bluuebunny/crossref_metadata_2025 huggingface dataset:
Control energy management system for photovoltaic with bidirectional converter using deep neural network
Authors: Widjonarko Widjonarko , Utomo Wahyu Mulyo , Omar Saodah , Baskara Fatah Ridha , Rosyadi Marwan
Published: January 2024

Rapid population growth propels technological advancement, heightening electricity demand. Obsolete fossil fuel-based power facilities necessitate alternative energy sources. Photovoltaic (PV) energy relies on weather conditions, posing challenges for constant energy consumption. This hybrid energy source system (HESS) prototype employs extreme learning machine (ELM) power management to oversee PV, fossil fuel, and battery sources. ELM optimally selects power sources, adapting to varying conditions. A bidirectional converter (BDC) efficiently manages battery charging, discharging, and secondary power distribution. HESS ensures continuous load supply and swift response for system reliability. The optimal HESS design incorporates a single renewable source (PV), conventional energy (PNL and genset), and energy storage (battery). Supported by a BDC with over 80% efficiency in buck and boost modes, it stabilizes voltage and supplies power through flawless ELM-free logic verification. Google Colab online testing and hardware implementation with Arduino demonstrate ELM's reliability, maintaining a direct current (DC) 24 V interface voltage and ensuring its applicability for optimal HESS.
Phase delay through slot-line beam switching microstrip patch array antenna design for sub-6 GHz 5G band applications
Authors: Das Debprosad , Hossain Md. Farhad , Hossain Md. Azad , Rahman Muhammad Asad , Hossain Md. Motahar , Hossam-E-Haider Md.
Published: January 2024

Two, four, eight, and sixteen-element patch array antennas for beam switching are presented in this study. For a 1×2 array, an aperture-coupled feeding mechanism is used to feed patches while a slot line on the ground plane provides the phase delay between antenna elements. The 1×2 array is used to create the 2×2, 4×2, and 8×2 arrays, and an equal power divider provides the signal for each. For applications in the 5G sub-6 GHz frequency spectrum, the antennas are modeled. With -37.14 dB, -17.85 dB, -21.51 dB, and -26.03 dB return loss for two, four, eight, and sixteen-element array antennas respectively the simulation demonstrates that the antennas are properly matched at the resonant frequency. The antennas can switch its radiated beam to ±24o, ±24o, ±28o, and ±26o with gains of 8.97 dBi, 11.19 dBi, 13.23 dBi, and 16.24 dBi, respectively at the resonance frequency. The directivity of the proposed antenna is found to be 9.17 dBi, 11.20 dBi, 13.40 dBi, and 16.45 dBi respectively. The antennas are constructed with two 0.8 mm-thick Teflon substrate layers. The ground plane between the two substrate layers contains the aperture and the slot line that generates the phase delay.
Performance analysis of 2D optical code division multiple access through underwater wireless optical medium
Authors: Islam Md. Rabiul , Islam Md. Jahedul , Mitra Bithi , Hossain Md. Amzad , Islam Jahedul , Dev Shuvo
Published: January 2024

The performance of a two-dimensional optical code division multiple access (2D-OCDMA) system using an underwater wireless optical (UWO) medium is assessed in this work. The optical source is an LED with a working wavelength of 532 nm, and the optical detector is a p–i–n photodiode. When calculating the bit error rate (BER), the phase-induced intensity noise (PIIN), thermal noises, and shot sounds are taken into account. The user code address is set using 2D perfect difference (2D-PD) codes. Link distance, inclination angle, beam divergence angle, transmitter power, and the number of concurrent users are all taken into account when determining the BER performance. For various water media, such as pure sea water (PSW), clear ocean water (CLOW), and coastal ocean water (CSOW), the performance of the suggested system is examined.
Convolutional neural network for estimation of harvest time of forage sorghum (sorghum bicolor) cultivar samurai-1
Authors: Suradiradja Kahfi Heryandi , Sitanggang Imas Sukaesih , Abdullah Luki , Hermadi Irman
Published: January 2024

One of the economic alternatives to improve the quality of ruminant feed is combining grass as the main feed with high-protein forages such as sorghum. To get a quality sorghum harvest during the period, it must be right when it has good biomass content, nutrients, and digestibility. The problem is that measuring quality in the laboratory has additional costs and time, which is not short, causing delays. An approach with machine learning using a convolutional neural network can be a better solution. This research uses a convolutional neural network algorithm with the right architecture to estimate sorghum harvest time from imaging results of unmanned aerial vehicles. The stages of this research include data collection, pre-processing, modeling, and finally, the evaluation stage. This research compares the results of several convolutional neural network (CNN) algorithm architectural models: simple CNN, ResNet50 V2, visual geometry group-16 (VGG-16), MobileNet V2, and Inception V3. The result is determining the CNN algorithm architectural model that can estimate sorghum harvest time with maximum accuracy. The best result is the simple CNN architectural model with an accuracy of 0.95. This research shows that the classification model obtained from the CNN algorithm with a simple CNN architecture is the choice model for estimating sorghum harvest time.
Design of storage benchmark kit framework for supporting the file storage retrieval
Authors: Naazre Vittal Rao Sanjay Kumar , Munegowda Keshava
Published: January 2024

An open-source software framework called the storage benchmark kit (SBK) is used to store the system benchmarking performance framework. The SBK is designed to perform any storage client or device using any data type as a payload. SBK simultaneously helps number of readers as well as writes to the storage system of large amounts of data as well as allows end-to-end latency benchmarking for multiple writers and readers. The SBK uses standardized performance measures for comparing and evaluating various storage systems and their combinations. Distributed file systems, distributed database systems, single or local node databases, systems of object storage, platforms of distributed streaming and messaging, and systems of key-value storage are the storage solutions supported by SBK. The SBK supports various storage systems like XFS, Kafka streaming storage systems, and Hadoop distributed file system (HDFS) performance benchmarking. The experimental results show that a proposed method achieves execution time of 65.530 s, 40.826 s and 30.351 s for the 100k, 500k and 1000k files respectively which ensures better improvement than the existing methods such as simple data interface and distributed data protection system.
Improving cyberbullying detection through multi-level machine learning
Authors: Salsabila Salsabila , Sarno Riyanarto , Ghozali Imam , Sungkono Kelly Rossa
Published: January 2024

Cyberbullying is a known risk factor for mental health issues, demanding immediate attention. This study aims to detect cyberbullying on social media in alignment with the third sustainable development goal (SDG) for health and well-being. Many previous studies employ single-level classification, but this research introduces a multi-class multi-level (MCML) algorithm for a more detailed approach. The MCML approach incorporates two levels of classification: level one for cyberbullying or not cyberbullying, and level two for classifying cyberbullying by type. This study used a dataset of 47,000 tweets from Twitter with six class labels and employed an 80:20 training and testing data split. By integrating bidirectional encoder representations from transformers (BERT) and MCML at level two, we achieved a remarkable 99% accuracy, surpassing BERT-based single-level classification at 94%. In conclusion, the combination of MCML and BERT offers enhanced cyberbullying classification accuracy, contributing to the broader goal of promoting mental health and well-being.
A novel dynamic enterprise architecture model: leveraging MAPE-K loop and case-based reasoning for context awareness
Authors: Ettahiri Imane , Doumi Karim
Published: January 2024

Nowadays, enterprises are required to take the uncertainty of the environment as decisive factor of success. For this reason, Enterprises should be prepared up-stream to react dynamically to the turbulent context. Considering that enterprise architecture is a tool drawing a blueprint that gives a holistic view of the enterprise, this blueprint should be able to represent this awareness to context and implements the techniques and mechanisms to react in a dynamic manner depending on the triggers of change. In this paper, the proposed model stipulates a “context-awareness” that monitors the internal and external context, and then adapt its reaction in alignment with the prefixed goals. The operationalization of our conception is realized through the monitor-analyze-plan-execute-knowledge (MAPE-K) loop, the case-based reasoning and machine learning techniques organized and orchestrated through a global algorithm of 6 main functions to monitor, compare, analyze, plan, execute and enrich the knowledge base. The results are verified in the light of a case study that demonstrates the applicability of our proposed model.
A review on machine learning based intrusion detection system for internet of things enabled environment
Authors: Nisha Nisha , Gill Nasib Singh , Gulia Preeti
Published: January 2024

Within an internet of things (IoT) environment, the fundamental purpose of various devices is to gather the abundant amount of data that is being generated and then transmit this data to the predetermined server over the internet. IoT connects billions of objects and the internet to communicate without human intervention. But network security and privacy issues are increasing very fast, in today's world. Because of the prevalence of technological advancement in regular activities, internet security has evolved into a necessary requirement. Because technology is integrated into every aspect of contemporary life, cyberattacks on the internet of things represent a bigger danger than attacks against traditional networks. Researchers have found that combining machine learning techniques into an intrusion detection system (IDS) is an efficient way to get beyond the limitations of conventional IDSs in an IoT context. This research presents a comprehensive literature assessment and develops an intrusion detection system that makes use of machine learning techniques to address security problems in an IoT environment. Along with a comprehensive look at the state of the art in terms of intrusion detection systems for IoT-enabled environments, this study also examines the attributes of approaches, common datasets, and existing methods utilized to construct such systems.
Comparison of Iris dataset classification with Gaussian naïve Bayes and decision tree algorithms
Authors: Dani Yasi , Artanta Ginting Maria
Published: January 2024

In this study, we apply two classification algorithm methods, namely the Gaussian naïve Bayes (GNB) and the decision tree (DT) classifiers. The Gaussian naïve Bayes classifier is a probability-based classification model that predicts future probabilities based on past experiences. Whereas the decision tree classifier is based on a decision tree, a series of tests that are performed adaptively where the previous test affects the next test. Both of these methods are simulated on the Iris dataset where the dataset consists of three types of Iris: setosa, virginica, and versicolor. The data is divided into two parts, namely training and testing data, in which there are several features as information on flower characteristics. Furthermore, to evaluate the performance of the algorithms on both methods and determine the best algorithm for the dataset, we evaluate it using several metrics on the training and testing data for each method. Some of these metrics are recall, precision, F1-score, and accuracy where the higher the value, the better the algorithm's performance. The results show that the performance of the decision tree classifier algorithm is the most outperformed on the Iris dataset.
Generating images using generative adversarial networks based on text descriptions
Authors: Turarova Marzhan , Bekbayeva Roza , Abdykerimova Lazzat , Aitimov Murat , Bayegizova Aigulim , Smailova Ulmeken , Kassenova Leila , Glazyrina Natalya
Published: January 2024

Modern developments in the fields of natural language processing (NLP) and computer vision (CV) emphasize the increasing importance of generating images from text descriptions. The presented article analyzes and compares two key methods in this area: generative adversarial network with conditional latent semantic analysis (GAN-CLS) and ultra-long transformer network (XLNet). The main components of GAN-CLS, including the generator, discriminator, and text encoder, are discussed in the context of their functional tasks—generating images from text inputs, assessing the realism of generated images, and converting text descriptions into latent spaces, respectively. A detailed comparative analysis of the performance of GAN-CLS and XLNet, the latter of which is widely used in the organic light-emitting diode (OEL) field, is carried out. The purpose of the study is to determine the effectiveness of each method in different scenarios and then provide valuable recommendations for selecting the best method for generating images from text descriptions, taking into account specific tasks and resources. Ultimately, our paper aims to be a valuable research resource by providing scientific guidance for NLP and CV experts.
Hand LightWeightNet: an optimized hand pose estimation for interactive mobile interfaces
Authors: Banzi Jamal Firmat , Leonard Stanley
Published: January 2024

In this paper, a hand pose estimation method is introduced that combines MobileNetV3 and CrossInfoNet into a single pipeline. The proposed approach is tailored for mobile phone processors through optimizations, modifications, and enhancements made to both architectures, resulting in a lightweight solution. MobileNetV3 provides the bottleneck for feature extraction and refinements while CrossInfoNet benefits the proposed system through a multitask information sharing mechanism. In the feature extraction stage, we utilized an inverted residual block that achieves a balance between accuracy and efficiency in limited parameters. Additionally, in the feature refinement stage, we incorporated a new best-performing activation function called “activate or not” ACON, which demonstrated stability and superior performance in learning linearly and non-linearly gates of the whole activation area of the network by setting hyperparameters to switch between active and inactive states. As a result, our network operated with 65% reduced parameters, but improved speed by 39% which is suitable for running in a mobile device processor. During experiment, we conducted test evaluation on three hand pose datasets to assess the generalization capacity of our system. On all the tested datasets, the proposed approach demonstrates consistently higher performance while using significantly fewer parameters than existing methods. This indicates that the proposed system has the potential to enable new hand pose estimation applications such as virtual reality, augmented reality and sign language recognition on mobile devices.
Predicting churn with filter-based techniques and deep learning
Authors: Quek Jia Yi Vivian , Ying Han Pang , Zheng You Lim , Shih Yin Ooi , How Khoh Wee
Published: January 2024

Customer churn prediction is of utmost importance in the telecommunications industry. Retaining customers through effective churn prevention strategies proves to be more cost-efficient. In this study, attribute selection analysis and deep learning are integrated to develop a customer churn prediction model to improve performance while reducing feature dimensions. The study includes the analysis of customer data attributes, exploratory data analysis, and data preprocessing for data quality enhancement. Next, significant features are selected using two attribute selection techniques, which are chi-square and analysis of variance (ANOVA). The selected features are fed into an artificial neural network (ANN) model for analysis and prediction. To enhance prediction performance and stability, a learning rate scheduler is deployed. Implementing the learning rate scheduler in the model can help prevent overfitting and enhance convergence speed. By dynamically adjusting the learning rate during the training process, the scheduler ensures that the model optimally adapts to the data while avoiding overfitting. The proposed model is evaluated using the Cell2Cell telecom database, and the results demonstrate that the proposed model exhibits a promising performance, showcasing its potential as an effective churn prediction solution in the telecommunications industry.
Swarm flip-crossover algorithm: a new swarm-based metaheuristic enriched with a crossover strategy
Authors: Kusuma Purba Daru , Hasibuan Faisal Candrasyah
Published: January 2024

A new swarm-based metaheuristic that is also enriched with the crossover technique called swarm flip-crossover algorithm (SFCA) is introduced in this work. SFCA uses swarm intelligence as its primary technique and the crossover as its secondary one. It consists of three searches in every iteration. The swarm member walks toward the best member as the first search. The central point of the swarm becomes the target in the second search. There are two walks in the second search. The first walk is getting closer to the target, while the second is avoiding the target. The better result between these two walks becomes the candidate for the replacement. In the third search, the swarm member performs balance arithmetic crossover with the central point of the space or jumps to the opposite location within the area (flipping). The assessment is taken by confronting SFCA with five new metaheuristics: slime mold algorithm (SMA), golden search optimization (GSO), osprey optimization algorithm (OOA), coati optimization algorithm (COA), and walrus optimization algorithm (WaOA) in handling the set of 23 functions. The result shows that SFCA performs consecutively better than SMA, GSO, OOA, COA, and WaOA in 20, 23, 17, 17, and 17 functions.
Performance enhancement of machine learning algorithm for breast cancer diagnosis using hyperparameter optimization
Authors: Hridoy Rashidul Hasan , Arni Arindra Dey , Ghosh Shomitro Kumar , Chakraborty Narayan Ranjan , Mahmud Imran
Published: January 2024

Breast cancer is the most fatal women’s cancer, and accurate diagnosis of this disease in the initial phase is crucial to abate death rates worldwide. The demand for computer-aided disease diagnosis technologies in healthcare is growing significantly to assist physicians in ensuring the effectual treatment of critical diseases. The vital purpose of this study is to analyze and evaluate the classification efficiency of several machine learning algorithms with hyperparameter optimization techniques using grid search and random search to reveal an efficient breast cancer diagnosis approach. Choosing the optimal combination of hyperparameters using hyperparameter optimization for machine learning models has a straight influence on the performance of models. According to the findings of several evaluation studies, the k-nearest neighbor is addressed in this study for effective diagnosis of breast cancer, which got a 100.00% recall value with hyperparameters found utilizing grid search. k-nearest neighbor, logistic regression, and multilayer perceptron obtained 99.42% accuracy after utilizing hyperparameter optimization. All machine learning models showed higher efficiency in breast cancer diagnosis with grid search-based hyperparameter optimization except for XGBoost. Therefore, the evaluation outcomes strongly validate the effectiveness and reliability of the proposed technique for breast cancer diagnosis.
SADCNN-ORBM: a hybrid deep learning model based citrus disease detection and classification
Authors: Saini Ashok Kumar , Bhatnagar Roheet , Srivastava Devesh Kumar
Published: January 2024

Citrus disease has a significant influence on agricultural productivity these days, so technology based on artificial intelligence has been developed for creating computer vision (CV) models. By spotting disease in its early stages and enabling necessary productivity actions, CV in agriculture improves the production of agricultural goods. In this paper, we developed a CV-based citrus disease detection model called the self-attention dilated convolutional neural network optimized restricted Boltzmann machine (SADCNN-ORBM) model, which consists of two crucial parts: a SADCNN for disease segmentation and an ORBM optimized by the self-adaptive coati optimization (SACO) algorithm to improve the classification performance of diseases, which successfully divides the disease type into three groups: anthracnose, melanose, and brown spot. Numerous feature sets, such as texture features, three-channel red, green, blue (RGB) features, local binary pattern (LBP) features, and speeded-up robust features (SURF) features, are combined and given as input into the classification layer in the proposed model. We compare our proposed model's performance with existing methods by using several evaluation metrics. The findings demonstrate the SADCNN-ORBM model's superiority in precisely recognizing and classifying citrus illnesses, outperforming all available techniques.
A trust based secure access control using authentication mechanism for interoperability in internet of things
Authors: Narayanappa Shashikala , Narayanareddy Anitha Tulavanur , Mishra Priti , Raichur Patil Herakal Renuka , Kolur Jayasudha
Published: January 2024

The internet of things (IoT) is a revolutionary innovation in many aspects of our society including interactions, financial activity, and global security such as the military and battlefield internet. Due to the limited energy and processing capacity of network devices, security, energy consumption, compatibility, and device heterogeneity are the long-term IoT problems. As a result, energy and security are critical for data transmission across edge and IoT networks. Existing IoT interoperability techniques need more computation time, have unreliable authentication mechanisms that break easily, lose data easily, and have low confidentiality. In this paper, a key agreement protocol-based authentication mechanism for IoT devices is offered as a solution to this issue. This system makes use of information exchange, which must be secured to prevent access by unauthorized users. Using a compact contiki/cooja simulator, the performance and design of the suggested framework are validated. The simulation findings are evaluated based on detection of malicious nodes after 60 minutes of simulation. The suggested trust method, which is based on privacy access control, reduced packet loss ratio to 0.32%, consumed 0.39% power, and had the greatest average residual energy of 0.99 mJoules at 10 nodes.
An efficient security framework for intrusion detection and prevention in internet-of-things using machine learning technique
Authors: Nagaraj Tejashwini , Channarayappa Rajani Kallhalli
Published: January 2024

Over the past few years, the internet of things (IoT) has advanced to connect billions of smart devices to improve quality of life. However, anomalies or malicious intrusions pose several security loopholes, leading to performance degradation and threat to data security in IoT operations. Thereby, IoT security systems must keep an eye on and restrict unwanted events from occurring in the IoT network. Recently, various technical solutions based on machine learning (ML) models have been derived towards identifying and restricting unwanted events in IoT. However, most ML-based approaches are prone to miss-classification due to inappropriate feature selection. Additionally, most ML approaches applied to intrusion detection and prevention consider supervised learning, which requires a large amount of labeled data to be trained. Consequently, such complex datasets are impossible to source in a large network like IoT. To address this problem, this proposed study introduces an efficient learning mechanism to strengthen the IoT security aspects. The proposed algorithm incorporates supervised and unsupervised approaches to improve the learning models for intrusion detection and mitigation. Compared with the related works, the experimental outcome shows that the model performs well in a benchmark dataset. It accomplishes an improved detection accuracy of approximately 99.21%.
Freshwater fish diversity and IUCN Red List status of glacial-fed (Bheri) and spring-fed (Babai) rivers in the wake of inter-basin water transfer
Authors: Khatri Kumar , Jha Bibhuti Ranjan , Gurung Smriti , Khadka Udhab Raj
Published: January 2024

Freshwater fish are crucial components of aquatic ecosystems that are affected by a range of anthropogenic activities. Freshwater bodies in Nepal are under different threats affecting biodiversity. Inter-basin water transfer (IBWT) involving damming and diversion of water from one river basin to another is considered a major threat to aquatic biodiversity. Impact assessment of such projects include generation of baseline information on different biotic and abiotic variables. The aim of this study was to generate baseline information on fish diversity from the glacial-fed (Bheri) and the spring-fed (Babai) rivers and their selected tributaries from western Nepal in the wake of the first proposed inter-basin water transfer from the former to the latter. A total of 10 sampling sites, five each from Bheri and Babai River systems, were chosen strategically. Electrofishing was conducted encompassing different seasons in 2018 following the standard method. A total of 32 species with catch per unit effort (CPUE) of 47±24 from Bheri and 42 species with CPUE of 63±52 from Babai River were recorded. Cyprinidae, followed by Nemacheilidae, were the most dominant families in both river systems, and Barilius vagra and Schistura beavani were the most dominant species in both. Species richness and abundance showed a significant difference between rivers, and differences in fish assemblages reflects differences in ecological regimes. Failure to observe migratory species such as Anguilla bengalensis suggests that migratory routes may already have been affected. Of the total 52 species recorded, eight are in the threatened categories of the IUCN Red List and need active conservation measures. The findings provide a reference to assess the impacts of water transfers on fish assemblages in these river systems.
Population status and habitat use of White-crested Kalij Pheasant Lophura leucomelanos hamiltoni (J.E. Gray, 1829) in the Limber Wildlife Sanctuary, Jammu & Kashmir, India
Authors: Arif Nabi Lone  , Bhat Bilal A. , Ahmad Khursheed
Published: January 2024

Understanding the population status and habitat use of a species is fundamental for initiating conservation action. The present study was conducted from March 2021 to February 2022 to assess the population status and habitat use of White-crested Kalij Pheasant Lophura leucomelanos hamiltoni in the Limber Wildlife Sanctuary. Line transects/trails (n = 7) were established across all the habitat types. A total of 45 direct sightings of the bird were recorded in the study area. The highest abundance was recorded in autumn (2.25 ± 0.53 birds/km) and the lowest in spring (0.22 ± 0.53 birds/km). Flock size ranged from one individual to nine individuals. Of the different habitat types identified, most sightings occurred in coniferous forests with high understory (n = 16). The agricultural terracings (n = 6) and grassy slopes (n = 2) exhibited the fewest sightings. Recognizing and mapping these habitats are fundamental initial measures for conserving the species within the landscape.
Two new weevil species of the genus Myllocerus Schoenherr, 1823 (Coleoptera: Curculionidae: Entiminae) from India
Authors: G. Mahendiran  , M.M. Nagaraja  , M. Sampathkumar
Published: January 2024

Two new species, Myllocerus vathalmalaensis sp. nov. and M. depressus sp. nov., are described from India. M. vathalmalaensis differs from M. lineaticollis (Boheman, 1842) in possessing a fine central carina on the impressed rostrum, the second funicle segment longer than the first one and tegmen without parameres. M. depressus differs from M. cardoni Marshall, 1916 by the dorsally depressed rostrum, which is longer than the head, a prothorax depressed and constricted near the posterior margin, the antennae and legs black in colour, the hind femora with a single spine and a spermatheca with curved nodulus.
Observations on the courtship behaviour of Deocata Pipefish Microphis deocata (Hamilton, 1822) (Actinopterygii: Syngnathiformes: Syngnathidae) in an aquarium
Authors: Saikia Anu , Nath Jayanta Kumar , Sarma Dandadhar
Published: January 2024

The Deocata Pipefish Microphis deocata, is the sole freshwater syngnathid found in the Himalayan range. This species is categorised as ‘Near Threatened’ by the IUCN Red List and is also listed as a Schedule I species under the Wildlife Protection Act of 1972. This species is highly sought after in the ornamental fish trade due to its attractive colouration and uniqueness. The courtship behaviour of M. deocata consists of three distinct phases (initial courtship, spawning, and swaying) characterized by substantial behavioural alterations. M. deocata is a species where the action of females is more visible for commencing courting and strengthening at later phases. The females exhibit skin fold ornamentation to signal their readiness to spawn mature eggs. The first courtship phase is characterised by female quivering along with head pointing by both sexes and often tail entangling. As a sign of embracing the male often overlies upon the female. In the second phase, the female transfers her eggs onto the male incubating ventral surface with an upside-down body posture. During the final phase, the male forms his body into a contracting wave and lastly settles down in the bottom of the tank.
A record of Chestnut-and-Black Royal Tajuria yajna istrodea De Nicéville, 1887 (Lepidoptera: Lycaenidae) from Arunachal Pradesh, India
Authors: Limbu Ruksha , Achint Ramandeep , Gogoi Renu , Upadhaya Roshan , Gaur Jyoti
Published: January 2024

The current study reports the first sighting and photographic evidence of Chestnut-and-Black Royal Tajuria yajna istroidea De Nicéville, 1887 from Arunachal Pradesh, India.
Protein Kinase A Is a Master Regulator of Physiological and Pathological Cardiac Hypertrophy
Authors: Bai Yingyu , Zhang Xiaoying , Li Ying , Qi Fei , Liu Chong , Ai Xiaojie , Tang Mingxin , Szeto Christopher , Gao Erhe , Hua Xiang , Xie Mingxing , Wang Xuejun , Tian Ying , Chen Yongjie , Huang Guowei , Zhang Junping , Xiao Weidong , Zhang Lili , Liu Xueyuan , Yang Qing , Houser Steven R , Chen Xiongwen
Published: January 2024

BACKGROUND:

The sympathoadrenergic system and its major effector PKA (protein kinase A) are activated to maintain cardiac output coping with physiological or pathological stressors. If and how PKA plays a role in physiological cardiac hypertrophy (PhCH) and pathological CH (PaCH) are not clear.

METHODS:

Transgenic mouse models expressing the PKA inhibition domain (PKAi) of PKA inhibition peptide alpha (PKIalpha)-green fluorescence protein (GFP) fusion protein (PKAi-GFP) in a cardiac-specific and inducible manner (cPKAi) were used to determine the roles of PKA in physiological CH during postnatal growth or induced by swimming, and in PaCH induced by transaortic constriction (TAC) or augmented Ca
2+
influx. Kinase profiling was used to determine cPKAi specificity. Echocardiography was used to determine cardiac morphology and function. Western blotting and immunostaining were used to measure protein abundance and phosphorylation. Protein synthesis was assessed by puromycin incorporation and protein degradation by measuring protein ubiquitination and proteasome activity. Neonatal rat cardiomyocytes (NRCMs) infected with AdGFP (GFP adenovirus) or AdPKAi-GFP (PKAi-GFP adenovirus) were used to determine the effects and mechanisms of cPKAi on myocyte hypertrophy. rAAV9.PKAi-GFP was used to treat TAC mice.

RESULTS:

(1) cPKAi delayed postnatal cardiac growth and blunted exercise-induced PhCH; (2) PKA was activated in hearts after TAC due to activated sympathoadrenergic system, the loss of endogenous PKIα (PKA inhibition peptide α), and the stimulation by noncanonical PKA activators; (3) cPKAi ameliorated PaCH induced by TAC and increased Ca
2+
influxes and blunted neonatal rat cardiomyocyte hypertrophy by isoproterenol and phenylephrine; (4) cPKAi prevented TAC-induced protein synthesis by inhibiting mTOR (mammalian target of rapamycin) signaling through reducing Akt (protein kinase B) activity, but enhancing inhibitory GSK-3α (glycogen synthase kinase-3α) and GSK-3β signals; (5) cPKAi reduced protein degradation by the ubiquitin-proteasome system via decreasing RPN6 phosphorylation; (6) cPKAi increased the expression of antihypertrophic atrial natriuretic peptide (ANP); (7) cPKAi ameliorated established PaCH and improved animal survival.

CONCLUSIONS:

Cardiomyocyte PKA is a master regulator of PhCH and PaCH through regulating protein synthesis and degradation. cPKAi can be a novel approach to treat PaCH.
The Realities of Anti-Corruption and Whistleblowing
Authors: Mantzaris Evangelos , Saruchera Munyaradzi
Published: January 2024

Despite the existence of anti-corruption whistleblowing laws, Zimbabwe’s public service has suffered from maladministration, corrupt politicians, “mediators” and businesses, malpractice and irregularities for a long time. This article, employing grounded theory within a qualitative research paradigm, discusses a study which draws on eight interviews and found that whistleblowers lack state protection because state institutions do not have the political will to protect them. These realities are associated with: 1) state organisation capacity and success, or lack of it, in whistleblower protection in the public sector; 2) the significance of legislative and regulatory measures that encourage citizens to become whistleblowers as a key deterrent against corruption; 3) the lack of operationalisation of whistleblower protection within public organisations; 4) the possibility of creative intervention strategies and tactics that would increase the role and success of whistleblowing initiatives and actions; and 5) future financial incentives in laws and regulations that could encourage whistleblowing.
Unravelling the disease ecology of snake fungal disease: high genetic variability and ecological features of Ophidiomyces ophidiicola in Switzerland
Authors: Joudrier Nicolas , Blanvillain Gaelle , Meier Gregoire , Hoyt Joseph , Chèvre Maxime , Dubey Sylvain , Origgi Francesco C. , Ursenbacher Sylvain
Published: January 2024

The discovery of the fungal pathogen Ophidiomyces ophidiicola (Oo), the aetiologic agent of Snake Fungal Disease (SFD), has raised a growing interest in the North American and European scientific communities, in particular toward conservation. This pathogen is known or suspected to be associated with the declines of some snake populations in North America and was detected later in Europe. Its ecology, distribution and phylogeography still remain largely unknown. In this study, we collected skin swabs from 271 free-ranging snakes in Switzerland across 8 different species and 13 sites. The overall pathogen prevalence was at least 28% with sequences consistent with both the European and the North American lineages (respectively Clade I and II) of Oo. Semi-aquatic snakes were more likely to be infected by Oo, and high human disturbance (human frequentation and direct impact on snakes) was associated with a higher Oo prevalence, whereas season, body condition and snake species introduction was not. This study suggests that Switzerland might represent a region characterised by high genetic variability in Oo, and where long-term monitoring might be particularly important to follow the evolution of the disease in free-ranging snakes.
Female Leadership Roles: Subjectivity and Self-Representation
Authors: Abdelaal Ola
Published: January 2024

This article delves into the impact of digital activism on the leadership style of female-led non-governmental organisations operating under restricted access to opportunities. The focus is on Muslim sisterhood activism, examining the shift from offline to online activism led by the younger generation. The article analyses the barriers that have historically prevented Muslim sisterhood activists from playing leadership roles within the Muslim Brotherhood group and compares offline and online leadership roles using the case study of the Revolutionary Coalition for Egyptian Women from 2014 to 2023, with a focus on 2014. The study reveals that the younger generation of Muslim women has adopted a language that prioritises self-representation and body politics over religious discourse, indicating a heightened awareness of gender politics during the period between 2014 and 2023. To overcome state-imposed restrictions, the younger generation of the Muslim sisterhood has contradicted their well-established values on women’s roles and focused their political agendas on regaining popularity in Egyptian society. By exploring the impact of different political opportunities on women’s leadership roles through the political process paradigm, this article bridges a gap in the literature on resistant politics, feminism, leadership, and digital activism. The study shows that the younger generation of Muslim women is spearheading a strategic change in the resistant activist discourse, utilising the new digital space for activism to promote their cause.
Senegal’s Political Character and its Connections with the Middle East
Authors: Bishku Michael B.
Published: January 2024

Senegal is a country that values diplomacy. It is a predominantly-Muslim populated, constitutionally secular, nonaligned state having relations with most countries in the world. Senegal has maintained a stable political system with peaceful transfers of power despite being located in a region of Africa witnessing unstable conditions in recent years. Its leadership is well regarded by countries with different political systems and through that reputation has been able to receive needed technical and economic assistance while maintaining an independent foreign policy. Senegal has exercised influence on the continent, and even beyond, greater than a number of African countries larger in size, since its independence from France in June 1960. Therefore, it has attracted the attention of outside powers, including those in the Middle East of middle size. Those countries, namely Saudi Arabia (together with Morocco and its allies in the Gulf), Türkiye, Iran and Israel, have regarded Senegal as a “gateway” to West Africa and the Sahel region. Yet while Senegal’s ties especially with France and its neighbors in Africa have received much scholarly attention, academics have only recently begun to investigate Senegal’s connections with the Middle East; it is indeed one important aspect for anyone wishing to study Senegal’s foreign relations in a more comprehensive fashion and to have a better understanding of how this country is quite successful in balancing those ties.
Forestry control in the Brazilian Amazon III: anatomy of wood and charcoal of tree species from sustainable forest management
Authors: da Silva Alana Assunção , de Sousa Kaick Coelho , de Souza Fernanda Ilkiu Borges , Nobre João Rodrigo Coimbra , de Paula Protásio Thiago , de Lima Melo Luiz Eduardo
Published: January 2024

The illegal harvest of timber and the subsequent production of charcoal have been contributing to the deforestation of the Brazilian Amazon and its degradation. Analysis of wood anatomy is a well-established tool used to detect illegally harvested wood and is commonly employed in the control of illegal forestry activities. This study aimed to contribute to a database used for the anatomical identification of wood and charcoal from tree species commonly harvested and marketed in the Amazon. The macro- and microscopic characteristics of wood and charcoal for 15 forest species were described. Characteristics that could be described in detail, such as vessel grouping, type of axial parenchyma, and in some cases, perforation plates and uniseriate rays, are fundamental to the identification of wood and charcoal that are illegally, or even legally, marketed. The study aids in the monitoring of the illegal marketing of wood and charcoal and will contribute to the construction of a database and reference collections that will be available for research, training, and use by forest managers, anatomists and anthracologists who study both wood and charcoal.
Tuber itzcuinzapotl sp. nov. (Tuberaceae, Pezizomycetes), the first edible truffle reported from Mexico with traditional biocultural importance
Authors: DE LA FUENTE JAVIER ISAAC , ROSALES-ROSALES WENDY , MARTÍNEZ-GONZÁLEZ CÉSAR RAMIRO , MARTÍNEZ-REYES MAGDALENA , ELIZONDO-SALAS ANDREA CAROLINA , PEREZ-MORENO JESUS
Published: January 2024

Tuber itzcuinzapotl is described as a new species to science. This species is characterized by its pale brown ascomata, finely granular peridium, pale brown to gray gleba, and 22–52 × 15–40 µm alveolate ascospores. The new species is putatively associated with Pinus patula in conifer mixed forests in the state of Veracruz, located in eastern Mexico. Phylogenetic analysis based on the nrITS region places the new species in the Maculatum clade, closely related, but morphologically distinctive, to T. miquihuanense and T. mexiusanum. The new species consumed by the Nahua people, traditionally named “Itzcuinzapotl”, constitutes the first edible truffle with biocultural importance in Mexico. Macro- and micromorphological characterization, results of phylogenetic analysis, and photographs are presented. Ethnomycological aspects related to the species are also briefly discussed.
Reducing a Tropical Cyclone Weak-Intensity Bias in a Global Numerical Weather Prediction System
Authors: McTaggart-Cowan Ron , Nolan David S. , Aider Rabah , Charron Martin , Chen Jan-Huey , Cossette Jean-François , Gaudreault Stéphane , Husain Syed , Magnusson Linus , Qaddouri Abdessamad , Separovic Leo , Subich Christopher , Yang Jing
Published: January 2024

The operational Canadian Global Deterministic Prediction System suffers from a weak-intensity bias for simulated tropical cyclones. The presence of this bias is confirmed in progressively simplified experiments using a hierarchical system development technique. Within a semi-idealized, simplified-physics framework, an unexpected insensitivity to the representation of relevant physical processes leads to investigation of the model’s semi-Lagrangian dynamical core. The root cause of the weak-intensity bias is identified as excessive numerical dissipation caused by substantial off-centering in the two time-level time integration scheme used to solve the governing equations. Any (semi)implicit semi-Lagrangian model that employs such off-centering to enhance numerical stability will be afflicted by a misalignment of the pressure gradient force in strong vortices. Although the associated drag is maximized in the tropical cyclone eyewall, the impact on storm intensity can be mitigated through an intercomparison-constrained adjustment of the model’s temporal discretization. The revised configuration is more sensitive to changes in physical parameterizations and simulated tropical cyclone intensities are improved at each step of increasing experimental complexity. Although some rebalancing of the operational system may be required to adapt to the increased effective resolution, significant reduction of the weak-intensity bias will improve the quality of Canadian guidance for global tropical cyclone forecasting.

Significance Statement

Global numerical weather prediction systems provide important guidance to forecasters about tropical cyclone development, motion, and intensity. Despite recent improvements in the Canadian operational model’s ability to predict tropical cyclone formation, the system systematically underpredicts the intensity of these storms. In this study, we use a set of increasingly simplified experiments to identify the source of this error, which lies in the numerical time-stepping scheme used to solve the model equations. By decreasing numerical drag on the tropical cyclone circulation, intensity predictions that resemble those of other global modeling systems are achieved. This will improve the quality of Canadian tropical cyclone guidance for forecasters around the world.
Evaluating Mortality Outcomes, Transfusion Characteristics, and Risk Factors Associated With Cirrhotic Trauma Patients Undergoing Emergency Laparotomy Versus Non-Operative Management: A National Analysis
Authors: Elkbuli Adel , Breeding Tessa , Martinez Brian , Patel Heli , Chin Brian , Wright D-Dre , Patterson Ryan , Roberts Jennifer , Rhodes Heather
Published: January 2024

Background

Patients with liver cirrhosis (LC) demonstrate significantly elevated mortality rates following a traumatic event. This study aims to examine and compare the clinical outcomes in adult trauma patients with pre-existing LC undergoing laparotomy or non-operative management (NOM). Additionally, the study aims to investigate various patient outcomes, including mortality rate based on transfusion needs and timing.

Methods

This retrospective cohort study utilized the American College of Surgeons Trauma Quality Program Participant Use File (ACS-TQIP-PUF) 2017-21 to compare laparotomy vs NOM in adults (≥18 years) with pre-existing LC who presented to trauma facilities with isolated blunt solid organ abdominal injuries (Injury Severity Score ≥16, Abbreviated Injury Scale solid organ abdomen ≥3).

Results

Among 929 patients, 38.2% underwent laparotomy, while 61.7% received NOM. The in-hospital mortality rate was lower for patients who received NOM (52.3% vs 20.0%, P < .01). The risk of in-hospital mortality was significantly associated with laparotomy (OR 5.22, 95% CI: 2.06-13.18, P < .01) and sepsis (OR 99.50, 95% CI: 6.99-1415.28, P < .01). On average an increase in blood units in 4 hours was observed among those who experienced an in-hospital mortality (OR 5.65, 95% CI: 3.05-8.24, P < .01) and those who underwent laparotomy (OR 3.85, 95% CI: 1.36-6.34, P < .01)

Conclusion

Trauma patients with moderate to severe isolated organ injury and Liver cirrhosis had significantly higher mortality rates, acute renal failure, whole blood units received, as well as longer ICU-LOS when undergoing laparotomy compared to non-operative management.