bluuebunny/crossref_metadata_2025 huggingface dataset:
THE EFFECT OF IMPLEMENTATION OF JIGSAW COOPERATIVE LEARNING MODEL ON THE STUDENT MATHEMATICS LEARNING OUTCOMES SD NEGERI 133 BONTOTIRO
Authors: Kasim Eman Wahyudi
Published: June 2024

The purpose of this study was to determine the effect of the implementation of the jigsaw cooperative learning model on the mathematics learning outcomes of elementary school students in SD Negeri 133 Bontotiro. This study uses an experimental approach with an experimental design of Pretest-Posttest Noneequivalent Group with data collection techniques through learning outcomes tests and teacher and student observation sheets. The data analysis technique in this research is descriptive analysis and inferential analysis which consists of normality test, homogeneity test and hypothesis testing. Based on the results of the research and discussion, it can be said that the implementation of the Jigsaw cooperative learning model on students at SDN 133 Bontotiro District is in the high category. Students' mathematics learning outcomes at SDN 133 Bontotiro District before the implementation of the Jigsaw Cooperative learning model obtained an average score of 34 and the learning outcomes after the implementation of the Jigsaw Cooperative learning model obtained an average score of 70 from the ideal score of 100. There is a positive influence from the implementation of the model Jigsaw cooperative learning to students' mathematics learning outcomes at SD 133 Bontotiro. The results of this study also show that the implementation of the Jigsaw cooperative learning model can be applied to improve student learning outcomes, especially in mathematics in elementary schools.                                                                           Keywords: Jigsaw Type Cooperative Learning, Student Mathematics Learning Outcomes
The activity of antioxidant enzymes and the content of uncoupling protein-1 in the brown adipose tissue of hypothyroid rats: comparison with effects of iopanoic acid
Authors: Petrović N , Cvijić G , Davidović V
Published: June 2024

The activity of antioxidant enzymes, copper-zinc superoxide dismutase (CuZnSOD), manganese superoxide dismutase (MnSOD) and catalase (CAT), as well as that of the mitochondrial FAD-dependent a-glycerophosphate dehydrogenase (a-GPD) in the rat interscapular brown adipose tissue (IBAT) were studied after the treatment with methimazole (MMI) for three weeks or with iopanoic acid (IOP) for five days. Besides, the mitochondrial concentration of uncoupling protein-1 (UCP-1) and the activity of catecholamine degrading enzyme monoamine oxidase (MAO) in the IBAT as well as the activity of the catecholamine synthesizing enzyme, dopamine b-hydroxylase (DBH) in rat serum were examined. Judging by the significantly enhanced level of serum DBH, which is an index of sympathetic activity, and that of IBAT MAO, the increase in MnSOD and CAT activities in the IBAT of hypothyroid (MMI-treated) rats seems to be due to elevated activity of sympathetic nervous system (SNS). However, CuZnSOD activity is not affected by SNS. On the contrary, IOP, which is a potent inhibitor of T4 deiodination into T3 producing "local" hypothyroidism, did not change either SNS activity or activities of IBAT antioxidant enzyme. However, both treatments significantly decreased IBAT UCP-1 content and a-GPD activity suggesting that the optimal T3 concentration in the IBAT is necessary for maintaining basal levels of these key mitochondrial parameters.
Sufis and Shari'a
Authors: Dajani Samer
Published: June 2024

In the first three centuries of Islam, many scholars believed that juristic differences were rooted in the sharia’s inherent flexibility. As this pluralistic attitude began to disappear, a number of mystics and Sufis defended and developed this idea through the centuries. They aimed to preserve the leniency and simplicity of the sharia against the complications and restrictions created by many jurists. This book focuses on those major mystics whose contributions to jurisprudence were strongly shaped by their mystical thought and their corresponding insistence on God’s vast mercy. Particular attention is given to four major figures: Ibn ʿArabī, al-Ḥakīm al-Tirmidhī, al-Shaʿrānī and Aḥmad ibn Idrīs. It gives a detailed analysis of their legal thought, revealing that they belonged to the same tradition and developed each other’s ideas, and also highlights their influence on other important Sufis all the way up to the 19th century. This is the first study to give a full picture of the role that Sufi thought played in the revivalist and reformist Islamic movements of the 18th, 19th and even 20th centuries.
Film Regulation in a Cultural Context
Authors: Sacco Daniel
Published: June 2024

This book examines film classification as a practice of regulative social labelling that relies heavily upon culturally constructed boundaries between “types” of films in distinctive national contexts. The analysis draws parallels and distinctions between governmental policies in these contexts, as well as between various social control mechanisms at work within a wide-reaching network of institutions beyond censorial bodies themselves, including news media, film festivals, and advocacy groups. The latter half of the study illustrates the means by (and ends to) which the regulation of film content persists in the “post-censorship” media landscape of Britain, Canada, Australia, and (where this model has most matured) the United States. While the case studies examined each involve distinct problematics, what links them conceptually is attention to how notions of, and related to, film classification, categorization, or labelling manifest in regulatory, artistic, and commercial market contexts: ranging from ratings institutions to journalistic criticism, film distribution, and advertising practices. The study also draws comparison between now obsolete formal censorship practices and machinations of the US “ratings” model of classification that Great Britain, Canada, and Australia moved swiftly toward during the late 1990s and early 2000s.
The review of concept image and concept definition: A hermeneutic phenomenological study on the derivative concepts
Authors: Prihandika Aditya , Perbowo Krisna Satrio
Published: June 2024

Calculus classes often focus on studying derivatives, a fundamental topic in mathematics. Upon finishing their studies, potential mathematics teachers will educate students about advanced concepts such as derivatives in the classroom. Therefore, comprehending derivative concepts is essential for teaching children effectively. This study aims to determine how potential mathematics teachers view themselves concerning derivative concepts based on their concept image and concept definition. The study utilized a hermeneutic phenomenological approach together with qualitative approaches in its research strategy. The research data was obtained through interviews and clinical tests from six participants from one of the universities in Kuningan Regency, Indonesia. The research findings indicate that participants' concept image of derivative concepts is limited to symbolic representations. Most participants did not view derivative concepts as providing a deeper understanding but rather as a technique to solve procedural problems. The results indicate that utilizing a range of representations in the learning process can improve the development of a more thorough conceptual understanding, leading to better comprehension of derivative concepts.
Online didactical design for subtraction fractions: A didactical design research through lesson study activities
Authors: Kuswara R. Didi , Almazroei Essa Eqal , Isnawan Muhamad Galang
Published: June 2024

When the COVID-19 pandemic struck, students encountered several obstacles during their learning, including the inability to implement optimal learning strategies. Therefore, this research aimed to develop an online didactic design that could potentially reduce barriers to student learning, particularly in fraction subtraction operations. The research employed a didactical design approach. At each step of the research design, the researcher used lesson study activities to produce a good online didactic design. Participants in this research were 56 students in grades VII and VIII, aged 13 to 18 years. The researcher was the main instrument, with several additional instruments, such as online didactic design, fraction tests, interview guides, and documentation studies. We used thematic analysis to analyze data on learning barriers, and qualitative analysis to analyze implementation data. The research results revealed that students indicated that they had experienced learning obstacles with ontogenic and epistemological obstacles during the COVID-19 pandemic. Upon compiling and implementing the online didactic design, we found no evidence of learning obstacles among the students. Although students encountered difficulties when solving fraction subtraction problems, this was not due to learning factors, but rather to internal factors such as a lack of study time.
Automated Recommendation of Aggregate Visualizations for Crowdfunding Data
Authors: Sharaf Mohamed A. , Helal Heba , Zaki Nazar , Alketbi Wadha , Alkaabi Latifa , Alshamsi Sara , Alhefeiti Fatmah
Published: June 2024

Analyzing crowdfunding data has been the focus of many research efforts, where analysts typically explore this data to identify the main factors and characteristics of the lending process as well as to discover unique patterns and anomalies in loan distributions. However, the manual exploration and visualization of such data is clearly an ad hoc, time-consuming, and labor-intensive process. Hence, in this work, we propose LoanVis, which is an automated solution for discovering and recommending those valuable and insightful visualizations. LoanVis is a data-driven system that utilizes objective metrics to quantify the “interestingness” of a visualization and employs such metrics in the recommendation process. We demonstrate the effectiveness of LoanVis in analyzing and exploring different aspects of the Kiva crowdfunding dataset.
New Multi-View Feature Learning Method for Accurate Antifungal Peptide Detection
Authors: Ferdous Sayeda Muntaha , Mugdha Shafayat Bin Shabbir , Dehzangi Iman
Published: June 2024

Antimicrobial resistance, particularly the emergence of resistant strains in fungal pathogens, has become a pressing global health concern. Antifungal peptides (AFPs) have shown great potential as a promising alternative therapeutic strategy due to their inherent antimicrobial properties and potential application in combating fungal infections. However, the identification of antifungal peptides using experimental approaches is time-consuming and costly. Hence, there is a demand to propose fast and accurate computational approaches to identifying AFPs. This paper introduces a novel multi-view feature learning (MVFL) model, called AFP-MVFL, for accurate AFP identification, utilizing multi-view feature learning. By integrating the sequential and physicochemical properties of amino acids and employing a multi-view approach, the AFP-MVFL model significantly enhances prediction accuracy. It achieves 97.9%, 98.4%, 0.98, and 0.96 in terms of accuracy, precision, F1 score, and Matthews correlation coefficient (MCC), respectively, outperforming previous studies found in the literature.
A Non-Gradient and Non-Iterative Method for Mapping 3D Mesh Objects Based on a Summation of Dependent Random Values
Authors: Volkau Ihar , Krasovskii Sergei , Mujeeb Abdul , Balinsky Helen
Published: June 2024

The manuscript presents a novel non-gradient and non-iterative method for mapping two 3D objects by matching extrema. This innovative approach utilizes the amplification of extrema through the summation of dependent random values, accompanied by a comprehensive explanation of the statistical background. The method further incorporates structural patterns based on spherical harmonic functions to calculate the rotation matrix, enabling the juxtaposition of the objects. Without utilizing gradients and iterations to improve the solution step by step, the proposed method generates a limited number of candidates, and the mapping (if it exists) is necessarily among the candidates. For instance, this method holds potential for object analysis and identification in additive manufacturing for 3D printing and protein matching.
Simulation of Calibrated Complex Synthetic Population Data with XGBoost
Authors: Gussenbauer Johannes , Templ Matthias , Fritzmann Siro , Kowarik Alexander
Published: June 2024

Syntheticdata generation methods are used to transform the original data into privacy-compliant synthetic copies (twin data). With our proposed approach, synthetic data can be simulated in the same size as the input data or in any size, and in the case of finite populations, even the entire population can be simulated. The proposed XGBoost-based method is compared with known model-based approaches to generate synthetic data using a complex survey data set. The XGBoost method shows strong performance, especially with synthetic categorical variables, and outperforms other tested methods. Furthermore, the structure and relationship between variables are well preserved. The tuning of the parameters is performed automatically by a modified k-fold cross-validation. If exact population margins are known, e.g., cross-tabulated population counts on age class, gender and region, the synthetic data must be calibrated to those known population margins. For this purpose, we have implemented a simulated annealing algorithm that is able to use multiple population margins simultaneously to post-calibrate a synthetic population. The algorithm is, thus, able to calibrate simulated population data containing cluster and individual information, e.g., about persons in households, at both person and household level. Furthermore, the algorithm is efficiently implemented so that the adjustment of populations with many millions or more persons is possible.
Effects of Catch Crops Cultivated for Green Manure on Soil C and N Content and Associated Enzyme Activities
Authors: Piotrowska-Długosz Anna , Wilczewski Edward
Published: June 2024

The influence of catch crop (field pea) management and the time of its application [plots with autumn (A.I.) or spring (S.I.) biomass incorporation vs. treatments without catch crop (C) use] on the activity of five soil enzymes associated with C- (CMC-cellulase—CEL, β-glucosidase—βG, invertase—INV) and N-cycling (urease—UR, nitrate reductase—NR), the content of mineral N, and the content of microbial biomass C and N (MBC, MBN) were evaluated in a 3-year experiment (2009–2011). Field pea was sown in the first half of August and the biomass was incorporated into the soil in the second half of October in 2008, 2009, and 2010 or left as a mulch during the winter and mixed with the soil in spring. The enzymatic and microbial properties were determined in soil samples collected from spring barley experimental plots four times a year (2009, 2010, and 2011): in March (before sowing of barley), in May (during the tillering phase), in June (during the shooting phase), and in August (after barley harvesting). Neither the catch crop management nor the sampling date had any effect on the content of total organic carbon (CORG) or total nitrogen (Nt). The incorporation of filed pea biomass significantly enhanced the soil mineral N content (up to 30%), as well as the microbial and enzymatic variables, compared to the control soil. The spring use of field pea biomass significantly increased the content of microbial biomass C (2009 and 2011) in contrast to autumn incorporation. On the other hand, the biomass-N and the activity of the studied enzymes did not reveal statistically significant changes (or the results were inconsistent) as regards the time of catch crop biomass incorporation. The assessed variables (except for CORG, Nt, microbial biomass N) showed significant seasonal variability, but the changes were not clear or associated with a specific property. However, we found one similarity; the majority of the determined variables were the highest in May and/or June. Our data confirmed that catch crops play a key function in the management of agroecosystems. Plant biomass incorporated into soil is a source of organic matter, which increases nutrient concentrations and enhances soil biological activity. Because the time of catch crop application did not reveal consistent changes in the studied properties, both spring and autumn applications can be recommended as a suitable practice in modern agriculture.
Subsoiling Combine with Layered Nitrogen Application Optimizes Root Distribution and Improve Grain Yield and N Efficiency of Summer Maize
Authors: Li Xiangling , Wang Rui , Lou Fei , Ji Pengtao , Wang Jian , Dong Weixin , Tao Peijun , Zhang Yuechen
Published: June 2024

No-tillage of summer maize after the harvest of winter wheat is the primary agricultural practice on the North China Plain. However, prolonged no-tillage without deep tillage practices negatively impacts soil properties, which is detrimental to the growth and yield of summer maize. In this study, Xianyu 688 and Jifeng 2 were used as test maize materials, no-tillage and surface fertilizing with normal nitrogen (N) (BC240), no-tillage and surface fertilizing with N reduction (BC180), subsoiling layered fertilization with normal N (FC240) and subsoiling layered fertilization with N reduction (FC180)were designed, in order to assess root distribution, N utilization and grain yield of summer maize. In the two maize cultivars, the FC240 and FC180 treatments significantly reduced soil bulk density in the 10–50 cm depth soil layer compared to the other two treatments, and also increased total N content in the 20–50 cm depth soil layer. Compare BC 240 treatment, the FC240 treatment significantly increased root length in the 20–40 cm soil layer and root rap bleeding. Additionally, the FC240 and FC180 treatments enhanced dry matter and N accumulation, grain yield, N uptake efficiency and N fertilizer partial factor productivity. In various treatment, Xianyu 688 exhibited increased grain yield, N uptake efficiency and N fertilizer partial factor productivity compare with Jifeng 2. When employing a total N application level of 180–240 kg N/hm2, the synergistic improvement of summer maize grain yield and N efficiency can be achieved by incorporating subsoiling combined with layered nitrogen application.
Periparturient Changes in Voluntary Intake, Digestibility, and Performance of Grazing Zebu Beef Cows with or without Protein Supplementation
Authors: David Gabriel Santos Souza , Matos Ellém Maria de Almeida , Domingos Bianca Rodrigues , Ebani Yuri Cesconetto , de Sousa Luiz Carlos Oliveira , Leite Gabriela Duarte Oliveira , Pereira Pedro Henrique Borba , Rennó Luciana Navajas , Lopes Sidnei Antônio , Valadares Filho Sebastião de Campos , Paulino Mário Fonseca
Published: June 2024

We aimed to understand the changes in nutritional parameters and performance of beef cows during the peripartum, whether receiving or not receiving protein supplements. Forty cows were used, divided into two treatments: CON—mineral mix and SUP—protein supplementation. Digestibility trial was performed at 45, 30, and 15 days (d) before the parturition and at 20 and 40 d of lactation. The ADG of cows pre- and postpartum was recorded along with the BCS in gestational (GT) and maternal (MT) tissues in the prepartum. There was an effect of treatment and period (p ≤ 0.044) for intakes of DM and CP. The forage intake was similar (p > 0.90) but varied with the effect of days related to parturition (p < 0.001). There was a 14.37% decrease in DM intake from d −30 to d −15 of prepartum. In the postpartum, at 20 d of lactation, there was an increase of 72.7% in relation to d −15 of prepartum. No differences were observed in postpartum ADG or BCS at parturition and postpartum (p ≥ 0.12). However, higher total and MT ADG (p ≤ 0.02) were observed in animals receiving supplementation, while ADG in GT remained similar (p > 0.14). In conclusion, there is a decrease in intake for pregnant cows close to parturition and greater performance of animals supplemented in prepartum.
A Novel OFDM Approach Using Error Correcting Codes and Continuous Phase Modulation for Underwater Acoustic Communication
Authors: Murad Mohsin , Tasadduq Imran A. , Alghamdi Mohammed J. , Otero Pablo , Luque-Nieto Miguel-Ángel
Published: June 2024

In this work, the performance of coded continuous phase modulation (CPM) transceivers for orthogonal frequency-division multiplexing (OFDM) in underwater acoustic channels is evaluated. The proposed technique employs the Bose–Chaudhuri–Hocquenghem (BCH) class of cyclic codes, and a CPM-based mapper is being used in place of a traditional OFDM mapper. Bit-error-rates (BERs) for various distances are evaluated. We use the Bellhop acoustic channel simulator that is considered to be very close to a real underwater channel. The performance of the proposed system is evaluated for 23 values of the CPM modulation index h as a function of the distance between the transmitter (Tx) and receiver (Rx). Based upon the error performance, we identified several best-performing CPM indices. We also observed that for Tx–Rx distances of 100 and 250 m, the proposed system gives acceptable performance without the need for equalization. We also compared the out-of-band radiation of the proposed system with PSK-OFDM and observed that the uncoded CPM-OFDM has a better out-of-band (OOB) performance than the traditional OFDM. Moreover, a comparison of BERs with the traditional PSK-OFDM system shows that the proposed system outperforms the traditional OFDM system by a wide margin.
An Assessment of the Eurocode 3 Simplified Formulas for Distortional Buckling of Cold-Formed Steel Lipped Channels
Authors: Martins André Dias , Peres Nuno , Jacinto Pedro , Gonçalves Rodrigo
Published: June 2024

This paper concerns the Eurocode 3 Part 1-3 (EN 1993-1-3) methods for calculating the distortional buckling (bifurcation) load of cold-formed steel-lipped channels subjected to axial force, major and minor axis bending. More specifically, the paper presents the results of a parametric study that assesses the accuracy of the simplified method in EN 1993-1-3, which relies on direct/iterative hand calculations and an approximate mechanical model, through comparison with “exact” numerical results, obtained using semi-analytical linearized buckling analyses based on Generalized Beam Theory, which are also allowed by the code. Isoline error maps are presented for a wide range of geometric and material parameters, covering common commercial profiles and corresponding to a dataset of more than 24,000 cases. These maps make it possible to identify the parameter ranges leading to an acceptable error and, even though they strongly depend on the loading, general remarks concerning the expected error pertaining to the simplified method are drawn.
Fault Diagnosis of Universal Circuit Breakers Based on Variational Mode Decomposition and WOA-DBN
Authors: Liu Guorui , Cheng Xinyang , Dai Hualin , Dai Shuidong , Zhang Tianlin , Yang Daoxuan
Published: June 2024

Universal circuit breakers are crucial devices in power systems, and the accuracy of their fault diagnosis is vital. However, existing diagnostic models suffer from low feature extraction rates and low diagnostic accuracy. In this paper, we propose a novel approach for fault diagnosis of universal circuit breakers based on analyzing vibration signals generated during the closing operation. Firstly, the vibration signal was decomposed into multiple modal components using Variable Mode Decomposition (VMD), and the modal components were subjected to time and frequency domain feature extraction. Then, the extracted features were fused and normalized to construct a training dataset for the proposed model. We propose a Deep Belief Network (DBN) diagnostic model based on the Whale Optimization Algorithm (WOA), where the WOA is employed to optimize the hyperparameters of the DBN. Experimental results demonstrate that the proposed VMD and WOA-DBN model achieved an average accuracy of 96.63%. This method enhanced the accuracy of feature extraction from vibration signals and outperformed traditional diagnostic models when using a single vibration signal for fault diagnosis of universal circuit breakers. It provides a novel solution for early fault diagnosis of universal circuit breakers.
Active Collision Avoidance for Robotic Arm Based on Artificial Potential Field and Deep Reinforcement Learning
Authors: Xu Qiaoyu , Zhang Tianle , Zhou Kunpeng , Lin Yansong , Ju Wenhao
Published: June 2024

To address the local minimum issue commonly encountered in active collision avoidance using artificial potential field (APF), this paper presents a novel algorithm that integrates APF with deep reinforcement learning (DRL) for robotic arms. Firstly, to improve the training efficiency of DRL for the collision avoidance problem, Hindsight Experience Replay (HER) was enhanced by adjusting the positions of obstacles, resulting in Hindsight Experience Replay for Collision Avoidance (HER-CA). Subsequently, A robotic arm collision avoidance action network model was trained based on the Twin Delayed Deep Deterministic Policy Gradient (TD3) and HER-CA methods. Further, a full-body collision avoidance potential field model of the robotic arm was established based on the artificial potential field. Lastly, the trained action network model was used to guide APF in real-time collision avoidance planning. Comparative experiments between HER and HER-CA were conducted. The model trained with HER-CA improves the average success rate of the collision avoidance task by about 10% compared to the model trained with HER. And a collision avoidance simulation was conducted on the rock drilling robotic arm, confirming the effectiveness of the guided APF method.
A Trajectory Optimisation-Based Incremental Learning Strategy for Learning from Demonstration
Authors: Wang Yuqi , Li Weidong , Liang Yuchen
Published: June 2024

The insufficient generalisation capability of the conventional learning from demonstration (LfD) model necessitates redemonstrations. In addition, retraining the model can overwrite existing knowledge, making it impossible to perform previously acquired skills in new application scenarios. These are not economical and efficient. To address the issues, in this study, a broad learning system (BLS) and probabilistic roadmap (PRM) are integrated with dynamic movement primitive (DMP)-based LfD. Three key innovations are proposed in this paper: (1) segmentation and extended demonstration: a 1D-based topology trajectory segmentation algorithm (1D-SEG) is designed to divide the original demonstration into several segments. Following the segmentation, a Gaussian probabilistic roadmap (G-PRM) is proposed to generate an extended demonstration that retains the geometric features of the original demonstration. (2) DMP modelling and incremental learning updating: BLS-based incremental learning for DMP (Bi-DMP) is performed based on the constructed DMP and extended demonstration. With this incremental learning approach, the DMP is capable of self-updating in response to task demands, preserving previously acquired skills and updating them without training from scratch. (3) Electric vehicle (EV) battery disassembly case study: this study developed a solution suitable for EV battery disassembly and established a decommissioned battery disassembly experimental platform. Unscrewing nuts and battery cell removal are selected to verify the effectiveness of the proposed algorithms based on the battery disassembly experimental platform. In this study, the effectiveness of the algorithms designed in this paper is measured by the success rate and error of the task execution. In the task of unscrewing nuts, the success rate of the classical DMP is 57.14% and the maximum error is 2.760 mm. After the optimisation of 1D-SEG, G-PRM, and Bi-DMP, the success rate of the task is increased to 100% and the maximum error is reduced to 1.477 mm.
The Comprehensive Vertical Ozone Observation Experiment and Result Analysis of Ozone Lidars in China
Authors: Cai Haiyang , Jin Junli , Lv Shanshan , Song Xiaorui , Wang Ningzhang , Long Guicai , Shi Wen , Qin Zhengxin , Wu Kui
Published: June 2024

To evaluate the detection performance of ozone lidars, the first comprehensive vertical ozone observation experiment in China was conducted at the Xilinhot National Climate Observatory in Inner Mongolia from August to December 2023. The ozone profiles and concentrations of four ozone lidars were systematically compared and assessed with ozone radiosonde measurements and ozone analyzer observations both at ground-based stations and on an Unmanned Aerial Vehicle. The results show that the relative deviations of four ozone lidars are less than 20% compared with ozone radiosonde measurements at a height between 150 and 400 m. Ozone lidars have better behavior between 400 m and 2000 m than the lower altitude, with the deviation within 10% and the correlation coefficient around 0.8. However, relative deviations of lidars increased with altitude above 2000 m. The surface ozone concentrations observed using ozone lidars agreed well with the ground-based ozone analyzer, especially during periods with ozone concentrations higher than 40 µg·m−3. The correlation coefficients for most models of ozone lidar are higher than 0.53. A further investigation of the influence of precipitation events on ozone lidar measurement has been conducted, which revealed that thick cloud layers, low cloud base, and an intensive precipitation event with large raindrop particles can result in high anomalies and reduce the inversion accuracy of the ozone lidar. During the experiment, four ozone lidars were assessed quantitatively according to the comprehensive performance, which could help to improve inversion algorithms and the system design of this promising technique.
Organic Vapors from Residential Biomass Combustion: Emission Characteristics and Conversion to Secondary Organic Aerosols
Authors: Li Ruijie , Li Siyuan , Jiang Xiaotong , Wu Yangzhou , Hu Kang
Published: June 2024

Residential biomass combustion emits a large amount of organic gases into ambient air, resulting in the formation of secondary organic aerosol (SOA) and various environmental and health impacts. In this study, we investigated the emission characteristics of non-methane organic compounds (NMOCs) from residential biomass fuels during vigorous combustion (flaming) and stable combustion (smoldering) conditions. We quantified NMOC emission factors based on the CO concentration for different combustion phases and found that NMOC emissions were higher during the smoldering phase and approximately two to four times greater than those during flaming. NMOCs were categorized into volatile organic compounds (VOCs) and intermediate-volatility organic compounds (IVOCs) through the modeling of the organic compound volatility distribution. The photochemical aging of NMOCs revealed furans, phenolics, and certain IVOCs as significant non-traditional SOA precursors, with over half being consumed during a short aging period. A parametric function was established, indicating that accounting for non-traditional SOA precursors and IVOC yields improves the representation of the net enhancement of measured organic aerosol (OA). This study emphasizes the importance of differentiating emissions from various phases of residential biomass combustion and recognizing non-traditional SOA precursors and IVOCs for accurate SOA assessment and prediction.
Impact of Cumulus Options from Weather Research and Forecasting with Chemistry in Atmospheric Modeling in the Andean Region of Southern Ecuador
Authors: Parra Rene
Published: June 2024

Cumulus parameterization schemes model the subgrid-scale effects of moist convection, affecting the prognosis of cloud formation, rainfall, energy levels reaching the surface, and air quality. Working with a spatial resolution of 1 km, we studied the influence of cumulus parameterization schemes coded in the Weather Research and Forecasting with Chemistry Version 3.2 (WRF-Chem 3.2) for modeling in an Andean city in Southern Ecuador (Cuenca, 2500 masl), during September 2014. To assess performance, we used meteorological records from the urban area and stations located mainly over the Cordillera, with heights above 3000 masl, and air quality records from the urban area. Firstly, we did not use any cumulus parameterization (0 No Cumulus). Then, we considered four schemes: 1 Kain–Fritsch, 2 Betts–Miller–Janjic, 3 Grell–Devenyi, and 4 Grell-3 Ensemble. On average, the 0 No Cumulus option was better for modeling meteorological variables over the urban area, capturing 66.5% of records and being the best for precipitation (77.8%). However, 1 Kain–Fritsch was better for temperature (78.7%), and 3 Grell–Devenyi was better for wind speed (77.0%) and wind direction (37.9%). All the options provided acceptable and comparable performances for modeling short-term and long-term air quality variables. The results suggested that using no cumulus scheme could be beneficial for holistically modeling meteorological and air quality variables in the urban area. However, all the options, including deactivating the cumulus scheme, overestimated the total amount of precipitation over the Cordillera, implying that its modeling needs to be improved, particularly for studies on water supply and hydrological management. All the options also overestimated the solar radiation levels at the surface. New WRF-Chem versions and microphysics parameterization, the other component directly related to cloud and rainfall processes, must be assessed. In the future, a more refined inner domain, or an inner domain that combines a higher resolution (less than 1 km) over the Cordillera, with 1 km cells over the urban area, can be assessed.
Sphingolipid Signaling and Complement Activation in Glioblastoma: A Promising Avenue for Therapeutic Intervention
Authors: Janneh Alhaji H.
Published: June 2024

Glioblastoma is the most common and aggressive type of malignant brain tumor with a poor prognosis due to the lack of effective treatment options. Therefore, new treatment options are required. Sphingolipids are essential components of the cell membrane, while complement components are integral to innate immunity, and both play a critical role in regulating glioblastoma survival signaling. This review focuses on recent studies investigating the functional roles of sphingolipid metabolism and complement activation signaling in glioblastoma. It also discusses how targeting these two systems together may emerge as a novel therapeutic approach.
Modeling of Magnetic Scaffolds as Drug Delivery Platforms for Tissue Engineering and Cancer Therapy
Authors: Lodi Matteo B. , Corda Eleonora M. A. , Desogus Francesco , Fanti Alessandro , Mazzarella Giuseppe
Published: June 2024

Magnetic scaffolds (MagSs) are magneto-responsive devices obtained by the combination of traditional biomaterials (e.g., polymers, bioceramics, and bioglasses) and magnetic nanoparticles. This work analyzes the literature about MagSs used as drug delivery systems for tissue repair and cancer treatment. These devices can be used as innovative drugs and/or biomolecules delivery systems. Through the application of a static or dynamic stimulus, MagSs can trigger drug release in a controlled and remote way. However, most of MagSs used as drug delivery systems are not optimized and properly modeled, causing a local inhomogeneous distribution of the drug’s concentration and burst release. Few physical–mathematical models have been presented to study and analyze different MagSs, with the lack of a systematic vision. In this work, we propose a modeling framework. We modeled the experimental data of drug release from different MagSs, under various magnetic field types, taken from the literature. The data were fitted to a modified Gompertz equation and to the Korsmeyer–Peppas model (KPM). The correlation coefficient (R2) and the root mean square error (RMSE) were the figures of merit used to evaluate the fitting quality. It has been found that the Gompertz model can fit most of the drug delivery cases, with an average RMSE below 0.01 and R2>0.9. This quantitative interpretation of existing experimental data can foster the design and use of MagSs for drug delivery applications.
Molecular Basis of Plant–Pathogen Interactions in the Agricultural Context
Authors: Ijaz Usman , Zhao Chenchen , Shabala Sergey , Zhou Meixue
Published: June 2024

Biotic stressors pose significant threats to crop yield, jeopardizing food security and resulting in losses of over USD 220 billion per year by the agriculture industry. Plants activate innate defense mechanisms upon pathogen perception and invasion. The plant immune response comprises numerous concerted steps, including the recognition of invading pathogens, signal transduction, and activation of defensive pathways. However, pathogens have evolved various structures to evade plant immunity. Given these facts, genetic improvements to plants are required for sustainable disease management to ensure global food security. Advanced genetic technologies have offered new opportunities to revolutionize and boost plant disease resistance against devastating pathogens. Furthermore, targeting susceptibility (S) genes, such as OsERF922 and BnWRKY70, through CRISPR methodologies offers novel avenues for disrupting the molecular compatibility of pathogens and for introducing durable resistance against them in plants. Here, we provide a critical overview of advances in understanding disease resistance mechanisms. The review also critically examines management strategies under challenging environmental conditions and R-gene-based plant genome-engineering systems intending to enhance plant responses against emerging pathogens. This work underscores the transformative potential of modern genetic engineering practices in revolutionizing plant health and crop disease management while emphasizing the importance of responsible application to ensure sustainable and resilient agricultural systems.
Extracellular Self-DNA Effects on Yeast Cell Cycle and Transcriptome during Batch Growth
Authors: Palomba Emanuela , Chiusano Maria Luisa , Monticolo Francesco , Langella Maria Chiara , Sanchez Massimo , Tirelli Valentina , de Alteriis Elisabetta , Iannaccone Marco , Termolino Pasquale , Capparelli Rosanna , Carteni Fabrizio , Incerti Guido , Mazzoleni Stefano
Published: June 2024

The cell cycle and the transcriptome dynamics of yeast exposed to extracellular self-DNA during an aerobic batch culture on glucose have been investigated using cytofluorimetric and RNA-seq analyses. In parallel, the same study was conducted on yeast cells growing in the presence of (heterologous) nonself-DNA. The self-DNA treatment determined a reduction in the growth rate and a major elongation of the diauxic lag phase, as well as a significant delay in the achievement of the stationary phase. This was associated with significant changes in the cell cycle dynamics, with slower exit from the G0 phase, followed by an increased level of cell percentage in the S phase, during the cultivation. Comparatively, the exposure to heterologous DNA did not affect the growth curve and the cell cycle dynamics. The transcriptomic analysis showed that self-DNA exposure produced a generalized downregulation of transmembrane transport and an upregulation of genes associated with sulfur compounds and the pentose phosphate pathway. Instead, in the case of the nonself treatment, a clear response to nutrient deprivation was detected. Overall, the presented findings represent further insights into the complex functional mechanisms of self-DNA inhibition.
Effect of Acute Nutritional Ketosis on Circulating Levels of Growth Differentiation Factor 15: Findings from a Cross-Over Randomised Controlled Trial
Authors: Charles Sanjali , Liu Yutong , Bharmal Sakina H. , Kimita Wandia , Petrov Maxim S.
Published: June 2024

Exogenous supplementation with ketone beverages has been shown to reduce plasma glucose levels during acute nutritional ketosis. It remains to be investigated whether growth differentiation factor 15 (GDF-15)—an anorexigenic hormone—is involved in this process. The aim was to investigate the effect of a ketone ester beverage delivering β-hydroxybutyrate (KEβHB) on plasma levels of GDF-15, as well as assess the influence of eating behaviour on it. The study was a randomised controlled trial (registered at clinicaltrials.gov as NCT03889210). Individuals were given a KEβHB beverage or placebo in a cross-over fashion. Blood samples were collected at baseline, 30, 60, 90, 120, and 150 min after ingestion. Eating behaviour was assessed using the three-factor eating questionnaire. GDF-15 levels were not significantly different (p = 0.503) after the KEβHB beverage compared with the placebo. This finding remained consistent across the cognitive restraint, emotional eating, and uncontrolled eating domains. Changes in the anorexigenic hormone GDF-15, irrespective of eating behaviour, do not appear to play a major role in the glucose-lowering effect of exogenous ketones.
Hydrochar Production by Hydrothermal Carbonization: Microwave versus Supercritical Water Treatment
Authors: Ojewumi Modupe Elizabeth , Chen Gang
Published: June 2024

Hydrochar, a carbonaceous material produced through hydrothermal carbonization of lignocellulosic biomass, has gained significant attention due to its versatile applications in agriculture, energy, and environmental protection. This review extensively explores hydrochar production by hydrothermal carbonization, specifically microwave and supercritical water treatment. These innovative approaches hold substantial promises in enhancing the efficiency and sustainability of hydrochar synthesis. The review commences with an in-depth analysis of the fundamental principles governing hydrochar production, emphasizing the distinct mechanisms of microwave and supercritical water treatment. Insightful discussions on the influence of critical process parameters, such as temperature, pressure, and residence time, underscore these factors’ pivotal role in tailoring hydrochar characteristics. Drawing on a wide array of research findings, the review evaluates the impact of different lignocellulosic biomass feedstocks on hydrochar properties, which is crucial for optimizing hydrochar production. The comparative assessment of microwave and supercritical water treatment sheds light on their unique advantages and challenges, guiding researchers toward informed decision-making in selection of methods. Furthermore, the review delves into the myriad applications of hydrochar, spanning soil amendment, carbon sequestration, and renewable energy. Environmental considerations and life cycle assessments associated with microwave and supercritical water treatment are also explored, providing a holistic perspective on the sustainability of hydrochar production. In conclusion, this comprehensive review synthesizes current knowledge on hydrochar production from diverse lignocellulosic biomass sources, emphasizing the efficacy of microwave and supercritical water methods.
TAARs as Novel Therapeutic Targets for the Treatment of Depression: A Narrative Review of the Interconnection with Monoamines and Adult Neurogenesis
Authors: Shemiakova Taisiia S. , Efimova Evgeniya V. , Gainetdinov Raul R.
Published: June 2024

Depression is a common mental illness of great concern. Current therapy for depression is only suitable for 80% of patients and is often associated with unwanted side effects. In this regard, the search for and development of new antidepressant agents remains an urgent task. In this review, we discuss the current available evidence indicating that G protein-coupled trace amine-associated receptors (TAARs) might represent new targets for depression treatment. The most frequently studied receptor TAAR1 has already been investigated in the treatment of schizophrenia, demonstrating antidepressant and anxiolytic properties. In fact, the TAAR1 agonist Ulotaront is currently undergoing phase 2/3 clinical trials testing its safety and efficacy in the treatment of major depressive disorder and generalized anxiety disorder. Other members of the TAAR family (TAAR2, TAAR5, TAAR6, TAAR8, and TAAR9) are not only involved in the innate olfaction of volatile amines, but are also expressed in the limbic brain areas. Furthermore, animal studies have shown that TAAR2 and TAAR5 regulate emotional behaviors and thus may hold promise as potential antidepressant targets. Of particular interest is their connection with the dopamine and serotonin systems of the brain and their involvement in the regulation of adult neurogenesis, known to be affected by the antidepressant drugs currently in use. Further non-clinical and clinical studies are necessary to validate TAAR1 (and potentially other TAARs) as novel therapeutic targets for the treatment of depression.
Management of Fibromyalgia: An Update
Authors: Jones Eric A. , Asaad Farrah , Patel Nishil , Jain Esha , Abd-Elsayed Alaa
Published: June 2024

Fibromyalgia, a chronic pain condition marked by abnormal pain processing, impacts a significant part of the population, leading to reduced quality of life and function. Hallmark symptoms include widespread persistent pain, sleep disturbances, fatigue, cognitive dysfunction, and mood changes. Through this updated review, we aim to contribute to the evolving understanding and management of fibromyalgia, offering insights into the diverse tools available to improve the lives of those affected by this challenging condition. Management begins with educating patients to ultimately relieve them of unnecessary testing and provide reassurance. Treatment emphasizes a comprehensive approach, combining nonpharmacological interventions such as aforementioned education, exercise, and psychotherapy, alongside pharmacologic management—namely duloxetine, milnacipran, pregabalin, and amitriptyline—which have consistent benefits for a range of symptoms across the spectrum of fibromyalgia. Notably, drugs like nonsteroidal anti-inflammatory drugs (NSAIDs) and acetaminophen are generally not recommended due to limited efficacy and associated risks. Lastly, a variety of other medications have shown promise, including NMDA-receptor antagonists, naltrexone, and cannabinoids; however, they should be used with caution due to a small amount of evidence and potential for adverse effects.
Combination of an Optically Induced Dielectrophoresis (ODEP) Mechanism and a Laminar Flow Pattern in a Microfluidic System for the Continuous Size-Based Sorting and Separation of Microparticles
Authors: Chu Po-Yu , Wu Ai-Yun , Tsai Kun-Yu , Hsieh Chia-Hsun , Wu Min-Hsien
Published: June 2024

Optically induced dielectrophoresis (ODEP)-based microparticle sorting and separation is regarded as promising. However, current methods normally lack the downstream process for the transportation and collection of separated microparticles, which could limit its applications. To address this issue, an ODEP microfluidic chip encompassing three microchannels that join only at the central part of the microchannels (i.e., the working zone) was designed. During operation, three laminar flows were generated in the zone, where two dynamic light bar arrays were designed to sort and separate PS (polystyrene) microbeads of different sizes in a continuous manner. The separated PS microbeads were then continuously transported in laminar flows in a partition manner for the final collection. The results revealed that the method was capable of sorting and separating PS microbeads in a high-purity manner (e.g., the microbead purity values were 89.9 ± 3.7, 88.0 ± 2.5, and 92.8 ± 6.5% for the 5.8, 10.8, and 15.8 μm microbeads harvested, respectively). Overall, this study demonstrated the use of laminar flow and ODEP to achieve size-based sorting, separation, and collection of microparticles in a continuous and high-performance manner. Apart from the demonstration, this method can also be utilized for size-based sorting and the separation of other biological or nonbiological microparticles.