bluuebunny/crossref_metadata_2025 huggingface dataset:
Validation of a teacher mathematics knowledge scale based on the Ernest framework among Malaysian teachers
Authors: Md Ghazali Nor Hasnida , Zaini Syaza Hazwani , Hamzah Mahizer , Bakar Noor Insyiraah Abu , Imami Muh Khairul Wajedi
Published: July 2024

The aim of the current research was to validate the Malaysian version of the mathematical knowledge scale based on the Ernest framework. The participants include 100 teachers from Kelantan, Malaysia. An exploratory factor analysis (EFA) with principal components analysis and varimax rotation was conducted. This research determines the number of factors through eigenvalues greater than one and frames the structure of the scale through factor loading. The value of the eigenvalue and factor loading are considered in this research to frame and develop the structure of the scale. Furthermore, the reliability of the scale was tested with Cronbach’s alpha. The results showed six factors on the scale explaining 67.39% of the variances. A total of 26 items with factor loading greater than 0.60 were determined for the scale. The structure of the scale was as three items in the first factor, two items in the second factor, five items in the third factor, four items in the fourth factor, 10 items in the fifth factor, and two items in the sixth factor. Furthermore, the reliability of the scale was 0.958. This research concluded that the scale was internally consistent in measuring the teacher’s mathematics knowledge.
Challenges of using ChatGPT in education: a digital pedagogy analysis
Authors: Murtiningsih Siti , Sujito Arie , Khin Soe Khin
Published: July 2024

This research explains into the challenges posed by the increasing utilization of Chat generative pre-trained transformer (ChatGPT) in educational settings and proposes solutions to mitigate its potential negative impacts. The widespread adoption of artificial intelligence, exemplified by ChatGPT, in learning environments from elementary to higher education levels, raises concerns about its impact on critical thinking and reflection among students. Through a comprehensive literature review encompassing journals, scientific articles, news, and previous research, this study identifies the challenges associated with ChatGPT’s usage in education in the digital era. One significant challenge is the potential reduction in students’ ability to think critically and engage with reality due to overreliance on ChatGPT for assignments. Moreover, excessive dependence on ChatGPT risks diminishing the quality of human resources in education. To address these challenges, educators need to develop strategies to harness technological advancements effectively while fostering critical thinking skills in students. This research underscores the importance of equipping educators with the necessary skills to navigate the digital landscape and prevent academic dishonesty facilitated by ChatGPT.
Development of diagnostic competencies of educational psychologist in professional training
Authors: Aralbaeva Ryszhamal , Naubaeva Khapiza , Abishev Nurlan , Taszhurekova Zhumagul , Zhexembayeva Zhadra
Published: July 2024

In the era of globalization, enhancing the development of diagnostic competencies among educational psychologists in Kazakhstan is a crucial aspect of improving higher education quality. The purpose of the study is to substantiate and implement the methodology for the development of diagnostic competencies of future educational psychologists in higher educational institutions of Kazakhstan as a guarantee of their readiness to carry out professional activities. The methodological approach involves developing methods for fostering diagnostic competencies, employing empirical techniques such as comparison, systematization, classification, and generalization of theoretical data, conducting surveys, testing, and modeling training methods for future educational psychologists. An experimental study conducted on the premises of Zhetysu University named after I. Zhansugurov has developed methodological tools to increase the readiness of future educational psychologists for the development of diagnostic competencies in professional training, namely: the implementation of readiness components with selected methods for their formation. After conducting an experimental study, promising areas for improving the methodology of readiness of future educational psychologists for the development of diagnostic competencies in Kazakhstan were established.
Quasi linear and stone geary utility functions based-internet service financing scheme with marginal costs and monitoring costs
Authors: Indrawati Indrawati , Puspita Fitri Maya , Yuliza Evi , Dwipurwani Oki , Octarina Sisca , Helmayanti Rizky
Published: July 2024

The use of computer network technology is currently increasing, especially on the internet network. To connect to the actual internet, it is a task for internet service provider (ISP). Providing advantages to ISPs, it requires a financing scheme. This study's goal is to present a modified model for internet service financing schemes, within the customer choices and consumer satisfaction levels to maintain the schemes. To achieve the best outcomes, this updated model is built through marginal costs and cost monitoring while taking into account service quality based on stone-geary utility functions and quasi-linear utility functions. This research provides a solution regarding the differences in increasing consumer interest with payment options on model modification that will be provided. Traffic Digilib in a local server in Palembang. According to this study, a usage-based financing strategy and a two-part pricing of IDR 2727.8 per kbps will yield the highest revenues.
Improved inception-V3 model for apple leaf disease classification
Authors: Sirait Dheo Ronaldo , Sutikno Sutikno , Sasongko Priyo Sidik
Published: July 2024

Apple, a nutrient-rich fruit belonging to the genus Malus, is recognized for its fiber, vitamins, and antioxidants, giving health benefits such as improved digestion and reduced cardiovascular disease risk. In Indonesia, the soil and climate create favorable conditions for apple cultivation. However, it is essential to prioritize the health of the plant. Biotic factors, such as fungal infections like apple scabs and pests, alongside abiotic factors like temperature and soil moisture, impact the health of apple plants. Computer vision, specifically convolution neural network (CNN) inception-V3, proves effective in aiding farmers in identifying these diseases. The output layer in inception-V3 is essential, generating predictions based on input data. For this reason, in this paper, we add an output layer in inception-V3 architecture to increase the accuracy of apple leaf disease classification. The added output layers are dense, dropout, and batch normalization. Adding a dense layer after flattening typically consolidates the extracted features into a more compact representation. Dropout can help prevent overfitting by randomly deactivating some units during training. Batch normalization helps normalize activations across batches, speeding up training and providing stability to the model. Test results show that the proposed method produced an accuracy of 99.27% and can increase accuracy by 1.85% compared to inception-V3. These enhancements showcase the potential of leveraging computer vision for precise disease diagnosis in apple crops.
BloFoPASS: A blockchain food palliatives tracer support system for resolving welfare distribution crisis in Nigeria
Authors: Aghware Fidelis Obukohwo , Adigwe Wilfred , Okpor Margareth Dumebi , Odiakaose Christopher Chukwufunaya , Ojugo Arnold Adimabua , Eboka Andrew Okonji , Ejeh Patrick Ogholorunwalomi , Taylor Onate Egerton , Ako Rita Erhovwo , Geteloma Victor Ochuko
Published: July 2024

With population rising to approximately 200 million Nigerians – fast-paced, urbanization has continued to advent food insecurity with maladministration, corruption, internal rife, and starvation. These, threatened the nation's unity with the lockdown of 2020; and consequently, have now become the trend. Nigeria must as a nation, re-examine her methods in the administration of palliatives (in lieu of food and relief) distribution – as the above-listed issues have become of critical need in the equitable distribution of reliefs, both from the humanitarian agency view, and the Government (State and Federal). They have noticed non-transparency, corruption, and data inadequacies, as major drawbacks in its management. Our study presents a blockchain ensemble for the administration of food palliatives distribution in Nigeria that first ensures, that all beneficiaries be registered, and the food palliatives are sensor-tagged and recorded on the blockchain. Results show the number of transactions per second and page retrieval abilities for the proposed chain were quite low with 30-TPS and 0.38seconds respectively – as compared to public blockchain. Proposed ensemble eliminates fraud that is herein rippled across the existing system, minimizes corrupt practices via sensor-based model, provides insight for stakeholders, and minimize the error in reported data on the supply chain.
Design of an efficient Transformer-XL model for enhanced pseudo code to Python code conversion
Authors: Kuche Snehal H. , Gaikwad Amit K. , Deshmukh Meghna
Published: July 2024

The landscape of programming has long been challenged by the task of transforming pseudo code into executable Python code, a process traditionally marred by its labor-intensive nature and the necessity for a deep understanding of both logical frameworks and programming languages. Existing methodologies often grapple with limitations in handling variable-length sequences and maintaining context over extended textual data. Addressing these challenges, this study introduces an innovative approach utilizing the Transformer-XL model, a significant advancement in the domain of deep learning. The Transformer-XL architecture, an evolution of the standard Transformer, adeptly processes variable-length sequences and captures extensive contextual dependencies, thereby surpassing its predecessors in handling natural language processing (NLP) and code synthesis tasks. The proposed model employs a comprehensive process involving data preprocessing, model input encoding, a self-attention mechanism, contextual encoding, language modeling, and a meticulous decoding process, followed by post-processing. The implications of this work are far-reaching, offering a substantial leap in the automation of code conversion. As the field of NLP and deep learning continues to evolve, the Transformer-XL based model is poised to become an indispensable tool in the realm of programming, setting a new benchmark for automated code synthesis.
A comprehensive analysis of dynamic PAPR reduction schemes in MIMO-OFDM systems
Authors: Dubala Ramadevi , Rao P. Trinatha
Published: July 2024

In this paper, an attempt develops three different methods, namely, Hybrid Maximal-Minimum (Max-Min) model with Decomposed Selective Mapping (D-SLM) in a UFMC, Modified Enhancement Asymmetric Arithmetic Coding Scheme (M-EAAC) and Dynamic Threshold-based Logarithmic Companding (DTLC) is carried out in Multiple-Input, Multiple-Output Orthogonal Frequency-Division Multiplexing (MIMO-OFDM) technology to enhance the PAPR reduction. These methods allow increased data rate request through threshold limit adjustment in a desired out-of-band (OOB) range, allows data transmission for the selected for the candidate sequences for maximizing the channel utility, data capacity and computational demands and varying threshold limit to analyse the nonlinear companding effect, respectively on D-UFMC-SLM, M-EAAC SCS-TI and DTLC. The extensive analysis shows that the proposed M-EAAC SCS-TI achieves a reduced CCDF PAPR, increased average spectral efficiency and redued Bit Error Rate (BER) than the other proposed DTLC and D-UFMC-SLM methods.
Slip angle control based DTC of open-end winding induction motor drive using dual randomized decoupled PWM for acoustic noise mitigation in EV application
Authors: Challa Ganesh , Reddy M. Damodar
Published: July 2024

Reduced vibration, acoustic noise, and higher DC link utilization are advantageous for industrial drives and/or electric vehicle (EV) drives. Direct torque controlled (DTC) induction motor drives fulfill the stringent demands of the EV and/or industries of the modern era. However, torque and flux ripples occur at steady state conditions, resulting in increased acoustical noise. As a result, EV and/or workplace noise has emerged as a major issue, both in terms of human health and safety. The space vector pulse width modulation (SVPWM) improves DC bus utilization. However, SVPWM is less effective in reducing acoustic noise. Many random PWM (RPWM) approaches, including random zero vector PWM (RZVPWM), random pulse position modulation (RPPM), random carrier frequency modulation (RCFM), and RCFM-RPPM are effective in reducing acoustic noise. However, due to the decreased level of randomization, reducing noise remains problematic. This research proposes a decoupled hybrid dual randomized RPWM (HDRRPWM) schemes for slip angle control based DTC of an open-end winding induction motor drive for acoustic noise mitigation in EV applications. The suggested schemes aim to demonstrate the efficacy of HDRRPWMs in dispersing the acoustic noise spectrum as compared to traditional methods.
Design an optimal robust integral signum of the error controller for electrical vehicle based on salp swarm optimization algorithm
Authors: Jassim Arkan Ali , Karam Ekhlas H. , Ali Mohammed Moanes E.
Published: July 2024

The electric vehicle (EV) has nowadays become a suitable alternative to clean and sustainable energy emissions in transportation, so researchers have become interested in modeling and controlling the electric vehicle.in this paper, an optimal robust integral signum of the error (ORISE) controller is designed to control the actuator speed of an electric vehicle. The actuator type of this vehicle is three-phase induction motor (IM). By reducing the discrepancy between the desired and actual output, the standard salp swarm algorithm (SSA) is utilized to find the optimal suggested ORISE parameter. The suggested controller tested by different desired velocity trajectory. Simulation results demonstrate that the ORISE have high performance, fast and accurate tracking for the EV speed, compare with PID controller that the output speed suffer from chattering and has higher oscillation. In particular, the SSA-based ORISE controller is superior to the proportional-integral-derivative (PID)-based SSA method in terms of no steady-state error and smallest overshoot (0.002% with ORISE while 0.05% with PID) prevention for electric vehicle (EV) speed despite the better results of settling time and rising time obtained in PID (1.532 s and 0.785 s) respectively while these values were (1.574 s and 1.915 s) respectively, in ORISE. The MATLAB (R2020a)/Simulink environment is used for all projects.
Electrical motors in the lift systems: a review
Authors: Nasir Nur Ashikin Mohd , Shukor Fairul Azhar Abdul , Abdullah Norrimah , Othman Raja Nor Firdaus Kashfi Raja
Published: July 2024

This paper discussed a few type linear motors for lift systems applications. First, a few types of lift systems are generally presented. Based on these types of lift systems, the common actuators used to operate the lifts are compared and analyzed. Basically, in traditional lift systems, rotational motors are commonly employed as actuators. However, to achieve simpler lift systems, linear motors are utilized instead of rotational motors in direct drive systems. There are three types of linear motors usually being adopted which are linear induction motors (LIM), permanent magnets linear synchronous motors (PMLSM) and switched-reluctance linear synchronous motors (SRLSM). LIM exhibits a simple structure but relatively have low performance, while the SRLSM demonstrates a similar simplicity yet delivering improved performance compared to the LIM. On the other hand, the PMLSM, despite its high-performance capabilities, suffers from notable cogging.
Improved scalar control based on slip compensation from virtual speeds in three-phase induction motor drives
Authors: Dinh Bach Hoang , Tran Cuong Dinh
Published: July 2024

The typical scalar control method adjusts the speed based on changes in power frequency while maintaining a constant V/f ratio corresponding to the nominal flux. The disadvantage of the typical scalar method is that the motor operating speed is always less than the reference speed by a slip value. This research proposes an improved scalar control method based on slip compensation for speed control in the induction motor drive. A virtual speed according to the rotor flux-based model reference adaptive system (RF-MRAS) technique is applied to the current model to estimate the proper compensation of the rotor slip. The suitability of the control method is verified through comparative simulations between the typical and proposed methods under various operating conditions by the MATLAB/Simulink software.
Effect of the placement capacitor bank on electrical power quality in the ILST fan drive system
Authors: Hermawan Asep Dadan , Negara I Made Yulistya , Nandar Cuk Supriadi Ali , Prastawa Andhika , Aryono Nur Aryanto , Damara Dio Randa , Dianti Dwie Vannie , Nugraha Heri
Published: July 2024

Power quality is a critical aspect of the effective and dependable operation of electrical systems. In today's technologically advanced world, where industries rely significantly on electricity, understanding and maintaining power quality is critical. National Research and Innovation Agency (BRIN) has an important laboratory for aerodynamic testing technology service activities in Indonesia. This facility contains a closed wind tunnel, a DC drive system, and a DC electric motor. This innovative fan drive system employs a parallel DC drive system, with the master and slave motors connected to a master DC drive system and a slave DC drive system, respectively. The usage of a new system results in poor electrical power quality, with a power factor value of less than 0.85, resulting in higher electricity costs and increased current, which has an impact on other equipment, such as a rise in temperature. To improve the quality of electrical power according to standard operating procedures and lower electricity bills, the current work evaluated the new system using field tests and literature studies on the impact of installing capacitor banks in parallel DC motor systems to boost the power factor value. This evaluation aims to improve the quality of the ILST parallel electric power system by including a capacitor bank in each motor.
A novel nonlinear controller design of three-phase grid-tied photovoltaic system
Authors: Zaghar Fatim-Zahra , Hekss Zineb , Rafi Mohamed , Echalih Salwa , Ridah Abdraouf
Published: July 2024

This paper explores the challenges associated with the control of a two-stage three-phase electrical grid connected to a photovoltaic (PV) system. The core control objectives encompass: i) Maximizing the utilization of available power from the PV panel; ii) Regulating the DC-link voltage to a predetermined reference; and iii) Ensuring power factor correction (PFC) on the grid side. To achieve these objectives, two loops nonlinear controller are developed. In the outer loop, the duty cycle of a boost converter is controlled using integral backstepping technique with the perturb & observe (P&O) algorithm, which provides reference voltages to track maximum power points, and Lyapunov theory to ensure the system’s stability. In addition, the Inner loop is designed using proportional integrator (PI) controller to ensure the unit power factor correction. An assessment of the proposed method is conducted through a comparative study with the backstepping technique, demonstrating the effectiveness of the suggested solution. The results have been validated by numerical simulation within MATLAB/Simulink power systems environment.
Common mode voltage mitigation in a three phase 21-level asymmetrical inverter fed induction motor
Authors: Narayana Mamatha , Rajappa Ramesh Honnappanahalli
Published: July 2024

For many years, numerous high-power applications have been found to cause mechanical damage to the rotating shaft. This research demonstrates that common mode voltage (CMV), which causes common mode leakage currents to flow into the motor bearings and spinning shaft, is one of the main contributing factors to mechanical shaft failures. By applying a carrier based Sinusoidal pulse width modulation (PWM) approach in conjunction with a proportional integral or PI controller technique in the feedback, the study aims to reduce leakage currents by mitigating the common mode voltages induced by the inverter output asymmetry. To accomplish the stated goal, an asymmetrical inverter architecture with a 21-level is employed. The literature emphasized the trade-off between total harmonic distortion (THD) and CMV, which is attempted to be kept well below IEEE's allowable bounds. The system stability is verified and proved by plotting the bode diagram.
Improved load frequency control in dual-area hybrid renewable power systems utilizing PID controllers optimized by the salp swarm algorithm
Authors: Sreenivasan Pushpa , Dhandapani Lakshmi , Natarajan Shanthi , Adaikalam Arul Doss , Sivakumar Amudhapriya
Published: July 2024

In this study, we utilize the salp swarm algorithm (SSA) to optimize proportional integral derivative (PID) controller gains for load frequency control (LFC) in a multi-area hybrid renewable nonlinear power system. Incorporating generation rate constraints and dead-bands into the governor model, we examine system nonlinearities. Performance evaluation employs both single- and multi-objective functions, with actual sun irradiation data validating SSA-PID controllers' efficacy in managing renewable energy source uncertainties. Comparing with alternative optimization techniques across various operational scenarios reveals the SSA-PID controller's 15% improvement in dynamic response time. The findings suggest SSA enhances LFC dynamic response in hybrid renewable power systems, with potential generalizability. These results underscore SSA's utility in addressing system complexities, offering implications for improved stability and efficiency across renewable energy integration scenarios.
Dual-axis solar tracker system utilizing Fresnel lens for web-based monitoring
Authors: Ayu Humairoh Ratu , Kurniawansyah Rifki Mohamad , Supriyanto Amir , Junaidi Junaidi
Published: July 2024

Solar energy produced using solar panels is a renewable source of electricity. Over the years, several studies have been developed in the field to increase the performance efficiency of these panels. Therefore, this study aims to develop dual-axis solar tracker with the addition of Fresnel lens to improve performance efficiency. The system implemented consisted of multisensors, servo motors, Fresnel lenses, Arduino nano, and NodeMCU ESP32. In the experiments, proposed tracking system with and without Fresnel lens were evaluated to compare the output of both setups. The results showed that the maximum power of dual-axis solar tracker with and without the device was 13.60 W and 15.78 W, respectively, at the same radiation intensity, temperature, and time. These findings showed that the proposed tracking system could increase the maximum power efficiency of solar panels by 16.03%. Furthermore, the maximum value was obtained when dual-axis solar tracker with Fresnel lens moved from E to W at 23° to the horizontal.
Design and energy performance of PV systems: a case study Kosova
Authors: Bylykbashi Blerina , Hoxha Bukurije
Published: July 2024

The energy and environmental crisis are increasing every day. Where the
focus of energy production is being driven by renewable energy sources. Solar
energy represents an inexhaustible source of energy that can be used almost
anywhere. This paper presents the analysis of the energy performance of
photovoltaic (PV) and photovoltaic thermal (PVT) panels for the climatic
conditions of Kosovo. The site analyzed is the building of the University
Clinical Center in Prishtina. The analysis included five types of photovoltaic
modules from where the highest energy performance is shown by the PVT
panels with a theoretical power produced during July 273 W while during
December 78 W. Also, with an efficiency of 59.77% during the month of
December and an efficiency of 17.08% during the month of July. While
among the other types of PV panels, polycrystalline panels have the best
performance with a theoretical power of 252 W during July and 72 W during
December. But they showed an efficiency of 48.78 during the month of
December and an efficiency of 13.94 during the month of July. The analysis
made is presented in an analytical and detailed manner for certain climatic
conditions of annual measurements.
A stacked LSTM model for day-ahead solar irradiance forecasting under tropical seasons in Java-Bali
Authors: Nugroho Muhammad Very , Prastawa Andhika , Mardiansah Fajril , Rezavidi Arya , Fudholi Ahmad , Palaloi Sudirman
Published: July 2024

Accurate short-term solar irradiance forecasting is essential for the efficient management and planning of power generation, especially for solar energy, which holds a major role in the Indonesian Government’s energy transition policy. A novel day-ahead solar irradiance forecasting is proposed using a stacked long short-term memory (LSTM) model to support the energy planning in the Java-Bali grid. The proposed model utilizes the first historical solar irradiance data of Java-Bali obtained from direct measurement to forecast the next day’s hourly irradiance. The results are compared with the methods of autoregressive integrated moving average (ARIMA) and recurrent neural network (RNN). This study revealed that the proposed model outperforms ARIMA and RNN, and regarded as a highly accurate forecast since root mean square error (RMSE), mean absolute percentage error (MAPE), and R2 are 25.56 W/m2, 7.27%, and 0.99, respectively. The stacked LSTM produces better forecasting in the dry season than in the wet season. The MAPE indicates that the LSTM's lowest accuracy for the dry season was 13.99%, which is categorized as a good forecast. The LSTM’s highest MAPE in the rainy season is 34.04%, which is categorized as a reasonable forecast. The proposed model shows its superiority and capability as a promising approach for short-term solar irradiance forecasting in Java-Bali.
Machine learning-based lithium-ion battery life prediction for electric vehicle applications
Authors: Ha Vo Thanh , Vinh Vo Quang , Truc Le Ngoc
Published: July 2024

The actual and anticipated battlefield creates a model capable of accurately estimating the lifetime of lithium-ion batteries used in electric cars. This inquiry uses a technique known as supervised machine learning, more particularly linear regression. In lithium-ion batteries, modeling temperature-dependent per-cells is the basis for capacity calculation. When a sufficient quantity of test data is accessible, a linear regression learning method will be utilized to train this model, ensuring a positive outcome in forecasting battery capacity. The conclusions drawn in the article are derived from the attributes of the initial one hundred charging and discharging cycles of the battery, enabling the determination of its remaining power. This determination facilitates the swift identification of battery manufacturing procedures and empowers consumers to detect flawed batteries when signs of performance degradation and reduced longevity are observed. MATLAB simulations have demonstrated the accuracy of the projected results, exhibiting a margin of error of approximately 9.98%. With its capacity to provide a reliable and precise means of estimating battery lifespan, the developed model holds the potential to revolutionize the electric vehicle industry.
Effect of annealing temperature on the optoelectrical synapse behaviors of A-ZnO microtube
Authors: Pan Yongman , Wang Qiang , He Anqing , Yan Yinzhou , Cao Xingzhong , Liu Peng , Jiang Yijian
Published: July 2024

Optoelectronic synapses with fast response, low power consumption, and memory function hold great potential in the future of artificial intelligence technologies. Herein, a strategy of annealing in oxygen ambient at different temperatures is presented to improve the optoelectronic synaptic behaviors of acceptor-rich ZnO (A-ZnO) microtubes. The basic synaptic functions of as-grown and annealed A-ZnO microtubes including excitatory postsynaptic current (EPSC), short-term memory (STM) to long-term memory (LTM) conversion, and paired-pulse facilitation (PPF), were successfully emulated. The results show that the annealing temperature of 600 °C yields high figures of merit compared to other annealed A-ZnO microtubes. The 4-fold and 20-fold enhancement dependent on the light pulse duration time and energy density have been achieved in the 600 °C annealed A-ZnO microtube, respectively. Furthermore, the device exhibited a PPF index of up to 238% and achieved four cycles of “learning-forgetting” process, proving its capability for optical information storage. The free exciton (FX) and donor–acceptor pair (DAP) concentrations significantly influenced the persistent photoconductivity (PPC) behavior of A-ZnO microtubes. Therefore, the LTM response can be controlled by the adjustment of numbers, powers, and interval time of the optical stimulation. This work outlines a strategy to improve the EPSC response through defect control, representing a step towards applications in the field of optoelectronic synaptic device.
C4-like Sesuvium sesuvioides (Aizoaceae) exhibits CAM in cotyledons and putative C4-like + CAM metabolism in adult leaves as revealed by transcriptome analysis
Authors: Siadjeu Christian , Kadereit Gudrun
Published: July 2024

Background

The co-occurrence of C4 and CAM photosynthesis in a single species seems to be unusual and rare. This is likely due to the difficulty in effectively co-regulating both pathways. Here, we conducted a comparative transcriptomic analysis of leaves and cotyledons of the C4-like species Sesuvium sesuvioides (Aizoaceae) using RNA-seq.

Results

When compared to cotyledons, phosphoenolpyruvate carboxylase 4 (PEPC4) and some key C4 genes were found to be up-regulated in leaves. During the day, the expression of NADP-dependent malic enzyme (NADP-ME) was significantly higher in cotyledons than in leaves. The titratable acidity confirmed higher acidity in the morning than in the previous evening indicating the induction of weak CAM in cotyledons by environmental conditions. Comparison of the leaves of S. sesuvioides (C4-like) and S. portulacastrum (C3) revealed that PEPC1 was significantly higher in S. sesuvioides, while PEPC3 and PEPC4 were up-regulated in S. portulacastrum. Finally, potential key regulatory elements involved in the C4-like and CAM pathways were identified.

Conclusions

These findings provide a new species in which C4-like and CAM co-occur and raise the question if this phenomenon is indeed so rare or just hard to detect and probably more common in succulent C4 lineages.
Evaluation of heritability partitioning approaches in livestock populations
Authors: Yuan Can , Gualdrón Duarte José Luis , Takeda Haruko , Georges Michel , Druet Tom
Published: July 2024

Background

Heritability partitioning approaches estimate the contribution of different functional classes, such as coding or regulatory variants, to the genetic variance. This information allows a better understanding of the genetic architecture of complex traits, including complex diseases, but can also help improve the accuracy of genomic selection in livestock species. However, methods have mainly been tested on human genomic data, whereas livestock populations have specific characteristics, such as high levels of relatedness, small effective population size or long-range levels of linkage disequilibrium.

Results

Here, we used data from 14,762 cows, imputed at the whole-genome sequence level for 11,537,240 variants, to simulate traits in a typical livestock population and evaluate the accuracy of two state-of-the-art heritability partitioning methods, GREML and a Bayesian mixture model. In simulations where a single functional class had increased contribution to heritability, we observed that the estimators were unbiased but had low precision. When causal variants were enriched in variants with low (< 0.05) or high (> 0.20) minor allele frequency or low (below 1st quartile) or high (above 3rd quartile) linkage disequilibrium scores, it was necessary to partition the genetic variance into multiple classes defined on the basis of allele frequencies or LD scores to obtain unbiased results. When multiple functional classes had variable contributions to heritability, estimators showed higher levels of variation and confounding between certain categories was observed. In addition, estimators from small categories were particularly imprecise. However, the estimates and their ranking were still informative about the contribution of the classes. We also demonstrated that using methods that estimate the contribution of a single category at a time, a commonly used approach, results in an overestimation. Finally, we applied the methods to phenotypes for muscular development and height and estimated that, on average, variants in open chromatin regions had a higher contribution to the genetic variance (> 45%), while variants in coding regions had the strongest individual effects (> 25-fold enrichment on average). Conversely, variants in intergenic or intronic regions showed lower levels of enrichment (0.2 and 0.6-fold on average, respectively).

Conclusions

Heritability partitioning approaches should be used cautiously in livestock populations, in particular for small categories. Two-component approaches that fit only one functional category at a time lead to biased estimators and should not be used.
A novel deep-benthic sea cucumber species of Benthodytes (Holothuroidea, Elasipodida, Psychropotidae) and its comprehensive mitochondrial genome sequencing and evolutionary analysis
Authors: He Yingying , Zhao Hancheng , Wang Yongxin , Qu Changfeng , Gao Xiangxing , Miao Jinlai
Published: July 2024

Background

The holothurians, commonly known as sea cucumbers, are marine organisms that possess significant dietary, nutritional, and medicinal value. However, the National Center for Biotechnology Information (NCBI) currently possesses only approximately 70 complete mitochondrial genome datasets of Holothurioidea, which poses limitations on conducting comprehensive research on their genetic resources and evolutionary patterns. In this study, a novel species of sea cucumber belonging to the genus Benthodytes, was discovered in the western Pacific Ocean. The genomic DNA of the novel sea cucumber was extracted, sequenced, assembled and subjected to thorough analysis.

Results

The mtDNA of Benthodytes sp. Gxx-2023 (GenBank No. OR992091) exhibits a circular structure spanning 17,386 bp, comprising of 13 protein-coding genes (PCGs), 24 non-coding RNAs (2 rRNA genes and 22 tRNA genes), along with two putative control regions measuring 882 bp and 1153 bp, respectively. It exhibits a high AT% content and negative AT-skew, which distinguishing it from the majority of sea cucumbers in terms of environmental adaptability evolution. The mitochondrial gene homology between Gxx-2023 and other sea cucumbers is significantly low, with less than 91% similarity to Benthodytes marianensis, which exhibits the highest level of homology. Additionally, its homology with other sea cucumbers is below 80%. The mitogenome of this species exhibits a unique pattern in terms of start and stop codons, featuring only two types of start codons (ATG and ATT) and three types of stop codons including the incomplete T. Notably, the abundance of AT in the Second position of the codons surpasses that of the First and Third position. The gene arrangement of PCGs exhibits a relatively conserved pattern, while there exists substantial variability in tRNA. Evolutionary analysis revealed that it formed a distinct cluster with B. marianensis and exhibited relatively distant phylogenetic relationships with other sea cucumbers.

Conclusions

These findings contribute to the taxonomic diversity of sea cucumbers in the Elasipodida order, thereby holding significant implications for the conservation of biological genetic resources, evolutionary advancements, and the exploration of novel sea cucumber resources.
Application of CuNPs and AMF alleviates arsenic stress by encompassing reduced arsenic uptake through metabolomics and ionomics alterations in Elymus sibiricus
Authors: Gatasheh Mansour K. , Shah Anis Ali , Kaleem Muhammad , Usman Sheeraz , Shaffique Shifa
Published: July 2024

Recent studies have exhibited a very promising role of copper nanoparticles (CuNPs) in mitigation of abiotic stresses in plants. Arbuscular mycorrhizae fungi (AMF) assisted plants to trigger their defense mechanism against abiotic stresses. Arsenic (As) is a non-essential and injurious heavy-metal contaminant. Current research work was designed to elucidate role of CuNPs (100, 200 and 300 mM) and a commercial inoculum of Glomus species (Clonex® Root Maximizer) either alone or in combination (CuNPs + Clonex) on physiology, growth, and stress alleviation mechanisms of E. sibiricus growing in As spiked soils (0, 50, and 100 mg Kg− 1 soil). Arsenic induced oxidative stress, enhanced biosynthesis of hydrogen peroxide, lipid peroxidation and methylglyoxal (MG) in E. sibiricus. Moreover, As-phytotoxicity reduced photosynthetic activities and growth of plants. Results showed that individual and combined treatments, CuNPs (100 mM) as well as soil inoculation of AMF significantly enhanced root growth and shoot growth by declining As content in root tissues and shoot tissues in As polluted soils. E. sibiricus plants treated with CuNPs (100 mM) and/or AMF alleviated As induced phytotoxicity through upregulating the activity of antioxidative enzymes such as catalase (CAT) and superoxide dismutase (SOD) besides the biosynthesis of non-enzymatic antioxidants including phytochelatin (PC) and glutathione (GSH). In brief, supplementation of CuNPs (100 mM) alone or in combination with AMF reduced As uptake and alleviated the As-phytotoxicity in E. sibiricus by inducing stress tolerance mechanism resulting in the improvement of the plant growth parameters.
Relationship between the severity of functional mitral regurgitation at admission and one-year outcomes in patients hospitalized for acute heart failure with mildly reduced ejection fraction
Authors: Wang Zhenhua , Zeng Yue , Qiu Hanzhao , Chen Li , Chen Jun , Li Chaosheng
Published: July 2024

Background

The epidemiological distribution of functional mitral regurgitation (FMR) in heart failure (HF) and mildly reduced ejection fraction (HFmrEF) patients and its impact on outcomes remains unclear. We attempt to investigate the prognosis of FMR in patients with HFmrEF.

Methods

The HF center registry study is a prospective, single, observational study conducted at the Second Affiliated Hospital of Shenzhen University, where 2330 patients with acute HF (AHF) were enrolled and 890 HFmrEF patients were included in the analysis. The patients were stratified into three categories based on the severity of FMR: none/mild, moderate, and moderate-to-severe/severe groups. Subsequently, a comparison of the clinical characteristics among these groups was conducted, along with an assessment of the incidence of the primary endpoint (comprising all-cause mortality and readmission for HF) during a one-year follow-up period.

Results

The one-year follow-up results indicated that the primary composite endpoint occurrence rates in the three groups were 23.5%, 32.9%, and 36.5%, respectively. The all-cause mortality rates in the three groups were 9.3%, 13.7%, and 16.4% respectively. Survival analysis demonstrated a statistically significant difference in the occurrence rates of the primary composite endpoint and all-cause mortality among the three groups (P < 0.05). Multifactor Cox regression revealed that moderate FMR and moderate-to-severe/severe FMR were independent risk factors for adverse clinical prognosis in HFmrEF patients, with hazard ratios and 95% confidence intervals of 1.382 (1.020–1.872, P = 0.037) and 1.546 (1.092–2.190, P = 0.014) respectively.

Conclusions

Moderate FMR and moderate-to-severe/severe FMR independently predict an unfavorable prognosis in patients with HFmrEF.
SurvdigitizeR: an algorithm for automated survival curve digitization
Authors: Zhang Jasper Zhongyuan , Rios Juan David , Pechlivanoglou Tilemanchos , Yang Alan , Zhang Qiyue , Deris Dimitrios , Cromwell Ian , Pechlivanoglou Petros
Published: July 2024

Background

Decision analytic models and meta-analyses often rely on survival probabilities that are digitized from published Kaplan–Meier (KM) curves. However, manually extracting these probabilities from KM curves is time-consuming, expensive, and error-prone. We developed an efficient and accurate algorithm that automates extraction of survival probabilities from KM curves.

Methods

The automated digitization algorithm processes images from a JPG or PNG format, converts them in their hue, saturation, and lightness scale and uses optical character recognition to detect axis location and labels. It also uses a k-medoids clustering algorithm to separate multiple overlapping curves on the same figure. To validate performance, we generated survival plots form random time-to-event data from a sample size of 25, 50, 150, and 250, 1000 individuals split into 1,2, or 3 treatment arms. We assumed an exponential distribution and applied random censoring. We compared automated digitization and manual digitization performed by well-trained researchers. We calculated the root mean squared error (RMSE) at 100-time points for both methods. The algorithm’s performance was also evaluated by Bland–Altman analysis for the agreement between automated and manual digitization on a real-world set of published KM curves.

Results

The automated digitizer accurately identified survival probabilities over time in the simulated KM curves. The average RMSE for automated digitization was 0.012, while manual digitization had an average RMSE of 0.014. Its performance was negatively correlated with the number of curves in a figure and the presence of censoring markers. In real-world scenarios, automated digitization and manual digitization showed very close agreement.

Conclusions

The algorithm streamlines the digitization process and requires minimal user input. It effectively digitized KM curves in simulated and real-world scenarios, demonstrating accuracy comparable to conventional manual digitization. The algorithm has been developed as an open-source R package and as a Shiny application and is available on GitHub: https://github.com/Pechli-Lab/SurvdigitizeR and https://pechlilab.shinyapps.io/SurvdigitizeR/.
Timeliness and quality of peripartum care provision during a health system strengthening initiative in rural Guinea-Bissau: a qualitative situation analysis
Authors: Damerow Sabine Margarete , Adrian Helene Vernon , Indjai Bucar , Cá Elsi José Carlos , Maaløe Nanna , Fisker Ane Bærent , Sørensen Jane Brandt
Published: July 2024

Guinea-Bissau has among the world’s highest maternal and perinatal mortality rates. To improve access to quality maternal and child health (MCH) services and thereby reduce mortality, a national health system strengthening initiative has been implemented. However, despite improved coverage of MCH services, perinatal mortality remained high. Using a systems-thinking lens, we conducted a situation analysis to explore factors shaping timeliness and quality of facility-based care during labour, childbirth, and the immediate postpartum period in rural Guinea-Bissau. We implemented in-depth interviews with eight peripartum care providers and participant observations at two health facilities (192 h) in 2021-22, and analysed interview transcripts and field notes using thematic network analysis. While providers considered health facilities as the only reasonable place of birth and promoted facility birth uptake, timeliness and quality of care were severely compromised by geographical, material and human-resource constraints. Providers especially experienced a lack of human resources and materials (e.g., essential medicines, consumables, appropriate equipment), and explained material constraints by discontinued donor supplies. In response, providers applied several adaptation strategies including prescribing materials for private purchase, omitting tests, and delegating tasks to birth companions. Consequences included financial barriers to care, compromised patient and occupational safety, delays, and diffusion of health worker responsibilities. Further, providers explained that in response to persisting access barriers, women conditioned care seeking on their perceived risk of developing birthing complications. Our findings highlight the need for continuous monitoring of factors constraining timeliness and quality of essential MCH services during the implementation of health system strengthening initiatives.
Investigating distinct clinical features and constructing a nomogram model for survival probability in adults with cerebellar high-grade gliomas
Authors: Chang Tao , Zhang Rui , Gan Jiahao , Yang Yuan , Liu Yanhui , Ju Yan , Niu Xiaodong , Mao Qing
Published: July 2024

Background

The clinical features of cerebellar high-grade gliomas (cHGGs) in adults have not been thoroughly explored. This large-scale, population-based study aimed to comprehensively outline these traits and construct a predictive model.

Methods

Patient records diagnosed with gliomas were collected from various cohorts and analyzed to compare the features of cHGGs and supratentorial HGGs (sHGGs). Cox regression analyses were employed to identify prognostic factors for overall survival and to develop a nomogram for predicting survival probabilities in patients with cHGGs. Multiple machine learning methods were applied to evaluate the efficacy of the predictive model.

Results

There were significant differences in prognosis, with SEER-cHGGs showing a median survival of 7.5 months and sHGGs 14.9 months (p < 0.001). Multivariate Cox regression analyses revealed that race, WHO grade, surgical procedures, radiotherapy, and chemotherapy were independent prognostic factors for cHGGs. Based on these factors, a nomogram was developed to predict 1-, 3-, and 5-year survival probabilities, with AUC of 0.860, 0.837, and 0.810, respectively. The model’s accuracy was validated by machine learning approaches, demonstrating consistent predictive effectiveness.

Conclusions

Adult cHGGs are distinguished by distinctive clinical features different from those of sHGGs and are associated with an inferior prognosis. Based on these risk factors affecting cHGGs prognosis, the nomogram prediction model serves as a crucial tool for clinical decision-making in patient care.