bluuebunny/crossref_metadata_2025 huggingface dataset:
Characterization of the electrical properties of an optical device manufactured with CMOS 0.35 μm technology
Authors: Yauri Ricardo , Gamero Vanessa , Alayo Marco
Published: November 2023

Currently, the relevance of optical devices has increased due to the physical limitations of the electrical transmission medium and the proximity of the limit of Moore's Law. Furthermore, the fabrication of optical devices on monolithic silicon substrates has gained importance in recent years thanks to manufacturing technologies in the microelectronics industry. For this reason, this paper aims to carry out the electrical characterization of an optical device manufactured with commercial austria micro syste m technology of complementary metal oxide semiconductors of 0.35 μm. The methodology consists of implementing an optical device, with an incandescent optical source called a microlamp, a waveguide and a photodiode. The microlamp was projected between two m etal layers connected by tungsten vias that act as filaments covered by SiO 2 dielectric to prevent oxidation. The results of the electrical characterization of the optical device show that the microlamp reaches a maximum current of 48 mA and stops working at higher currents. The waveguide was designed with a SiO 2 core and it was discovered that the TiN layers were found to be part of the waveguide causing it to behave as an emitter in the 2.5 - 5 µm region.
Internet of things-based floor cleaning robot
Authors: Kassim Abdul Hafiz , Yusof Mat Zain Mohamad , Abdul Talib Mat Yusoh Mohd , Nazrul Sidek Mohd , Mohd Noorhafizi Raja Daud Raja , Izzat Mod Arifin Ahmad , Firdaus Mohd Zin Mazratul
Published: November 2023

Internet of t hings (IoT) based floor cleaning robot (FC - Rob) is a floor cleaning robot that uses a smartphone to assist users, primarily housewives and mothers, in completing their chores. The NodeMCU ESP8266 serves as the robot’ s “brain” and is controlled by a smartphone application for the purpose of this research. In accordance with current trends, a n iOS and Android application using Blynk has also been developed for users to control the robot’ s movements, making it the ideal solution for time - crunched individuals. FC - Rob is propelled by two d irect current (DC) motors to ensure comprehensive floor cl eaning. The results of this research are strengthened by tests conducted on battery life and two types of fabric, as well as a comparison with two types of commercially available robots. The positive findings of these tests on this robot demonstrate its effectiveness and efficiency in cleani ng houses, as well as its reasonable cost and educational value for children.
Internet of things-based garbage monitoring system integrated with Telegram
Authors: Nur Syuhada Ahmad Tarmizi Siti , Nur Shaadah Nik Dzulkefli Nik , Abdullah Rina , Izawana Ismail Syila , Omar Suziana
Published: November 2023

This paper presents the development of smart garbage monitoring system usin g internet of things (IoT) to keep the environment clean thus reducing cleaners’ burden. The present era is characterized by smart cities, where precision and organization are the norm. This initiative was launched because population is progressing rapidly, increasing more garbage hence esclating cleaner’s frequency of dustbin checking daily whether the dustbin is full or not which mean more labour costs. The main purpose of this researc h is to develop a systematic garbage monitoring system which can help cleaners schedule their work in monitoring and picking up garbage from dustbins. It used node microcontroller (NodeMCU) ESP8266 Wi - Fi module as the main controller to control ultrasonic and rain input sensors and provide notifications via Telegram. A limit switch is used to detect whether the lid is open or closed. When the lid is closed, the ultrasonic sensor is activated and measures the garbage distance depending on the amount. If an overrun of the maximum amount is detected, the red - light emitting diode (LED) will turn on that connects to the Wi - Fi module, which sends notification to the cleaners. As a result, the IoT based garbage monitoring system was fully functioned and accomplish ed its objectives.
Optimal chest position of auscultation for chronic obstructive pulmonary disease diagnosis using machine learning
Authors: Amose John , Vairavan Manimegalai
Published: November 2023

Digital Stethoscopes over recent years have gained acceptance among pulmonologists to perform auscultations due to their advantages over traditional stethoscopes. During the previous decade, researchers have prominently contributed to the development of algorithms aimed at enabling objective diagnosis of respiratory sounds and conditions, thereby affording individuals lacking medical expertise the capability to auscultate themselves. However, auscultation requires the personnel to be aware of the optimal chest position to place the device for a reliable diagnosis as well. This study aims to identify the optimal chest position to place a digital stethoscope's diaphragm to objectively diagnose Chronic Obstructive Pulmonary Disease (COPD). Lung sound recordings from seven chest positions with data available in the ICBHI 2017 database namely, Anterior left (Al), Anterior right (Ar), Lateral left (Ll), Lateral right (Lr), Posterior left (Pl), Posterior right (Pr) and Trachea (Tc), were analyzed in this study. COPD+ and COPD- at diagnosis, each chest position was done objectively using Mel-Frequency Cepstral Coefficients (MFCC) features and machine learning models namely Support Vector Machine and Decision Tree. The results indicate that the Posterior right (Pr) chest position offers superior precision, recall, and F1 score, with a recognition accuracy of 99.7% in COPD screening.
Advances of vehicular ad hoc network using machine learning approach
Authors: Thian Meng See , Yogarayan Sumendra , Fatimah Abdul Razak Siti , Kannan Subarmaniam , Azman Afizan
Published: November 2023

Vehicular Ad hoc Networks (VANETs) play a crucial role in Intelligent Transportation Systems (ITS), enabling seamless communication between vehicles and other entities. VANETs provide a wide range of services, allowing vehicles to communicate with each other and with roadside infrastructure. With the increasing amount of data generated by VANETs, machine learning approaches have emerged as valuable tools to address complex challenges in this domain. This paper presents a comprehensive literature review on the application of machine learning in VANETs. The paper discusses the potential challenges and future research directions in the field, emphasizing the need for more accessible machine learning solutions for VANETs. This review emphasizes the significant role of machine learning approach in advancing the capabilities of VANETs and shaping the future of intelligent transportation systems.
Implementation of a long range-based monitoring system for environmental remote safety schemes
Authors: Jalal Ahmad Alsammarraie Mahmood , Hussein Ali Adnan , A. Al-Hilali Aqeel , Q. Mohammad Monir Hind , Sameer Jabbar Mohannad , Bashar Qasim Haitham
Published: November 2023

The effects of poor air quality on people are profound. Air pollution and health issues are linked to millions of deaths. The World Health Organization (WHO)and the environmental protection agency (EPA)have produced several regulations and guidelines to control and enhanceair quality. Several authors have recently suggested low-cost tools for instantly assessing air quality. the abundance of inexpensive ways to assess air quality and collect sensor data. The design and employment of remote safety monitoring for humidity, temperature, and carbon monoxide has been merged into a single tool in this work. The results findings can be observed on the actual measured instrument’s organic light-emitting diode (OLED)show and the liquid crystal display (LCD)remote monitoring system. This device uses ESP32 microcontroller with long range (LoRa)wireless communication technology.The DHT22 sensor is used to detect humidity and temperature, while the MQ-7 sensor is used to monitor carbon monoxide levels. Testing measurements are calibrated using instruments that meet industrial standards (AS8700A CO meter and 4,000 NV weather tracker) used in environmental station. If the abnormalities in the recorded parameters are detected, then alert will be triggered. This instrument can help patients and workers to check their surroundings and provide remote monitoring to health service providers.
Systematic literature review on global software development based software cost estimation models and cost drivers
Authors: Ahmed Mehmood , Ibrahim Noraini , Nasir Wasif , Ahmed Adeel
Published: November 2023

Global software development (GSD) is a well - established discipline of software engineering that focuses on the advantage of a global environment. Effective cost estimation is critical for the success of GSD projects. Cost estimation in a GSD environment is a challenging task. As a re sult, GSD must emphasize cost estimation. Findings show that a number of researchers over the past few decades have emphasized GSD - based cost estimation in GSD; to the best of our knowledge, however, existing cost estimation have not taken into account man y GSD - based cost drivers that must be considered when estimating costs. Motivated by all this, the purpose of this study is to review the existing GSD - based cost estimation models/techniques and cost drivers that influence the accuracy of cost estimation. To identify and compile relevant research papers, a systematic literature review was carried out. From twenty - seven selected studies, initially, 86 GSD - based cost drivers and 12 GSD - based cost estimation models/techniques were extracted. After filtration, 26 cost drivers were identified as significant and to be considered in GSD - based cost estimation. This study significantly identifies GSD - based cost drivers and existing cost estimation techniques.
Classifying product review quality based on semantic and structural features
Authors: Akhyar Firdaus Ilham , Rolliawati Dwi , Kunaefi Anang , Firdaus Firdaus
Published: November 2023

Product reviews are written opinions submitted by consumers in assessing a product. The existence of product reviews is important because it can help consumers make better product purchasing decisions. But product reviews can also be unimportant if the quality of the information from the reviews is not helpful. This can be minimized if a classification is carried out to find out which reviews are helpful or not. For this to be achieved, this research will apply a support vector machine model using semantic and structural features to be able to classify review texts based on their characteristics. By applying the appropriate preprocessing stages, the final results show that the semantic features produce the highest F1-score value of 0.825. Whereas the structural features produce the highest F1-score value of 0.823. From this, it can be concluded that semantic features can be used to identify the characteristics of a review text that are helpful or not properly. This success also shows outstanding performance in classifying reviews as helpful or not compared to previous studies.
Ataxia severity classification using enhanced feature selection and ranking optimization through machine learning model
Authors: Durganivas Seetharama Pavithra , Math Shrishail
Published: November 2023

The examination of neurological disorders and the monitoring of ataxic gait are major scientific topics that benefit from digital signal processing techniques and machine learning (ML) technologies. In this research, an ML approach is optimized with the use of Spatio-temporal data obtained from a kinect-sensor to differentiate between normal gait and ataxic. The current ML-based approaches perform very poorly because they cannot build feature-correlation among many gait characteristics. Furthermore, current ML-based techniques generate more false-positive whenever data is imbalanced in nature; especially for performing multi-label classification. This work presents a feature selection and ranking (FSR) based on extreme gradient boost (XGB) for ataxia severity classification. The FSR-XGB introduce an enhanced misclassification minimization error optimization and presents a novel feature selection and ranking to introduce feature importance using new cross-validation mechanism, both of which are aimed at solving the multi-label classification research problems. Results from experiments demonstrate that the presented FSR-XGB approach outperforms other ML-based and deep learning-based approaches.
Adaptive traffic aware clustering and routing model for wireless sensor networks
Authors: Veeregowdanadoddi Chandraiah Rudramurthy , Ramalingappa Aparna
Published: November 2023

Designing effective clustering algorithms plays a very important role in improving network lifetime target coverage (NLTC) in heterogeneous wireless sensor networks (HWSNs). However, the current clustering mechanism failed to address hotspot problem resulting in poor coverage and network lifetime performance. Recently, unequal clustering has been used for addressing hotspot problem in HWSNs. However, when performing inter-cluster routing, load balancing is not efficient and the energy degradation is high, which affects the overall network performance. This work introduces an effective model namely adaptive traffic aware clustering and routing (ATACR) model to address hotspot and load balancing issues. The ATACR introduces a novel unequal clustering and ideal distribution of load in different cluster levels. The ATACR model improves network lifetime, reduces control channel overhead, and attains better throughput in comparison with existing routing models.
Poultry disease early detection methods using deep learning technology
Authors: Yajie Liu , Md Johar Md Gapar , Iqbal Hajamydeen Asif
Published: November 2023

Poultry production is a pivotal contributor to global economic growth, playing a central role in promoting human ecosystem sustainability. It offers affordable and readily accessible protein sources, encompassing meat, eggs, and other by-products. Beyond its direct nutritional benefits, poultry production enhances household income, bolsters food security, and aids in poverty reduction, making it integral to worldwide economic advancement. However, as the global population surges, so does the demand for poultry meat and eggs. Concurrently, poultry disease management emerges as a paramount challenge, leading to significant threats to food security and economic stability. Leveraging cutting-edge technology offers promising avenues to devise strategies that not only bolster farm profitability but also mitigate environmental impacts and foster the well-being of both animals and humans. This study systematically reviews the latest literature concerning poultry disease diagnosis based on deep learning techniques, elucidating the clinical manifestations associated with various ailments. The analysis indicates that emerging technological solutions, especially image processing and deep learning (DL), substantially outperform conventional manual inspection methods in early disease detection and warning in the poultry sector. Such innovations underscore their potential for revolutionizing poultry health management and disease mitigation.
Enhanced negation handling for sentiment analysis on Twitter using deep neural networks
Authors: Mylarappa Mamatha , Kumar B. N Shiva , J. Gowda Thriveni , K. Rajuk Venugopal
Published: November 2023

Sentiment analysis is a tool to identify and measure the emotion in a piece of text. Negation handling is an important aspect of natural language processing (NLP) for Twitter data. It is a process of correctly interpreting the sentences containing negation words, such as, "never", "no", "neither" and so on. Negation words are used in machine learning to express negative sentiment or indicate the absence of something. In this article, a negation handling technique using deep learning models. Artificial neural networks (ANNs) and convolutional neural networks (CNNs) for classification is proposed. The system is evaluated on SemEval-2017 dataset. The classification performance is improved by using ANN and CNN on the negative tweets. The study aims to improve the classification accuracy by considering negation words in the text. The paper compares the performance of ANNs and CNNs in handling negation words and evaluates them on the tweets data. This study provides insights into the effectiveness of using deep learning techniques for negation handling in sentiment analysis and highlights the importance of considering negation words in the text for improved sentiment analysis performance. The proposed negation strategy attains a superior performance accuracy over machine learning models by preventing misclassified tweets.
Patient data management using blockchain technology
Authors: Bidve Vijaykumar , Kakakde Kiran , Sarasu Pakiriswamy , Kediya Shailesh , Tamkhade Pradip , Sudarsanan Nair Suprakash
Published: November 2023

The patient data management is an essential component of healthcare systems, the secure and efficient data processing is important for the medical data. Data security, interoperability and privacy are the key requirements of data storage systems of healthcare organizations. The electronic medical records have become a key technique to maintain patient information in hospitals due to the technology revolution. Some hospital systems are also using server-based patient detail management systems, they require considerable storage to record all of the patient's medical reports, limiting scalability. They are facing difficulties, including interoperability, security and privacy worries, cyberattacks on centralized storage, and maintaining medical policy compliance simultaneously. The blockchain technology has come up with solution having decentralized and irreversible data storage. A distributed secure ledger of blockchain is the solution, enabling safe storage and retrieval of data. The proposed work yields effectively deployed smart contracts based on the system's functions, real-time patient health monitoring. The main goal of this system is to bring the whole medical data together on a single platform, employing a secured decentralized approach to store and retrieve medical information effectively.
Brain computer interfaces in computer science and engineering areas: a systematic study
Authors: Katona Jozsef , Kovari Attila , Guzsvinecz Tibor , Szűcs Judit , Demeter Robert , Szücs Veronika
Published: November 2023

Brain-computer interfaces (BCI) are a channel that implements direct communication between the brain and some external unit. Developments of BCIs can provide new application opportunities in a large number of fields of use. In the development of BCI devices, the development of technology and digital technology represented a big change, as it provided the necessary computing power to implement and run the continuously developing signal processing algorithms that ensure processing and evaluation. The aim of this paper is to provide an overview of BCI research results which were published in the engineering field. In the present study, articles that had a greater impact, where the annual average number of citations is greater than 30, in the BCI field were reviewed and processed in a systematic way, in order to make individual research more comparable. The systematic processing was focused on the aims of application, used device/ dataset, applied data process and achieved best accuracy. This systematic study summarizes the most effective methods used in the BCI processing and highlights the future trends. The results showed an accuracy of 85% thanks to increasingly reliable, accurate and cost-effective signal detection and processing devices, as well as algorithms.
Performance evaluation of technical indicators for forecasting the moroccan stock index using deep learning
Authors: Razouk Ayoub , El Mehdi Falloul Moulay , Harkati Ayman , Touhami Fatima
Published: November 2023

Navigating the complex terrain of financial markets requires accurate forecasting tools, underscoring the need for effective forecasting methods to assist investors and policymakers alike. This paper explores deep learning techniques for forecasting the Moroccan all shares index (MASI), a prominent indicator of the Moroccan stock market. The study aims to evaluate the performance of technical indicators in enhancing the accuracy of MASI predictions. A comprehensive dataset of daily closing prices of the MASI index is collected and 26 technical indicators are computed from the historical price data. Deep learning models based on artificial neural networks (ANNs) are trained and optimized using the dataset. The performance of the models is evaluated using standard metrics such as mean absolute error (MAE), mean square error (MSE), mean absolute percentage error (MAPE), and root mean square error (RMSE). Additionally, feature selection techniques are employed to identify the subset of technical indicators that contribute most significantly to the prediction accuracy. The findings provide insights into the effectiveness of deep learning models and the impact of technical indicators on MASI prediction accuracy. This research has important implications for investors, financial analysts, and policymakers, enhancing investment strategies and risk management approaches.
Optimization deep learning with rough set approach model classification Otitis
Authors: Arief Wisky Irzal , Ade Putra Teri
Published: November 2023

Otitis is a disease that occurs in the middle ear in the form of inflammation. This research aims to develop an analysis model for the classification of Otitis disease based on knowledge patterns based on symptoms and type of disease. The analysis methods used include the performance of the certainty factor (CF), rough set (RS), artificial neural network (ANN), and decision tree (DT) methods. CF and RS performance can be used to generate classification rule patterns. These rule patterns become new knowledge in the classification analysis process using the concept of deep learning (DL). DL analysis with ANN and DT performance can work optimally in exploring and discovering hidden knowledge. Based on the results of performance testing, the combination of CF and RS in preprocessing can present a classification pattern of 106 rules. The output of DL analysis results is proven to produce precise and accurate classification results with an accuracy of 89%. Based on these results, the analytical model developed was proven to be effective in classifying Otitis disease. Not only that, this research is also able to contribute to updating the knowledge-based system in the classification process.
Forecast earthquake precursor in the Flores Sea
Authors: Jufriansah Adi , Anggraini Ade , Zulfakriza Zulfakriza , Khusnani Azmi , Pramudya Yudhiakto
Published: November 2023

Artificial intelligence (AI) can use seismic training data to discover relationships between inputs and outcomes in real-world applications. Despite this, particularly when using geographical data, it has not been used to predict earthquakes in the Flores Sea. The algorithm will read the seismic data as a pattern of iterations throughout the operation. The output data is created by grouping based on clusters using the most effective WCSS analysis, while the input features are derived from the original international resource information system (IRIS) web service data. Given that earthquake prediction is an effort to reduce seismic disasters, this research is essential. By generating predictions, it can reduce the devastation caused by earthquakes. Using the support vector machine (SVM), hyperparameter support vector machine (HP-SVM), and particle swarm optimization support vector machine (PSO-SVM) algorithms, this study seeks to forecast the Flores Sea earthquake. According to the estimation results, the SVM algorithm’s evaluation value is less precise than that of the HP-SVM, especially the linear HP-SVM and HP-SVM Polynomial models. However, the HP-SVM RBF model’s accuracy rating is identical to that of the traditional SVM model.
 The improvement of the PSO-SVM model, which has the finest gamma position and a value of 9.
The unknown house of an old neighbor: the nest of Zethus (Zethoides) nodosus Zavattari (Hymenoptera: Vespidae)
Authors: Lopes Rogério Botion
Published: November 2023

Despite being mostly solitary and nesting in cavities, Zethus is more widely known by its few representatives that build external aerial nests. Here, nests of Zethus nodosus collected in the first decades of the twentieth century in surrounding areas of the Museu de Zoologia da Universidade de São Paulo are described and ponderations about the absence of more recent material are made.
Immediate Implant Placement Using the Socket Shield Technique: Clinical, Radiographic, and Volumetric Results Using 3D Digital Techniques—A Case Series
Authors: Natale Massimo , Soardi Carlo M. , Saleh Muhammad H. A. , Ponzi Armando , Tagliaferri Davide , Filannino Fabio Manuel , Fontana Fausto , Decker Ann , Marinotti Fabio , d'Ambrosio Aniello , Wang Hom-Lay
Published: November 2023

Extraction-site alveolar remodeling is a major concern due to negative volumetric hard and soft tissue changes that inevitably limit rehabilitation options and diminish esthetic outcomes. Surgical techniques employed to minimize alveolar dimensional changes are not always predictable. Utilizing a socket shield with an immediate surgical implant procedure helps maintain a thin portion of the root in the vestibular area and thus minimizes bone resorption, especially at the coronal cortical aspect. This case series assesses the dimensional changes in peri-alveolar structures via superimposition of the preoperative and 6-month postoperative 3D digital quantification of soft tissue. Fifty patients with 50 sites fulfilled the inclusion criteria. Implant survival was 100%, with no incidence of complications. Tissue changes were as follows: –0.85 mm at the mesial papilla, –0.95 mm at the distal papilla, –0.7 mm at both the vertical and horizontal central margins, –0.21 mm at a distance 4 mm from the margin, and –0.64 mm at the palatal central margin. The buccal contour was clinically convex in all cases. The site with highest frequency of > 1 mm of dimensional loss was the distal papilla (42% of sites), and the site with the lowest frequency was the point 4 mm from the midfacial margin (0% of sites).
The State of Acculturation and Identity Distress among Stranded Adolescent Biharis in Bangladesh
Authors: Islam Md Rafiqul , Ahmed Sharmin
Published: November 2023

The Urdu-speaking Bihari people in Bangladesh, living in dilapidated camps with compromised rights and opportunities, constitute one of the most vulnerable diasporas in the South Asian region. These camps have existed for generations and consequently house many adolescents. Yet studies on the acculturation and identity distress of adolescent Biharis are nonexistent. This paper addresses this significant topic directly. Based on a survey using the Abbreviated Multidimensional Acculturation Scale (AMAS-ZABB) and the Identity Distress Survey (IDS) frameworks, this study found that Bihari adolescent youths preferred to associate themselves more with the host identity and culture than with their own. They showed high language competence in their host and origin languages, but only high cultural competence in their host culture. Their identity distress was found to be low across identity, social relationships, religion and group loyalties, but was moderate and evenly distributed around long-term goals and career plans. The recommendations of the study support the integration of the Bihari population into Bangladesh’s population framework, since this diaspora appears to be qualified for this transition.
Social Cohesion in the Indian Community in Vietnam
Authors: Thuong Nguyen Le Thy , Van Linh Nguyen
Published: November 2023

Social cohesion holds significant importance within immigrant communities, providing them with a sense of belonging to a group of people who share many similarities while they are adapting to an unfamiliar environment. This article explores social cohesion in the Indian community in Vietnam through the primary method of surveying and in-depth interviews. The authors believe that the Indian community in Vietnam currently is not closely connected, but that its members show a certain dependence on and trust in the members of the community. Regarding cohesion methods, the article explores the main methods of engagement, such as participation in community activities, in the activities of representative organisations, and in interactive groups on social media. In all three ways it is undeniable that Indians living in Vietnam have made efforts to stick together and unite, but the results have not really been what they wanted.
Feelings matter! Emotions in Medieval Arabic
Authors: Bray Julia
Published: November 2023

The history of emotions is an established field in European studies. It covers all periods and many disciplines. Its premise is that emotions are shaped by culture, and that their expression and effects should be contextualised in time and place. More than this, the history of emotions asserts that emotion and intellect are interconnected, and that understanding emotion is central to understanding historical motivations and actions. This talk argues that we need an Arabic history of emotions, for which the written materials are both abundant and challenging. Identifying and interpreting emotions in medieval Arabic writing — those that it depicts, and those that it arouses in the reader — makes us ask where the task leads us. I have used an example that throws light on how adab functions as an overarching mode of thinking, and in reading it have tried to address the fundamental problem in text-based emotions history, which is distinguishing between literary (imagined) and lived emotions.
Two new species of Xyalidae Chitwood, 1951 (Nematoda, Monhysterida) from Chinese Sea Area
Authors: LI TING , BAN SHUYAN , HUANG YONG
Published: November 2023

Two new nematode species of the family Xyalidae were found in the intertidal zone of the Chinese coast, and they are described here as Daptonema sinica sp. nov. and Metadesmolaimus bulbosus sp. nov. Daptonema sinica sp. nov. is characterised by a relatively large body with a broad funnel-shaped buccal cavity; relatively long cephalic setae; a circular amphidial fovea posterior to the buccal cavity; slender spicules bending into a hook shape in the posterior half; a small plate-like gubernaculum without apophysis; and a conico-cylindrical slender tail with terminal setae. Metadesmolaimus bulbosus sp. nov. can be identified by its large buccal cavity, long cervical setae, multiple bulb-like pharyngeal muscular swellings, conico-cylindrical tail narrowing abruptly immediately posterior to the cloaca, straight clavated spicules with a broad proximal end and hooked distal end, and gubernaculum absent. An updated dichotomous key to the species of Metadesmolaimus is given.
A new species of Cardiodectes Wilson C.B., 1917 (Copepoda: Siphonostomatoida: Pennellidae) from Spinyjaw greeneye, Chlorophthalmus corniger Alcock, 1894 off the Indian Ocean
Authors: ANEESH PANAKKOOL THAMBAN , HELNA AMERI KOTTARATHIL , KUMAR APPUKUTTANNAIR BIJU , MARAN BALU ALAGAR VENMATHI
Published: November 2023

A new species of flesh penetrating parasitic copepod of the genus Cardiodectes Wilson C.B., 1917 (Siphonostomatoida: Pennellidae) parasitizing the fish Spinyjaw greeneye, Chlorophthalmus corniger Alcock, 1894 (Chlorophthalmidae), inhabiting at depths of 265 to 458 meters from the south-west coast of India is described and illustrated. The new species, Cardiodectes vampire sp. nov., can be distinguished from its other 16 congeners by the following features: (i) trunk 3.15 times as long as wide; (ii) extremely long (3.5 times as long as total body length) and uncoiled egg sacs; (iii) difference in the armature of legs 1–3; (iv) comparatively longer trunk (0.7 times as long as total body length). A checklist of valid global species of Cardiodectes with its host and distribution is also provided.
Anomalous Propagation and the Sinking of the Russian Warship Moskva
Authors: Norin Lars , Wellander Niklas , Devasthale Abhay
Published: November 2023

On 13 April 2022, the Russian warship Moskva was hit by two Ukrainian Neptune anti-ship missiles in the Black Sea, leading to its demise. Before launching an anti-ship missile, a target must first be detected and positioned, for example, by an accompanying radar system. However, when the missiles hit the Moskva she was well beyond the normal radar horizon of any ground-based radar system, making the ship undetectable under normal circumstances. Using meteorological reanalysis data, we show that at the time of the missile launch the prevailing weather conditions allowed a ground-based radar to detect targets far beyond the normal radar horizon through anomalous propagation conditions. During such conditions, the atmospheric index of refraction decreases rapidly with height, making electromagnetic radiation bend downward to, partly or fully, compensate the curvature of the Earth. The results show that atmospheric conditions must be considered carefully, even during warfare, as their impact on radar wave propagation can be considerable.
40-Year Statistics of Warm-Season Extreme Hourly Precipitation over Southwest China
Authors: Jiang Rouyi , Cui Xiaopeng , Lin Jian , Tian Jia
Published: November 2023

Southwest China (SWC) possesses complicated topography with frequent geological activities, where heavy precipitation occurs frequently in warm seasons. Few previous studies on extreme precipitation were carried out at hourly scales. In this study, spatiotemporal variations of the extreme hourly precipitation (EHP) over SWC during the warm season of 1981–2020 and the involved mechanisms are investigated. Results show that the threshold and intensity of EHP present similar spatial distribution—lower (higher) in the west (east) part of SWC, while the EHP frequency is opposite. The long-term trend of EHP amount shows a more significant positive tendency than that of hourly precipitation (HP) amount due to synchronous increases in intensity and frequency. The significant increasing trend of EHP occurs in areas above 500-m terrain height, with a weak increasing trend below 500 m (e.g., Chongqing and eastern Sichuan). EHP appears mainly from June to August and exhibits a bimodal distribution in diurnal variation. The mechanism analysis demonstrates that occurrences of EHP are generally accompanied by positive anomalies of temperature, humidity, and geopotential height. Anomalous cyclonic circulation can also be found in the low-level wind field. The westward and northward extension of the western North Pacific subtropical high (WNPSH) as well as temperature rise may be the primary reason for the increase of EHP. For Chongqing and eastern Sichuan, the anticyclone circulation in low-level and the significantly weakened water vapor flux convergence cause poor moisture and dynamic conditions, inhibiting the growth of EHP.

Significance Statement

Heavy precipitation occurs frequently during the warm season in Southwest China (SWC), often causing severe impacts on human safety and economic property. This study analyses spatiotemporal variations of the extreme hourly precipitation (EHP) over SWC during the warm season of 1981–2020 and the involved mechanisms. The increasing trend of EHP far exceeds that of hourly precipitation (HP), especially in areas above 500 m. The westward and northward extension of the western North Pacific subtropical high (WNPSH) and temperature rise may be the main reason for the increase of EHP. For areas below 500 m (e.g., Chongqing and eastern Sichuan), poor moisture and dynamic conditions inhibited the growth of EHP.
The Significance of the Melt-Pond Scheme in a CMIP6 Global Climate Model
Authors: Diamond Rachel , Schroeder David , Sime Louise C. , Ridley Jeff , Feltham Danny
Published: November 2023

The impact of melt ponds on sea ice albedo has been observed and documented. In general circulation models, ponds are now accounted for through indirect diagnostic treatments (“implicit” schemes) or prognostic melt-pond parameterizations (“explicit” schemes). However, there has been a lack of studies showing the impacts of these schemes on simulated Arctic climate. We focus here on rectifying this using the general circulation model HadGEM3, one of the few models with a detailed explicit pond scheme. We identify the impact of melt ponds on the sea ice and climate, and associated ice–ocean–atmosphere interactions. We run a set of constant forcing simulations for three different periods and show, for the first time, that using mechanistically different pond schemes can lead to very significantly different sea ice and climate states. Under near-future conditions, an implicit scheme never yields an ice-free summer Arctic, while an explicit scheme yields an ice-free Arctic in 35% of years and raises autumn Arctic air temperatures by 5° to 8°C. We find that impacts on climate and sea ice depend on the ice state: under near-future and last-interglacial conditions, the thin sea ice is very sensitive to pond formation and parameterization, whereas during the preindustrial period the thicker sea ice is less sensitive to the pond scheme choice. Both of these two commonly used parameterizations of sea ice albedo yield similar results under preindustrial conditions but in warmer climates lead to very different Arctic sea ice and ocean and atmospheric temperatures. Thus, changes to physical parameterizations in the sea ice model can have large impacts on simulated sea ice, ocean, and atmosphere.

Significance Statement

This study investigates the impacts of melt ponds on Arctic sea ice under different climate conditions, using the HadGEM3-GC3.1-LL general circulation model (GCM). Additionally, we study the impact of changing the type of pond scheme used. We find that changing the pond scheme causes large differences to how a GCM simulates Arctic sea ice, the ocean, and the atmosphere, for both near-future and warmer paleoclimate conditions. These large differences have not been found previously, because this is one of the first GCM studies of this type. Our results demonstrate the importance of melt ponds, and their wider impacts on ocean and atmosphere. Furthermore, they suggest that better evaluation of the representation of sea ice processes is vital for the robust projection of future climate change.
Seasonal Variations and Spatial Patterns of Arctic Cloud Changes in Association with Sea Ice Loss during 1950–2019 in ERA5
Authors: Jenkins Matthew T. , Dai Aiguo , Deser Clara
Published: November 2023

The dynamic and thermodynamic mechanisms that link retreating sea ice to increased Arctic cloud amount and cloud water content are unclear. Using the fifth generation of the ECMWF Reanalysis (ERA5), the long-term changes between years 1950–79 and 1990–2019 in Arctic clouds are estimated along with their relationship to sea ice loss. A comparison of ERA5 to CERES satellite cloud fractions reveals that ERA5 simulates the seasonal cycle, variations, and changes of cloud fraction well over water surfaces during 2001–20. This suggests that ERA5 may reliably represent the cloud response to sea ice loss because melting sea ice exposes more water surfaces in the Arctic. Increases in ERA5 Arctic cloud fraction and water content are largest during October–March from ∼950 to 700 hPa over areas with significant (≥15%) sea ice loss. Further, regions with significant sea ice loss experience higher convective available potential energy (∼2–2.75 J kg−1), planetary boundary layer height (∼120–200 m), and near-surface specific humidity (∼0.25–0.40 g kg−1) and a greater reduction of the lower-tropospheric temperature inversion (∼3°–4°C) than regions with small (<15%) sea ice loss in autumn and winter. Areas with significant sea ice loss also show strengthened upward motion between 1000 and 700 hPa, enhanced horizontal convergence (divergence) of air, and decreased (increased) relative humidity from 1000 to 950 hPa (950–700 hPa) during the cold season. Analyses of moisture divergence, evaporation minus precipitation, and meridional moisture flux fields suggest that increased local surface water fluxes, rather than atmospheric motions, provide a key source of moisture for increased Arctic clouds over newly exposed water surfaces during October–March.

Significance Statement

Sea ice loss has been shown to be a primary contributor to Arctic warming. Despite the evidence linking large sea ice retreat to Arctic warming, some studies have suggested that enhanced downwelling longwave radiation associated with increased clouds and water vapor is the primary reason for Arctic amplification. However, it is unclear how sea ice loss is linked to changes in clouds and water vapor in the Arctic. Here, we investigate the relationship between Arctic sea ice loss and changes in clouds using the ERA5 dataset. Improved knowledge of the relationship between Arctic sea ice loss and changes in clouds will help further our understanding of the role of the cloud feedback in Arctic warming.
Spatiotemporal Characteristics of the Near-Surface Turbulent Cascade at the Submesoscale in the Drake Passage
Authors: Tedesco P. F. , Baker L. E. , Naveira Garabato A. C. , Mazloff M. R. , Gille S. T. , Caulfield C. P. , Mashayek A.
Published: November 2023

Submesoscale currents and internal gravity waves achieve an intense turbulent cascade near the ocean surface [depth of 0–O(100) m], which is thought to give rise to significant energy sources and sinks for mesoscale eddies. Here, we characterize the contributions of nonwave currents (NWCs; including eddies and fronts) and internal gravity waves (IGWs; including near-inertial motions, lee waves, and the internal wave continuum) to near-surface submesoscale turbulence in the Drake Passage. Using a numerical simulation, we combine Lagrangian filtering and a Helmholtz decomposition to identify NWCs and IGWs and to characterize their dynamics (rotational versus divergent). We show that NWCs and IGWs contribute in different proportions to the inverse and forward turbulent kinetic energy cascades, based on their dynamics and spatiotemporal scales. Purely rotational NWCs cause most of the inverse cascade, while coupled rotational–divergent components of NWCs and coupled NWC–IGWs cause the forward cascade. The cascade changes direction at a spatial scale at which motions become increasingly divergent. However, the forward cascade is ultimately limited by the motions’ spatiotemporal scales. The bulk of the forward cascade (80%–95%) is caused by NWCs and IGWs of small spatiotemporal scales (L < 10 km; T < 6 h), which are primarily rotational: submesoscale eddies, fronts, and the internal wave continuum. These motions also cause a significant part of the inverse cascade (30%). Our results highlight the requirement for high spatiotemporal resolutions to diagnose the properties and large-scale impacts of near-surface submesoscale turbulence accurately, with significant implications for ocean energy cycle study strategies.
The Potential Roles of Preexisting Airmass Boundaries on a Tornadic Supercell Observed by TORUS on 28 May 2019
Authors: Axon Kristen L. , Houston Adam L. , Ziegler Conrad L. , Weiss Christopher C. , Rasmussen Erik N. , Coniglio Michael C. , Argrow Brian , Frew Eric , Swenson Sara , Reinhart Anthony E. , Wilson Matthew B.
Published: November 2023

On 28 May 2019, a tornadic supercell, observed as part of Targeted Observation by UAS and Radars of Supercells (TORUS) produced an EF-2 tornado near Tipton, Kansas. The supercell was observed to interact with multiple preexisting airmass boundaries. These boundaries and attendant air masses were examined using unoccupied aircraft system (UAS), mobile mesonets, radiosondes, and dual-Doppler analyses derived from TORUS mobile radars. The cool-side air mass of one of these boundaries was found to have higher equivalent potential temperature and backed winds relative to the warm-side air mass; features associated with mesoscale air masses with high theta-e (MAHTEs). It is hypothesized that these characteristics may have facilitated tornadogenesis. The two additional boundaries were produced by a nearby supercell and appeared to weaken the tornadic supercell. This work represents the first time that UAS have been used to examine the impact of preexisting airmass boundaries on a supercell, and it provides insights into the influence environmental heterogeneities can have on the evolution of a supercell.
A Case Study Investigating the Low Summertime CAPE Behavior in the Global Forecast System
Authors: Sun Xia , Heinzeller Dominikus , Bernardet Ligia , Pan Linlin , Li Weiwei , Turner David , Brown John
Published: November 2023

Convective available potential energy (CAPE) is an important index for storm forecasting. Recent versions (v15.2 and v16) of the Global Forecast System (GFS) predict lower values of CAPE during summertime in the continental United States than analysis and observation. We conducted an evaluation of the GFS in simulating summertime CAPE using an example from the Unified Forecast System Case Study collection to investigate the factors that lead to the low CAPE bias in GFS. Specifically, we investigated the surface energy budget, soil properties, and near-surface and upper-level meteorological fields. Results show that the GFS simulates smaller surface latent heat flux and larger surface sensible heat flux than the observations. This can be attributed to the slightly drier-than-observed soil moisture in the GFS that comes from an offline global land data assimilation system. The lower simulated CAPE in GFS v16 is related to the early drop of surface net radiation with excessive boundary layer cloud after midday when compared with GFS v15.2. A moisture-budget analysis indicates that errors in the large-scale advection of water vapor does not contribute to the dry bias in the GFS at low levels. Common Community Physics Package single-column model (SCM) experiments suggest that with realistic initial vertical profiles, SCM simulations generate a larger CAPE than runs with GFS IC. SCM runs with an active LSM tend to produce smaller CAPE than that with prescribed surface fluxes. Note that the findings are only applicable to this case study. Including more warm-season cases would enhance the generalizability of our findings.

Significance Statement

Convective available potential energy (CAPE) is one of the key parameters for severe weather analysis. The low bias of CAPE is identified by forecasters as one of the key issues for the NOAA operational global numerical weather prediction model, Global Forecast System (GFS). Our case study shows that the lower CAPE in GFS is related to the drier atmosphere than observed within the lowest 1 km. Further investigations suggest that it is related to the drier atmosphere that already exists in the initial conditions, which are produced by the Global Data Assimilation System, in which an earlier 6-h GFS forecast is combined with current observations. It is also attributed to the slightly lower simulated soil moisture than observed. The lower CAPE in GFS v16 when compared with GFS v15.2 in the case analyzed here is related to excessive boundary layer cloud formation beginning at midday that leads to a drop of net radiation reaching the surface and thus less latent heat feeding back to the low-level atmosphere.