bluuebunny/crossref_metadata_2025 huggingface dataset:
Penguin search with Harris-Hawk optimization algorithm to improve clustering performance in wireless network
Authors: Sabapathy Ranganathan Chitra , Sampathrajan Rajeshkumar
Published: January 2024

Integrating optimal search algorithm concepts across the wireless core and cluster structure enables next-generation wireless networks to effectively provide reliable low-delay communications and connectivity for internet of things (IoT) devices. This article describes penguin search with the Harris-Hawk optimization algorithm (PHHO) to improve clustering performance in wireless networks. The penguin search optimization algorithm (PSO) algorithm computes the fitness value for feature selection from the database. Harris-Hawk optimization (HHO) algorithm to reduce the time and energy required for network transmission. This mechanism builds the clusters based on node communication range. The node direction, node mobility, node bandwidth availability, and energy parameters to decide the cluster head (CH) by applying the HHO algorithm. This approach uses a PSO algorithm fitness function to select the feature subset to minimize error and overhead in the network. Using a network simulator (NS)-3, this method assesses and chooses the most efficient way for data transmission, and the result is compared to a baseline mechanism.
Fuzzy adaptive resonance theory failure mode effect analysis non-healthcare setting for infectious disease: review
Authors: Samjun Aysha , Lias Kasumawati , Zulhilmi Firdaus Rosli Mohd. , Mohamed Basri Hazrul , Chee Shee Chai , Kipli Kuryati
Published: January 2024

Fuzzy adaptive resonance theory (ART) is an ART network that is developed as one of the alternative methods to evaluate risk priority number (RPN) in failure mode and effect analysis (FMEA). Not only is FMEA are common technique as an analysis tool in industrial sectors, but also, especially during the global emergency COVID-19 pandemic hits, FMEA is used in prevention and mitigation measures. Many alternative methods have been proposed. However, not many investigations use clustering models such as Fuzzy ART in FMEA. This paper aims to provide a comprehensive review and then propose a model for systematic risk analysis which implement the fuzzy ART model, named clustering- transmission causes and effects analysis (c-TCEA), for the prevention and mitigation of infectious diseases.
Improving time efficiency in big data through progressive sampling-based classification model
Authors: Bangera Nandita , Kayarvizhy Kayarvizhy , Luharuka Shubham , S. Manek Asha
Published: January 2024

The proposed system aims to overcome challenges posed by large databases, data imbalance, heterogeneity, and multidimensionality through progressive sampling as a novel classification model. It leverages sampling techniques to enhance processing performance and overcome memory restrictions. The random forest regressor feature importance technique with the gini significance method is employed to identify important characteristics, reducing the data’s features for classification. The system utilizes diverse classifiers such as random forest, ensemble learning, support vector machine (SVM), k-nearest neighbors’ algorithm (KNN), and logistic regression, allowing flexibility in handling different data types and achieving high accuracy in classification tasks. By iteratively applying progressive sampling to the dataset with the best features, the proposed technique aims to significantly improve performance compared to using the entire dataset. This approach focuses computational resources on the most informative subsets of data, reducing time complexity. Results show that the system can achieve over 85% accuracy even with only 5-10% of the original data size, providing accurate predictions while reducing data processing requirements. In conclusion, the proposed system combines progressive sampling, feature selection using random forest regressor feature importance (RFRFI-PS), and a range of classifiers to address challenges in large databases and improve classification accuracy. It demonstrates promising results in accuracy and time complexity reduction.
Optimizing ant colony system algorithm with rule-based data classification for smart aquaculture
Authors: Mizan Munif Mohd , Jamal Abdul Nasir Husna , Imran Ahmad Muhammad
Published: January 2024

Aquaculture is one of many industries where the use of artificial intelligence (AI) techniques has increased dramatically in recent years. Internet of things (IoT), AI, and big data are just a few of the technologies being used in smart aquaculture to increase productivity, efficiency, and system sustainability of aquaculture systems. Data classification, which involves finding patterns and relationships in huge datasets, is one of the most important tasks in smart aquaculture. The ant colony system (ACS) has been used to solve a number of optimization issues, including data classification. To provide a more practical and successful solution, this study proposes an improved ACS algorithm for rule-based data classification in smart aquaculture. The proposed algorithm combines the advantages of ACS and rule-based classification to optimize the number of rules and accuracy. The experimental results showed that the proposed algorithm outperformed the traditional AntMiner algorithm in terms of the number of rules and accuracy. The improved pheromone update technique could potentially increase data classification accuracy and convergence in smart aquaculture systems.
Graph attention-driven document image classification through DualTune learning
Authors: Shilpa Shilpa , Soma Shridevi
Published: January 2024

Document image classification is a challenging task due to the complexity of information contained within documents, including text, images, and their spatial arrangement. Deep learning has become a pivotal tool for extracting and learning complex patterns. However, conventional methods often grapple with integrating different data modalities and minimizing redundancy, leading to a need for more advanced and efficient deep learning strategies. This study presents a new approach to document image classification, named graph attention-driven with dual tune learning (GAD-DTL). GAD-DTL employs dual-tune learning and graph attention networks. The methodology creates semantic region embedding within document images, which incorporate both textual and spatial data. A key feature of this approach is the adaptive fusion layer, which integrates different modalities and uses a graph attention layer to capture context within each region. To minimize redundancy in learned features, we implement two distinct learning techniques, relational and non-relational learning. This approach enhances document image classification by ensuring invariant representation and minimal redundancy in features.
A proposed model for detecting defects in software projects
Authors: Nabil Mahmoud Alia , Abdelaziz Ahmed , Santos Vitor , M. Freire Mario
Published: January 2024

Defective modules that cause software execution failures are common in large software projects. Source code for a significant number of modules may be found in several software repositories. This software repository includes each module’s software metrics and the module’s faulty status. Software companies face a considerable problem detecting defects in sizeable and complex programming code. In addition, many international reports, such as the comprehensive human appraisal for originating (CHAOS) report, have mentioned that there are countless reasons for the failure of software projects, including the inability to detect errors and defects in the programming code of those projects at an early stage. This research employs a statistical analysis technique to reveal the characteristics that indicate the faulty status of software modules. It is recommended that statistical analysis models derived from the retrieved information be merged with existing project metrics and bug data to improve prediction. When all algorithms are merged with weighted votes, the results indicate enhanced prediction abilities. The proposed statistical analysis outperforms the state-of-the-art method (association rule, decision tree, Naive Bayes, and neural network) in terms of accuracy by 9.1%, 10.3%, 13.1%, and 13.1%, respectively.
Determination of children's nutritional status with machine learning classification analysis approach
Authors: Yanto Musli , Hadi Febri , Arlis Syafri
Published: January 2024

Malnutrition is a problem that is often faced by every country around the world. Various facts show that malnutrition is of particular concern to many researchers. To can overcome this problem, every effort has been made such as developing analytical models in identification, classification, and prediction. This study aims to determine the nutritional status of children using the machine learning (ML) classification analysis approach. The methods used in the ML analysis process consist of cluster K-Means, artificial neural network (ANN), sum square error (SSE), pearson correlation (PC), and decision tree (DT). The dataset for this study uses data on child nutrition cases that occurred in the previous and was sourced from the provincial general hospital (RSUP) M. Djamil, Padang, West Sumatera. Based on the research presented, ML performance in the nutritional status classification analysis gave maximum results. These results are reported based on the level of precision with an accuracy of 99.23%. The results of the analysis can also present a knowledge-based nutritional status classification. This research can contribute to and update the analytical model in determining nutritional status. The results of this study can also provide benefits in handling nutritional status problems that occur in children.
A hybrid spectral-spatial fusion technique for hyperspectral object classification
Authors: Mani Radhakrishna , Raguttapalli Chowdareddy Manjunatha
Published: January 2024

In the field of object classification, hyperspectral imaging (HSI) has been widely used, due to its spectral-spatial, and temporal resolution of larger areas. The HSI is generally used to identify the objects physical properties in accurate manner and as well as to identify similar object with acceptable spectral signatures. Thus, the HSI has been widely used for object identification applications in different fields such as precision agriculture, environmental study, crop monitoring, and surveillance. However, the object classification is time consuming due to extremely large size; thus, the feature fusion of both spectral and spatial have been done. The current feature fusion method fails to retain semantic object intrinsic feature; further, current classification technique induces higher misclassification. In addressing the research issues this paper introduces a hybrid spectral-spatial fusion (HSSF) technique to reduce feature size and retains object intrinsic properties. Finally, in reducing misclassification a soft-margins kernel is introduced in support vector machine (SVM). Experiment is conducted on standard Indian Pines dataset; the result shows the HSSF-SVM model attain much higher accuracy and Kappa coefficient performance.
Car make and model recognition using convolutional neural network: fine-tune AlexNet architecture
Authors: Asifur Rahman Khan Md. , Talukder Raju , Anwar Hossen Md. , Jahan Nusrat
Published: January 2024

Artificial intelligence (AI) has significantly contributed to car make and model recognition in this current era of intelligent technology. By using AI, it is much easier to identify car models from any picture or video. This paper introduces a new model by fine-tuning the AlexNet architecture to determine the car model from images. First of all, our car image dataset has been created. Some of these images were taken by us, and others were taken from the website of the car connection. Then we cleaned all the unwanted images for better performance. Our dataset has ten classes containing 5,000 car images split into train and test data. After that, we augmented our data with random flip, rotation, and zoom to reduce overfitting. Finally, we used a pre-trained convolutional neural network (CNN) model AlexNet architecture. We fine-tuned AlexNet (FT-AlexNet) by adding three extra layers for better classification and compared it with the original AlexNet. To measure the performance of these models, accuracy, precision, recall, and F1-score were used. The results show that fine-tune AlexNet architecture outperforms the original AlexNet architecture. The results prove that recognition accuracy has increased due to our improvement approach.
Modification of SHA-512 using Bcrypt and salt for secure email hashing
Authors: Eljim S. Castelo Sean , Jolo L. Apostol IV Ruben , Michael A. Cortez Dan , M. Dioses Raymund , Christopher R. Blanco Mark , A. Agustin Vivien
Published: January 2024

Email security, particularly against phishing, spoofing, and distributed denial-of-service (DoS) attacks, is a pressing concern given the essential role email plays in accessing various online accounts. The study introduced a modified SHA-512 algorithm, implementing additional security layers including randomly generated salt and the Bcrypt algorithm. The modified SHA-512 was comprehensively evaluated on parameters like hash construction, computational efficiency, data integrity, collision resistance, and attack resistance. The results showed its avalanche percentage exceeded the 50% target, reaching 50.08%. Experimental hash-cracking failed to decode the hashes created by the modified algorithm, verifying its protective efficiency. The algorithm also successfully demonstrated data integrity and collision resistance. This indicates that the enhanced SHA-512 algorithm is an effective, more secure hashing method, particularly applicable to email addresses.
Design, security and implementation of learning focal point algorithm in a docker container
Authors: Eddine Mansour Salah , Sakhi Abdelhak , Kzaz Larbi , Tali Oussama , Sekkaki Abderrahim
Published: January 2024

Artificial intelligence is not smart enough. This is why we are looking for complementary algorithms in order to increase the performance of machine learning and neural networks (NN). We have innovated an algorithm called learning focal point (LFP). This algorithm will help us increase the intelligence of machine learning and the NN. In this article, we will present the algorithm in detail, starting with the mathematical and theoretical principles, passing through the development and deployment in cloud docker containers, and ending with the security of its application programming interface (API). Finally, we are going to do a test in which we apply it in the case of using tin cans.
Power flow analysis in a distributed network for a smart grid system
Authors: Jothi Thangavel , Arun Manoharan , Varadarajan Murugesan
Published: January 2024

This article presents the implementation of a hybrid renewable energy-based smart grid in a distributed system. Photovoltaic (PV) and wind generation are variable and time-dependent, yet they are very efficient and correlated, making them perfect for a two-source hybrid system. To maximize the generated power, using the maximum power point tracker (MPPT) technique, the incremental conductance (IC) algorithm is employed. The proportional integral (PI)-based MPPT controller is chosen to improve the efficiency of conventional MPPT controllers. A battery system is implemented as an energy management system (EMS) to aid in transferring or managing the high load throughout peak and off-peak hours. The proposed system uses an optimization technique called genetic algorithm (GA) to control the inverter voltage. The GA-tuned PI controller performs efficiently and has less harmonic distortion than the traditional sinusoidal pulse width modulation (SPWM) control method. The designed system uses real-time measurable parameters as inputs and is simulated in Matlabtool. The system generates 42 kW of solar power and 250 kW of wind power; the total harmonic distortion (THD) value is 5% less than the SPWM technique. For future work, flexible alternating current transmission system (FACTS) devices can improve the power quality and lower the oscillations.
Convolutional neural network hyperparameters for face emotion recognition using genetic algorithm
Authors: Sam'an Muhammad , Safuan Safuan , Munsarif Muhammad
Published: January 2024

The development of artificial intelligence in facial emotion recognition (FER) is rapidly growing and has been widely applied in various fields. Deep learning (DL) techniques with evolutionary algorithms have become the preferred choice for solving various security, health, gaming, and other related problems. This research proposes the use of a genetic algorithm (GA) as the main method to optimize hyperparameters in the convolutional neural network (CNN) model for FER. The required computation time is approximately 37 hours 57 minutes 55 seconds, with generation 3 taking the longest time at around 16 hours 45 minutes 4 seconds. However, generation 3 achieved an accuracy of 76.11%, which is the highest compared to other generations. The results indicate that the more generations are involved, the higher the achievable accuracy. Furthermore, the proposed CNN-GA model in this study outperforms previous models that have been examined. Thus, this study makes a significant contribution to improving the understanding of using GAs to optimize the performance of CNN models for FER.
Blockchain based drug supply chain for decentralized network
Authors: Bidve Vijaykumar , Hamine Aryan , Akre Sharwari , Ghan Yash , Sarasu Pakiriswamy , Pakle Ganesh
Published: January 2024

The concept of supply and demand drives the scales of various markets in today’s world. When it comes to producing a quality product, the right kind of steps need to be taken to ensure that its quality can be supplemented with the process of its making. A supply chain is a business process that delineates the creation of a product. One such supply chain is the drug supply chain, focusing on the manufacturing and distribution of drugs. It is implied that there is an immense importance of traceability in the drug supply chain to ensure transparency amongst various actors and ultimately the end user. Improving on this crucial parameter allows drug supply chains to be carefully monitored and adhere to the various compliances from governing bodies. This work aim is to provide organizations with solutions that allow them to ameliorate the supply chain management. Using the blockchain technology, various transactions recorded in the supply chain can be checked against providing strong traceability and secure record-keeping. The positives that are provided by the blockchain transform the supply chain to a much more efficient and improved operation, impacting various facets of the process for the better.
Forecasting water quality through machine learning and hyperparameter optimization
Authors: Elvin Elvin , Wibowo Antoni
Published: January 2024

Forecasting water quality through machine learning and hyperparameter optimization is a research endeavor aimed at enhancing the water quality prediction process. The primary goal of this study is to employ various machine learning algorithms for water quality prediction and to refine existing models from previous research. The paper encompasses a comprehensive literature review of previous water quality prediction studies and introduces novel theoretical insights. The research employs a classic machine learning problem-solving approach, predominantly utilizing the extreme gradient boost (XGBoost) algorithm. Additionally, it evaluates other machine learning algorithms, including the random forest (RF) classifier, decision tree (DT) classifier, adaptive boosting (AdaBoost) classifier, support vector machine (SVM), Naïve Bayes, and extra tree classifier for comparison. The evaluation process utilizes a classification report, providing insights into the precision, recall, f1-score, and accuracy of each machine learning model. Notably, the XGBoost model exhibits superior performance, achieving an impressive 97.06% accuracy. Precision stands at 94.22%, recall at 81.5%, and F1-score at 87.4%. These results represent a significant advancement over prior water quality prediction models, emphasizing the potential of machine learning and hyperparameter optimization to enhance water quality forecasting in environmental monitoring.
Challenges in big data adoption for Malaysian organizations: a review
Authors: Qi Zian Lee , Zareen Zulkarnain Nur , Jaya Kumar Yogan
Published: January 2024

Big data has played an ever-increasing role in various sectors of the economy. Despite the availability of big data technologies, many companies and organizations in Malaysia remain reluctant to adopt them. Numerous studies have been published on big data adoption; however, there is a lack of research focusing on identifying the challenges faced by Malaysian organizations. Therefore, this study will implement the technology-organization-environment (TOE) framework to examine the challenges faced by Malaysian organizations with regards to big data adoption. A systematic literature review (SLR) was conducted to examine the challenges. From the result of this study, it was found that the factors from technology context are deemed to be the major challenge faced in big data adoption followed by organization and environment factors. Furthermore, the insights derived from the TOE framework-based information can help address concerns that hinder big data adoption among organizations in Malaysia. Finally, this study concludes with several recommendations.
Development of a patient health monitoring system based on the internet of things with a module for predicting vital signs
Authors: Zaitin Yerlan , Mansurova Madina , Kunelbayev Murat , Tyulepberdinova Gulnur , Sarsembayeva Talshyn , Shomanov Adai
Published: January 2024

Recent issues related to human health in the world have shown the importance of telemedicine considering necessities to perform the remote monitoring of patients. In this study, using a patient smart monitoring system (PSMS), we collected 5,000 samples of heart rate and blood saturation vital signs from 4 volunteers and tried to find better correlation algorithms to develop a module to predict what these vital signs will be in the next 60 seconds. The following regression algorithms recurrent neural network (long short-term memory) (RNN(LSTM)), autorregresive integrated moving average (ARIMA), value-added reseller vector autoregression (VAR) were used to forecast the patient's state of health in the next 60 seconds. Further, the support vector machine (SVM) and Naive Bayes classification algorithms use the data forecasted by the regression algorithms as input to predict the health status of the patients. When comparing algorithms, we focused on the F measure, a metric used to evaluate the performance of machine learning algorithms. As a result, RNN(LSTM) and SVM showed the performance score value of machine learning algorithms F 0.84, RNN(LSTM) and Naive Bayes 0.81, VAR and SVM 0.82, and VAR and Naive Bayes 0.75. Compared to them, the correlation of ARIMA regression algorithms and SVM classification showed a better F score of 0.86 for machine learning algorithms than the others.
Morphological features of lung white spots based on the Otsu and Phansalkar thresholding method
Authors: Supriyanti Retno , Luke Dzihniza Syadzwina , Alqaaf Muhammad , Rifqi Kurniawan Muhammad , Ramadhani Yogi , Budi Widodo Haris
Published: January 2024

COVID-19 is a disease that causes respiratory system disorders, so various tests are needed. One of them uses a chest X-ray or thorax. A chest X-ray will depict the lungs as a whole so that patches like white shadows will be visible. In this study, the number of lung areas and white spots can be observed and detected using segmentation techniques in image processing. But before entering the segmentation stage, the image will go through the preprocessing stage using the tri-threshold fuzzy intensification operators (fuzzy IO) method. It then segmented the lungs using the Otsu method by changing the digital image from grey to black and white based on comparing the threshold value with the pixel colour value of the digital image. Then, further segmentation was carried out using the Phansalkar method to detect and simultaneously count the number of white spots. Referring to the experiments we have carried out, Otsu Phansalkar's segmentation performance promises to be developed further.
Predicting progression of Alzheimer’s disease using new survival analysis approach
Authors: Saad Zawawi Nour , Gamal Saber Heba , Hashem Mohamed
Published: January 2024

It is critical to determine the risk of Alzheimer’s disease (AD) in people with mild cognitive impairment (MCI) to begin treatment early. Its development is affected by many things, but how each effect and how the disease worsens is unclear. Nevertheless, an in-depth examination of these factors may provide a reasonable estimate of how long it will take for patients at various stages of the disease to develop Alzheimer’s. Alzheimer’s disease neuroimaging initiative (ADNI) database had 900 people with 63 features from magnetic resonance imaging (MRI), genetic, cognitive, demographic, and cerebrospinal fluid data. These characteristics are used to track AD progression. A hybrid approach for dynamic prediction in clinical survival analysis has been developed to track progression to AD. The method uses a random forest cox regression approach to figure out how long it will take for MCI to turn into AD. In order to evaluate the result concordance index is used. The concordance index measures the rank correlation between predicted risk scores and observed time points. The concordance index was statistically considerably higher in the suggested work than in previous approaches with a score of 95.3%, which is higher than others.
Blockchain-based key-value store to support dynamic smart contract interaction in the agricultural sector
Authors: Saputra Irwansyah , Arkeman Yandra , Jaya Indra , Hermadi Irman , Sutedja Indrajani
Published: January 2024

In the era of supply chain digitalization, adaptability and transparency are key to enhancing efficiency and trust. Although blockchain technology and smart contract offer innovative solutions, the limitations of static smart contract hinder their full potential. This article introduces a new approach using dynamic smart contract capable of managing various commodities in the supply chain with a key-value store. While this advantage provides flexibility, it still poses challenges in managing increasingly complex interactions among various actors, especially when the number of commodities increases. To address these challenges, this study introduces the concept of smart contract interaction that facilitates the automation and management of interactions with high efficiency. The implementation results show that smart contract interaction outperforms conventional approaches in terms of speed, resilience, and ease of management. Through the combination of dynamic smart contract and smart contract interaction, demonstrating how efficiency, transparency, adaptability, and scalability can be achieved in the supply chain, providing new insights into the utilization of blockchain technology for the modern industry.
Aquaculture monitoring system using multi-layer perceptron neural network and adaptive neuro fuzzy inference system
Authors: Hassan Abdullah Abu , Bin Sudin Sukhairi , Syahir Ahmad Saad Fathinul , Khairul Ali Hassan Muhamad , Imran Ahmad Muhammad , Aizat bin Abdul Khalid Kamarul
Published: January 2024

The water quality is the most important parameter for aquatic species health and growth. The condition is very critical and is essential to monitor continuously. Poor water quality will affect health, growth and ability of the animal to survive. These also affected their harvesting yields based on the amount and size of the animal. The main water parameters such dissolved oxygen (DO), pH, temperature, salinity and turbidity are monitored and control for good water quality. The data were acquired by the developed instrument and send wirelessly through GPRS/GSM module to cloud-based database. The data were retrieved and the water quality is predicted using fuzzy logic and multi-layer perceptron. MATLAB software was used for the model which is developed based on Mamdani fuzzy interface system. The membership functions of fuzzy were generated, as well as the simulation and analysis of the water quality system. Results show that the performance of fuzzy method can improve system performance in monitoring the water quality. This system also provides alert signals to farmers based on specific limit value for the water quality parameters. This will help the breeders to make certain adjustment to ensure suitable water quality for the aquaculture system.
Mundos (in)compossíveis: o fundamento em Leibniz e Deleuze
Authors: Teixeira Yasmin de Oliveira Alvez
Published: January 2024

Para construir seu próprio edifício filosófico em Diferença e Repetição, Deleuze dedicou-se no início de sua trajetória à pesquisa sistemática daquilo que chamou de “linhagem menor” em história da filosofia. Neste artigo buscamos explorar especificamente o embate deleuziano com o pensamento de Leibniz, situado no limiar dessa linhagem. Embora uma monografia mais extensiva tenha sido publicada por Deleuze apenas décadas mais tarde, seguimos a hipótese de que seus primeiros esboços críticos sobre o pensamento leibniziano aparecem já no seminário O que é fundar?, endereçado ao problema do fundamento. À luz desse texto, abre-se uma perspectiva produtiva para a compreensão da relação entre as filosofias dos dois pensadores.
máquina s'imaginante de Spinoza
Authors: Lanciote Diego
Published: January 2024

Esse texto propõe a hipótese da “máquina s’imaginante” como sendo o constructo fundamental da teoria da imaginação de Spinoza e pretende justificar certa leitura que permita tal denominação. Para tanto, parte-se da noção de contemplari externè para, em seguida, explanar a noção interpretativa de simul desconcertante, a qual desempenha importante papel a fim de forjar a noção de centramento em si, noção que permite captar os efeitos de interioridade e exterioridade relativas ao indivíduo humano e cujo engendramento faz a natureza das coisas ser apreendida teleologicamente. A partir do centramento em si são examinadas e interpretadas categorias relevantes da imaginação, sendo a mais importante delas a concatenatio. Por fim, são indicados alguns desdobramentos da hipótese de leitura proposta.
Curadoria de ideias: resenha para a nova edição do Tratado da Emenda do Intelecto, de Espinosa
Authors: Oliva Luís César Guimarães
Published: January 2024

Trata-se de uma resenha sobre a tradução da obra Tratado da Emeneda do Intelecto, de Espinosa.
Systematic review of the genera Rhabdostyla Kent 1880 and Orborhabdostyla Foissner et al. 2010 (Ciliophora, Peritrichia, Epistylididae)
Authors: SOUZA PEDRO MENDES DE , ROSSI MARIANA FONSECA , DIAS ROBERTO JÚNIO PEDROSO
Published: January 2024

The genus Rhabdostyla is one of the oldest of the family Epistylididae, it was described over 120 years ago and has more than 40 species considered valid. This group went through several systematic changes throughout its existence and due to the lack of taxonomic information, said to be essential, for most species that compose it, some of these changes are still questioned. In order to better understand and validate the species and relationships identified for this genus, the present work makes a brief review for the genus Rhabdostyla and its related genera, Opisthostyla and Orborhabdostyla. A 18S-rDNA phylogenetic analysis was also performed to understand the relationship between these groups. As a result of this review, 45 diagnoses were compiled and reorganized for the current model, accompanied by new graphical representations for the species considered valid for the genus Rhabdostyla and its associates. The major changes that the genus underwent, such as synonymy and separation to a new genus, were discussed in light of new phylogenetic information, but despite this, other information is still needed for a better validation of these changes.
A new species of deep-sea Booralana Bruce, 1986 (Crustacea: Isopoda: Cirolanidae) from The Bahamas, Western North Atlantic
Authors: SHIPLEY OLIVER N , BRUCE NIEL L. , HIGGS NICHOLAS D , RAWLINS SCOTT , IRSCHICK DUNCAN J , GUTIERREZ ROBERT , MOYER JOSHUA K , VIOLICH MACKELLAR , TALWAR BRENDAN S , SCHNEIDER ERIC VC , ROBINSON NATHAN J , BROOKS EDWARD J
Published: January 2024

Booralana nickorum sp. nov. is described from the deep-water slope of the Exuma Sound, The Bahamas, from depths of 540 to 560 metres. It is the fourth species to be assigned to the genus and the second species described from the Western North Atlantic. The species can be distinguished from Booralana tricarinata Camp and Heard, 1988 and the other species by the sub-triangular pleotelson and the uropodal exopod of mature males being far longer than endopod, with both rami extending well beyond the posterior margin of the pleotelson. Additionally, pleopods 3 and 4 lack a prominent angle at midpoint of ramus.
MJO Structure–Propagation Nexus and Impacts of Background Mean States in CMIP6 Models
Authors: Xiang Yuhang , Li Juan , Wang Bin , Ma Libin , Zhu Zhiwei
Published: January 2024

Eastward propagation is an essential feature of the Madden–Julian oscillation (MJO). Yet, it remains a challenge to realistically simulate it by global climate system models, and the reasons are not fully understood. This study evaluates the capability of 20 Coupled Model Intercomparison Project phase 6 (CMIP6) models in simulating MJO’s eastward propagation and its intrinsic links with the dynamic–thermodynamic structures and the background mean states, aiming at better understanding the sources of the simulation errors. The metrics to evaluate the MJO internal dynamics consists of six parameters: 1) the east–west asymmetry in the low-level circulation, 2) the boundary layer moisture convergence propagation, 3) the vertical tilt of equivalent potential temperature or moist static energy, the vertical structures of 4) diabatic heating and 5) available potential energy generation, and 6) upper-level diabatic heating and divergence. We also gauge the performance of three MJO-related background mean-state fields, including precipitation, sea surface temperature, and low-level moist static energy. It is argued that these parameters are relevant internal and external factors that could affect MJO eastward propagation. We find that the boundary layer moisture convergence is most tightly coupled with the eastward propagation of MJO and controls the premoistening, destabilization, and the leading low-level diabatic heating and available potential energy generation. The CMIP6 models exhibit significant improvements against CMIP5 models in simulating MJO dynamic–thermodynamic structures and the mean states. The diagnostics in this study could help to identify the possible processes related to CMIP6 models’ shortcomings and shed light on how to improve simulation of MJO eastward propagation in the future.
Level 1a Evidence Comparing Use of Antibiotics Versus No Antibiotics in Management of Acute Left-Sided Uncomplicated Diverticulitis
Authors: Sokhal Balamrit Singh , Mostafa Omar Ezzat Saber , Ramasamy Sadhasivam , Spyridon Roditis , Zaman Shafquat , Hajibandeh Shahab , Hajibandeh Shahin
Published: January 2024

Background

Acute uncomplicated diverticulitis (AUD) is a common cause of acute abdominal pain. Recent guidelines advise selective use of antibiotics in AUD patients. This meta-analysis aimed to compare the effectiveness of no antibiotics vs antibiotics in AUD patients.

Methods

This review was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses to identify randomized controlled trials (RCTs) involving AUD patients which compared the use of antibiotics with no antibiotics. Pooled outcome data was calculated using random effects modeling with 95% confidence intervals (CIs).

Results

5 RCTs with 1934 AUD patients were included. 979 patients were managed without antibiotics (50.6%). Patients in the no antibiotic and antibiotic groups had comparable demographics (age, sex, and body mass index) and presenting features (temperature, pain score, and C-reactive protein levels). There was no significant difference in rates of complicated diverticulitis (OR: .61, 95% CI: 0.27-1.36, P = .23), abscess (OR: .51, 95% CI: .08-3.25, P = .47) or fistula (OR: .33, 95% CI: .03-3.15, P = .33) formation, perforation (OR: .98, 95% CI: .32-3.07, P = .98), recurrence (OR: .96, 95% CI: .66-1.41, P = .85), need for surgery (OR: 1.36, 95% CI: .47-3.95, P = .37), mortality (OR: 1.27, 95% CI: .14-11.76, P = .82), or length of stay (MD: .215, 95% CI: −.43-.73, P = .61) between the 2 groups. However, the likelihood of readmission was higher in the antibiotics group (OR: 2.13, 95% CI: 1.43-3.18, P = .0002).

Conclusion

There is no significant difference in baseline characteristics, clinical presentation, and adverse health outcomes between AUD patients treated without antibiotics compared to with antibiotics.
The Business Value of Gamification
Authors: Jacobides Michael G. , Ma M. Dalbert , Trantopoulos Konstantinos , Vassalos Vasilis
Published: January 2024

This article analyzes the connection between gamification and business success, focusing on customer retention, new customer acquisition, and transforming user perceptions. Based on a qualitative comparative analysis of 40 high-profile gamification projects, it shows that a combination of three key features—virtualization, social comparison, and tangible rewards—explain the various pathways to success. Each pathway requires the presence—and sometimes absence—of different design features, and firms do best when they focus on one or two objectives rather than all three at once. The article presents a framework for designing and implementing gamification more strategically and effectively, noting the ethical questions that arise.
Exploring the Timing of Disengagement From Nondriving Related Tasks in Scheduled Takeovers With Pre-Alerts: An Analysis of Takeover-Related Measures
Authors: Bai Jiming , Sun Xu , Cao Shi , Wang Qingfeng , Wu Jiang
Published: January 2024

Objectives

This study aimed to investigate drivers’ disengagement from nondriving related tasks (NDRT) during scheduled takeovers and to evaluate its impact on takeover performance.

Background

During scheduled takeovers, drivers typically have sufficient time to prepare. However, inadequate disengagement from NDRTs can introduce safety risks.

Method

Participants experienced scheduled takeovers using a driving simulator, undergoing two conditions, with and without an NDRT. We assessed their takeover performance and monitored their NDRT disengagement from visual, cognitive, and physical perspectives.

Results

The study examined three NDRT disengagement timings (DTs): DT1 (disengaged before the takeover request), DT2 (disengaged after the request but before taking over), and DT3 (not disengaged). The impact of NDRT on takeover performance varied depending on DTs. Specifically, DT1 demonstrated no adverse effects; DT2 impaired takeover time, while DT3 impaired both takeover time and quality. Additionally, participants who displayed DT1 exhibited longer eye-off-NDRT duration and a higher eye-off-NDRT count during the prewarning stage compared to those with DT2 and DT3.

Conclusion

Drivers can benefit from earlier disengagement from NDRTs, demonstrating resilience to the adverse effects of NDRTs on takeover performance. The disengagement of cognition is often delayed compared to that of eyes and hands, potentially leading to DT3. Moreover, visual disengagement from NDRTs during the prewarning stage could distinguish DT1 from the other two.

Application

Our study emphasizes considering NDRT disengagement in designing systems for scheduled takeovers. Measures should be taken to promote early disengagement, facilitate cognitive disengagement, and employ visual disengagement during the prewarning period as predictive indicators of DTs.
Personnel Control and Local Officials’ Attention to Economic Development: Evidence from China
Authors: Yan Yang , Christensen Tom , Yang Zhusong
Published: January 2024

Compared to democracies, our understanding of policy attention in authoritarian governments remains limited. Using data from 612 Chinese municipal party secretaries’ official activities, we investigate the structural factors within the authoritarian bureaucratic system influencing new officials’ attention for economic issues. The findings reveal that younger officials and those transferred from other regions exhibit a clear preference for economic issues. Moreover, higher economic growth expectations from superior governments are linked to increased attention to urban economic affairs by officials. This study enriches the literature on comparative agenda research by discussing the relationship between policy attention allocation and authoritarian cadre management.
Investigating Experimental Treatments for Rhinitis: A State-of-the-Art Systematic Review
Authors: Sarfraz Zouina , Sarfraz Azza , Cherrez-Ojeda Ivan
Published: January 2024

Background: Rhinitis is a common inflammatory condition that affects the nasal passages, significantly impacting quality of life and placing a considerable burden on healthcare systems. While traditional treatments offer limited relief, there is a growing interest in novel therapies. This systematic review aims to analyze investigational new treatments for rhinitis. Methods: A search was conducted in ClinicalTrials.gov, the World Health Organization International Clinical Trials Registry Platform, and the European Union Clinical Trials Register, as well as PubMed, Web of Science, and the Cochrane Library. Both ongoing and completed clinical trials exploring innovative therapies for rhinitis, including immunotherapy, probiotics, and stem cell therapy, were included. Results: This systematic review compiled information from 74 clinical trials—51 completed and 23 ongoing—focused on new treatments for rhinitis. A significant portion of the completed studies (44) focused on various forms of immunotherapy, which showed potential for long-term effectiveness and had a high safety profile. Another seven completed trials investigated probiotics as a treatment method, yielding mixed results, though they did show promise in managing symptoms, particularly when combined with other treatments. The ongoing trials are primarily investigating immunotherapy, with a smaller number looking at probiotics and stem cell therapy. This shows a continued exploration of innovative and diverse therapies for managing rhinitis. Conclusion: This study highlights the potential of emerging rhinitis therapies to improve patient outcomes and enhance quality of life. Continued research is recommended for developing more effective, personalized, and targeted therapeutic strategies for rhinitis.
Comparison of the Extended Perichondrium-Cartilage Butterfly Inlay Technique and Over-Underlay Technique for Repairing Subtotal Perforation: A 2-Year Follow-up Study
Authors: Zhao Chaowei
Published: January 2024

Objective:

To compare graft outcomes and postoperative complications between the extended perichondrium-cartilage butterfly inlay (PCBI) technique and the perichondrium-cartilage over-underlay (PCOU) technique for repairing subtotal perforations over a 2-year follow-up period.

Methods and Materials:

Patients with subtotal perforations were prospectively randomized to either the extended PCBI (n = 52) or PCOU (n = 51) group. Evaluation metrics included operation time, graft success rate, hearing gain, and complications at 24 months postoperatively.

Results:

The study included 103 patients with 103 ears. Follow-up loss occurred in 11 of 52 patients (21.2%) in the PCBI group and 13 of 51 patients (25.5%) in the PCOU group ( P = .773). The final analysis included 41 of 52 patients (78.9%) in the PCBI group and 38 of 51 patients (74.5%) in the PCOU group. The average operation time was significantly shorter in the PCBI group (31.2 ± 1.9 min) compared to the PCOU group (52.8 ± 6.3 min, P < .001). At 24 months postoperatively, the graft success rate was 82.9% (34 patients) in the PCBI group and 92.1% (35 patients) in the PCOU group ( P = .374). No significant group difference was noted in mean air–bone gap (ABG) gain ( P = .759). High-resolution computed tomography (HRCT) demonstrated well-pneumatized mastoids and middle ears in both groups. Altered taste was reported in no PCBI group patients and 23.7% (9 patients) of the PCOU group patients ( P < .05). Graft cholesteatoma was found in 4.9% (two patients) in the PCBI group and 7.9% (three patients) in the PCOU group ( P = .930).

Conclusion:

The endoscopic extended PCBI technique is safe and effective for repairing subtotal perforation. It avoids raising a tympanomeatal flap and demonstrates high long-term graft success with minimal complications.
Classroom Sensing Tools: Revolutionizing Classroom-Based Research in the 21st Century
Authors: Foster Tiffany J. , Justice Laura , Gonzalez Villasanti Hugo , Irvin Dwight , Messinger Daniel
Published: January 2024

Sensing technologies that provide continuous, real-time information about teachers’ and students’ individual experiences are increasingly being applied to classroom-based research. Sensing technologies provide a possible alternative to costly and time-intensive in-person or hand-coded observations and have the potential to increase our present understanding of the vastly different experiences students within the same classroom often have. The goal of the present article is to provide an overview of sensing technologies, an explanation of how these technologies can be applied in early childhood classroom-based research, and examples of existing studies that have successfully implemented sensing technologies in the classroom environment.
Predictive factors of psoriatic arthritis in a diverse population with psoriasis
Authors: Loo Wai Yang , Tee Ying Chew , Han Winn Hui , Faheem Nik Aimee Azizah , Yong Shin Shen , Kwan Zhenli , Pok Lydia Say Lee , Yahya Fariz
Published: January 2024

Objective

We aimed to analyze the clinical characteristics of patients with psoriasis and determine the predictive factors of psoriatic arthritis (PsA).

Methods

This retrospective cohort study was performed among patients with psoriasis. Demographic and clinical data were collected. Psoriasis treatment was categorized as topical agents, phototherapy, oral therapy, and biologics. Predictive factors of PsA development were determined using logistic regression analyses.

Results

We included 330 patients with psoriasis, and 83 (25%) patients developed PsA. Thirty-eight (45.8%) patients who developed PsA were Malay, 24 (28.9%) were Chinese, and 21 (25.3%) were Indian. The mean age of patients with PsA was 54.2 (±15.8) years, and the duration from diagnosis of psoriasis to diagnosis of PsA was 36 (3.5–114) months. Predictive factors for developing PsA were female sex (odds ratio [OR] = 3.33, 95% confidence interval [CI] 1.78–6.22), presence of nail involvement (OR = 5.36, 95% CI 2.50–11.51), severe psoriasis (OR = 27.41, 95% CI 7.58–99.11), and oral systemic therapy prior to PsA diagnosis (OR = 4.09, 95% CI 2.04–8.22).

Conclusion

Patients with psoriasis who are female, have nail involvement, severe skin psoriasis, and require oral systemic therapy for psoriasis may have an increased risk of developing PsA.