bluuebunny/crossref_metadata_2025 huggingface dataset:
Fast Prediction of Combustion Heat Release Rates for Dual-Fuel Engines Based on Neural Networks and Data Augmentation
Authors: Wei Mingxin , Shuai Xiuyun , Ma Zexin , Liu Hongyu , Wang Qingxin , Zhao Feiyang , Yu Wenbin
Published: February 2025

As emission regulations become increasingly stringent, diesel/natural gas dual-fuel engines are regarded as a promising solution and have attracted extensive research attention. However, their complex combustion processes pose significant challenges to traditional combustion modeling approaches. Data-driven modeling methods offer an effective way to capture the complexity of combustion processes, but their performance is critically constrained by the quantity and quality of the test data. To address these limitations, this study proposes a combustion prediction model framework for dual-fuel engines based on neural networks and data augmentation, aiming to achieve high-quality and fast predictions of the heat release rate curve. First, a hybrid regression data augmentation architecture based on an improved Generative Adversarial Network (GAN) is introduced to enable high-quality dataset augmentation. Subsequently, a Bayesian Neural Network (BNN) is employed to construct a Wiebe parameter prediction model for dual-fuel engines with an accelerated and optimized training model. Meanwhile, an adaptive weight allocation method is proposed based on the model’s precision performance, achieving balanced accuracy distribution across multiple output dimensions and further enhancing the model’s generalization ability. Overall, the proposed modeling approach introduces tradeoff optimizations in both data and model dimensions, enhancing the training and learning efficiency, which offers a valuable direction for data-driven prediction models with practical significance.
Role of Cervical Elastography in Predicting Progression to Active Phase in Labor Induction in Term Nulliparous Women
Authors: Hong Su-Jung , Jung Young-Mi , Hwang Jeong-Eun , Lee Ki-Su , Cho Geum-Joon , Oh Min-Jeong
Published: February 2025

Background/Objectives: Several factors, such as age, parity, body mass index, a favorable cervix, and fetal birth weight, are known to be related to the success of labor induction. With advancements in ultrasound technology, these factors have been studied to predict the success of vaginal delivery. However, there has been limited research on ultrasound measures that can effectively predict entry into the active phase of labor. Thus, we aimed to assess the use of cervical quantitative strain sonoelastography to predict entry into the active phase of labor induction. Methods: This prospective study included nulliparous term singleton pregnant women scheduled for labor induction between July 2018 and July 2022. Sonographic parameters were obtained using a transvaginal ultrasound approach with semiautomatic quantitative strain elastography software (E-Cervix; Samsung WS80A ultrasound device with a VR5-9 transducer, Samsung Medison Co., Ltd., Seoul, Republic of Korea), which provides objective measurements through the pixel-based analysis of elastographic maps. Univariate and multivariate logistic regression and area-under-the-curve analyses were used to evaluate the diagnostic performance of the variables under consideration in predicting the onset of the active phase of labor. Results: A total of 71 women were included in the study, and 29 progressed to the active phase. The cervical length, angle of progression, and mean strain from the external cervical os were significantly associated with successful entry into the active phase. The receiver operating characteristic (ROC) curve model also indicated a higher predictive value when the elastographic parameters were combined. Conclusions: Cervical elastography can be used as a sonographic index to predict progression to the active phase of labor.
Financial and Economic Determinants of Banks Financial Distress in MENA Region
Authors: Metwally Abdelmoneim Bahyeldin Mohamed , Yasser Mai M. , Ahmed Eman Adel , Ali Mohamed Ali Shabeeb
Published: February 2025

This study investigates the influences of financial performance and economic determinants (inflation rate and economic growth) on financial distress (FD) in the MENA region in the context of the contagion effect theory and Minsky’s financial instability theory. This paper examines the determinants of financial distress in the MENA region from 2002 until 2020 using pooled OLS, fixed effect, and GMM panel estimation models; then the results are used to estimate the effect over the long run. The results show that the things that cause financial distress are changing a lot between countries in the MENA region. This shows how important it is to separate the effects of economic and financial factors. The results show the significance of economic growth, ROA, ROE, inflation, and stock market profitability using fixed effects. The results changed when we used GMM, concluding that economic growth, ROA, ROE, and stock market profitability were significant, while inflation was not significant. Therefore, there is a significant and negative relationship between financial distress and economic growth in GCC-MENA as well as other MENA countries. Our results can be of importance to investors and regulators. The introduction of a more stable political environment and engagement in international economic and financial markets will decrease the negative impacts of financial distress and boost economic growth and its sustainability in the MENA region.
The Role of Copper Intake in Bone Health: A Quantitative Analysis in Postmenopausal Spanish Women
Authors: Canal-Macías María Luz , Puerto-Parejo Luis Manuel , Lavado-García Jesús María , Roncero-Martín Raúl , Pedrera-Zamorano Juan Diego , López-Espuela Fidel , Rey-Sánchez Purificación , Sánchez-Fernández Antonio , Morán José M.
Published: February 2025

(1) Background: Copper is a crucial trace element which is vital to growth and development and is especially important in bone health. Copper intake is now the focus of much broader research beyond its associations with nail growth, looking at copper’s potential in contributing to bone integrity to prevent a high risk of osteoporosis as well. (2) Methods: This study included postmenopausal women from a larger longitudinal study conducted between 2019 and 2022. Bone health was assessed using three quantitative techniques: heel QUS, DXA and pQCT. Copper intake was evaluated using a 131-item, 7-day food frequency questionnaire. Data from these assessments were used to analyze the relationship between copper intake and bone health. (3) Results: In the unadjusted multiple linear regression model, associations were found between copper intake levels and both BUA (dB/MHz) and pQCT cortical + subcortical density (mg/cm3), with copper intake acting as a negative predictor in both instances. However, these associations lost statistical significance after adjusting for participant age and weight. No further associations were identified for the other parameters assessed. (4) We conclude that our study does not reveal an association between copper intake and bone health in postmenopausal Spanish women.
Research on Intelligent Verification of Equipment Information in Engineering Drawings Based on Deep Learning
Authors: Zhang Zicheng , He Yurou
Published: February 2025

This paper focuses on the crucial task of automatic recognition and understanding of table structures in engineering drawings and document processing. Given the importance of tables in information display and the urgent need for automated processing of tables in the digitalization process, an intelligent verification method is proposed. This method integrates multiple key techniques: YOLOv10 is used for table object recognition, achieving a precision of 0.891, a recall rate of 0.899, mAP50 of 0.922, and mAP50-95 of 0.677 in table recognition, demonstrating strong target detection capabilities; the improved LORE algorithm is adopted to extract table structures, breaking through the limitations of the original algorithm by segmenting large-sized images, with a table extraction accuracy rate reaching 91.61% and significantly improving the accuracy of handling complex tables; RapidOCR is utilized to achieve text recognition and cell correspondence, solving the problem of text-cell matching; for equipment name semantic matching, a method based on BERT is introduced and calculated using a comprehensive scoring method. Meanwhile, an improved cuckoo search algorithm is proposed to optimize the adjustment factors, avoiding local optima through sine optimization and the catfish effect. Experiments show the accuracy of equipment name matching in semantic similarity calculation approaches 100%. Finally, the paper provides a concrete system practice to prove the effectiveness of the algorithm. In conclusion, through experimental comparisons, this method exhibits excellent performance in table area location, structure recognition, and semantic matching and is of great significance and practical value in advancing table data processing technology in engineering drawings.
A Framework for Embedded Non-Volatile Memory Development: Examples from Pb(ZrxTi1−x)O3 Ferroelectric Memory Development at Texas Instruments
Authors: Moise Ted , Summerfelt Scott , Rodriguez John
Published: February 2025

An overview of the steps employed to advance non-volatile Pb(ZrxTi1−x)O3-based materials from parallel capacitor array test structures to embedded 130 nm (1.5 V operation) memory product release is presented. Specific development stages include parallel capacitor array evaluation, capacitor characterization array development, memory macro creation and measurement, and initial product design and qualification. Representative data, learning goals, and critical outputs will be presented for each development phase. We note that the cost and complexity of the development effort increase dramatically as the new technology approaches high-volume manufacturing. We hope that the documentation of our experiences in this manuscript may be of assistance to those teams striving to create the next generations of non-volatile embedded memory technology.
Grid Peak Shaving and Energy Efficiency Improvement: Advances in Gravity Energy Storage Technology and Research on Its Efficient Application
Authors: Wang Shaojun , Xiao Hao , Zhao Zhaoquan , Li Dezhao , Hu Dong , Hu Qi , Shen Chen , Zhang Xingyu , Hu Jiahao , Chi Cheng , Cheng Xin , Zhang Wei , Bu Erjun , Zhao Chenxu , Wang An , Wang Lu
Published: February 2025

Global energy issues have spurred the development of energy storage technology, and gravity-based energy storage (GBES) technology has attracted much attention. This comprehensive review examines the principles, applications, and prospects of GBES technology, a promising solution for mitigating the intermittency of renewable energy sources and enhancing grid stability. GBES harnesses potential energy by elevating solid or liquid mediums, offering distinct advantages over other energy storage technologies such as pumped hydro storage and batteries. The study examines various GBES configurations, emphasizing the importance of system design, control strategies, and efficiency. This review also evaluates the economic, environmental, and social benefits of GBES, emphasizing its cost-effectiveness and potential for local economic growth. The need for policy support, technological innovation, and a robust regulatory framework is highlighted to promote the widespread adoption of GBES, which holds significant potential for enhancing grid stability and supporting the integration of renewable energy.
Efficiency-Based Modeling of Aeronautical Proton Exchange Membrane Fuel Cell Systems for Integrated Simulation Framework Applications
Authors: Aliberti Paolo , Minneci Marco , Sorrentino Marco , Cuomo Fabrizio , Musto Carmine
Published: February 2025

Proton exchange membrane fuel cell system (PEMFCS)-based battery-hybridized turboprop regional aircraft emerge as a promising solution to the urgency of reducing the environmental impact of such airplanes. The development of integrated simulation frameworks consisting of versatile and easily adaptable models and control strategies is deemed highly strategic to guarantee proper component sizing and in-flight, onboard energy management. This need is here addressed via a novel efficiency-driven PEMFCS model and a degradation-aware battery-PEMFCS unit specification-independent control algorithm. The proposed model simplifies stack voltage and current estimation while maintaining accuracy so as to support, in conjunction with the afore-introduced versatile control strategy, the development of architectures appropriate for subsequent fully integrated (i.e., at the entire aircraft design level) simulation frameworks. The model also allows assessing the balance of plant impact on the fuel cell system’s net power, as well as the heat generated by the stack and related cooling demand. Finally, the multi-stack configuration meeting the DC bus line 270 V constraint, as currently assumed by the aviation industry, is determined.
Enhancement of Bio-H2 Purification Performance in a Multi-Stage Desulfurization Process Using Mining Waste and LaNi5
Authors: Kitayama Shuto , Hamazaki Mayu , Kumon Shoichi , Sato Kimitaka , Dowaki Kiyoshi
Published: February 2025

The fuel-cell (FC) power system, utilizing biohydrogen from biomass resources, is a promising alternative to fossil fuels. However, hydrogen sulfide (H2S) in bio-syngas can severely degrade FC performance and increase environmental impact, necessitating impurity removal. This study investigates a multi-stage desulfurization process using neutralized sediment (NS) and a metal hydride (LaNi5) as H2S adsorbents. NS, a mining waste material, can potentially reduce environmental impact when repurposed as an adsorbent, with its performance influenced by pore configuration and Fe content. However, the purified gas does not fully meet FC fuel specifications. To address this, LaNi5, which selectively absorbs and releases hydrogen, was incorporated to achieve higher purification levels. In our study, H2S adsorption tests were conducted using two fixed-bed flow reactors heated to 250 °C, where a gas mixture containing 196 ppm of H2S flowed through the system. The proposed multi-stage system achieved a breakthrough time of 182.5 h with purified gas remaining under 0.1 ppm and an adsorption capacity of 16.4 g/g-sorbent. These results demonstrate the high desulfurization performance achieved using NS and LaNi₅.
A Multilayer Perceptron Feedforward Neural Network and Particle Swarm Optimization Algorithm for Optimizing Biogas Production
Authors: Abdurrakhman Arief , Sutiarso Lilik , Ainuri Makhmudun , Ushada Mirwan , Islam Md Parvez
Published: February 2025

Efficient biogas production significantly impacts greenhouse gas (GHG) emissions and carbon sequestration by reducing emissions and enhancing carbon storage. Nonetheless, the consistency and optimization of biogas production are hindered by fluctuations in key input variables, namely, pH, moisture content, organic loading rate (OLR), and temperature, which significantly impact the quality of agricultural waste biomass and biogas production. Any fluctuations in these variables can affect biogas productivity. This study aims to provide valuable optimization parameters for maximum biogas production using rice straw and cow dung as agricultural waste biomass for biogas input materials. Therefore, machine learning techniques such as multilayer perceptron feedforward neural networks with a particle swarm optimization (PSO) combination generate optimal values for each variable for maximum biogas production. This study uses three variants of the training function for neural networks, namely gradient descent with momentum and adaptive learning rate, gradient descent with momentum, and gradient descent with adaptive learning rate. The findings reveal that, under an optimum pH value of 6.0000, a humidity of 62.3176%, an OLR of 67.6823 kg.m3/day, and a temperature of 37.0482 °C, biogas production has the potential to increase to 2.91 m³/day with a high accuracy testing value of R2 = 0.90. These methods in use accurately predict the optimal parameters, with a maximum deviation of 8.48% from experimentally derived values and mean square error (MSE) of 0.0051243. This study emphasizes the benefits of using multilayer perceptron feedforward neural networks and particle swarm optimization to optimize operational parameters and accurately predict biogas production.
Torque Ripple Minimization for Switched Reluctance Motor Drives Based on Harris Hawks–Radial Basis Function Approximation
Authors: Oloo Jackson , Laszlo Szamel
Published: February 2025

Switched reluctance motor drives are becoming attractive for electric vehicle propulsion systems due to their simple and cheap construction. However, their operation is degraded by torque ripples due to the salient nature of the stator and rotor poles. There are several methods of mitigating torque ripples in switched reluctance motors (SRMs). Apart from changing the geometrical design of the motor, the less costly technique involves the development of an adaptive switching strategy. By selecting suitable turn-on and turn-off angles, torque ripples in SRMs can be significantly reduced. This work combines the benefits of Harris Hawks Optimization (HHO) and Radial Basis Functions (RBFs) to search and estimate optimal switching angles. An objective function is developed under constraints and the HHO is utilized to perform search stages for optimal switching angles that guarantee minimal torque ripples at every speed and current operating point. In this work, instead of storing the θon, θoff  values in a look-up table, the values are passed on to an RBF model to learn the nonlinear relationship between the columns of data from the HHO and hence transform them into high-dimensional outputs. The values are used to train an enhanced neural network (NN) in an adaptive switching strategy to address the nonlinear magnetic characteristics of the SRM. The proposed method is implemented on a current chopping control-based SRM 8/6, 600 V model. Percentage torque ripples are used as the key performance index of the proposed method. A fuzzy logic switching angle compensation strategy is implemented in numerical simulations to validate the performance of the HHO-RBF method.
Driving the Energy Transition in Colombia for Off-Grid Regions: Microgrids and Non-Conventional Renewable Energy Sources
Authors: Cerón Jaime Alberto , Gómez-Luna Eduardo , Vasquez Juan C.
Published: February 2025

At present, the Colombian government is faced with the challenge of guaranteeing access to energy services for all its inhabitants. However, as there are isolated populations or populations with difficult access to conventional electricity grids in the country, it is necessary to seek innovative and appropriate solutions to the conditions and needs of the so-called non-interconnected zones (NIZs), which allow the generation and consumption of energy in a local, efficient, and safe way for all users. For this reason, this research consisted of studying and proposing technological solutions that use distributed energy resources, making the most of the energy potential in each area, as a proposed solution to the problems faced by NIZs with energy shortages. As a result, a series of proposals with microgrids are obtained, taking advantage of their flexible characteristics and using NRES as energy sources, mitigating pollution and contributing to the energy transition sought by the Colombian government.
Analysis of Corporate Acceptance of Hydrogen Energy Technology Based on the Extended Technology Acceptance Model
Authors: Hou Jianchao , Wang Han
Published: February 2025

Hydrogen holds an important strategic position in the energy systems of many countries. Many studies have analyzed the acceptance of hydrogen energy technology from the public’s perspective, but few have examined it from the corporate perspective. This paper establishes a technology acceptance model and employs structural equation modeling to investigate the factors affecting the acceptance of hydrogen energy technology within enterprises. After conducting questionnaire surveys among employees of energy enterprises, electric power companies, and new energy vehicle manufacturers, the results indicate that, while most of the interviewed enterprises have positive attitudes towards hydrogen technology, their willingness to develop hydrogen business does not appear to be correspondingly positive. In addition, government trust, perceived benefit, and social influence positively impact corporate acceptability indirectly, whereas perceived risk exhibits a negative indirect effect on corporate acceptance. Finally, this paper discusses the results of the above studies and makes corresponding policy recommendations.
Harnessing Backpack Lidar Technology: A Novel Approach to Monitoring Moso Bamboo Shoot Growth
Authors: Li Chen , Li Chong , Pan Chunyu , Yan Yancun , Zhou Yufeng , Sun Jingyi , Zhou Guomo
Published: February 2025

Bamboo, characterized by its high growth speed and short maturation period, occupies 0.875% of the global forest area and significantly contributes to terrestrial carbon cycling. The state of shoot growth can essentially indicate a bamboo forests’ health and productivity. This study explored the potential of backpack laser scanning (BLS) for monitoring the growth of Moso bamboo shoots (Phyllostachys edulis), a key economic species in subtropical China. Initially, the accuracy of BLS in extracting attributes of bamboo and shoots (including diameter at breast height (DBH), height, and real-world coordinates) was validated. An optimized method was developed to address the lower precision of BLS in extracting the DBH for thinner species. Subsequently, this research analyzed the impact of spatial structure and other indicators on shoot emergence stage and growth rate using a random forest model. The results indicate that BLS can accurately extract Moso bamboo and shoot height (RMSE = 0.748 m) even in dense bamboo forests. After optimization, the error in DBH extraction significantly decreased (RMSE = 0.835 cm), with the average planar and elevation errors for Moso bamboo being 0.227 m and 0.132 m, respectively. The main indicators affecting the coordinate error of Moso bamboo were the distance to the start (DS) and the distance to the trajectory (DT). The emergence time of shoots was mainly influenced by the surrounding Moso bamboo quantity, with the leaf area index (LAI) and competition index (CI) positively related to the growth rate of shoots. The importance ranking of spatial structure for the carbon storage of shoots was similar to that of the growth rate of shoots, with both identifying LAI as the most significant indicator. This study has validated the value of BLS in monitoring the growth of shoots, providing a theoretical support for the sustainable management and conservation of bamboo forests.
Bioactivity of the Tree of Heaven Leaf Extracts Incorporated into Biopolymer Matrix Against Spongy Moth Larvae
Authors: Milanović Slobodan D. , Simović Nemanja , Dobrosavljević Jovan , Milenković Ivan Lj. , Branković Zorica , Ćirković Jovana , Radojković Aleksandar , Perać Sanja , Jovanović Jelena , Tadić Vanja , Žugić Ana , Branković Goran
Published: February 2025

The bioactivity of the Ailanthus altissima crude leaf extract (CLE) and a leaf extract incorporated into a biopolymer matrix (BPM) was tested against Lymantria dispar larvae. The crude leaf extracts and those incorporated into a chitosan–gelatin polymer matrix were examined in choice and non-choice assays at 0.01, 0.05, 0.5, and 1% concentrations for feeding deterrent activity, contact, and digestive toxicity. The CLE exhibited moderate deterrent activity at all concentrations, whereas the BPM showed a very strong deterrent effect at 0.5% and 1% and a strong effect at 0.1% and 0.01%. No significant differences in digestive or contact toxicity were observed between the CLE and BPM groups and the control groups. The BPM also influenced larval behavior after digestion, decreasing consumption and growth and increasing development time. The higher bioactivity of the CLE compared to the control group is attributed to its high content of total phenols, flavonoids, and tannins, whereas the enhanced bioactivity of the BPM is due to its incorporation into the biopolymer matrix. Given its very strong deterrent activity, and absence of contact and digestive toxicity, the BPM can be recommended as a potential environmentally friendly bioproduct for forest pest control after field evaluation.
Effects of Rumen-Degradable Starch Levels on In Vitro Rumen Fermentation and Microbial Protein Synthesis in Alfalfa Silage
Authors: Guo Wenliang , Liu Yulan , Na Meila , Zhang Yu , Na Renhua
Published: February 2025

Alfalfa silage has a high proportion of rumen-degradable protein content. Increasing dietary rumen-degradable starch (RDS) can enhance ruminal microbial protein synthesis. This study was conducted to investigate the influence of RDS levels in substrates with alfalfa silage on in vitro rumen fermentation and nitrogen (N) utilization. Rumen fluid was collected and dispensed into anaerobic fermentation bottles, each containing 1 g of substrate and 60 mL of rumen fluid–buffer mixture. The substrate was composed of 40% alfalfa silage and five different RDS levels: 14.85% RDS, 16.40% RDS, 18.67% RDS, 20.21% RDS, and 21.62% RDS. For each RDS level, three replicates were prepared. Each substrate was then incubated at 39 °C for 3, 6, 12, and 24 h. After incubation, the following parameters were measured: gas production, pH, α-amylase activity, ammonia nitrogen (NH3-N), bacterial protein (BCP), and short-chain fatty acid (SCFA) concentrations were measured. Total gas production increased linearly with increasing RDS levels from 3 to 10 h of incubation (p < 0.01), with no difference observed among five levels after 11 h. At 3 h of incubation, pH decreased linearly with increasing RDS levels (p < 0.05). BCP concentrations and α-amylase activity increased linearly or quadratically with increasing RDS levels (p < 0.01), while the R4 group had the highest concentrations of BCP and the R5 group had the highest activity of α-amylase (p < 0.01). At 6 h of incubation, the NH3-N concentration decreased linearly or quadratically with increasing RDS levels (p < 0.05), and the α-amylase activity, acetate, propionate, and total SCFA concentrations increased linearly (p < 0.01). The R4 group had the highest activity of α-amylase (p < 0.01), and the R5 group had the highest concentrations of acetate (p < 0.05) and propionate (p < 0.01). At 12 h of incubation, BCP, NH3-N, and propionate concentrations, as well as α-amylase activity, increased linearly or quadratically with increasing RDS levels (p < 0.05). At 24 h of incubation, the α-amylase activity increased linearly with increasing RDS levels (p < 0.05). The highest multiple-factor associative effects index was observed in the 20.21% RDS substrate, indicating that an RDS level of 20.21% in the alfalfa silage substrate resulted in a desirable rumen N utilization.
Association of Escherichia coli O157:H7 Density Change with Hydrogen Peroxide but Not Carbohydrate Concentration in the Leaf Content of Different Lettuce Types and Spinach
Authors: Brandl Maria T. , Hua Sui S. T. , Sarreal Siov B. L.
Published: February 2025

Leafy greens injuries occur from farm to table, causing leakage of cellular contents that promote the multiplication of foodborne pathogens and impose oxidative stress. Fresh beverages made from blended uncooked fruit and vegetables have become a popular food. The effect of cellular contents of different leafy greens on the multiplication of the important pathogen Escherichia coli O157:H7 (EcO157) under temperature abuse was investigated. Leafy greens consisted of spinach and different lettuce types (romaine, iceberg, butterhead, green leaf, and red leaf). Fructose, glucose, and sucrose concentrations in the leaves were quantified by HPLC. H2O2 concentration was measured via a peroxidase-based assay. Young leaves of iceberg, romaine, and green leaf lettuce held significantly greater total amounts of the three carbohydrates than middle-aged leaves. Except for iceberg and red leaf lettuce, all middle-aged leaves contained greater H2O2 than young leaves. EcO157 density change in leaf contents over 5 h incubation related neither to individual nor total carbohydrate concentration but was negatively associated with H2O2 concentration (regression analysis; p < 0.05). Given the common use of antioxidants to maintain the organoleptic aspects of homogenized produce beverages and certain fresh-cut produce, the antimicrobial effect of reactive oxygen species may be important to preserve in ensuring their microbial safety.
Dynamics Analysis of Nonlinear Differential Equation Systems Applied to Low-Grade Gliomas and Their Treatment
Authors: Carmona-Moreno Felipe J. , Gallegos Armando , Barba-Franco José J. , Urenda-Cázares Ernesto , Jiménez-Guerrero Enrique V. , Macías-Díaz Jorge E.
Published: February 2025

Low-grade gliomas are a group of brain tumors that mostly affect people in early adulthood. A glioma is a tumor that originates in glial cells. They are classified into four levels according to their level of proliferation, with grades 1 and 2 called low-grade gliomas. In this research, we conduct an analysis focused on a mathematical model that emulates the behavior of low-grade gliomas with chemotherapy treatment based on a system of nonlinear differential equations. An analysis of the model is carried out in the absence of treatment, resulting in a predictive solution for the behavior of glioma if it left untreated for some reason. In turn, a stability analysis is carried out on the system of equations to find the critical points for treating glioma. In addition, the numerical results of the model are obtained, presenting the state variables at each instant. Finally, some simulations are presented, varying the moments of treatment initiation and the applied doses of Temozolomide.
On a Certain Class of GA-Convex Functions and Their Milne-Type Hadamard Fractional-Integral Inequalities
Authors: Moumen Abdelkader , Debbar Rabah , Meftah Badreddine , Zennir Khaled , Saber Hicham , Alraqad Tariq , Alshawarbeh Etaf
Published: February 2025

In this article, we prove a new Milne-type inequality involving Hadamard fractional integrals for functions with GA-convex first derivatives. The limits of the error estimates involve incomplete gamma and confluent hypergeometric functions. The results of this study open the door to further investigation of this subject, as well as extensions to other forms of generalized convexity, weighted formulas, and higher dimensions.
Nucleotide Excision Repair: Insights into Canonical and Emerging Functions of the Transcription/DNA Repair Factor TFIIH
Authors: Zachayus Amélie , Loup-Forest Jules , Cura Vincent , Poterszman Arnaud
Published: February 2025

Nucleotide excision repair (NER) is a universal cut-and-paste DNA repair mechanism that corrects bulky DNA lesions such as those caused by UV radiation, environmental mutagens, and some chemotherapy drugs. In this review, we focus on the human transcription/DNA repair factor TFIIH, a key player of the NER pathway in eukaryotes. This 10-subunit multiprotein complex notably verifies the presence of a lesion and opens the DNA around the damage via its XPB and XPD subunits, two proteins identified in patients suffering from Xeroderma Pigmentosum syndrome. Isolated as a class II gene transcription factor in the late 1980s, TFIIH is a prototypic molecular machine that plays an essential role in both DNA repair and transcription initiation and harbors a DNA helicase, a DNA translocase, and kinase activity. More recently, TFIIH subunits have been identified as participating in other cellular processes, including chromosome segregation during mitosis, maintenance of mitochondrial DNA integrity, and telomere replication.
Developmental Expression Patterns of miRNA in Mythimna separata Walker (Lepidotera: Noctuidae)
Authors: Liu Yuhan , Tian Huiman , Ren Shaoqiu , Chen Xiulin , Luo Kun , Li Guangwei , Li Boliao
Published: February 2025

Background/Objectives: miRNAs are a family of single-stranded non-coding RNAs that regulate gene expression by targeting messenger RNAs (mRNAs) for suppression, with an average length of 22 nt. The oriental armyworm, Mythimna separata Walker, is a pest insect with long-distance migratory capability, which causes severe loss of grains and pastures in Eastern Asia, Southeastern Asia, and Oceania. This study aims to elucidate the post-transcriptional regulatory mechanisms of miRNAs in the development of this pest. Methods: We carried out small RNA sequencing on samples from eggs, third instar larvae, pre-pupae, pupae, and adults. Results: A total of 400 miRNAs were identified, among which 40 were known and 360 were novel miRNAs. Dynamic trend analysis of miRNAs revealed that 199 miRNAs were highly expressed in eggs (profile 12), while 173 miRNAs were highly expressed in both eggs and pupae (profile 13). The results of differential expression analysis of miRNAs (DEmiR) revealed that 75 miRNAs were significantly more abundant in eggs compared to other developmental stages. Furthermore, more up-regulated miRNAs were observed than down-regulated miRNAs in adults relative to 3rd instar larvae, pre-pupae, and pupae. The core genes for miRNA biosynthesis—Pasha, Dicer1, and Ago1—were highly expressed in eggs but poorly expressed in 3rd instar larvae. KEGG enrichment analyses indicated that several genes in the pentose and glucuronate interconversion pathway, as well as the fructose and mannose metabolism pathway, were regulated by DEmiRs. Conclusions: DEmiRNAs targeted most genes of M. separata, resulting in a complex miRNA–mRNA regulation mode.
Earthquake Risk Severity and Urgent Need for Disaster Management in Afghanistan
Authors: Akhundzadah Noor Ahmad
Published: February 2025

Afghanistan is located on the Eurasian tectonic plate’s edge, a highly seismically active region. It is bordered by the northern boundary of the Indian plate and influenced by the collisional Arabian plate to the south. The Hindu Kush and Pamir Mountains in Afghanistan are part of the western extension of the Himalayan orogeny and have been uplifted and sheared by the convergence of the Indian and Eurasian plates. These tectonic activities have generated numerous active deep faults across the Hindu Kush–Himalayan region, many of which intersect Afghanistan, resulting in frequent high-magnitude earthquakes. This tectonic interaction produces ground shaking of varying intensity, from high to moderate and low, with the epicenters often located in the northeast and extending southwest across the country. This study maps Afghanistan’s tectonic structures, identifying the most active geological faults and regions with heightened seismicity. Historical earthquake data were reviewed, and recent destructive events were incorporated into the national earthquake dataset to improve disaster management strategies. Additionally, the study addresses earthquake hazards related to building and infrastructure design, offering potential solutions and directions to mitigate risks to life and property.
Geoinformatics and Machine Learning for Shoreline Change Monitoring: A 35-Year Analysis of Coastal Erosion in the Upper Gulf of Thailand
Authors: Chawalit Chakrit , Boonpook Wuttichai , Sitthi Asamaporn , Torsri Kritanai , Kamthonkiat Daroonwan , Tan Yumin , Suwansaard Apised , Nardkulpat Attawut
Published: February 2025

Coastal erosion is a critical environmental challenge in the Upper Gulf of Thailand, driven by both natural processes and human activities. This study analyzes 35 years (1988–2023) of shoreline changes using geoinformatics, machine learning algorithms (Random Forest, Support Vector Machine, Maximum Likelihood, Minimum Distance), and the Digital Shoreline Analysis System (DSAS). The results show that the Random Forest algorithm, utilizing spectral bands and indices (NDVI, NDWI, MNDWI, SAVI), achieved the highest classification accuracy (98.17%) and a Kappa coefficient of 0.9432, enabling reliable delineation of land and water boundaries. The extracted annual shorelines were validated with high accuracy, yielding RMSE values of 13.59 m (2018) and 8.90 m (2023). The DSAS analysis identified significant spatial and temporal variations in shoreline erosion and accretion. Between 1988 and 2006, the most intense erosion occurred in regions 4 and 5, influenced by sea-level rise, strong monsoonal currents, and human activities. However, from 2006 to 2018, erosion rates declined significantly, attributed to coastal protection structures and mangrove restoration. The period 2018–2023 exhibited a combination of erosion and accretion, reflecting dynamic sediment transport processes and the impact of coastal management measures. Over time, erosion rates declined due to the implementation of protective structures (e.g., bamboo fences, rock revetments) and the natural expansion of mangrove forests. However, localized erosion remains persistent in low-lying, vulnerable areas, exacerbated by tidal forces, rising sea levels, and seasonal monsoons. Anthropogenic activities, including urban development, mangrove deforestation, and aquaculture expansion, continue to destabilize shorelines. The findings underscore the importance of sustainable coastal management strategies, such as mangrove restoration, soft engineering coastal protection, and integrated land-use planning. This study demonstrates the effectiveness of combining machine learning and geoinformatics for shoreline monitoring and provides valuable insights for coastal erosion mitigation and enhancing coastal resilience in the Upper Gulf of Thailand.
Emerging Role of Hypoxia-Inducible Factors (HIFs) in Modulating Autophagy: Perspectives on Cancer Therapy
Authors: Jalouli Maroua
Published: February 2025

Hypoxia-inducible factors (HIFs) are master regulators of cellular responses to low oxygen levels and modulate autophagy, a conserved process essential for maintaining homeostasis. Under hypoxic conditions, HIFs regulate the expression of autophagy-related genes and influence autophagic flux and cellular stress responses. Dysregulated hypoxia-induced autophagy promotes cancer cell survival, metabolism, and metastasis, thereby contributing to treatment resistance. Targeting HIF-mediated pathways or modulating autophagic processes offers the potential to improve traditional cancer therapies and overcome drug resistance. Pharmacological inhibitors of HIFs or autophagy, either alone or in combination with other treatments, may disrupt the pro-survival mechanisms within the hypoxic tumor microenvironment. Further research is needed to elucidate the intricate interplay between HIF signaling and the autophagy machinery in cancer cells. Understanding these processes could pave the way for novel therapeutic strategies to enhance treatment outcomes and combat drug resistance. This review highlights the complex relationship between HIFs and autophagy in cancer development and therapy, offering insights into how targeting these pathways may improve patient outcomes.
Predicting the Beneficial Effects of Cognitive Stimulation and Transcranial Direct Current Stimulation in Amnestic Mild Cognitive Impairment with Clinical, Inflammation, and Human Microglia Exposed to Serum as Potential Markers: A Double-Blind Placebo-Controlled Randomized Clinical Trial
Authors: Alcalá-Lozano Ruth , Carmona-Hernández Rocio , Ocampo-Romero Ana Gabriela , Sosa-Millán Adriana Leticia , Morelos-Santana Erik Daniel , Abarca Diana Zapata , Castro-de-Aquino Dana Vianey , Cabrera-Muñoz Edith Araceli , Ramírez-Rodríguez Gerardo Bernabé , Sosa Ortiz Ana Luisa , Garza-Villarreal Eduardo A. , Saracco-Alvarez Ricardo , González Olvera Jorge Julio
Published: February 2025

In amnestic mild cognitive impairment (aMCI), neuroinflammation evolves during disease progression, affecting microglial function and potentially accelerating the pathological process. Currently, no effective treatment exists, leading to explorations of various symptomatic approaches, though few target the underlying physiological mechanisms. Modulating inflammatory processes may be critical in slowing disease progression. Cognitive stimulation (CS) and transcranial direct current stimulation (tDCS) applied to the left dorsolateral prefrontal cortex (l-DLPFC) show promise, but the results are heterogeneous. Thus, a randomized, double-blind, placebo-controlled clinical trial is currently underway. The first-stage results were examined after three weeks of intervention in two groups: active tDCS combined with CS and sham tDCS combined with CS. Twenty-two participants underwent two assessments: T0 (baseline) and T1 (after 15 sessions of tDCS, active or sham, and 9 sessions of CS). The results demonstrated that CS improved cognition, increased brain-derived neurotrophic factor (BDNF) levels, and reduced peripheral proinflammatory cytokine levels (interleukin IL-6 and chemokine CX3CL1) in serum. This decrease in IL-6 may promote microglial proliferation and survival as a modulatory effect response, while the increase in BDNF might suggest a regulatory mechanism in microglia–neuron interaction responses. However, tDCS did not enhance the cognitive or modulatory effects of CS, suggesting that longer interventions might be required to achieve substantial benefits.
Density Functional Theory Study of Methylene Blue Demethylation as a Key Step in Degradation Mediated by Reactive Oxygen Species
Authors: González Silvia , Jaramillo-Fierro Ximena
Published: February 2025

Methylene blue (MB), a widely used organic dye, poses significant environmental challenges due to its stability and persistence in aquatic ecosystems. This study employs density functional theory (DFT) to investigate the demethylation mechanisms of MB mediated by reactive oxygen species (ROS), a critical initial step in its photocatalytic degradation. Computational analyses reveal that demethylation is energetically favorable, particularly when mediated by hydroxyl radicals (•OH) and hydroxyl ions (OH−) with reaction energies of −154 kcal/mol and −214 kcal/mol, respectively. These pathways lead to the formation of key intermediates, such as Azure B, methanol (CH3OH), and formaldehyde (CH2O), which align with experimentally detected degradation byproducts. The study further demonstrates that the dissociation of hydrogen peroxide species (H2O2, H2O2−, H2O2+) plays a fundamental role in generating the ROS required for MB degradation. Potential energy surface analyses confirm that these ROS-driven processes are thermodynamically and kinetically viable. The findings provide a theoretical framework that bridges existing knowledge gaps in MB degradation, reinforcing the role of ROS in advanced photocatalytic systems and contributing to the optimization of wastewater treatment strategies. This work underscores the importance of integrating computational and experimental approaches to develop more effective strategies for the remediation of recalcitrant pollutants in wastewater.
The Potential Impact of Edible Fruit Extracts on Bacterial Nucleases in Preliminary Research—In Silico and In Vitro Insight
Authors: Szeleszczuk Łukasz , Brożyna Malwina , Dudek Bartłomiej , Czarnecki Marcin , Junka Adam , Czerwińska Monika E.
Published: February 2025

The extracts from fruits of Chaenomeles japonica (Thunb.) Lindl. ex Spach (CJE), Cornus mas L. (CME), and Hippophaё rhamnoides L. (HRE) are known inhibitors of a variety of eukaryotic hydrolases, engaged in the digestion of fats and polysaccharides. However, there are no data on their potential interaction with the bacterial hydrolases participating in the replication of microbial nucleic acids. This analysis predicted the interaction of the most abundant constituents of HRE, CJE, and CME with the bacterial nucleases. The analysis covered the molecular docking of isorhamnetin glycosides, procyanidins C1 and B2, epicatechin, loganic acid, and cornuside with bacterial enzymes (Escherichia coli endonuclease 1, colicin E9, and ribonuclease H; or Staphylococcus aureus thermonuclease and nuclease SbcCD). The suggested complexes have been subjected to molecular mechanics with generalized Born and surface area solvation (MM/GBSA) calculations. The second aim was the in vitro evaluation of the influence of the CJE, HRE, and CME on the metabolic activity of bacterial biofilm of selected microbial strains, as well as fibroblasts (L929) and adenocarcinoma intestinal cells (Caco-2) toxicity. Among all extracts, CME showed the most relevant effect on the survival of planktonic cells and biofilm of E. coli and Pseudomonas aeruginosa. As a result of in silico studies, most virtual hits were predicted to inhibit the proteins under investigation, except for procyanidin C1. Further research on the direct interaction of phytochemicals and selected enzymes in vitro is required and challenged.
Metformin’s Effects on Cognitive Function from a Biovariance Perspective: A Narrative Review
Authors: Chele Dimitrie , Sirbu Carmen-Adella , Mitrica Marian , Toma Mihai , Vasiliu Octavian , Sirbu Anca-Maria , Authier Francois Jerome , Mischianu Dan , Munteanu Alice Elena
Published: February 2025

This study examines the effects of metformin on brain functions focusing on the variability of the results reported in the literature. While some studies suggest that metformin may have neuroprotective effects in diabetic patients, others report an insignificant impact of metformin on cognitive function, or even a negative effect. We propose that this inconsistency may be due to intrinsic cellular-level variability among individuals, which we term “biovariance”. Biovariance persists even in demographically homogeneous samples due to complex and stochastic biological processes. Additionally, the complex metabolic actions of metformin, including its influence on neuroenergetics and neuronal survival, may produce different effects depending on individual metabolic characteristics.
Early Detection of Skin Diseases Across Diverse Skin Tones Using Hybrid Machine Learning and Deep Learning Models
Authors: Aquil Akasha , Saeed Faisal , Baowidan Souad , Ali Abdullah Marish , Elmitwally Nouh Sabri
Published: February 2025

Skin diseases in melanin-rich skin often present diagnostic challenges due to the unique characteristics of darker skin tones, which can lead to misdiagnosis or delayed treatment. This disparity impacts millions within diverse communities, highlighting the need for accurate, AI-based diagnostic tools. In this paper, we investigated the performance of three machine learning methods -Support Vector Machines (SVMs), Random Forest (RF), and Decision Trees (DTs)-combined with state-of-the-art (SOTA) deep learning models, EfficientNet, MobileNetV2, and DenseNet121, for predicting skin conditions using dermoscopic images from the HAM10000 dataset. The features were extracted using the deep learning models, with the labels encoded numerically. To address the data imbalance, SMOTE and resampling techniques were applied. Additionally, Principal Component Analysis (PCA) was used for feature reduction, and fine-tuning was performed to optimize the models. The results demonstrated that RF with DenseNet121 achieved a superior accuracy of 98.32%, followed by SVM with MobileNetV2 at 98.08%, and Decision Tree with MobileNetV2 at 85.39%. The proposed methods overcome the SVM with the SOTA EfficientNet model, validating the robustness of the proposed approaches. Evaluation metrics such as accuracy, precision, recall, and F1-score were used to benchmark performance, showcasing the potential of these methods in advancing skin disease diagnostics for diverse populations.