C and 100°C. The ozone treated jute fabric/epoxy composites showed extended temperature range of 100°C–120°C to restrict segmental mobility of epoxy matrix and depicted higher interfacial shear strength properties from the microbead pull out test.
Porphyromonas gingivalis Evades Immune Clearance by Regulating Lysosome Efflux
Authors: Liu M. , Shao J. , Zhao Y. , Ma B. , Ge S.
Published: February 2023

Porphyromonas gingivalis, a major periodontal pathogen, invades autophagosomes of cells, including gingival epithelial cells, endothelial cells, gingival fibroblasts, macrophages, and dendritic cells, to escape antimicrobial autophagy and lysosome fusion. However, it is not known how P. gingivalis resists autophagic immunity, survives within cells, and induces inflammation. Thus, we investigated whether P. gingivalis could escape antimicrobial autophagy by promoting lysosome efflux to block autophagic maturation, leading to intracellular survival, and whether the growth of P. gingivalis within cells results in cellular oxidative stress, causing mitochondrial damage and inflammatory responses. P. gingivalis invaded human immortalized oral epithelial cells in vitro and mouse oral epithelial cells of gingival tissues in vivo. The production of reactive oxygen species (ROS) increased upon bacterial invasion, as well as mitochondrial dysfunction-related parameters with downregulated mitochondrial membrane potential and intracellular adenosine triphosphate (ATP), upregulated mitochondrial membrane permeability, intracellular Ca2+ influx, mitochondrial DNA expression, and extracellular ATP. Lysosome excretion was elevated, the number of intracellular lysosomes was diminished, and lysosomal-associated membrane protein 2 was downregulated. Expression of autophagy-related proteins, microtubule-associated protein light chain 3, sequestosome-1, the NLRP3 inflammasome, and interleukin-1β increased with P. gingivalis infection. P. gingivalis may survive in vivo by promoting lysosome efflux, blocking autophagosome-lysosome fusion, and destroying autophagic flux. As a result, ROS and damaged mitochondria accumulated and activated the NLRP3 inflammasome, which recruited the adaptor protein ASC and caspase 1, leading to the production of proinflammatory factor interleukin-1β and inflammation.
Impact of Treatment on Host Responses in Young Individuals with Periodontitis
Authors: Branco-de-Almeida L.S. , Velsko I.M. , de Oliveira I.C.V. , de Oliveira R.C.G. , Shaddox L.M.
Published: February 2023

Grade C periodontitis in young individuals is characterized by severe/rapid periodontal destruction, usually early onset, in systemically healthy individuals. An individual’s host response, triggered by a dysbiotic subgingival biofilm, has been reported as a contributor to the tissue destruction, although mechanisms of this response and contributions to such disease remain poorly understood. Nonsurgical treatment has resulted in positive clinical responses for both localized (now molar-incisor pattern) and generalized forms of grade C periodontitis, especially when adjunctive systemic antibiotics are used. Nonsurgical treatment may also affect host responses, although mechanisms leading to significant changes in this response remain unclear. Significant effects on inflammatory response to antigens/bacteria have been described posttreatment, but evidence for long-term effects remains limited. Nonsurgical treatment in these individuals may also modulate a variety of host markers in serum/plasma and gingival crevicular fluid along with clinical parameter improvements. The impact of other adjuncts to nonsurgical treatment focusing on controlling exacerbated immunoinflammatory responses needs to be further explored in grade C periodontitis in young individuals. Recent evidence suggests that nonsurgical treatment with adjunctive laser therapy may modulate host and microbial responses in those subjects, at least in the short term. Available evidence, while very heterogeneous (including variations in disease definition and study designs), does not provide clear conclusions on this topic yet provides important insights for future studies. In this review, studies within the past decade evaluating the impact of nonsurgical treatment on systemic/local host responses in young individuals with grade C periodontitis, as well as long-term clinical responses posttreatment, will be critically appraised and discussed.
Rethinking Dispositions in Teaching and Teacher Education: Virtue and the Manners of Democracy as a Way of Life
Authors: Bullough Robert V.
Published: February 2023

As long as there have been teachers, there has been an expectation that they be of good character and model virtue. The author describes developments in the thought about teacher dispositions and identifies specific shortcomings in the effort to define and assess them as part of teacher education program accreditation. Virtue ethics as an alternative conceptual possibility is considered but found wanting. Seeking to identify a moral framework capable of supporting education, including teacher education program development, he suggests focus on the “manners” of democracy that arise from an analysis of democracy as a distinctive way of life, an essential social and civic practice. Five manners are suggested: hospitality, attuned listening, voice, reflectivity, and evidential discernment. Each manner is briefly described and discussed.
Career Impact of High-Performance Work System: A Kaleidoscope Perspective
Authors: Tran Huy Phuong , Vu Hoang Ngan
Published: February 2023

High-performance work system (HPWS) is considered a solid predictor of individual outcomes, but there is less evidence about the impact HPWS on subjective career success (SCS). The current study examines the direct impact of HPWS on SCS through the lens of the Kaleidoscope Career Model. In addition, employability orientation is expected to mediate the relationship while employees’ HPWS attribution is hypothesized to moderate the HPWS-SCS linkage. A quantitative research design with two-wave survey collected data from 365 employees working in 27 firms in Vietnam. Partial least square structural equation modelling (PLS-SEM) is used to test the hypotheses. Results indicate that HPWS is significantly associated with SCS through the achievements of career parameters. In addition, employability orientation mediates the above relationship while HPWS external attribution moderates the relationship between HPWS and SCS. This research suggests that HPWS may influence employees’ outcomes beyond the current employment, such as career success. HPWS fosters employability orientation which may encourage the employees to seek career advancements outside the current employer. Therefore, HPWS-implementing organizations should provide career opportunities to the employees. In addition, attention should be paid to employees’ evaluative reports of HPWS implementation.
Utilizing Publicly Available Community Data to Address Social Determinants of Health: A Compendium of Data Sources
Authors: Lindenfeld Zoe , Pagán José A. , Chang Ji Eun
Published: February 2023

To compile a compendium of data sources representing different areas of social determinants of health (SDOH) in New York City. We conducted a PubMed search of the peer-reviewed and gray literature using the terms “social determinants of health” and “New York City,” with the Boolean operator “AND.” We then conducted a search of the “gray literature,” defined as sources outside of standard bibliographic databases, using similar terms. We extracted publicly available data sources containing NYC-based data. In defining SDOH, we used the framework outlined by the CDC’s Healthy People 2030, which uses a place-based framework to categorize 5 domains of SDOH: (1) healthcare access and quality; (2) education access and quality; (3) social and community context; (4) economic stability; and (5) neighborhood and built environment. We identified 29 datasets from the PubMed search, and 34 datasets from the gray literature, resulting in 63 datasets related to SDOH in NYC. Of these, 20 were available at the zip code level, 18 at the census tract-level, 12 at the community-district level, and 13 at the census block or specific address level. Community-level SDOH data are readily attainable from many public sources and can be linked with health data on local geographic-levels to assess the effect of social and community factors on individual health outcomes.
Is Helicobacter pylori Anyway Pathogen in Children?
Authors: Manfredi Marco , Gismondi Pierpacifico , Iuliano Silvia
Published: February 2023

Helicobacter pylori ( H. pylori) infection is a continuous challenge for both gastroenterologists and pediatricians. The international guidelines regarding diagnostic and treatment pathways differ between adults and children. The pediatric guidelines are more restrictive because children are rarely affected by serious consequences, particularly in Western countries. Therefore, infected children should be treated only after a careful case-by-case evaluation by a pediatric gastroenterologist. In any case, recent studies are confirming an increasingly all-around pathological role of H. pylori even in asymptomatic children. For these reasons, following the current evidence, we feel that H. pylori-infected children could be treated starting in pre-adolescence, particularly in Eastern countries, because their stomachs have already begun to develop the biomarkers of gastric damage. Therefore, we believe that H. pylori is anyway pathogen in children. Nevertheless, the possible beneficial role of H. pylori in humans has not yet been conclusively disproved.
Factors Associated with Psychosocial Symptoms Experienced by Students in Response to COVID-19 Pandemic: A Multivariate Analysis
Authors: Kadir Abdul , Sunarno Stevan Deby Anbiya Muhammad , Oktaviana Amalia , Zulfah Aldila Faza , Maharani Cynthia Febrina , Santoso Nanda Trio , Lestari Fatma , Erwandi Dadan , Dhesi Surindar Kishen
Published: February 2023

The COVID-19 pandemic has significantly impacted many sectors, including the education sector. After in-person school activities were suspended due to the pandemic, a number of educational institutions in Indonesia reported concerns in implementing online learning due to the institutions’ unpreparedness. This issue may potentially induce mental health disorders among students and triggers long-term stress. This study aimed to examine factors linked to the psychosocial symptoms of anxiety, stress, and depression in response to the early stage of the COVID-19 pandemic. An online cross-sectional study in Indonesia was conducted with 433 undergraduate and senior high school students aged 15 to 26 years old, both female and male. The self-reported symptoms were analyzed using both bivariate and multivariate linear regression methods. It was found that the percentage of participants with depression symptoms was 66%, while 61% and 43% participants experienced stress and anxiety respectively. The bivariate analysis presented strong correlations between anxiety and gender, learning duration and use of gadgets, internet expenses, and highly-interrupted learning. Furthermore, the multivariate regression revealed that only anxiety was significantly linked to internet expenses. This study indicates that many students are affected by COVID-19, the impacts of which manifest in psychosocial issues in the form of anxiety. We suggest that creating a supportive and positive family environment would help to alleviate some of these issues.
Estimating the Impact of New Health Price Transparency Policies
Authors: Parente Stephen T.
Published: February 2023

This analysis investigates and scores the impact of new health price transparency rules. Using a set of novel data sources, we estimate substantial savings are possible following the implementation of the insurer price transparency rule. Specifically, we estimate annual savings to consumers, employers, and insurers by 2025, assuming a robust set of tools to allow consumers to purchase medical services. We matched claims with 70 HHS defined shoppable services by CPT and DRG codes and replaced them with an estimated median commercial allowed payment multiplied by a reduced cost of 40% based on estimates found from literature for the difference in cost between negotiated and cash payment for medical services. We consider 40% to be an upper bound estimate of the potential savings based on existing literature. Several databases are used to estimate the potential benefits of insurer price transparency. Two different all-payer claim databases were used, representing the entire insured population in the US. For this analysis, only the private insurer commercial population was examined, comprised of over 200 million covered lives as of 2021. The estimated impact of price transparency will vary significantly by region and income level. The national upper bound estimate is $80.7 billion. The national lower bound estimate is $17.6 billion. For the upper bound, the region with the most significant impact in the US will be the Midwest, with $20 billion in potential savings and an 8% reduction in medical expenditure. The region with the lowest impact will be the South, with only a 5.8% reduction. Concerning income, those at lower levels of income will have the most significant impact with a −7.4% (<100% Federal Poverty Level) to −7.5% (100%-137% Federal Poverty Level) impact. Overall, the total impact could be a 6.9% reduction for the whole privately insured population in the United States. In summary, a unique set of national data resources were used to estimate the cost savings impact medical price transparency. This analysis suggests price transparency for shoppable services may yields significant savings between $17.6 to $80.7 billion by 2025. Consumers may have strong incentives to shop with the rise in the use of high deductibles, health plans, and health savings accounts. How these potential saving are to be shared by consumers, employers and health plans has yet to be determined.