bluuebunny/crossref_metadata_2025 huggingface dataset:
A community scale hybrid renewable energy system for sustainable power supply during load shedding
Authors: Paend Bakht Muhammad , Haji Mohd Mohd Norzali , Md. Ayob Shahrin , Khan Nuzhat , Lawan Bukar Abba
Published: May 2023

Load shedding is an operating condition in which the electrical grid is temporarily disconnected from the load. The objective is to minimize the gap between available generation capacity and load demand while maintaining an equitable supply for all consumers. Load shedding is a prominent problem for many developing countries. To address this issue, this paper explores the potential of a hybrid energy system (HES) to provide uninterrupted power supply at the distribution feeder despite load shedding from electrical grid. The proposed HES in this work combines photovoltaic (PV) array, battery storage system (BSS) and diesel generator (DG). The HES is equipped with energy management scheme (EMS) that ensures continuous power supply, improves energy efficiency, and minimizes the electricity cost. To accomplish these tasks, the EMS operates the system in one of three modes: grid mode, renewable energy source mode and the diesel generator mode. Besides, the proposed methodology allows injecting surplus PV energy into the grid, thus maximizing PV utilization and improving power system’s reliability. The results of this study will assist policymakers to determine the prospect of renewable based hybrid system to supply sustainable power and eliminate the energy problems in the power deficit countries.
Behaviour based botnet detection with traffic analysis and flow intervals at the host level
Authors: Padhiar Sneha , Patel Ritesh
Published: May 2023

A botnet is one of the most dangerous forms of security issues. It infects unsecured computers and transmit malicious commands. By using botnet, the attacker can launch a variety of attacks, such as distributed denial of service (DDoS), data theft, and phishing. The botnet may contain a lot of infected hosts and its size is usually large. In this paper, we addressed the problem of botnet detection based on network’s flows records and activities in the host. We proposed a host-based approach that detects a host, that has been compromised by observing the flow of in-out bound traffic. To prove the existence of command and control communication, we examine host network flow. Once the bot process has been identified in the host being monitored, this knowledge allows blocking any in/out traffic with the bot’s server. In addition to providing information about the compromised machine’s IP address and how it communicates with servers, the log file is generated, which can provide data about the command and control (C&C) servers. Most existing work on detecting botnet is based on flow-based traffic analysis by mining their communication patterns. Our work distinguishes itself from other methods of bot detection from its ability to use real-time host-related data for detection.
Age and gender classification with bone images using deep learning algorithms
Authors: Sundarasamy Sathyavathi , Kuttuva Rajendran Baskaran
Published: May 2023

In paediatrics, bone age is a crucial indicator of how a child's skeleton is developing. They have had great success ever since the creation of deep learning (DL)-based bone age prediction tools. Deep features learning, however, has a significant computing overhead problem. Deep convolution layers are used in this technique to learn representative features in the small yet useful regions that are extracted for feature learning. This work suggests using an extreme learning machine algorithm as the fundamental architecture in the final bone age assessment study to realise the rapid computation speed and feature interaction. The viability and efficacy of the suggested strategy have been verified by experiments using data that is openly accessible. The suggested model is explicitly trained using a cutting-edge end-to-end learning architecture employing bone scans to extract the most discriminative patches from the original high-resolution image. The bone picture is the foundation of the procedure. Our main objective is to categorize individuals by age using convolution neural network (CNN) classification models, such as the Xception and Mobile Net models. As a result, we have achieved results that are 90% and 94% accurate in classifying people by age using CNN models.
A new modification CNN using VGG19 and ResNet50V2 for classification of COVID-19 from X-ray radiograph images
Authors: Haruna Usman , Ali Rozniza , Man Mustafa
Published: May 2023

Coronavirus often called COVID-19 is a deadly viral disease that causes as a result of severe acute respiratory syndrome coronavirus-2 that needs to be identified especially at its early stages, and failure of which can lead to the further spread of the virus. Despite with the huge success recorded towards the use of the original convolutional neural networks (CNN) of deep learning models. However, their architecture needs to be modified to design their modified versions that can have more powerful feature layer extractors to improve their classification performance. This research is aimed at designing a modified CNN of a deep learning model that can be applied to interpret X-rays to classify COVID-19 cases with improved performance. Therefore, we proposed a modified convolutional neural network (shortened as modification CNN) approach that uses X-rays to classify a COVID-19 case by combining VGG19 and ResNet50V2 along with putting additional dense layers to the combined feature layer extractors. The proposed modified CNN achieved 99.24%, 98.89%, 98.90%, 99.58%, and 99.23% of the overall accuracy, precision, specificity, sensitivity, and F1-Score, respectively. This demonstrates that the results of the proposed approach show a promising classification performance in the classification of COVID-19 cases.
Fault tolerant and load balancing model for software defined networking controllers
Authors: Choukri Ihssane , Ouzzif Mohammed , Bouragba Khalid
Published: May 2023

Previous years have seen increased interest in a new network paradigm, Software defined networking (SDN). The basic idea behind this new concept consists of removing the smart parts of the connectivity components and moving them to a seul control point known as a controller. This centralized network view makes the network maintenance and management easier and facilitates the creation of new services. Despite many advantages of SDN, the concentration of network intelligence in a single controller raises serious challenges that impact SDN scalability, performance, and fault tolerance. One of the main problems in SDN is controller failure. In this article, we develop a fault tolerant model called fault-tolerant load balancing (FTLBC) for SDN controllers. To reduce the cascading failure problem, the proposed model requires the load of the failing controller to be shared among other controllers. In the case of a controller failure, The FTLBC model concentrates on distributing the load among the remaining controllers based on the load of the orphans' switches and the load of the remaining controllers.
Human resource optimization using linear regression machine learning model: case study SUNAT
Authors: Marín Gloria Salazar , Obregon Patricia Condori , Vidal Carlos Palomino
Published: May 2023

The continue searching for organization’s process improvement for reduce cost and increase efficiency is a big challenge for organizations nowdays. This paper is about to recognize the importance of process improvement focusing in the right human resource allocation. The research predict best optime human re- source allocation in the Superintendencia Nacional de Aduanas (SUNAT) in the chemical materials control area using a linear regression machine learning (ML) algorithm. This model was validated with recollected data in the SUNAT’s con- trol locations, the results were compared with historical data to determine their efficiency obtained a mean square error (MSE) 0.434 that is lower comparing to logistic regression and support vector machine algorithm. This research rec- ommend the implementation of this model in all SUNAT’s controls locations in Peru.
Steering angle prediction via neural networks
Authors: Saeed Faizi Fayez , Khorsheed Alsulaifanie Ahmed
Published: May 2023

Methods to calculate the correct steering angle are an important aspect of developing self-driving vehicles. Recently, steering angle prediction methods based on deep neural networks have achieved accurate results that outperform other methods over a range of road types. This paper investigates steering angle prediction for an autonomous vehicle as a regression problem and solves it using deep neural networks. The proposed approach obtains data in the form of the coordinates of lane lines and examines if they belong to one or more lines. Based on that, the method locates the path line that the vehicle is moving on. It then determines the equation that represents that path. Finally, the coefficients of this equation are fed into a trained neural network, which predicts the steering angle for that video frame. Extensive tests were performed on the Comma.ai and Udacity benchmarks to evaluate the performance of the approach. State-of-the-art results were achieved, with mean absolute error 0.64 and root mean square error 0.87 for the Comma.ai dataset, and mean absolute error 1.04 and root mean square error 2.33 for the Udacity dataset.
Efficient matrix key homomorphic encryption of medical images
Authors: Krishnegowda Prabhavathi , M. Boregowda Anandaraju
Published: May 2023

A sure way of providing privacy to sensitive images is to encrypt them, especially when they are stored in a public cloud server. Homomorphic encryption enables arithmetic operations over encrypted data without access to the secret key. This facility can be well harnessed for secure outsourced image processing by exploiting the available computational capabilities of modern cloud servers. This paper presents a new homomorphic image encryption scheme that uses integer matrix keys. The homomorphic operations are carried out in the finite field Zp to avail the advantages of integer arithmetic and to limit the cipher text sizes to reasonable levels. Our method does not use any error vector as in learning with errors (LWE) to improve the security.
Nested approach for X-ray image enhancement based on contrast adaptive using histogram equalization
Authors: Satea Mostafa
Published: May 2023

Medical image enhancement is a topic of great interest to researchers due to the rapid evolution of technology and advancements in communication. There are many types of medical images such as X-ray images, computed tomography (CT) scans, magnetic resonance imaging (MRI) scans, ultrasound images, positron emission tomography (PET) scans, single photon emission computed tomography (SPECT) scans, digital radiography images, mammography images and Fluoroscopy images. X-ray imaging is a valuable tool for diagnosis, monitoring, and treatment of many medical conditions, and its non-invasive, widely available, low cost and fast nature makes it a popular choice for many medical professionals. The proposed approach presents an algorithm for enhancing X-ray images, improving their visual appearance and making their content more useful and meaningful. The results of the algorithm show that enhanced images have a more natural look and provide accurate details of the objects in the X-ray images. Overall, this algorithm can aid in the diagnostic process by providing clearer and more detailed images for medical professionals to interpret.
An innovative smart glass for blind people using artificial intelligence
Authors: G. Gollagi Shantappa , D. Bamane Kalyan , Manish Patil Dipali , B. Ankali Sanjay , M. Akiwate Bahubali
Published: May 2023

The "Smart Glasses" are made to make it easier for blind persons to read and decipher written English-language content. A blind people find it exceedingly challenging to travel alone, and they run the danger of getting lost and regular sticks won't allow the person to go around independently in public without things growing worse. The objective of our work is to help blind people communicate more easily by developing a smart assistive glasses using artificial intelligence. The function of the glasses is to read out any text picture as audio text, which can then be heard through a headset attached to the spectacles. OpenCV, optical character recognition and efficient and accurate scene text (EAST) detector were used to identify the text in the image; ultrasonic sensor in the glasses is used to calculate the distance to snap a clear picture. The motion sensor directs the blind to the lecture halls, classrooms, and laboratory locations using an radio frequency identification reader. The results shows that combination of optical character recognizer and EAST detector produced a fairly accurate result, demonstrating the potential of the glasses to recognize the text. Currently, the language supported by the glasses is English, and the distance covered is 40 to 150 cm.
Five-level cascaded H-bridge inverter for renewable energy applications
Authors: Ali Md. Hazrat , Mainul Emtiaz Ahmed , Rahman Tawfikur , Hossen Md. Shabuj , Alam Khan Md. Kamrul , Rahmann Mohammad Obaidur
Published: May 2023

This paper presents a five-level inverter based on the phase-disposition pulse width modulation (PD-PWM) technique for renewable energy applications. The conventional cascaded H-bridge (CHB) five-level inverter structure is reproduced, and an LC filter is designed to get optimum results from the inverter output. Two batteries are used as renewable energy sources to investigate the performance of the designed inverter. The system can provide 220 volts and 50 Hz AC supply with a 6.9 kW power supply to the load/grid. The designed output is compared with filter and without filter circuit. It provides sinusoidal voltage and current output with a very low total harmonic distortion (THD) of 2.75% which is compliant with IEEE 519 standards. MATLAB/Simulink software tool is used for the entire design. The inverter’s output is graphically represented, and discussed various parameters like switching frequency, THD, modulation index, and rated voltage.
Application of ontology-based engineering and stem approach in learning
Authors: Madina Bazarova , Saltanat Adikanova , Gulnaz Zhomartkyzy , Yuriy Vais , Ainur Alpyssova , Akmaral Jaxalykova , Meruyert Kaidarova , Roza Bekbayeva
Published: May 2023

Currently, there is a need to improve the educational system and develop interdisciplinary studies at all stages of education, from school to postgraduate education. The implementation of cross-curriculum connections promotes a holistic view of natural phenomena and the relationship between them, that is, this knowledge becomes more meaningful and applicable in practice. The article proposes an approach to building a conceptual model of the content of education in the form of a thesaurus and ontology, the use of which will ensure that the educational information is adaptively selected and put straight. The article discusses the possibility of and experience in using ontological modeling and engineering for the conceptual description of school and higher education. The article discusses the development of the ontological science, technology, engineering, and mathematics (STEM) Education at School model. The article builds an ontological model, which is an integration of the ontology of school and the ontology of university. When filling in the knowledge base, it becomes possible to identify interdisciplinary relationships. The use of ontological engineering methods will improve the quality of education for schoolchildren and students through the semantic description of knowledge of the subject area by using interdisciplinary and STEM approaches in the process of education.
Swarm energy efficient power efficient gathering in sensor information systems protocol in wireless sensor networks
Authors: Chinnaiah Kandrakunta , Nageswara Rao Kunjam
Published: May 2023

Wireless sensor network (WSN) is a network, which has more numbers of sensors that are small in nature and are self organised. The inbuilt battery system is provided in the sensor nodes through which the nodes can communicate and do good operations among the other nodes available in the network. Lifetime is the most essential parameter which needs to be maximised for the WSN. This measure is more important for conservation of energy, for adequate and efficient performance of sensor networks. This paper proposes a swarm energy efficient power efficient gathering in sensor information systems (PEGASIS) (SEE-PEG) based energy optimization algorithm for WSN in which clustering and clustering head selection are done by using modified particle swarm optimization (MPSO) algorithm with respect to minimizing the consumption of power in WSN. The parameters evaluated for the proposed method is compared with existing technique like energy efficient PEGASIS without optimization. The simulation results are obtained using matlab tool.
Improvement detection system on complex network using hybrid deep belief network and selection features
Authors: Sharipuddin Sharipuddin , Arip Winanto Eko , Zain Mohtar Zulwaqar , Kurniabudi Kurniabudi , Sani Wijaya Ibnu , Sandra Dodi
Published: May 2023

The challenge for intrusion detection system on internet of things networks (IDS-IoT) as a complex networks is the constant evolution of both large and small attack techniques and methods. The IoT network is growing very rapidly, resulting in very large and complex data. Complex data produces large data dimensions and is one of the problems of IDS in IoT networks. In this work, we propose a dimensional reduction method to improve the performance of IDS and find out the effect of the method on IDS-IoT using deep belief network (DBN). The proposed method for feature selection uses information gain (IG) and principle component analysis (PCA). The experiment of IDS-IoT with DBN successfully detects attacks on complex networks. The calculation of accuracy, precision, and recall, shows that the performance of the combination DBN with PCA is superior to DBN with information gain for Wi-Fi datasets. Meanwhile, the Xbee dataset with information gain is superior to using PCA. The final result of measuring the average value of accuracy, precision, and recall from each IDSDBN test for IoT is 99%. Other results also show that the proposed method has better performance than previous studies increasing by 4.12%.
Energy efficient data fusion approach using squirrel search optimization and recurrent neural network
Authors: Varatharajan Arulkumar , Ramasamy Poonkodi , Marappan Suguna , Ananthavadivel Devipriya , Govardanan Chemmalar Selvi
Published: May 2023

Sensor networks have helped wireless communication systems. Over the last decade, researchers have focused on energy efficiency in wireless sensor networks. Energy-efficient routing remains unsolved. Because energyconstrained sensors have limited computing capabilities, extending their lifespan is difficult. This work offers a simple, energy-efficient data fusion technique employing zonal node information. Using the witness-based data fusion technique, the evaluated network lifetime, energy consumption, communication overhead, end-to-end delay, and data delivery ratio. Energyefficient data fusion optimizes energy utilization using squirrel search optimization and a recurrent neural network. The method allows the system to recognize a sensor with excessive energy dissipation and relocate data fusion to a more energy-efficient node. The proposed model was compared against artificial neural network-particle swarm optimization (ANN-PSO), cuckoo optimization algorithm-back propagation neural network (COABPNN), Elman neural network-whale optimization algorithm (ENN-WOA), and extreme learning machine-particle swarm optimization (ELM-PSO). The model achieved 94.50% network lifetime, 26.63% communication overhead, 93.85% data delivery ratio, 10.50 ms end-to-end delay, and 282 J energy usage.
Review: machine and deep learning methods in Malaysia for COVID-19
Authors: Kunna Azrag Mohammed Adam , Mohamad Zain Jasni , Asmawaty Abdul Kadir Tuty , Yusoff Marina , Hai Tao
Published: May 2023

The global pandemic of the coronavirus disease COVID-19 has impacted a variety of operations. This dilemma is also attributable to the lockdown measures taken by the afflicted nations. The entire or partial shutdown enacted by nations across the globe affected the majority of hospitals and clinics until the pandemic was contained. The judgements made by the authorities of each impacted nation vary based on a number of variables, including the nation's severity of reported cases, the availability of vaccines, beds in intensive care unit (ICU), staff number, patient number, and medicines. Consequently, this work offers a thorough analysis of the most recent machine learning (ML) and deep learning (DL) approaches for COVID-19 that can assist the medical field in offering quick and exact COVID-19 diagnosis in Malaysia. This research aims to review the machine learning and deep learning methods that were used to help diagnose COVID-19 in Malaysia.
Segmentation approach for offline handwritten Kannada scripts
Authors: Sandyal Krupashankari S. , Y. Chandrappa Kiran
Published: May 2023

India has more than 1,600 official languages, making it a multilingual country. Kannada, one of the major languages, originated in the state of Karnataka and is currently ranked 33rd among the accents that are most often spoken throughout the world. However, the survey shows that much more effort is needed to create a complete handwritten identification system. Segmentation is one of the crucial steps in a handwriting identification system that extracts significant objects from an image. The feature extraction and classification phases of handwritten text recognition will be more successful if the segmentation approaches selected are efficient. In the proposed system, segmentation was accomplished using bounding box and contour tracing methods. The result got is delivered to the next step of handwritten identification system. An average accuracy of 92.6% is worked out for line segmentation and word segmentation.
Aura detection using thermal camera with convolutional neural network method for mental health diagnosis
Authors: Ariswati Her Gumiwang , Soetjiatie Liliek
Published: May 2023

Mental health is an important aspect in realizing overall health. For this reason, non-invasive medical equipment is needed for people with mental disorders. This study aimed to create a psychological health diagnostic tool by detecting auras using a thermal camera from facial objects. The contribution of this study is that the tool can detect the patient's aura without physical contact so that the patient is more comfortable and does not feel invaded. This research designed a system for detecting electromagnetic wave radiation energy emitted by the body using a thermal camera. Face detection in the input image was performed using a convolutional neural network (CNN) model single shot multibox detector (SSD), which is one of the CNN models that implements a bounding box to estimate the localization of detected objects. In this case, system testing was used to evaluate the performance of the CNN system algorithm for aura detection in terms of color (main color or average color). The results obtained were detectibility by 80%, selectivity by 88.88%, precision by 70%, sensitivity by 87.5%, and accuracy by 63.63%. The design of the aura detection system in this study will make it easier for psychiatrists and psychologists to help make a noninvasive diagnosis.
Implementation of image file security using the advanced encryption standard method
Authors: Ikhwan Ali , Aliana A. Raof Rafikha , Ehkan Phaklen , Yacob Yasmin , Aslami Nuri
Published: May 2023

The application of technology in this era has entered digitalization and is modern. Therefore, we are already in an era of advanced and rapid technological development. It has become a human need to exchange information in every activity. Documents that contain information that is frequently sought or used. The document's use also includes essential information. Document security is undoubtedly a significant factor in prioritizing important information in a document to prevent unauthorized people from misusing the document's vital information. Cryptography is a method of overcoming document security issues so that third parties cannot read the information or messages contained within the document. The 128-bit advanced encryption standard (AES) algorithm is one of the algorithms included in the cryptography technique. Additionally, it can be combined with operation modes such as electronic codebook (ECB) and cipher block chaining (CBC) to create an application that can generate random codes to improve the security of the data contained in the document.
Bone osteosarcoma tumor classification
Authors: Hussien Rahouma Kamel , Salama Abdellatif Ahmed
Published: May 2023

Osteosarcoma is a malignant bone tumor that usually affects children and adolescents. Early detection of osteosarcoma tumors increases the likelihood of successful therapy. Manual identification of osteosarcoma requires highly skilled doctors. In this study, we attempt to create a model to automatically diagnose tumors into three categories; non-tumor, viable-tumor, and osteosarcoma tumor. The suggested methodology can help medical professionals identify tumors correctly and quickly. The proposed approach uses the gray level co-occurrence matrix (GLCM) to extract features for feature extraction and three different classifiers for tumor detection. The used classifier are XG-Boost, support vector machine (SVM), and K-nearest neighbors. Finally, ensemble voting is used by combining the predictions from these classifiers. The system achieves 91.8% accuracy.
A new approach to develop biometric fingerprint using human right thumb fingernail
Authors: Al-Sultan Tahani Ghanim , Abduljabar Aza Qays , Hashim Alkhaled Warqaa , Husham Al-Sawaff Zaid , Kandemirli Fatma
Published: May 2023

In this article, we analyzed the structure of human nails to develop a modern biometric authentication system based on the nail bed and finger lunula. The results of the studies on the collected images proved that each fingernail has distinctive characteristics in terms of the length and width of the nail and the lunula, even identical twins. We focused on the fingernail of the right thumb because of its large size and the accuracy and clarity of the nail. The mediation of the indicative points on the nail bed and on the lunula was used to form the pentagonal structure and use it as a region of interest. Then an ensemble independent component analysis, principal component analysis, haar wavelet, and scale invariant feature transformation, were used. Later we classified these algorithms using support vector machine and Naive Bayes techniques, the performance of each algorithm was analyzed by feature extraction with two classifiers. This study was conducted on 100 participants and showed that this new method could be used as a biometric identification system for humans. There was no similarity in results for all verified samples.
Usos do conceito de indigenização nos museus franceses
Authors: Roustan Mélanie , Theyss Regiane Queiroz
Published: May 2023

Se considerarmos o movimento global de descolonização do pensamento museológico, como explicar que algumas instituições adotaram o conceito de indigenização como um princípio unificador, enquanto outras o abandonaram deliberadamente? Este artigo, situa e analisa o caso francês na temática proposta. Após uma análise de contexto – reconfiguração dos museus de etnografia e requalificação das suas coleções –, passamos a uma análise crítica sobre diferentes caminhos percorridos: museologia colaborativa, restituição de restos humanos e valorização da arte contemporânea. Consideramos, em seguida, o exemplo da abordagem da cultura Maori pelo Museu Quai Branly, e a restituição de cabeças tatuadas mumificadas pela França à Nova Zelândia, como eixo de reflexão sobre o impacto do conceito de “indigenização” nos museus franceses. A conclusão apresenta a formulação de algumas hipóteses sobre a relutância francesa – sua resistência? – relacionada às novas lógicas transnacionais relativas ao conceito de indigenização; e questiona a ação dos museus com relação à herança colonial, e ao seu papel na manutenção de relações materiais com o sagrado.
Pure and Other Phenomenologically Oriented Psychology
Authors: Nenon Thomas
Published: May 2023

This paper inquires into the necessity and limits of what Edmund Husserl calls a “pure” phenomenological psychology. It argues that there may be merit to this notion as a kind of philosophical psychology, the notion of purity in clinical psychology would unnecessarily limit the kinds of factors that the psychologist must take into account in understanding and treating most of the psychological conditions the therapist faces. The paper suggests that phenomenological psychology nonetheless has value in providing a counter-balance to naturalistically inclined psychology that for its part neglects the meaning of various life events for the agent and can serve as means of critical reflection on the operative concepts guiding psychological practice. It can also help correct a deterministic tendency in modern psychology that does not take seriously enough human beings’ capacity for self-reflection and for self-responsibility that can often serve as key elements in psychological therapy and healing.
Extraction and normalization of IR indexing terms and phrases in a highly inflectional language
Authors: Gakis Panagiotis , Kokkinos Theodoros , Tsalidis Christos
Published: May 2023

Term-based indexing of documents is conventionally implemented by stemmers or their corpus-based improvements, both of which encode implicit linguistic information. Terms are directly derived from document content such that a unique indexing approach is available at indexing run-time. For highly inflectional languages where term variation is high, such techniques are more error-prone. The main focus of the current study is the extraction and normalization of single terms and phrases and the proposal of authenticated control of indexing. The proposed approach relies on the use of explicit linguistic knowledge, appropriately encoded in large language resources. Such control guarantees the highest possible expansion factor for indexing terms as well as indexing consistency. Moreover, it offers a framework where different and eventually contradicting indexing criteria can be practiced, conventional and Natural Language Processing (NLP)-based Information Retrieval (IR) applications can be served, while adaptations can be made for tuning to a specific domain or corpus.
Devotional Synergy and Expanding Shi’i Space- Times: Preliminary Observations on Changing ‘Azādārī Practices in the Bhit Shah Area
Authors: Huang Pei-ling , Lanjwani Bisharat Ali
Published: May 2023

Bhit Shah is the abode of venerated Sufi mystic Shah Abdul Latif Bhittai. During the last few decades, the saint’s shrine has also become a center for Shi’i practices of ‘azādārī – mourning congregations in memory of Imam Husain. This article contributes to the literature on intertwining Sufi and Shi’i practices by examining how saintly and Husaini devotion repertoires converged in the Bhit Shah area and how this convergence contributed to transformations in ritual action and identity over the twentieth century. We explore how local devotees started adopting the remembrance of Imam Husain and Karbala through devotion to Shah Latif, which initially took place without changes in the devotees’ religious affiliation. However, with the precolonial Talpurs’ influence and interaction with other devotees from Shi’i urban centers of Sindh, many residents began to affirm Twelver Shi’a identification and expand their mourning practices in space and duration.
Distinct Meteorological Mode Associated with High-PM2.5 Episodes in Seoul, South Korea
Authors: Choi Daniel , Lee Hyo-Jung , Chang Lim-Seok , Jo Hyun-Young , Jo Yu-Jin , Park Shin-Young , Yang Geum-Hee , Kim Cheol-Hee
Published: May 2023

In this study, high–particulate matter (PM2.5) pollution episodes were examined in Seoul, the capital city of South Korea, which, based on the episode characteristics, were influenced by a distinct meteorological mode, long-range transport (LRT), from two-level meteorological observations: surface and 850–500-hPa level. We performed two-step statistical analysis including principal component (PC) analysis of meteorological variables based on the observation data, followed by multiple linear regression (MLR). The meteorological variables included surface temperature (Tsfc), wind speed (WSsfc), and the east–west (usfc) and north–south (υsfc) components of wind speed, as well as wind components at 850-hPa geopotential height (u850 and υ850, respectively) and the vertical temperature gradient between 850 and 500 hPa. Our two-step analysis of data collected during the period 2018–19 revealed that the dominant factors influencing high-PM2.5 days in Seoul (129 days) were upper-wind characteristics in winter, including positive u850 and negative υ850, that were controlled by the presence of continental anticyclones that increased the likelihood of LRT of PM2.5 pollutants. Regional-scale meteorological variables, including surface and upper-meteorological variables on normal and high-PM2.5 days, showed distinct covariation over Seoul, a megacity in the eastern part of northeast Asia with large anthropogenic emissions. Although this study examined only two atmospheric layers (surface and 500–850 hPa), our results clearly detected high-PM2.5 episodes with LRT characteristics, suggesting the importance of considering both geographical distinctiveness and seasonal meteorological covariability when scaling down continental to local response to emission reduction.
A Machine Learning Tutorial for Operational Meteorology. Part II: Neural Networks and Deep Learning
Authors: Chase Randy J. , Harrison David R. , Lackmann Gary M. , McGovern Amy
Published: May 2023

Over the past decade the use of machine learning in meteorology has grown rapidly. Specifically neural networks and deep learning have been used at an unprecedented rate. To fill the dearth of resources covering neural networks with a meteorological lens, this paper discusses machine learning methods in a plain language format that is targeted to the operational meteorological community. This is the second paper in a pair that aim to serve as a machine learning resource for meteorologists. While the first paper focused on traditional machine learning methods (e.g., random forest), here a broad spectrum of neural networks and deep learning methods is discussed. Specifically, this paper covers perceptrons, artificial neural networks, convolutional neural networks, and U-networks. Like the Part I paper, this manuscript discusses the terms associated with neural networks and their training. Then the manuscript provides some intuition behind every method and concludes by showing each method used in a meteorological example of diagnosing thunderstorms from satellite images (e.g., lightning flashes). This paper is accompanied with an open-source code repository to allow readers to explore neural networks using either the dataset provided (which is used in the paper) or as a template for alternate datasets.
Predictors of Mortality in Trauma patients Receiving massive Transfusion Protocol
Authors: Jebbia Mallory , Nguyen Jade , Marty Makenna , Carcamo Rosa , Alvarez Claudia , Tay Lasso Erika , Barrios Cristobal , Lugo Brian
Published: May 2023

Introduction

Massive transfusion protocol (MTP) is often defined as the transfusion of ≥10 units of packed red blood cells (PRBCs) in 24 hours. The purpose of this study is to determine which factors most significantly contribute to mortality in patients receiving MTP after trauma.

Methods

An initial database search followed by retrospective chart review was performed on patients treated at four trauma centers in Southern California. Data were collected on all patients who received MTP, defined as at least 10 units PRBCs within the first 24 hours of admission, between January 2015 and December 2019. Patients with isolated head injuries were excluded. Univariate and multivariate analyses were used to determine which factors most significantly influenced mortality.

Results

Of 1278 patients who met our inclusion criteria in the database, 596 (46.6%) survived and 682 (53.4%) died. On univariate analysis initial vitals and labs, except for initial hemoglobin and initial platelet count were significant predictors of mortality. A multivariate regression model showed the strongest predictors of mortality were pRBC transfusions at 4 hours (OR 1.073, CI 1.020-1.128, P = .006) and 24 hours (OR 1.045, CI 1.003-1.088, P = .036), and FFP transfusion at 24 hours (OR 1.049, CI 1.016-1.084, P = .003).

Conclusion

Our data indicates that several factors may contribute to mortality in patients receiving MTP. In particular age, mechanism, initial GCS, and PRBC transfusions at 4 and 24 hours provided the strongest correlation. Further multicenter trials are indicated to provide further guidance in deciding when to discontinue massive transfusion.
Venous Thrombosis Recurrence After Catheter-Related Upper Extremity Deep Venous Thrombosis in Cancer Patients: A Retrospective Analysis
Authors: Hakem Rabiaa , Soudet Simon , Diouf Momar , Sevestre Marie Antoinette
Published: May 2023

Long-term indwelling central venous catheters (CVC) are frequently used to secure vascular access to deliver injectable treatment. Catheter-related thrombosis (CRT) occurs in approximately 2–6% of cancer patients. We conducted a single-center retrospective study to assess the rate of venous thromboembolism (VTE) recurrence in cancer patients; 200 patients were included. Mean age was 56 ± 15.15 years, median follow-up duration was 16.5 [range: 10–36] months. The incidence of recurrence was estimated using Gray’s method for competing risk with death as the competing event of VTE. Recurrent VTE occurred in 25.5% of patients with a median occurrence time of 6.5 [range: 5–11.25] months. In case of recurrence, 94.6% of patients were treated for cancer and 80.4% of them received anticoagulants; 4 major bleeds and 17 non-major bleeds occurred during follow-up. In multivariate analysis, previous VTE (Hazard Ratio (HR) 2.48 (95% CI 1.42–4.32) and presence of CVC (HR 5.56 (95% CI 1.96–15.75) were significant recurrence risk factors. After a first episode of CRT, 25.5% of patients experienced VTE recurrence as UEDVT in 30 cases (55.5%), PE in 17 cases (31.5%), and DVT in 7 cases (13%), mostly during anticoagulation therapy. Anticoagulation therapy does not avoid CRT in case of cancer and must be balanced with hemorrhagic risk.
Shifted Excitation Raman Difference Spectroscopy Combined with Wide Area Illumination and Sample Rotation for Wood Species Classification
Authors: Sowoidnich Kay , Towrie Michael , Matousek Pavel
Published: May 2023

Raman spectroscopy has found its way into a wide range of applications and is successfully applied for qualitative and quantitative studies. Despite significant technical progress over the last few decades, there are still some challenges that limit its more widespread usage. This paper presents a holistic approach to addressing simultaneously the problems of fluorescence interference, sample heterogeneity, and laser-induced sample heating. Long wavelength shifted excitation Raman difference spectroscopy (SERDS) at 830 nm excitation combined with wide-area illumination and sample rotation is presented as a suitable approach for the investigation of selected wood species. Wood as a natural specimen represents a well-suited model system for our study as it is fluorescent, heterogeneous, and susceptible to laser-induced modifications. Two different subacquisition times (50 and 100 ms) and two sample rotation speeds (12 and 60 r/min) were exemplarily assessed. Results demonstrate that SERDS can effectively separate the Raman spectroscopic fingerprints of the wood species balsa, beech, birch, hickory, and pine from intense fluorescence interference. Sample rotation in conjunction with 1 mm-diameter wide-area illumination was suitable to obtain representative SERDS spectra of the wood species within 4.6 s. Using partial least squares discriminant analysis, a classification accuracy of 99.4% for the five investigated wood species was realized. This study highlights the large potential of SERDS combined with wide-area illumination and sample rotation for the effective analysis of fluorescent, heterogeneous, and thermally sensitive specimens in a wide range of application areas.
Resource Wealth: A “Curse” for Labor Rights?
Authors: Blanton Robert G. , Blanton Shannon Lindsey , Peksen Dursun
Published: May 2023

Though a great deal of research has examined the economic and political consequences of natural resource wealth, its implications for labor remain empirically under-examined. In this article, we contend that resource abundance undermines labor rights due to the inelastic demand for resource-intensive products, which serves to insulate these states from internal and external pressures to protect these rights. In addition to this direct linkage, we posit that resource wealth indirectly undercuts labor rights through its adverse impact on civil society and bureaucratic capacity. Examining these linkages across 148 countries for the years 1994–2010, we find that resource wealth has a negative and significant impact on labor rights practices, though not on labor rights laws. Results from a causal mediation analysis show that resource wealth also undermines labor rights through its negative impact on labor organizations and bureaucratic capacity
The Metamorphosis of the Public Sphere in India over Time: Unfolding the Dichotomy between Public and Private Spheres in the Pre and Post-Independence Era
Authors: Raiguru Surjit
Published: May 2023

The public sphere in India has undergone significant changes during colonial rule and the national movement, leading to its susceptibility to the recent rise of Hindutva. The current state of the public sphere in India is shaped by the ambiguities of the national movement, which were influenced by nationalist responses to colonial rule. To fully understand the public sphere, its relationship with the private sphere must be considered, as the public sphere’s definition and shape are derived from this relationship. To institutionalize multiculturalism in the public sphere, it is necessary to renegotiate the relationship between the public and private spheres. Therefore, it is imperative to explore ways to recreate the public sphere in a manner that reflects the country’s diversity effectively.
Wheels to Meals: Measuring the Impact of Micromobility on Restaurant Demand
Authors: Kim Kyeongbin , McCarthy Daniel Minh
Published: May 2023

Dockless shared micromobility services have grown substantially in recent years, but their impact on consumer demand has remained largely unstudied. The authors estimate how the largest and fastest-growing segment of this market—the dockless electric scooter (“e-scooter”) sharing industry—impacts spending in one of the largest segments of the local economy, the restaurant industry. Using data covering 391 companies in 98 U.S. cities, the authors find that the introduction of e-scooters in a city significantly impacts restaurant spending, increasing spending by approximately 5.2% for e-scooter users, driving incremental spending of at least $11.3 million annually across all cities that first allowed e-scooters to operate over summer 2018. Impact varies by restaurant subcategory, with a stronger positive effect on fast-food restaurant spending, and a weaker effect on sit-down restaurant spending. E-scooter entry has a larger impact on companies with higher historical revenues selling at lower prices. It facilitates discovery of new restaurants from prospective customers and repeat business from already-acquired customers.
The Art of Slowness: Slow Motion Enhances Consumer Evaluations by Increasing Processing Fluency
Authors: Stuppy Anika , Landwehr Jan R. , McGraw A. Peter
Published: May 2023

Slow motion is a popular video editing tool used to enhance short-form videos (e.g., reels, stories, GIFs), which are commonly found in media entertainment and marketing communications. This research shows that slow motion increases the virality (e.g., likes, votes, views) of short-form videos and boosts brand liking, choice, and willingness to pay. The effect occurs because slow motion enhances the hedonic component of the viewing experience via processing fluency. By documenting how the success of slow motion is subject to moderators, this work shows marketers, entertainment producers, and everyday people how to use slow motion more effectively. Across a large-scale field data set and six experiments, the authors highlight that slow motion is especially effective when applied to short-form videos that are inherently pleasant and that involve complex movements that are difficult to perceive at regular speed. However, even simple movements benefit from slow motion when content creators zoom in on subtle movements to increase complexity. Moreover, slow motion is more effective when viewers engage in less elaborate processing. Finally, the authors show that the perceived disfluency of fast-motion editing is effective at boosting brand evaluations when viewers desire excitement.
Intermediary Organizations, Technocratic Discourses, and the Rise of Accountability Regimes in Teacher Education
Authors: Aydarova Elena
Published: May 2023

Intermediary organizations’ (IOs) involvement in teacher education policies has grown in recent years. Apart from advocating for the introduction of alternative routes into the teaching profession, IOs have facilitated the spread of outcomes-based teacher preparation accountability. While previous studies examined the neoliberal market-based logic of their proposals, less is known about technocracy as a discourse informing teacher education redesign. To address this gap, I use the tools of critical policy and critical discourse analysis to examine how IOs advocated for and participated in the construction of outcomes-based accountability regimes. This analysis captures key elements of technocracy, such as the depoliticization of social issues, scientism, and the dismissal of opposition on which accountability regimes are built. By attending to the assumptions and inherent contradictions of technocratic discourses, I shed light on the ways in which accountability regimes dismiss opposition and seek to refashion governance structures in teacher education.
Neoliberal Progressivism: Charter Schools as Claims to Urban Space, Asian American Self-Determination, and Multiracial Solidarity
Authors: Liu Roseann
Published: May 2023

Much of the scholarship on charter schools has examined the reform through the lens of neoliberalism, rightly critiquing it for its negative effects on democracy, public schools, and marginalized communities. While we have developed important understandings of how progressive values are often repurposed toward strengthening neoliberal projects in education, I argue for a serious exploration of the possibilities of using neoliberal tools as a way of advancing progressive aims. Using the case study of a charter school in Philadelphia’s Chinatown that was founded expressly as a claim to urban space, Asian American self-determination, and multiracial solidarity, I show how some of the progressive aims we hope to achieve can come from unexpected neoliberal techniques.