bluuebunny/crossref_metadata_2025 huggingface dataset:
Four-Week Mentalizing Imagery Therapy for Family Dementia Caregivers: A Randomized Controlled Trial with Neural Circuit Changes
Authors: Jain Felipe A. , Chernyak Sergey V. , Nickerson Lisa D. , Morgan Stefana , Schafer Rhiana , Mischoulon David , Bernard-Negron Richard , Nyer Maren , Cusin Cristina , Ramirez Gomez Liliana , Yeung Albert
Published: March 2022

Introduction: Family caregivers of patients with dementia suffer a high burden of depression and reduced positive emotions. Mentalizing imagery therapy (MIT) provides mindfulness and guided imagery skills training to improve balanced mentalizing and emotion regulation. Objective: Our aims were to test the hypotheses that MIT for family caregivers would reduce depression symptoms and improve positive psychological traits more than a support group (SG), and would increase dorsolateral prefrontal cortex (DLPFC) connectivity and reduce subgenual anterior cingulate cortex (sgACC) connectivity. Methods: Forty-six caregivers participated in a randomized controlled trial comparing a 4-week MIT group (n = 24) versus an SG (n = 22). Resting state neuroimaging was obtained at baseline and post-group in 28 caregivers, and questionnaires completed by all participants. The primary outcome was change in depression; secondary measures included anxiety, mindfulness, self-compassion, and well-being. Brain networks with participation of DLPFC and sgACC were identified. Connectivity strengths of DLPFC and sgACC with respective networks were determined with dual regression. DLPFC connectivity was correlated with mindfulness and depression outcomes. Results: MIT significantly outperformed SG in improving depression, anxiety, mindfulness, self-compassion, and well-being, with moderate to large effect sizes. Relative to SG, participants in MIT showed significant increases in DLPFC connectivity – exactly replicating pilot study results – but no change in sgACC. DLPFC connectivity change correlated positively with mindfulness and negatively with depression change. Conclusions: In this trial, MIT was superior to SG for reducing depression and anxiety symptoms and improving positive psychological traits. Neuroimaging results suggested that strengthening DLPFC connectivity with an emotion regulation network might be mechanistically related to MIT effects.
Minimal Change Disease Secondary to Graft-versus-Host Disease after Allogeneic Hematopoietic Cell Transplant for Myelodysplastic Syndrome
Authors: Elghawy Omar , Wang John S. , Hafey Alexander C. , Renaghan Amanda D. , Whitehair Rachel M. , Kindwall-Keller Tamila L.
Published: March 2022

Chronic graft-versus-host disease (cGVHD) is a leading cause of non-relapse mortality in allogeneic hematopoietic cell transplant (HCT) recipients. While the current standard of care is proactive in detecting cGVHD in the lungs, liver, and skin, cGVHD involving kidneys is an underrecognized and likely underdiagnosed cause of post-HCT renal dysfunction. Nephrotic syndrome (NS) is a very rare complication of HCT that is postulated to be a glomerular manifestation of cGVHD. Herein, we report 2 cases of post-HCT minimal change disease likely secondary to cGVHD. In both cases, the onset of NS coincided with tapering of calcineurin inhibitors, and 1 patient had previously been diagnosed with cGVHD of the lungs. One patient was treated with corticosteroids alone and the other with a corticosteroids and tacrolimus. Complete, sustained remission was achieved in both cases. Our cases illustrate the implications of the association between cGVHD and post-HCT NS for patient care, including the importance of obtaining a renal biopsy to establish an accurate histopathological diagnosis and guide-appropriate treatment.
Diffuse Peritoneal Malignant Mesothelioma Presenting with Abnormal Uterine Bleeding: Case Report
Authors: Rizzuto Ivana , Power Theresa , Oehler Martin Klaus
Published: March 2022

This report describes a highly unusual case of malignant peritoneal mesothelioma (MPM), who presented with abnormal menstrual bleeding due to diffuse infiltration of the uterus. MPM is a rare entity, which on initial clinical presentation can be indistinguishable from a primary gynecological malignancy such as ovarian cancer. As differential diagnosis is challenging among primary care physicians, gynecologists, gynecological oncologists, and pathologists, misdiagnosis and subsequent mismanagement are not uncommon. Immunohistochemical stains were required in our case to help to make the final diagnosis. We included multiple mesothelial markers such as calretinin, CK5/6, WT-1, and D240 in our analysis, in addition to epithelial markers such as Claudin-4, BerEP4, B72.3, and PAX-8, to exclude metastatic adenocarcinoma.
Synovial Chondromatosis in the Acromioclavicular Joint: Diagnosis and Management
Authors: Pire Jonathan R. , Goodloe J. Brett , Emanuel Anthony , Woolf Shane
Published: March 2022

A 31-year-old male presented to clinic with 2 months of aching shoulder pain without inciting injury or recent illness. Radiographs and MRI were obtained and the leading diagnosis was synovial chondromatosis (SC) of the acromioclavicular joint. He was treated with surgical resection. This case represents a rare location for SC, a disease defined by chondral tissue growth within the synovium most commonly affecting the knee and hip. We present this case report and consolidate information published in previous reports to provide guidance and discuss management for a rare and difficult location of this disease.
Design and performance analysis of human body communication digital transceiver for wireless body area network applications
Authors: Bangalore Lokanatha Sujaya , Bhanu Prashanth Sompura Basavaraju
Published: March 2022

Wireless body area network (WBAN) is a prominent technology for resolving health-care concerns and providing high-speed continuous monitoring and real-time help. Human body communication (HBC) is an IEEE 802.15.6 physical layer standard for short-range communications that is not reliant on radio frequency (RF). Most WBAN applications can benefit from the HBC's low-latency and low-power architectural features. In this manuscript, an efficient digital HBC transceiver (TR) hardware architecture is designed as per IEEE 802.15.6 standard to overcome the drawbacks of the RF-wireless communication standards like signal leakage, on body antenna and power consumption. The design is created using a frequency selective digital transmission scheme for transmitter and receiver modules. The design resources are analyzed using different field programmable gate array (FPGA) families. The HBC TR utilizes <1% slices, consumes 101 mW power, and provides a throughput of 24.31 Mbps on Artix-7 FPGA with a latency of 10.5 clock cycles. In addition, the less than 10-4bit error rate of HBC is achieved with a 9.52 Mbps data rate. The proposed work is compared with existing architectures with significant improvement in performance parameters like chip area, power, and data rate.
Economic dispatch by optimization techniques
Authors: Ali Abttan Rana , Hasan Tawafan Adnan , Jaafar Ismael Samar
Published: March 2022

The current paper offers the solution strategy for the economic dispatch problem in electric power system implementing ant lion optimization algorithm (ALOA) and bat algorithm (BA) techniques. In the power network, the economic dispatch (ED) is a short-term calculation of the optimum performance of several electricity generations or a plan of outputs of all usable power generation units from the energy produced to fulfill the necessary demand, although equivalent and unequal specifications need to be achieved at minimal fuel and carbon pollution costs. In this paper, two recent meta-heuristic approaches are introduced, the BA and ALOA. A rigorous stochastically developmental computing strategy focused on the action and intellect of ant lions is an ALOA. The ALOA imitates ant lions' hunting process. The introduction of a numerical description of its biological actions for the solution of ED in the power framework. These algorithms are applied to two systems: a small scale three generator system and a large scale six generator. Results show were compared on the metrics of convergence rate, cost, and average run time that the ALOA and BA are suitable for economic dispatch studies which is clear in the comparison set with other algorithms. Both of these algorithms are tested on IEEE-30 bus reliability test system.
A novel fault location approach for radial power distribution systems
Authors: Hamdouche Tarek , Bendjeghaba Omar , Noureddine Brakta , Aimad Ahriche
Published: March 2022

Power distribution systems (PDS) are increasingly complex and spread over long distances and in different locations. Given their radial configuration, the loads could be inserted at the same distances from the substation. This leads to multiple estimation of fault location (FL) and yields time consuming for iterative FL algorithms. In this article, we provide a novel practical approach to fault localization in order to defeat these limitations. The central idea of the proposed approach is to divide the multilateral distribution system into a possible number of mono-lateral sub systems (MLS) using a proposed communicating sensor. Next, we apply two different fault locator algorithms only to the defective MLS. The first variant of the approach is based on the impedance method, while the second variant is non-parametric used only when there is lack in the line data. To test the proposed technique in practice, we used the IEEE 13 Node test feeder, and a real Algerian PDS. The results obtained clearly show the contribution of the dedicated method for locating faults in the PDS.
Optimization of automobile active suspension system using minimal order
Authors: Amertet Finecomes Sairoel , Gebre Fisseha L. , Mesene Abush M. , Abebaw Solomon
Published: March 2022

This paper presents an analysis and design of linear quadratic regulator for reduced order full car suspension model incorporating the dynamics of the actuator to improve system performance, aims at benefiting: Ride comfort, long life of vehicle, and stability of vehicle. Vehicle’s road holding or handling and braking for good active safety and driving pleasure, and keeping vehicle occupants comfortable and reasonably well isolated from road noise, bumps, and vibrations are become a key research area conducted by many researchers around the globe. Different researchers were tested effectiveness of different controllers for different vehicle model without considering the actuator dynamics. In this paper full vehicle model was reduced to a minimal order using minimal realization technique. The entire system responses were simulated in MATLAB/Simulink environment. The effectiveness of linear quadratic regulator controller was compared for the system model with and without actuator dynamics for different road profiles. The simulation results were indicated that percentage reduction in the peak value of vertical and horizontal velocity for the linear quadratic regulator with actuator dynamics relative to linear quadratic regulator without actuator dynamics was 28.57%. Overall simulation results were demonstrated that proposed control scheme has able to improve the effectiveness of the car model for both ride comfort and stability.
State feedback control for human inspiratory system
Authors: Jumaa Alkurawy Lafta Esmaeel , Mohammed Khalid Gadban , Ismael Ammar Issa
Published: March 2022

The mathematical modeling of human respiratory system is an essentially part in saving precision information of diagnostic about the disease of cardiovascular respiratory system. The physics of respiratory system and cardiovascular are completely interconnected with each other. In this paper, we will study the state feedback control for the inspiratory system during study the characteristics of the response output with the stability. The model of system is nonlinear and linearized it by Tayler method to be simple to matching with the control theory. We convert the system from differential equation to state equation to find the optimal control that helps to drive the respiratory system. Simulations are managed to indicate the proposed method effectiveness. The results of simulations are validated by using a real information form the health center.
Field-programmable gate array design of image encryption and decryption using Chua’s chaotic masking
Authors: Al-Musawi Wisal Adnan , Wali Wasan A. , Ali Al-Ibadi Mohammed Abd
Published: March 2022

This article presents a simple and efficient masking technique based on Chua chaotic system synchronization. It includes feeding the masked signal back to the master system and using it to drive the slave system for synchronization purposes. The proposed system is implemented in a field programmable gate array (FPGA) device using the Xilinx system generator tool. To achieve synchronization, the Pecora-Carroll identical cascading synchronization approach was used. The transmitted signal should be mixed or masked with a chaotic carrier and can be processed by the receiver without any distortion or loss. For different images, the security analysis is performed using the histogram, correlation coefficient, and entropy. In addition, FPGA hardware co-simulation based Xilinx Artix7 xc7a100t-1csg324 was used to check the reality of the encryption and decryption of the images.
Fish classification using extraction of appropriate feature set
Authors: Badawi Usama A.
Published: March 2022

The field of wild fish classification faces many challenges such as the amount of training data, pose variation and uncontrolled environmental settings. This research work introduces a hybrid genetic algorithm (GA) that integrates the simulated annealing (SA) algorithm with a back-propagation algorithm (GSB classifier) to make the classification process. The algorithm is based on determining the suitable set of extracted features using color signature and color texture features as well as shape features. Four main classes of fish images have been classified, namely, food, garden, poison, and predatory. The proposed GSB classifier has been tested using 24 fish families with different species in each. Compared to the back-propagation (BP) algorithm, the proposed classifier has achieved a rate of 87.7% while the elder rate is 82.9%.
A deep locality-sensitive hashing approach for achieving optimal ‎image retrieval satisfaction
Authors: Karamti Hanen , Shaiba Hadil , Mahmoud Abeer M.
Published: March 2022

Efficient methods that enable high and rapid image retrieval are continuously needed, especially with the large mass of images that are generated from different sectors and domains like business, communication media, and entertainment. Recently, deep neural networks are extensively proved higher-performing models compared to other traditional models. Besides, combining hashing methods with a deep learning architecture improves the image retrieval time and accuracy. In this paper, we propose a novel image retrieval method that employs locality-sensitive hashing with convolutional neural networks (CNN) to extract different types of features from different model layers. The aim of this hybrid framework is focusing on both the high-level information that provides semantic content and the low-level information that provides visual content of the images. Hash tables are constructed from the extracted features and trained to achieve fast image retrieval. To verify the effectiveness of the proposed framework, a variety of experiments and computational performance analysis are carried out on the CIFRA-10 and NUS-WIDE datasets. The experimental results show that the proposed method surpasses most existing hash-based image retrieval methods.
Combining 3D run-length encoding coding and searching techniques for medical image compression
Authors: Arif Arif Sameh , Jassim Muntaha Abood
Published: March 2022

The field of image compression became a mandatory tool to face the increasing and advancing production of medical images, besides the inevitable need for smaller size of medical images in telemedicine systems. In spite of its simplicity, run-length encoding (RLE) technique is a considerably effective and practical tool in the field of lossless image compression. Such that, it is widely recommended for 2D space that utilizes common searching techniques like linear and zigzag. This paper adopts a new algorithm taking advantage of the potential simplicity of the run-length algorithm to contribute a volumetric RLE approach for binary medical data in the 3D form. The proposed volumetric-RLE (VRLE) algorithm differs from the 2D RLE approach utilizing correlations of intra-slice only, which is used for compressing binary medical data utilizing voxel-correlations of inter-slice. Furthermore, several forms of scanning are used to extending proposed technique like Hilbert and Perimeter, which determines the best possible procedure of scanning suitable for data morphology considering the segmented organ. This work employs proposed algorithm on four image datasets to get as sufficient as possible evaluation. Experimental results and benchmarking illustrate that the performance of the proposed technique surpasses other state-of-the-art techniques with 1:30 enhancement on average.
Landslide assessment using interferometric synthetic aperture radar in Pacitan, East Java
Authors: Bayu Ichsandya Dimas , Dimyati Muhammad , Ash Shidiq Iqbal Putut , Zulkarnain Faris , Sri Rahatiningtyas Nurul , Putera Syamsuddin Riza , Makarim Zein Farhan
Published: March 2022

Landslides are a common type of disaster in Indonesia, especially in steep-slope areas. The landslide process can be well understood by measuring the surface deformation. Currently, there are no practical solutions for measuring surface deformation at landslide locations other than field surveys in the Pacitan Regency. We apply LiCSBAS, to identify surface deformation in several landslide locations in a specific non-urban area with mixed topographical features. LiCSBAS is a module that utilizes data from the project of looking inside the continent from space (LiCS), using the new small baseline area subset (NSBAS) method. This study utilizes the leaf area index (LAI) to validate the ability of LiCSBAS to detect surface deformation values at landslide locations. The study succeeded in identifying surface deformations at 100 landslide locations, with deformation values ranging from 15.1 to 10.9 millimeters per year. Most of the landslide locations are closely related to volcanic rocks and volcanic sediments on slopes of 30–35°. The NSBAS method in the LiCSBAS module can reduce gaps error in the sentinel-1 image network. However, the utilization of the C-band at a pixel size of 100 meters made surface deformation only well detectable in a large open landslide area.
Optimum consultation for serial distributed detection systems
Authors: Al-Ababneh Nedal , Aldiabat Hasan
Published: March 2022

This paper considers a distributed detection system which consists of  sensors that are connected in series. The observations of each sensor in this system design are considered to be statistically independent of all other sensors. In contrast to the popular serial decision fusion systems, we assume that consultations are allowed in a serial manner between successive sensors that make up the system. In addition, the paper demonstrates the similarity between the proposed consulting serial system and the optimal serial one in terms of detection probabilities for a give probability of false alarm. However, it should be emphasized that the proposed system has the benefit of conditional nonrandom consultation among the sensors. Consequently, its survivability is higher than that of serial systems. Numerical evaluations for the cases of two and three sensors are provided and compared with those of the serial as well as the centralized schemes.
Data protection based neural cryptography and deoxyribonucleic acid
Authors: Kadum Sahar Adill , Al-Sultan Ali Yakoob , Hadie Najlaa Adnan
Published: March 2022

The need to a robust and effective methods for secure data transferring makes the more credible. Two disciplines for data encryption presented in this paper: machine learning and deoxyribonucleic acid (DNA) to achieve the above goal and following common goals: prevent unauthorized access and eavesdropper. They used as powerful tool in cryptography. This paper grounded first on a two modified Hebbian neural network (MHNN) as a machine learning tool for message encryption in an unsupervised method. These two modified Hebbian neural nets classified as a: learning neural net (LNN) for generating optimal key ciphering and ciphering neural net CNN) for coding the plaintext using the LNN keys. The second granulation using DNA nucleated to increase data confusion and compression. Exploiting the DNA computing operations to upgrade data transmission security over the open nets. The results approved that the method is effective in protect the transferring data in a secure manner in less time
A new similarity-based link prediction algorithm based on combination of network topological features
Authors: Saeidinezhad Hasan , Parvinnia Elham , Boostani Reza
Published: March 2022

In recent years, the study of social networks and the analysis of these networks in various fields have grown significantly. One of the most widely used fields in the study of social networks is the issue of link prediction, which has recently been very popular among researchers. A link in a social network means communication between members of the network, which can include friendships, cooperation, writing a joint article or even membership in a common place such as a company or club. The main purpose of link prediction is to investigate the possibility of creating or deleting links between members in the future state of the network using the analysis of its current state. In this paper, three new similarities, degree neighbor similarity (DNS), path neighbor similarity (PNS) and degree path neighbor Similarity (DPNS) criteria are introduced using neighbor-based and path-based similarity criteria, both of which use graph structures. The results have been tested based on area under curve (AUC) and precision criteria on datasets and it shows well the superiority of the work over the criteria that only use the neighbor or the path.
Simulation for predictive maintenance using weighted training algorithms in machine learning
Authors: Jittawiriyanukoon Chanintorn , Srisarkun Vilasinee
Published: March 2022

In the production, the efficient employment of machines is realized as a source of industry competition and strategic planning. In the manufacturing industries, data silos are harvested, which is needful to be monitored and deployed as an operational tool, which will associate with a right decision-making for minimizing maintenance cost. However, it is complex to prioritize and decide between several results. This article utilizes a synthetic data from a factory, mines the data to filter for an insight and performs machine learning (ML) tool in artiﬁcial intelligence (AI) to strategize a decision support and schedule a plan for maintenance. Data includes machinery, category, machinery, usage statistics, acquisition, owner’s unit, location, classiﬁcation, and downtime. An open-source ML software tool is used to replace the short of maintenance planning and schedule. Upon data mining three promising training algorithms for the insightful data are employed as a result their accuracy figures are obtained. Then the accuracy as weighted factors to forecast the priority in maintenance schedule is proposed. The analysis helps monitor the anticipation of new machines in order to minimize mean time between failures (MTBF), promote the continuous manufacturing and achieve production’s safety.
An efficient enhanced k-means clustering algorithm for best offer prediction in telecom
Authors: Fraihat Malak , Fraihat Salam , Awad Mohammed , AlKasassbeh Mouhammd
Published: March 2022

Telecom companies usually offer several rate plans or bundles to satisfy the customers’ different needs. Finding and recommending the best offer that perfectly matches the customer’s needs is crucial in maintaining customer loyalty and the company’s revenue in the long run. This paper presents an effective method of detecting a group of customers who have the potential to upgrade their telecom package. The used data is an actual dataset extracted from call detail records (CDRs) of a telecom operator. The method utilizes an enhanced k-means clustering model based on customer profiling. The results show that the proposed k-means-based clustering algorithm more effectively identifies potential customers willing to upgrade to a higher tier package compared to the traditional k-means algorithm. Our results showed that our proposed clustering model accuracy was over 90%, while the traditional k-means accuracy was under 70%.
Peanut leaf spot disease identification using pre-trained deep convolutional neural network
Authors: Patayon Urbano B. , Crisostomo Renato V.
Published: March 2022

Reduction of quality and quantity of agricultural products, particularly peanut or groundnut, is usually associated with disease. This could be solved through automatic identification and diagnoses using deep learning. However, this technology is not yet explored and examined in the case of peanut leaf spot disease due to some aspects, such as the availability of sufficient data to be used for training and testing the model. This study is intended to explore the use of pre-trained visual geometry group–16 (VGG16), visual geometry group–19 (VGG19), InceptionV3, MobileNet, DenseNet, Xception, InceptionResNetV2, and ResNet50 architectures and deep learning optimizers such as stochastic gradient descent (SGD) with Momentum, adaptive moment estimation (Adam), root mean square propagation (RMSProp), and adaptive gradient algorithm (Adagrad) in creating a model that can identify leaf spot disease by using a total of 1,000 images of leaves captured using a mobile camera. Confusion matrix was used to assess the accuracy and precision of the results. The result of the study shows that DenseNet-169 trained using SGD with momentum, Adam, and RMSProp attained the highest accuracy of 98%, while DenseNet-169 trained using RMSProp achieved the highest precision of 98% among pre-trained deep convolutional neural network architectures. Furthermore, this result could be beneficial in agricultural automation and disease identification systems for peanut or groundnut plants.
Functions of fuzzy logic based controllers used in smart building
Authors: Baniyounes Ali M. , Ghadi Yazeed Yasin , Radwan Eyad , Al-Olimat Khalid S.
Published: March 2022

The main aim of this study is to support design and development processes of advanced fuzzy-logic-based controller for smart buildings e.g., heating, ventilation and air conditioning, heating, ventilation and air conditioning (HVAC) and indoor lighting control systems. Moreover, the proposed methodology can be used to assess systems energy and environmental performances, also compare energy usages of fuzzy control systems with the performances of conventional on/off and proportional integral derivative controller (PID). The main objective and purpose of using fuzzy-logic-based model and control is to precisely control indoor thermal comfort e.g., temperature, humidity, air quality, air velocity, thermal comfort, and energy balance. Moreover, this article present and highlight mathematical models of indoor temperature and humidity transfer matrix, uncertainties of users’ comfort preference set-points and a fuzzy algorithm.
Fake accounts detection system based on bidirectional gated recurrent unit neural network
Authors: Benabbou Faouzia , Boukhouima Hanane , Sael Nawal
Published: March 2022

Online social networks have become the most widely used medium to interact with friends and family, share news and important events or publish daily activities. However, this growing popularity has made social networks a target for suspicious exploitation such as the spreading of misleading or malicious information, making them less reliable and less trustworthy. In this paper, a fake account detection system based on the bidirectional gated recurrent unit (BiGRU) model is proposed. The focus has been on the content of users’ tweets to classify twitter user profile as legitimate or fake. Tweets are gathered in a single file and are transformed into a vector space using the GloVe word embedding technique in order to preserve the semantic and syntax context. Compared with the baseline models such as long short-term memory (LSTM) and convolutional neural networks (CNN), the results are promising and confirm that using GloVe with BiGRU classifier outperforms with 99.44% for accuracy and 99.25% for precision. To prove the efficiency of our approach the results obtained with GloVe were compared to Word2vec under the same conditions. Results confirm that GloVe with BiGRU classifier performs the best results for detection of fake Twitter accounts using only tweets content feature.
A multithreaded hybrid framework for mining frequent itemsets
Authors: Ponmudiyan Poovan Jashma Suresh , Udupi Dinesh Acharya , Subba Reddy Nandanavana Veerappareddy
Published: March 2022

Mining frequent itemsets is an area of data mining that has beguiled several researchers in recent years. Varied data structures such as Nodesets, DiffNodesets, NegNodesets, N-lists, and Diffsets are among a few that were employed to extract frequent items. However, most of these approaches fell short either in respect of run time or memory. Hybrid frameworks were formulated to repress these issues that encompass the deployment of two or more data structures to facilitate effective mining of frequent itemsets. Such an approach aims to exploit the advantages of either of the data structures while mitigating the problems of relying on either of them alone. However, limited efforts have been made to reinforce the efficiency of such frameworks. To address these issues this paper proposes a novel multithreaded hybrid framework comprising of NegNodesets and N-list structure that uses the multicore feature of today’s processors. While NegNodesets offer a concise representation of itemsets, N-lists rely on List intersection thereby speeding up the mining process. To optimize the extraction of frequent items a hash-based algorithm has been designed here to extract the resultant set of frequent items which further enhances the novelty of the framework.
Optimizing the placement of cloud data center in virtualized environment
Authors: Al-Karawi Yassir , S. Alhumaima Raad , Hussein Khudair Khalid , Ahmed Abdulmunem
Published: March 2022

In cloud mobile networks, precise assessment for the position of the virtualization powered cloud center would improve the capacity limit, latency and energy efficiency (EEf). This paper utilized the Monte Carlo oriented particle swarm optimization (PSO) and genetic algorithm (GA) to first, obtain the optimal number of virtual machines (VMs) that maximize the EEf of the mobile cloud center, second, optimize the position of the mobile data center. To fulfil such examination, a power evaluation framework is proposed to shape the power utilization of a virtualized server while hosting an amount of VMs. In addition, the total power consumption of the network is examined, including data center and radio units (RUs). This evaluation is based on linear modelling of the network parameters, such as resource blocks, number of VMs, transmitted and received powers, and overhead power consumption. Finally, the EEf is constrained to many quality of service (QoS) metrics, including number of resource blocks, total latency and minimum user's data rate.
Clinical Trial Design Principles and Outcomes Definitions for Device-Based Therapies for Hypertension: A Consensus Document From the Hypertension Academic Research Consortium
Authors: Kandzari David E. , Mahfoud Felix , Weber Michael A. , Townsend Raymond , Parati Gianfranco , Fisher Naomi D.L. , Lobo Melvin D. , Bloch Michael , Böhm Michael , Sharp Andrew S.P. , Schmieder Roland E. , Azizi Michel , Schlaich Markus P. , Papademetriou Vasilios , Kirtane Ajay J. , Daemen Joost , Pathak Atul , Ukena Christian , Lurz Philipp , Grassi Guido , Myers Martin , Finn Aloke V. , Morice Marie-Claude , Mehran Roxana , Jüni Peter , Stone Gregg W. , Krucoff Mitchell W. , Whelton Paul K. , Tsioufis Konstantinos , Cutlip Donald E. , Spitzer Ernest
Published: March 2022

The clinical implications of hypertension in addition to a high prevalence of both uncontrolled blood pressure and medication nonadherence promote interest in developing device-based approaches to hypertension treatment. The expansion of device-based therapies and ongoing clinical trials underscores the need for consistency in trial design, conduct, and definitions of clinical study elements to permit trial comparability and data poolability. Standardizing methods of blood pressure assessment, effectiveness measures beyond blood pressure alone, and safety outcomes are paramount. The Hypertension Academic Research Consortium (HARC) document represents an integration of evolving evidence and consensus opinion among leading experts in cardiovascular medicine and hypertension research with regulatory perspectives on clinical trial design and methodology. The HARC document integrates the collective information among device-based therapies for hypertension to better address existing challenges and identify unmet needs for technologies proposed to treat the world’s leading cause of death and disability. Consistent with the Academic Research Consortium charter, this document proposes pragmatic consensus clinical design principles and outcomes definitions for studies aimed at evaluating device-based hypertension therapies.
Phenotype‐Directed Management of Hypertension in Pregnancy
Authors: McLaughlin Kelsey , Snelgrove John W. , Sienas Laura E. , Easterling Thomas R. , Kingdom John C. , Albright Catherine M.
Published: March 2022

Hypertensive disorders of pregnancy are among the most serious conditions that pregnancy care providers face; however, little attention has been paid to the concept of tailoring clinical care to reduce associated adverse maternal and perinatal outcomes based on the underlying disease pathogenesis. This narrative review discusses the integration of phenotype‐based clinical strategies in the management of high‐risk pregnant patients that are currently not common clinical practice: real‐time placental growth factor testing at Mount Sinai Hospital, Toronto and noninvasive hemodynamic monitoring to guide antihypertensive therapy at the University of Washington Medical Center, Seattle. Future work should focus on promoting more widespread integration of these novel strategies into obstetric care to improve outcomes of pregnancies at high risk of adverse maternal‐fetal outcomes from these complications of pregnancy.
Emergence of Self-Reproducing Metabolisms as Recursive Algorithms in an Artificial Chemistry
Authors: Kruszewski Germán , Mikolov Tomáš
Published: March 2022

One of the main goals of Artificial Life is to research the conditions for the emergence of life, not necessarily as it is, but as it could be. Artificial chemistries are one of the most important tools for this purpose because they provide us with a basic framework to investigate under which conditions metabolisms capable of reproducing themselves, and ultimately, of evolving, can emerge. While there have been successful attempts at producing examples of emergent self-reproducing metabolisms, the set of rules involved remain too complex to shed much light on the underlying principles at work. In this article, we hypothesize that the key property needed for self-reproducing metabolisms to emerge is the existence of an autocatalyzed subset of Turing-complete reactions. We validate this hypothesis with a minimalistic artificial chemistry with conservation laws, which is based on a Turing-complete rewriting system called combinatory logic. Our experiments show that a single run of this chemistry, starting from a tabula rasa state, discovers—with no external intervention—a wide range of emergent structures including ones that self-reproduce in each cycle. All of these structures take the form of recursive algorithms that acquire basic constituents from the environment and decompose them in a process that is remarkably similar to biological metabolisms.
Tangible Care: Design as a Vehicle for Materializing Shifting Relationships between Clinicians and Patients
Authors: Dankl Kathrina , Akoglu Canan
Published: March 2022

This article explores a collaboration between a hospital and a design team working on shared decision making. The analysis shows that designers have materialized care on different levels. One level concerns the combination of top-down and bottom-up stakeholder input—in other words, the representation of voices that are not equally equipped with power. By evidence of our research we argue that designers’ main cultural achievement lies in an illustration of a currently changing medical practice with shifting roles for “modern” patients and clinicians. Thus the main outcome of the design process is a re-organization of the relationship between patients and clinicians.
Architecture of the Avant-Apocalypse: Preservation beyond Preservation of the Species
Authors: Vujosevic Tijana
Published: March 2022

In an age in which human presence on Earth has become precarious, architects have begun to contemplate their discipline on two parallel timelines: that of human history on the one hand and that of geologica or evolutionary history on the other. Anxieties about the potential disappearance of the human species call into question accepted notions of cultural heritage. The author focuses on the emergence of nonhuman intelligences in architectural discourse by looking at two examples of new materials and their aesthetic qualities—the idea of “self-assembly” in concrete science and an invention involving “guided growth.” She speculates on how including nonhuman agents in a discussion of architecture changes the way we conceive of cultural and civilizational continuity.
Moving from Accessible Filmmaking toward Creative Media Accessibility
Authors: Romero-Fresco Pablo
Published: March 2022

Accessible filmmaking, a new model that proposes the integration of translation and accessibility into the filmmaking process through collaboration between filmmakers and translators, is leading to increasingly creative examples of media accessibility. In this article, the author puts forward a definition of creative media accessibility and illustrates it with examples of current professional practice. Situated at the crossroads between creation and translation, these practices point to a new way to approach audiovisual translation and, more importantly, filmmaking itself.
Anatomy of avian distress calls: structure, variation, and complexity in two species of shorebird (Aves: Charadrii)
Authors: Miller Edward H. , Kostoglou Kristal N. , Wilson David R. , Weston Michael A.
Published: March 2022

Birds often vocalize when threatened or captured by a predator. We present detailed qualitative analyses of calls from 24 red-capped plover (Charadrius ruficapillus) and 117 masked lapwing (Vanellus miles) chicks (Charadriidae) that we recorded during handling. Calls were structurally complex and differed between species. Calls showed moderate structure at higher levels of organization (e.g., similarity between successive calls; sequential grading). Some call characteristics resembled those in other bird species in similar circumstances (e.g., in nonlinear phenomena). Most calls consisted of several different parts, which combined in different ways across calls. Past studies have overlooked most features of distress calls and calling in charadriids due to small sample sizes and limited spectrographic analyses. Understanding interspecific patterns in call structure, and determination of call functions, will require: detailed knowledge of natural history; detailed behavioural descriptions, acoustic analysis, and analyses of development and growth; and experimental investigations of call functions.
Review of the genus Leucoma Hübner, 1822 (Lepidoptera: Erebidae: Lymantriinae) from China, with description of two new species
Authors: XIE LINZHE , WANG MING , CHEN YUSI , WANG HOUSHUAI
Published: March 2022

The genus Leucoma Hübner from China is reviewed. Based on morphological characters and DNA barcode data, fourteen species belonging to this genus are recognized, with descriptions of two new species: L. nyingchiensis sp. nov. and L. sichuana sp. nov.. Illustrations of adults and their genitalia are provided along with a key to all currently known species of Leucoma in China.
Introduction
Authors: Zicari Massimo
Published: March 2022

This volume analyses the interpretations of those arias from the so-called bel canto repertoire which Luisa Tetrazzini recorded between 1904 and 1922. This introduction provides context and draws attention to the technical constraints and the rudimentary technologies involved in the production of early discs. Many limitations affected the manner in which music was recorded and a number of important decisions had to be taken, which we should bear in mind when listening to them. For instance, small ensembles substituted for full orchestras and music scores had to be cut to fit the disc length. Each chapter seeks to reconstruct the phases of the nineteenth-century performing tradition and focuses on the main characteristics of those arias taken into consideration. Given the much-insisted-upon importance of the connection between the dramatic settings and the music, each aria is discussed with regard to the connection between its lyrics and musical structure.
On the Emergence of Frequency Bias from Accumulating or Disaggregating Bias-Corrected Quantitative Precipitation Forecasts
Authors: Veenhuis Bruce A. , Brill Keith F.
Published: March 2022

Quantitative precipitation forecast (QPF) applications often demand accumulations of precipitation for both long- and short-duration time intervals. It is desired that the shorter-duration forecasts sum to the longer-duration accumulations spanning the same time period. In the context of calibration, it is further desired that both the subinterval and longer interval accumulations be similarly corrected to have near unit frequency bias on a spatial domain. This study examines two methods of achieving these goals for 6- and 24-h accumulation intervals: 1) the accumulation method bias corrects the 6-h forecasts and accumulates them to create the 24-h accumulations; and 2) the disaggregation method bias corrects the 24-h accumulation and then proportionately disaggregates the 24-h accumulation back into 6-h accumulations. The experiments for the study are done retrospectively so that a “perfect” bias correction is possible for each method. The results of the study show that neither method accomplishes the stated goal for the calibration because QPF placement and/or timing errors contribute to frequency bias in the course of accumulation or disaggregation. However, both methods can improve the frequency bias for both the subinterval and longer interval accumulations. The choice of method may hinge most strongly on the relative tolerance of bias for the subinterval accumulations versus the longer interval accumulation.
Spilled Gallstones Presenting as Left Lower Quadant Abdominal Pain Consistent With Diverticulitis
Authors: McNamee Molly , Chambers James G
Published: March 2022

It has been reported that the incidence of spilled gallstones during laparoscopic cholecystectomy can be from 6-40%. Treatment of these sequelae are often reactive to symptoms produced from the presence of the spilled stones. Herein is presented a case of a 57 year old male with a history of left lower quadrant abdominal pain that had been present for several years and was thought to be due to diverticulitis. On diagnostic laparoscopy he was found to have multiple stones causing an inflammatory response in his left lower quadrant. These stones were removed and the left lower quadrant was washed out. The patient recovered well and had abatement of symptoms at his two months postoperative visit. This case is an example of a spilled gallstones being confused as diverticulitis. Recognition of the spilled stones on diagnostic laparoscopy spared the patient a larger operation and resulted in his symptoms resolving.
Left-Sided Amyand Hernia
Authors: Murugan Seema , Grenn Emily E. , Morris Michael W.
Published: March 2022

An Amyand’s hernia is defined by the presence of the vermiform appendix in the inguinal hernia sac. Most cases of Amyand’s hernia are incidentally diagnosed intraoperatively while surgically reducing a right inguinal hernia. Right-sided Amyand’s hernias are more common than left-sided Amyand’s hernias for two reasons: the appendix is naturally located on the right side and right inguinal hernias are more common. The prevalence of an Amyand’s hernia has been reported as 1%, but the true prevalence is much lower than that. We report a particularly rare case of a 5-month-old boy with a left-sided Amyand’s hernia surgically repaired without appendectomy.
Protect Black Women Teachers: Influencing Sustainability Through Mental Health Literacy
Authors: Lee Shanique J. , Thomas Stephanie
Published: March 2022

Critical Black women teachers (BWTs) play a vital role in education. They employ pedagogies that are political in nature and effective in outcome, particularly for Black students in urban schools. However, despite their impact, BWTs leave the profession at rates higher than all other teachers. Therefore, this study engaged sista circle methodology to explore how post-service critical BWTs reflected on their teaching experiences, specifically regarding their mental health and the role it had on their decision to leave the profession. Based on the findings, recommendations are made to key stakeholders to support critical BWTs’ mental health and sustainability in urban schools.
Controlling Land, Controlling People: Urban Greening and the Territorial Turn in Theories of Urban Planning in the Soviet Union, 1931-1932
Authors: Conterio Johanna
Published: March 2022

This article explores the relationship between urban planning and social order in the Soviet Union in the 1930s. From the mid-1930s, urban planners sought to shape the social order by reducing urban population density, limiting urban growth, and controlling mobility. The article explores how urban planners translated the ideal of less densely populated cities into designed built environments, through a study of how they theorized urban green space. This study ties the history of urban planning to the history of urban policing, mass operations, and the social repressions of the Stalinist 1930s through the lens of territoriality. It treats the rise of urban planning and urban policing as part of a single, state project to establish social order in cities, through establishing control over territory, implicating Soviet urban planners in the violent processes of social engineering of Stalinism.
The end of social media? How data attraction model in the algorithmic media reshapes the attention economy
Authors: Liang Meng
Published: March 2022

Douyin, which is also known as the Chinese version of Tiktok, is currently the most valuable digital advertisement platform in China. One of the most significant features of this short-video platform is the heavy reliance on algorithmic production and distribution of media. In this emergent configuration, algorithms and data shape the production and circulation of media beyond social networks. Such a system develops by meshing grassroots and professionally generated content, leading to the audience engaging in the production of commercial content for profit. My essay explores the political context and economic logic that underpins these developments. It draws specifically on official reports from Douyin, as well as interviews with users, including individual users and Multiple Channel Network (MCN) employees. This essay proposes the idea of the ‘data attraction model’ based on the investigation of the emergence of new forms of algorithmic production and distribution. It argues that the data attraction model is characterised by an extreme logic of flexible accumulation, which is radically transforming the content production of participatory media in China