bluuebunny/crossref_metadata_2025 huggingface dataset:
Advanced hybrid algorithms for precise multipath channel estimation in next-generation wireless networks
Authors: Rekkal Kahina , Rekkal Sara , Bassou Abdesselam
Published: January 2024

Multipath channels continue to present challenges in wireless communication for both 5G and 6G networks. A multipath channel is a phenomenon in wireless communications where signals traverse from the sender to the receiver along various paths. This end occurs due to the reflection, diffraction, and refraction of signals of various objects and structures in the environment. Such pathways can cause symbol interference in the transmitted signal, leading to communication issues. To this end, our paper proposes the integration of three algorithms: teaching-learning-based optimization (TLBO), particle swarm optimization (PSO), and artificial neural networks (ANN). This combination effectively analyzes and stabilizes the transmission channel, minimizing symbol interference. We have developed, simulated, and evaluated this hybrid approach for multipath fading channels. We apply it to various coding schemes, including tail-biting convolutional code, turbo codes, low-density parity-check, and polar code. Additionally, we have explored various decoding methods such as Viterbi, maximum logarithmic maximum a posteriori, minimum sum, and cyclic redundancy check soft cancellation list. Our study encompasses new channel equalization schemes and coding gains derived from simulations and mathematical analysis. Our proposed method significantly enhances channel equalization, reducing interference and improving error correction in wireless communication systems.
Improved design and performance of the global rectenna system for wireless power transmission applications around 2.45 GHz
Authors: En-Naghma Walid , Halaq Hanan , El Ougli Abdelghani
Published: January 2024

This work proposes a new conception of the global microstrip rectenna system operating around 2.45 GHz. This improved rectenna system associates a receiving antenna with a rectifier circuit. This rectenna is printed on an FR4 substrate. The proposed antenna is a 1×4 microstrip antenna patch array with pentagonal patches using the defective ground structure method and operates with circular polarization. To show the effectiveness of this array, the results obtained by the computer simulation technology microwave studio (CST MWS) software prove that this array is good in terms of high gain, high directivity, high efficiency, wideband, small volume, and well-adaptation, and all these results are confirmed by another solver high-frequency structure simulator (HFSS). The improved rectifier is a microstrip rectifier that uses an HSMS2852 Schottky diode by using a series topology. The effectiveness of this rectifier is proved by the simulation results using advanced design system (ADS) software in terms of well-matching input impedance, high efficiency, and important output direct current (DC) voltage value. The proposed rectenna system is more efficient compared with the existing works and is very appropriate for several applications of wireless power transmission to power supply electronic instruments in various fields cleanly on our planet.
Non-binary codes approach on the performance of short-packet full-duplex transmissions
Authors: Vuong Bao Quoc , Trang Kien , Nguyen An Hoang , Do Hung Ngoc
Published: January 2024

This paper illustrates the enhancement of the performance of short-packet full-duplex (FD) transmission by taking the approach of non-binary low density parity check (NB-LDPC) codes over higher Galois ﬁeld. For the purpose of reducing the impacts of self-interference (SI), high order of modulation, complexity, and latency decoder, a blind feedback process composed of channels estimation and decoding algorithm is implemented. In particular, this method uses an iterative process to simultaneously suppress SI component of FD transmission, estimate intended channel, and decode messages. The results indicate that the proposed technique provides a better solution than both the NB-LDPC without feedback and the binary LDPC feedback algorithms. Indeed, it can signiﬁcantly improve the performance of overall system in two important factors, which are bit-error-rate (BER) and mean square error (MSE), especially in high order of modulation. The suggested algorithm also shows a robustness in reliability and power consumption for both short-packet FD transmissions and high order modulation communications.
Agriculture crop yield prediction using inertia based cat swarm optimization
Authors: Reddy Dwaram Jayanarayana , Madapuri Rudra Kumar
Published: January 2024

Crop yield prediction is among the most important and main sources of income in the Indian economy. In this paper, the improved cat swarm optimization (ICSO) based recurrent neural network (RNN) model is proposed for crop yield prediction using time series data. The inertia weight parameter is added to position equation that is selected randomly, and a new velocity equation is produced which enhances the searching ability in the best cat area. By using inertia weight, the ICSO enhances performance of feature selection and obtains better convergence in minimum iteration. The RNN is applied to produce direct graph using sequence of data and decides current layer output by involving all other existing calculations. The performance of the model is estimated using coefficient of determination (R2), root mean square error (RMSE), mean squared error (MSE), and mean absolute error (MAE) on the yield from the years 2011 to 2021 with an annual prediction for 120 records of approximately 8 million nuts. The evaluated result shows that the proposed ICSO-RNN model delivers metrics such as R2, MAE, MSE, and RMSE values of 0.99, 0.77, 0.68, and 0.82 correspondingly, which ensures accurate yield prediction when compared with the existing methods which are hybrid reinforcement learning-random forest (RL-RF) and machine learning (ML) methods.
Detection of diseases in rice leaf using convolutional neural network with transfer learning based on ResNeXt
Authors: Veeramreddy Rajasekhar , Gnanasekaran Arulselvi , Kunchala Suresh Babu
Published: January 2024

Rice is the most cultivated crop in the agricultural field and is a major food source for more than 60% of the population in India. Occurrence of disease in rice leaves, majorly affects the rice quality, production and also the farmers’ income. Nowadays, new variety of diseases in rice leaves are identified and detected periodically throughout the world. Manual monitoring and detection of plant diseases proves to be time consuming for the farmer and also a costly affair for using chemicals in the disease treatment. In this paper, a deep learning method of convolutional neural networks (CNN) with a transfer learning technique is proposed for the detection of a variety of diseases in rice leaves. This method uses the ResNeXt network model for classifying the images of disease-affected plants. The proposed model’s performance is evaluated using accuracy, precision, recall, F1-score and specificity. The experimental results of ResNeXt model measured for accuracy, precision, recall, F1-score, and specificity, are respectively 99.22%, 92.87%, 91.97%, 90.95%, and 99.05%, which proves greater accuracy improvement than the existing methods of SG2-ADA, YOLOV5, InceptionResNetV2 and Raspberry Pi.
Strengthening data integrity in academic document recording with blockchain and InterPlanetary file system
Authors: Suseno Taufiq Rizky Darmawan , Afrianto Irawan , Atin Sufa
Published: January 2024

A diploma is a certificate or official document given by a school or college that is useful for continuing education, applying for jobs, and assessing student intelligence. The main problem with diplomas and other academic documents is that many are forged. This study aims to develop a prototype for recording student academic data using blockchain and blockchain and InterPlanetary file system (IPFS). The research stages were conducted with system conceptualization, data modeling, smart contract development, IPFS integration, data transaction development, user interface/user experience (UI/UX) development, and system testing. A blockchain is a permanent information structure formed by data blocks that are interconnected with transaction data blocks before and after it. The transaction data for each block are encrypted using asymmetric cryptography. IPFS is a peer-to-peer network protocol for storing and sharing data in a distributed file system applying the concept of decentralization to make the manipulation more difficult. The results show that student academic data and documents were successfully stored in a blockchain network using smart contracts and IPFS. Blockchain technology, smart contracts, and IPFS strengthen the value of these documents into documents that are safe, difficult to counterfeit, and easy to trace, such that authentication and integration are better preserved.
Optimal model of vehicular ad-hoc network assisted by unmanned aerial vehicles and information-centric networking to enhance network performance
Authors: Houari Abdeslam , Mazri Tomader
Published: January 2024

Vehicular ad-hoc network (VANET) is a promising project related to intelligent transportation systems (ITS), which aims at connecting vehicles and providing a set of functionalities for the efficient management of the network. However, the high mobility of the network nodes is considered a significant challenge for implementing a reliable, secure, and efficient exchange system. Furthermore, VANET faces the issue of packet delivery due to the high mobility of the nodes and packet collisions complicate the process of sending and receiving packets. We propose to combine two technologies which are unmanned aerial vehicle (UAV) and information centric networks (ICN) and apply it in VANET architecture as supporting technology. The UAV are more reliable and less affected by channel fading. And can be used in areas where we cannot install network infrastructure. The UAV has many advantages that we have cited in this article and can solve many issues of VANET. Using ICN can solve some of the problems of VANET since ICN has many strategies to capture and retrieve data. This study proposes a new VANET model based on an UAV and ICN, to reduce the overload of the vehicles, which in most cases require more resources and have a limited time to process and act especially in case of an accident or emergency.
Cloud service ranking with an integration of k-means algorithm and decision-making trail and evaluation laboratory approach
Authors: Goyal Pooja , Singh Deora Sukhvinder
Published: January 2024

The present research focuses on ranking cloud services by using the k-means algorithm with multi-criteria decision-making (MCDM) approaches that are the prime factor in the decision-making process and have been used to choose cloud services. The tools offered by MCDM can solve almost any decision-making problem. When faced with a selection challenge in the cloud environment, the trusted party would need to weigh the client’s choice against a predetermined list of criteria. There is a wide range of approaches to evaluating the quality of cloud services. The deep learning model has been considered a branch of artificial intelligence that assesses datasets to perform training and testing and makes decisions accordingly. This paper presents a concise overview of MCDM approaches and discusses some of the most commonly used MCDM methods. Also, a model based on deep learning with the k-means algorithm based decision-making trial and evaluation laboratory (kDE-MATEL) and analytic network process (ANP) is proposed as k-means algorithm based decision-making trial and evaluation laboratory with analytic network process (kD-ANP) for selecting cloud services. The proposed model uses the k-means algorithm and gives different levels of priority and weight to a set of criteria. A traditional model is also compared with a proposed model to reflect the efficiency of the proposed approach.
Development of system for generating questions, answers, distractors using transformers
Authors: Barlybayev Alibek , Matkarimov Bakhyt
Published: January 2024

The goal of this article is to develop a multiple-choice questions generation system that has a number of advantages, including quick scoring, consistent grading, and a short exam period. To overcome this difficulty, we suggest treating the problem of question creation as a sequence-to-sequence learning problem, where a sentence from a text passage can directly mapped to a question. Our approach is data-driven, which eliminates the need for manual rule implementation. This strategy is more effective and gets rid of potential errors that could result from incorrect human input. Our work on question generation, particularly the usage of the transformer model, has been impacted by recent developments in a number of domains, including neural machine translation, generalization, and picture captioning.
Interoperability for intelligent traffic management systems in smart cities
Authors: Alanazi Fayez , Alenezi Mamdouh
Published: January 2024

Intelligent traffic management systems (ITMS) are essential for safe and livable smart cities. However, achieving seamless interoperability between diverse devices and services is challenging due to the lack of universal open standards. This study examines different types of interoperability (syntactic, semantic, network, middleware, and security) and their relationships with ITMS in the smart city context. By discussing requirements, challenges, and potential standards, this research provides a comprehensive understanding of interoperability issues in ITMS. It highlights the importance of standardization and collaboration among stakeholders, including policymakers, urban planners, and technology providers, to achieve interoperability. Addressing these challenges can optimize ITMS performance and contribute to smarter, sustainable cities. The study categorically examines challenges and potential standards, offering a framework for future research and practice. By advancing our understanding of ITMS interoperability, this research facilitates improved traffic management and smarter city development, enhancing urban residents’ quality of life. It makes a significant contribution to the field by emphasizing the critical role of interoperability in effective traffic management systems and the advancement of smart cities. By addressing interoperability challenges, we can create safer, more efficient, and sustainable transportation networks, fostering the development of livable cities.
Classification of pathologies on digital chest radiographs using machine learning methods
Authors: Aitimov Murat , Shekerbek Ainur , Pestunov Igor , Bakanov Galitdin , Ostayeva Aiymkhan , Ziyatbekova Gulzat , Mediyeva Saule , Omarova Gulmira
Published: January 2024

This article is devoted to the research and development of methods for classifying pathologies on digital chest radiographs using two different machine learning approaches: the eXtreme gradient boosting (XGBoost) algorithm and the deep convolutional neural network residual network (ResNet50). The goal of the study is to develop effective and accurate methods for automatically classifying various pathologies detected on chest X-rays. The study collected an extensive dataset of digital chest radiographs, including a variety of clinical cases and different classes of pathology. Developed and trained machine learning models based on the XGBoost algorithm and the ResNet50 convolutional neural network using pre-processed images. The performance and accuracy of both models were assessed on test data using quality metrics and a comparative analysis of the results was carried out. The expected results of the article are high accuracy and reliability of methods for classifying pathologies on chest radiographs, as well as an understanding of their effectiveness in the context of clinical practice. These results may have significant implications for improving the diagnosis and care of patients with chest diseases, as well as promoting the development of automated decision support systems in radiology.
Feature selection using non-parametric correlations and important features on recursive feature elimination for stock price prediction
Authors: Priyatno Arif Mudi , Ramadhan Sudirman Wahyu Febri , Musridho Raja Joko
Published: January 2024

Stock price prediction using machine learning is a rapidly growing area of research. However, the large number of features that can be used can complicate the learning process. The feature selection method that can be used to overcome this problem is recursive feature elimination. Standard recursive feature elimination carries the risk of producing inaccurate algorithms because the top-ranked features are not necessarily the most important features. This research proposes a feature selection method that combines important features and nonparametric correlation in recursive feature elimination for stock price prediction. The data features used are technical indicators and stock price history. The recursive feature elimination method is modified with important features and nonparametric correlation features. The strategy for combining important features and non-parametric features is average weight, 25:75% weight, 75:25% weight, maximum weight, and minimum weight. The performance evaluation results show that the proposed feature selection method succeeded in obtaining small error values. The proposed method for predicting PT Bank Rakyat Indonesia Tbk (BBRI) stock prices obtains mean squared error, root mean square error, mean absolute error, and mean absolute percentage error evaluation values of 0.0000336, 0.00577, 0.00459, and 1.78%, respectively. This shows that recursive feature elimination with feature selection that combines important features and non-parametric correlation works better than the original recursive feature elimination at predicting stock prices.
Efficient intelligent crawler for hamming distance based on prioritization of web documents
Authors: Dange Amol Subhash , Byranahalli Eraiah Manjunath Swamy , Rao Manju More Eshwar , Hanumanthaiah Asha Kethaganahalli , Ganganayaka Sunil Kumar
Published: January 2024

Search engines play a crucial role in today's Internet landscape, especially with the exponential increase in data storage. Ranking models are used in search engines to locate relevant pages and rank them in decreasing order of relevance. They are an integral component of a search engine. The offline gathering of the document is crucial for providing the user with more accurate and pertinent findings. With the web’s ongoing expansions, the number of documents that need to be crawled has grown enormously. It is crucial to wisely prioritize the documents that need to be crawled in each iteration for any academic or mid-level organization because the resources for continuous crawling are fixed. The advantages of prioritization are implemented by algorithms designed to operate with the existing crawling pipeline. To avoid becoming the bottleneck in pipeline, these algorithms must be fast and efficient. A highly efficient and intelligent web crawler has been developed, which employs the hamming distance method for prioritizing the pages to be downloaded in each iteration. This cutting-edge search engine is specifically designed to make the crawling process more streamlined and effective. When compared with other existing methods, the implemented hamming distance method achieves a high value of 99.8% accuracy.
Application of deep learning methods for automated analysis of retinal structures in ophthalmology
Authors: Kassymova Akmaral , Konyrkhanova Assem , Issembayeva Aida , Saimanova Zagira , Saltayev Alisher , Ongarbayeva Maral , Issakova Gulnur
Published: January 2024

This article examines a current area of research in the field of ophthalmology the use of deep learning methods for automated analysis of retinal structures. This work explores the use of deep learning methods such as EfficientNet and DenseNet for the automated analysis of retinal structures in ophthalmology. EfficientNet, originally proposed to balance between accuracy and computational efficiency, and DenseNet, based on dense connections between layers, are considered as tools for identifying and classifying retina features. Automated analysis includes identifying pathologies, assessing the degree of their development and, possibly, diagnosing various eye diseases. Experiments are performed on a dataset containing a variety of images of retinal structures. Results are evaluated using metrics of accuracy, sensitivity, and specificity. It is expected that the proposed deep learning methods can significantly improve the automated analysis of retinal images, which is important for the diagnosis and monitoring of eye diseases. As a result, the article highlights the significance and promise of using deep learning methods in ophthalmology for automated analysis of retinal structures. These methods help improve the early diagnosis, treatment and monitoring of eye diseases, which can ultimately lead to improved healthcare quality and improved patient lives.
Collusion-resistant multiparty data sharing in social networks
Authors: Shetty Nisha P. , Muniyal Balachandra , Proothi Nandini , Gopal Bhavya
Published: January 2024

The number of users on online social networks (OSNs) has grown tremendously over the past few years, with sites like Facebook amassing over a billion users. With the popularity of OSNs, the increase in privacy risk from the large volume of sensitive and private data is inevitable. While there are many features for access control for an individual user, most OSNs still need concrete mechanisms to preserve the privacy of data shared between multiple users. The proposed method uses metrics such as identity leakage (IL) and strength of interaction (SoI) to fine-tune the scenarios that use privacy risk and sharing loss to identify and resolve conflicts. In addition to conflict resolution, bot detection is also done to mitigate collusion attacks. The final decision to share the data item is then ascertained based on whether it passes the threshold condition for the above metrics.
Efficient criticality oriented service brokering policy in cloud datacenters
Authors: Subramanian Shanmugapriya , Natarajan Priya
Published: January 2024

Cloud service provider (CSP) offers a huge number of datacenters and virtual servers to the users for processing their workloads in an infrastructure as a service (IaaS) cloud computing environment. Due to the heterogeneous volume of these resources and the immense number of user workloads arriving simultaneously in the cloud, it is necessary to use an effective load distribution technique for scheduling the resources to achieve high performance and high user satisfaction. Service brokering policy and load balancing techniques are the two crucial areas to be focused on while selecting the datacenters and virtual machines, respectively. In this study, we have proposed a dynamic efficient criticality-oriented service brokering policy for load allocations among datacenters by considering task criticality, datacenter proximity, and traffic, the size of the datacenter, its present load and makespan value. The proposed methodology is examined against the current policies in the CloudAnalyst simulation tool and the analysis report confirms that our proposed policy gives priority to processing the urgent loads and chooses the optimum datacenter to diminish the load response time, datacenter processing time, minimizes the cost, achieves optimum resource utilization and workload balancing among resources.
Recognition of music symbol notation using convolutional neural network
Authors: Setyo Ciara , Kusuma Gede Putra
Published: January 2024

Musical notation is one thing that needs to be learned to play music. This notation has an important role in music because it can help in visualizing instructions for playing musical instruments and singing. Unfortunately, musical symbols that are commonly written in musical notation are difficult for beginners who have just started learning music. This research proposed a solution to create an optical music recognition (OMR) using a deep learning model to classify musical notes more accurately with some of the latest convolutional neural network (CNN) architectures. The research was carried out by implementing vision transformer (ViT), CoAtNet-0, and ConvNeXt-Tiny architecture. The training process was also combined with data augmentation to provide more information for the model to learn. Then the accuracy results of each model were compared to find out the best model for the OMR solution in this research. This experiment uses the Andrea dataset and Attwenger dataset which both get the best result by using the augmentation method and ConvNeXt-Tiny as the model. The best accuracy for the Andrea dataset is 98.15% and for the Attwenger dataset is 98.43%.
Situational judgment test measures administrator computational thinking with factor analysis
Authors: Indrawati Cicilia Dyah Sulistyaningrum , Permansah Sigit , Ninghardjanti Patni , Subarno Anton , Winarno Winarno , Rusmana Dede
Published: January 2024

Computational thinking skills (CTS) play a crucial role across diverse domains, involving a thinking process that allows the execution of solutions by information processing agents. Measuring the level of CTS becomes essential to ensure that administrators effectively leverage technology. However, finding suitable instruments to measure and justify CTS levels in administration can be challenging. The selection of situational judgement test (SJT) is supported by its validity and reliability in assessing attributes, including professionalism. The instrument’s development includes face validity, discriminant validity (using Pearson correlation and Cronbach’s alpha), and exploratory factor analysis (EFA). The study involved 111 undergraduate administration students from various Indonesian universities, and data were collected in 2023. Following a discriminant validity analysis, ten items were eliminated, while 23 met the criteria with p0.185. Subsequently, EFA yielded 16 items forming seven components, covering algorithmic thinking, problem-solving, technology literacy, problem abstraction, problem reformulation, data management in administration technology, and administrative data presentation with loading factor variations (0.421-0.868). The final instrument, consisting of 16 valid items and seven components, effectively evaluates the level of administrator computational thinking skills (ACTS) among administration students.
Taxi-out time prediction at Mohammed V Casablanca Airport
Authors: Zbakh Douae , El Gonnouni Amina , Benkacem Abderrahmane , Said Kasttet Mohammed , Lyhyaoui Abdelouahid
Published: January 2024

Airports are vital for global connectivity. However, the increasing volume of air travel has presented significant challenges in airport managing. Accurate predictions of taxi-out times (TXOT) offer potential to enhance airport performance, minimize delays, optimize airline schedules, and enhance customer satisfaction. This paper focuses on developing a machine learning model to forecast taxi-out times at Mohammed V Airport. Historical taxiing data from various airports will be analyzed to predict taxi-out times based on diverse runway-stand combinations and congestion levels. we used neural network (NN), support vector machines (SVM), and regression tree (RT) in order to create a real-time model that forecasts TXOT and congestion levels for different runway-stand combinations. The result showed that the NN model outperformed other forecasting models when their performances are compared using the mean absolute percentage error, root mean square error as accuracy measures.
A deep learning framework for accurate diagnosis of colorectal cancer using histological images
Authors: Attia Maria M. , F. Areed Nihal Fayez , Amer Hanan M. , El-Seddek Mervat
Published: January 2024

Colorectal cancer (CRC) is one of the most prevalent malignancies worldwide, with high mortality and incidence rates. Early detection of the disease may increase the probability of survival, making it critical to develop effective procedures for precise treatment. In the past few years, there has been an increased use of deep learning techniques in image classification that aid in the detection of various types of cancer. In this study, convolutional neural network (CNN) models were used to classify colorectal cancer into benign and malignant. After applying various data preprocessing techniques to the image dataset, we evaluated our prototypes using three distinct subsets of testing data, representing 20%, 30%, and 40% of the total dataset. Additionally, four pre-trained CNN models (ResNet-18, ResNet-50, GoogLeNet, and MobileNetV2) were trained, and the network architectural techniques were compared by applying the Adam optimizer. Finally, we assessed the performance of algorithms in terms of accuracy, sensitivity, specificity, precision, F1-score, and area under the receiver operating characteristic (ROC) curve (AUC). In this research, deep learning approaches demonstrated high efficacy in accurately diagnosing colorectal cancer. This indicates that these techniques have an important and significant value for advancing medical research.
Hyperspectral object classification using hybrid spectral-spatial fusion and noise tolerant soft-margin technique
Authors: Mani Radhakrishna , Raguttapalli Chowdareddy Manjunatha
Published: January 2024

Because of its spectral-spatial and temporal resolution of greater areas, hyperspectral imaging (HSI) has found widespread application in the field of object classification. The HSI is typically used to accurately determine an object's physical characteristics as well as to locate related objects with appropriate spectral fingerprints. As a result, the HSI has been extensively applied to object identification in several fields, including surveillance, agricultural monitoring, environmental research, and precision agriculture. However, because of their enormous size, objects require a lot of time to classify; for this reason, both spectral and spatial feature fusion have been completed. The existing classification strategy leads to increased misclassification, and the feature fusion method is unable to preserve semantic object inherent features; This study addresses the research difficulties by introducing a hybrid spectral-spatial fusion (HSSF) technique to minimize feature size while maintaining object intrinsic qualities; Lastly, a soft-margins kernel is proposed for multi-layer deep support vector machine (MLDSVM) to reduce misclassification. The standard Indian pines dataset is used for the experiment, and the outcome demonstrates that the HSSF-MLDSVM model performs substantially better in terms of accuracy and Kappa coefficient.
Auto retry circuit breaker for enhanced performance in microservice applications
Authors: Punithavathy E. , Priya N.
Published: January 2024

Cloud-hosted distributed systems are much more resilient to failure. Resilience is the key factor in avoiding cascading failures in microservice architectures, where circuit-breaker, retry, bulkhead, and rate-limiter patterns are implemented. Through the implementation of these patterns, availability and reliability factors can be greatly improved. Circuit-breaker emphasizes a fail-fast mechanism, so it is highly recommended. Although they are beneficial, some factors, such as configuration, need to be improved to increase the system's throughput. In this paper, an approach called auto retry circuit breaker (ARCB) is implemented, which is a modified version of existing patterns of circuit breaker, and it has been found that the throughput of the system can be increased by 36% when compared to the existing manual circuit breaker pattern.
Developing a smart system for infant incubators using the internet of things and artificial intelligence
Authors: Aryanto I Komang Agus Ady , Maneetham Dechrit , Triandini Evi
Published: January 2024

This research is developing an incubator system that integrates the internet of things and artificial intelligence to improve care for premature babies. The system workflow starts with sensors that collect data from the incubator. Then, the data is sent in real-time to the internet of things (IoT) broker eclipse mosquito using the message queue telemetry transport (MQTT) protocol version 5.0. After that, the data is stored in a database for analysis using the long short-term memory network (LSTM) method and displayed in a web application using an application programming interface (API) service. Furthermore, the experimental results produce as many as 2,880 rows of data stored in the database. The correlation coefficient between the target attribute and other attributes ranges from 0.23 to 0.48. Next, several experiments were conducted to evaluate the model-predicted value on the test data. The best results are obtained using a two-layer LSTM configuration model, each with 60 neurons and a lookback setting 6. This model produces an R2 value of 0.934, with a root mean square error (RMSE) value of 0.015 and a mean absolute error (MAE) of 0.008. In addition, the R2 value was also evaluated for each attribute used as input, with a result of values between 0.590 and 0.845.
Checklist of hawkmoths (Lepidoptera: Bombycoidea: Sphingidae) in the Central Highlands of Vietnam
Authors: Le Trang Q. , Vu Lien V.
Published: January 2024

Study on hawkmoths (Sphingidae family) in the Central Highlands of Vietnam (Kon Tum, Gia Lai, Dak Lak, Dak Nong, and Lam Dong provinces) was carried out during the years 2016–2022. Light traps and butterfly nets were used to collect specimens of the Sphingidae family in the night and day times. An update checklist of Sphingidae was based on collected specimens in the studied area and combine the review data. Totally, 113 Sphingidae species from the Central Highlands of Vietnam are recorded. Among them, 78 species are recorded from collected specimens and 35 species without collected specimens are compiled from the literatures. Addition, 14 species are herein added to the known list, including one very rare species, Eurypteryx geoffreyi Cadiou & Kitching, 1990, and two endemic species, Griseosphinx marchandi Cadiou, 1996 and Callambulyx schintlmeisteri Brechlin, 1997.
Trade of skulls as novelty and aquarium objects are an additional threat to porcupines
Authors: Chavez Jessica , Kuntayuni  , Nijman Vincent
Published: January 2024

The commercial exploitation of wildlife affects many species, including porcupines that are traded for meat, as pets, and for medicinal purposes. Here we report on a novel trade in Sunda Porcupine Hystrix javanica skulls in Bali specifically for curios and as aquarium novelty objects. Since 2018, the species is protected within Indonesia and the sale of skulls is illegal. Between December 2022 and November 2023, we visited wildlife shops in seven towns, and conducted a survey of online sellers. We recorded 44 porcupine skulls in 11 shops and 10 skulls online. Skulls sold as curios were intricately carved and were priced at USD 61, whereas skulls sold as hiding caves for aquarium fish were not carved and priced at USD 22. Porcupine skulls were sourced from Bali and Java. Especially the carved skulls are marketed primarily for international tourists and may be inadvertently taken abroad. The novel trade in skulls adds to a multitude of threats faced by porcupines, and there is a need for improved regulations and enforcement against illegal trade. Appropriate mitigation measures need to be developed to protect porcupines from unsustainable and illegal exploitation and will require the full operation of the commercial sector, local and national governments, the tourism industry and the Indonesian public.
Contribution to the knowledge of Gongolaria barbata (Sargassaceae, Fucales) from the Mediterranean: insights into infraspecific diversity
Authors: FALACE ANNALISA , MANFRIN CHIARA , FURNARI GIOVANNI , D’AMBROS BURCHIO SARA , PALLAVICINI ALBERTO , DESCOURVIERES EMMANUELLE , KALEB SARA , LOKOVŠEK ANA , GRECH DANIELE , ALONGI GIUSEPPINA
Published: January 2024

Gongolaria barbata (Sargassaceae, Fucales) is a widespread species for which several infraspecific taxa have been described, indicating its polymorphism. This study contributes to the understanding of the molecular, nomenclatural, morphological and ecological aspects of G. barbata in the Mediterranean and sheds light on the infraspecific diversity and its implications for the taxonomy of this species. Molecular analyses were performed using sequencing of the mitochondrial cox1 gene on both haptophytic and pleustophytic forms from different sites in the Adriatic and Tyrrhenian Seas. Vegetative and reproductive morphology was studied on thalli samples from the Adriatic. Our results showed that there are different morphotypes within G. barbata populations related to specific environmental conditions, suggesting infraspecific variation. In contrast, molecular analyses showed no differences between samples, regardless of whether individuals are growing “attached” to a substrate or “unattached”. We also discussed the taxonomic status and nomenclatural issues related to certain infraspecific taxa previously proposed for G. barbata. In particular, the confusion surrounding Cystoseira aurantia is clarified.
Association Between Posterior Tibial Slope and ACL Injury in Pediatric Patients: A Systematic Review and Meta-analysis
Authors: Farid Alexander R. , Pradhan Pratik , Stearns Stephen A. , Kocher Mininder S. , Fabricant Peter D.
Published: January 2024

Background:

The posterior tibial slope (PTS) has been proposed to be a radiographic risk factor for anterior cruciate ligament (ACL) injury in adults. However, this has not been well established in pediatric patients.

Purpose:

This systematic review and meta-analysis was performed to investigate any association between PTS and ACL tears in the pediatric population.

Study Design:

Systematic review and meta-analysis; Level of evidence, 4.

Methods:

A systematic review was performed to identify studies that examined the relationship between PTS, medial tibial slope (MTS), and lateral tibial slope (LTS) and ACL tears in children and adolescents aged ≤18 years. Full-text observational studies comparing PTS, MTS, and/or LTS values between pediatric (≤18 years of age) patients with and without ACL injury were included in this analysis. Review articles and case series were excluded. The authors calculated the mean difference (MD) via a restricted maximum-likelihood estimator for tau square and a Hartung-Knapp adjustment for random-effects model.

Results:

A total of 348 articles were identified in the initial database search, yielding 10 for final inclusion and analysis. There was no statistically significant association between PTS (MD, 1.13°; 95% CI, –0.55° to 2.80°; P = .10), MTS (MD, 0.36°; 95% CI, –0.37° to 1.10°; P = .27), or LTS (MD, 1.41°; 95% CI, –0.20° to 3.02°; P = .075) and risk for ACL injury in this population.

Conclusion:

The current study found that unlike what has been shown in adult populations, increased PTS may not be a significant risk factor for ACL tears in pediatric and adolescent patents. LTS was the only measured parameter that neared statistical significance, perhaps suggesting a potential role for this measurement in determining ACL risk if further research is done in this population.
Mild obstructive sleep apnoea in females:post hocanalysis of the MERGE randomised controlled trial
Authors: Wimms Alison J. , Kelly Julia L. , Turnbull Christopher D. , McMillan Alison , Craig Sonya E. , O'Reilly John F. , Nickol Annabel H. , Decker Meredith D. , Willes Leslee A. , Calverley Peter M.A. , Benjafield Adam V. , Stradling John R. , Morrell Mary J. ,   on behalf of the MERGE Trial Investigators
Published: January 2024

Background

Apost hocanalysis of the MERGE trial was conducted, to investigate whether sex differences are evident at the mildest end of the disease spectrum, for symptoms associated with obstructive sleep apnoea (OSA) and the response to continuous positive airway pressure (CPAP) treatment.

Methods

MERGE participants with mild OSA (apnoea–hypopnoea index 5–15 events·h−1; American Academy of Sleep Medicine 2012 criteria) were randomised to either CPAP plus standard care (sleep hygiene counselling) or standard care alone for 3 months. Quality of life (QoL) was measured by questionnaires completed before and after the 3 months. Thispost hocanalysis of participants of the MERGE trial compared the symptom presentation, and response to CPAP, between the sexes.

Results

233 patients were included; 71 (30%) were female. Females were more symptomatic at baseline in all QoL questionnaires. Specifically, females had lower 36-item Short-Form Health Survey (SF-36) Vitality scores (mean±sd39.1±10.1versus44.8±10.3) and higher Epworth Sleepiness Scale (ESS) scores (mean±sd11.0±4.2versus9.5±4.4). Both sexes experienced snoring, but more females reported fatigue and more males reported witnessed apnoeas. All symptoms improved with CPAP for both sexes; however, females had larger improvements in SF-36 Vitality scores, which was the primary outcome of the MERGE trial (mean change 9.4 (95% CI 6.8–12.0)versus6.0 (95% CI 4.3–7.7); p=0.034), and ESS (mean change −4.1 (95% CI −5.1– −3.0)versus−2.5 (95% CI −3.1– −1.8); p=0.015), after adjustment for baseline scores and CPAP usage.

Conclusions

Sex differences are apparent in patients with mild OSA. Females experience worse QoL symptoms than males at presentation to the sleep clinic; however, these improve significantly with CPAP treatment.
Adjustments to maintenance therapy and the reasoning behind them among COPD outpatients in Austria: the STEP study
Authors: Vafai-Tabrizi Florian , Schwab Ulrich , Brecht Stephan , Funk Georg-Christian
Published: January 2024

Background

Adjustments to COPD maintenance treatment are based on different guidelines. In Austria, there is a lack of real-world data on treatment adjustments of COPD outpatients and their underlying rationale. The STEP study characterised change patterns of pharmacological maintenance therapy in COPD outpatients in predefined categories of step-up, step-down and switch, the underlying reasons, and predictors in clinical routine in Austria.

Methods

STEP was a single-visit non-interventional study in Austria. 77 pulmonologists based in outpatient clinics documented previous and adapted COPD therapy, reason for change, patient characteristics, COPD phenotype, and lung function. Patients’ COPD symptom burden was assessed by using the COPD Assessment Test (CAT). Predictors for therapy changes were identified.

Results

1137 patients were studied (mean±sdage 67±10 years; 56.9% male; mean forced expiratory volume in 1 s 56.3% predicted; Global Initiative for Chronic Obstructive Lung Disease B and E stages 66% and 19%, respectively; mean CAT score 17.5). Therapy step-up was observed in 59.3%, treatment switch in 21.7% and step-down in 19.0% of patients. Triple therapy comprised the biggest proportion of inhalation treatment (53.3%). Physicians reported lung function, symptom burden and exacerbations as the main reasons for step-up or step-down, whereas switches within the same treatment class were predominantly caused by device issues. Predictors for step-up were comorbid asthma and exacerbations among others.

Conclusions

STEP was the first study to investigate COPD therapy changes in clinical routine in Austria. The most frequent treatment adjustment was step-up, followed by treatment switch and step-down. Symptom burden, stable or improved lung function and inhalation device handling were the most frequently given reasons for adjustments.
Real-world walking cadence in people with COPD
Authors: Delgado-Ortiz Laura , Ranciati Saverio , Arbillaga-Etxarri Ane , Balcells Eva , Buekers Joren , Demeyer Heleen , Frei Anja , Gimeno-Santos Elena , Hopkinson Nicholas S. , de Jong Corina , Karlsson Niklas , Louvaris Zafeiris , Palmerini Luca , Polkey Michael I. , Puhan Milo A. , Rabinovich Roberto A. , Rodríguez Chiaradia Diego A. , Rodriguez-Roisin Robert , Toran-Montserrat Pere , Vogiatzis Ioannis , Watz Henrik , Troosters Thierry , Garcia-Aymerich Judith
Published: January 2024

Introduction

The clinical validity of real-world walking cadence in people with COPD is unsettled. Our objective was to assess the levels, variability and association with clinically relevant COPD characteristics and outcomes of real-world walking cadence.

Methods

We assessed walking cadence (steps per minute during walking bouts longer than 10 s) from 7 days’ accelerometer data in 593 individuals with COPD from five European countries, and clinical and functional characteristics from validated questionnaires and standardised tests. Severe exacerbations during a 12-month follow-up were recorded from patient reports and medical registries.

Results

Participants were mostly male (80%) and had mean±sdage of 68±8 years, post-bronchodilator forced expiratory volume in 1 s (FEV1) of 57±19% predicted and walked 6880±3926 steps·day−1. Mean walking cadence was 88±9 steps·min−1, followed a normal distribution and was highly stable within-person (intraclass correlation coefficient 0.92, 95% CI 0.90–0.93). After adjusting for age, sex, height and number of walking bouts in fractional polynomial or linear regressions, walking cadence was positively associated with FEV1,6-min walk distance, physical activity (steps·day−1, time in moderate-to-vigorous physical activity, vector magnitude units, walking time, intensity during locomotion), physical activity experience and health-related quality of life and negatively associated with breathlessness and depression (all p<0.05). These associations remained after further adjustment for daily steps. In negative binomial regression adjusted for multiple confounders, walking cadence related to lower number of severe exacerbations during follow-up (incidence rate ratio 0.94 per step·min−1, 95% CI 0.91–0.99, p=0.009).

Conclusions

Higher real-world walking cadence is associated with better COPD status and lower severe exacerbations risk, which makes it attractive as a future prognostic marker and clinical outcome.
Methyl β-Cyclodextrin-sperm-mediated gene editing (MBCD-SMGE): a simple and efficient method for targeted mutant mouse production
Authors: Moradbeigi Parisa , Hosseini Sara , Salehi Mohammad , Mogheiseh Asghar
Published: January 2024

Background

Generating targeted mutant mice is a crucial technology in biomedical research. This study focuses on optimizing the CRISPR/Cas9 system uptake into sperm cells using the methyl β-cyclodextrin-sperm-mediated gene transfer (MBCD-SMGT) technique to generate targeted mutant blastocysts and mice efficiently. Additionally, the present study elucidates the roles of cholesterol and reactive oxygen species (ROS) in the exogenous DNA uptake by sperm.

Results

In this study, B6D2F1 mouse sperm were incubated in the c-TYH medium with different concentrations of MBCD (0, 0.75, 1, and 2 mM) in the presence of 20 ng/µl pCAG-eCas9-GFP-U6-gRNA (pgRNA-Cas9) for 30 min. Functional parameters, extracellular ROS, and the copy numbers of internalized plasmid per sperm cell were evaluated. Subsequently, in vitro fertilization (IVF) was performed and fertilization rate, early embryonic development, and transfection rate were assessed. Finally, our study investigated the potential of the MBCD-SMGT technique in combination with the CRISPR-Cas9 system, referred to as MBCD-SMGE (MBCD-sperm-mediated gene editing), for generating targeted mutant blastocysts and mice. Results indicated that cholesterol removal from the sperm membrane using MBCD resulted in a premature acrosomal reaction, an increase in extracellular ROS levels, and a dose-dependent influence on the copy numbers of the internalized plasmids per sperm cell. Moreover, the MBCD-SMGT technique led to a larger population of transfected motile sperm and a higher production rate of GFP-positive blastocysts. Additionally, the current study validated the targeted indel in blastocyst and mouse derived from MBCD-SMGE technique.

Conclusion

Overall, this study highlights the significant potential of the MBCD-SMGE technique for generating targeted mutant mice. It holds enormous promise for modeling human diseases and improving desirable traits in animals.

Graphical Abstract