Average Research Articles

The Pengenbangan

Improving of physics learning process in vocational schools is very urgent to do so that it can improve the problem-solving skills. The purpose of this study was to produce student worksheets based on the engineering design process to practice physics problem-solving skills. This research method is research and development (R&D) using the ADDIE model. The research instruments consisted of learning observation sheets, interview sheets, feasibility & practicality assessment sheets, student response questionnaires, and questions. Product implementation as well as legibility assessment using 10th-grade automotive engineering students. Student worksheets are equipped with assignments to train students' problem-solving skills. To find out the criteria for increasing students' problem-solving abilities, the N-Gain equation is used. The results of the assessment showed that the student worksheets met the criteria of being very eligible (92.67%) and Very Practical (98.20%). Analysis of problem-solving tasks shows an average value of 88.89. As for the test results, it is known that as many as 78% of students are included in the High criteria, and 22% of students are included in the Moderate criteria. The conclusion of the research is that the use of EDP-based student worksheets can improve the problem-solving skills of students.

Remote Sensing-Based Extension of GRDC Discharge Time Series - A Monthly Product with Uncertainty Estimates

The Global Runoff Data Center (GRDC) data set has faced a decline in the number of active gauges since the 1980s, leaving only 14% of gauges active as of 2020. We develop the Remote Sensing-based Extension for the GRDC (RSEG) data set that can ingest legacy gauge discharge and remote sensing observations. We employ a stochastic nonparametric mapping algorithm to extend the monthly discharge time series for inactive GRDC stations, benefiting from satellite imagery- and altimetry-derived river width and water height observations. After a rigorous quality assessment of our estimated discharge, involving statistical validation, tests and visual inspection, results in the extension of discharge records for 3377 out of 6015 GRDC stations. The quality of discharge estimates for the rivers with a large or medium mean discharge is quite satisfactory (average KGE value > 0.5) however for river reaches with a low mean discharge the average KGE value drops to 0.33.The RSEG data set regains monitoring capability for 83% of total river discharge measured by GRDC stations, equivalent to 7895 km3/month.

An numerical investigation of the three dimensional multi-level force chain network of the sample with a single fissure under uniaxial compression

A novel three-dimensional grain-based model based on Particle Flow Code is proposed. Within this model, both quantitative calculations and multi-level classifications of force chain networks are achieved. A batch of numerical samples with a single pre-existing fissure with different inclination angle αs is constructed and then applied to the uniaxial compression. The result shows that the general force chains (GF) are approximately uniformly distributed in each orientation. The number of high-strength force chains (HF) reaches its maximum at the peak load moment, and the main orientation is consistent with the loading direction. The average value of the force chain in the tip regions of the fissure at any characteristic moment is the highest. In the whole region and the upper and lower regions of the fissure, the level of the force chain network rises as αs increases. Conversely, within the tip regions of the fissure, both the average value and sum value of GF decrease. The fracture resistance of intragranular structures is the highest, surpassing that of both all structures and intergranular structures. The fracture resistance within the tip regions of the fissure is the lowest compared to the entire region and the upper and lower regions of the fissure.

Investigating the Essential Factors for Thresher Losses and Waste in Peanut Harvester Considering Several Varieties of Crop

Abstract This research investigated the performance of a model traction combine harvester (TCH) for harvesting several varieties of peanuts. The effect of independent factors including tested varieties at three levels (local Moghan, Goli Astana, and Turkish), tractor forward speed (FS) at three levels (4, 5, and 6 km·h−1), and rotational speed (RS) of the thresher at three levels (500, 600, 700 rpm) on losses and wastes were studied and investigated. To analyse the obtained data, a factorial experimental design was used in the form of randomised complete blocks, and Duncan‘s multi-range test was used to compare the mean values. According to the findings, the effects of tractor FS and rotary speed of the thresher on the percentage of threshing losses were significant at the probability level of 1%. Also, the interaction effects of variety type and tractor FS, tractor FS, and rotary speed of the thresher on the percentage of threshing losses were significant at the 1% probability level. Also, the effects of variety type, tractor FS, rotary speed of the thresher, and all mutual effects on the percentage of threshing waste were significant at the 1% probability level. The results of comparing the average effects of the main factors showed that the FS of 6 km·h−1 had the highest average threshing loss (82.89%). Threshing losses were obtained in the Turkish variety with the lowest average value (2.51%) and in the local variety with the highest average value (3.53%).

Continuous Running vs. Interval Training on VO2Max Increase in 16-17-Year-Old Football Athletes at Bintang Kejora Football School Deli Serdang Regency

This study compares the Vo2Max effects of continuous running and interval training on 16-17-year-old football participants at Bintang Kejora football school in Deli Serdang Regency in 2022. The research method is experimental quantitative research. This study used purposive sampling to pick Bintang Kejora football school athletes under certain conditions. The study lasted six weeks with three training sessions per week. The statistical analysis of Vo2Max increase in the continuous running group showed a tcount of 6.56 and a ttable of 2.75, with α = 0.05. We reject H0 and accept Ha since the tcount is bigger than the ttable (6.56 > 2.75). In 2022, the continuous running exercise group improved the Vo2Max results of 16-17-year-old football participants at Bintang Kejora football school (SSB), Deli Serdang Regency. Hypothesis II on interval training's Vo2Max rise had a tcount value of 6.11, higher than the crucial ttable value of 2.75. This means H0 is rejected and Ha is accepted. Thus, the interval training group affects the Vo2Max results of 16–17-year-old football participants at Bintang Kejora in Deli Serdang Regency. Upon assessing hypothesis III, the average standard deviation tcount value was 1.32, while the ttable value was 2.23, with α = 0.05. Low tcount values (tcount < ttable) support the null hypothesis (H0) and reject the alternative hypothesis (Ha). This analysis suggests that continuous running or interval training have similar effects on Vo2Max outcomes for 16-17-year-old football players at the Bintang Kejora football school in Deli Serdang Regency.

An assessment of heterogenous ensemble classifiers for analyzing change‐proneness in open‐source software systems

AbstractSoftware managers constantly look out for methods that ensure cost effective development of good quality software products. An important means of accomplishing this is by allocating more resources to weak classes of a software product, which are prone to changes. Therefore, correct prediction of these change‐prone classes is critical. Though various researchers have investigated the performance of several algorithms for identifying them, the search for an optimum classifier still persists. To this end, this study critically investigates the use of six Heterogenous Ensemble Classifiers (HEC) for Software Change Prediction (SCP) by empirically validating datasets obtained from 12 open‐source software systems. The results of the study are statistically assessed using three robust performance indicators (AUC, F‐measure and Mathew Correlation Coefficient) in two different validation scenarios (within project and cross‐project). They indicate the superiority of Average Probability Voting Ensemble, a heterogenous classifier for determining change‐proneness in the investigated systems. The average AUC values of software change prediction models developed using this ensemble classifier exhibited an improvement of 3%‐9% and 3%‐11% respectively when compared with its base learners and homogeneous counter parts. Similar observations were inferred using other investigated performance measures. Furthermore, the evidence obtained from the results suggests that the change in number of base learners or type of meta‐learner does not exhibit significant change in the performance of corresponding heterogenous ensemble classifiers.

Improved mechanical properties of GNPs/Al composites by eliminating alumina and obtaining a strong bonded GNPs-Al direct contact interface and Mg-rich phases

The interface between Graphene Nanosheets and Aluminum (GNPs-Al) without carbide and transition layers is referred to as a direct-contact GNPs-Al interface. In this work, the GNPs-reinforced aluminum matrix composites are fabricated by ultrasonic casting process. The interfacial structure of GNPs-Al is investigated using transmission electron microscopy, and the strength of the fabricated direct-contact GNPs-Al interface is analyzed. The results identified the presence of weak amorphous aluminum oxide at the original GNPs-Al interface. The direct contact GNPs-Al interface was obtained by introducing Mg. The migration of Al atoms formed an Al/C mixing zone between GNPs-Al, which led to the enhancement of the interface strength. The average UTS value has improved by about 20.5% due to the optimization of the interface structure. The enhancement of strength at the same toughness is achieved by the load transfer of GNPs, bridging, and the enhancement of dislocation storage capacity by the Mg-rich phase. The present work provides a new reference for the reinforcement of cast aluminum alloys by GNPs.

Finite element method analysis of bone stress for variants of locking plate placement

This study investigates the optimal placement of locking plate screws for bone fracture stabilization in the humerus, a crucial factor for enhancing healing outcomes and patient comfort. Utilizing Finite Element Method (FEM) modeling, the research aimed to determine the most effective screw configuration for achieving optimal stress distribution in the humerus bone. A computer tomography (CT) scan of the humerus was performed, and the resulting images were used to create a detailed model in SOLIDWORKS 2012. This model was then analyzed using ANSYS Workbench V13 to develop a finite element model of the bone. Four different screw configurations were examined: 4 × 0°, 4 × 10°, 4 × 20°, 2 × 20°; 2 × 0°. These configurations were subjected to bending in the XZ and YZ planes, as well as tension and compression along the Z axis. The research identified the 2 × 20°+2 × 0° configuration as the most beneficial, with average stress values below 30 MPa and peak stress values below 50 MPa in 3-point bending at the first screw. This configuration consistently showed the lowest stress values across all loading scenarios. Specifically, stress levels ranged from 20 MPa to 50 MPa for bending in the XZ plane, 20 MPa–35 MPa for bending in the YZ plane, 20 MPa–30 MPa for extension in the Z-axis, and 18 MPa–25 MPa for compression in the Z-axis. The 4 × 10° and 4 × 20° configurations also produced satisfactory results, with stress levels not exceeding 70 MPa. However, the 4 × 0° configuration presented considerable stress during bending and compression in the Z-axis, with stress values exceeding 100 MPa, potentially leading to mechanical damage. In conclusion, the 2 × 20°; 2 × 0° screw configuration was identified as the most effective in minimizing stress on the treated bone. Future work will involve a more detailed analysis of this methodology and its potential integration into clinical practice, with a focus on enhancing patient outcomes in bone fracture treatment.

Radon Levels and Risk Assessment due to its Ingestion and Inhalation from Groundwater of Lapai, North-Central Nigeria

Radon exposure through ingestion or inhalation from groundwater can be a significant public health concern due to its carcinogenic effects. This study assessed the health risks of radon exposure from groundwater in Lapai, Nigeria, using a Rad7 detector to measure radon concentration levels. The measured concentrations ranged from 5.36 ± 0.22 BqL-1 to 0.52 ± 0.06 BqL-1 with an average value of 2.82 ± 0.14 BqL-1. The annual effect dose due to ingestion of Radon obtained varies in the range of 2.76 to 28.34 μSva-1, 1.02 to 10.43 μSva-1, and 1.33 to 13.69 μSva-1 for infants, children, and adults, respectively. While the effective dose per year from inhalation of Radon released from water by adults has values in the range of 1.31 to 13.50 μSva-1. Radon levels were within permissible limits of UNSCEAR and USEPA, and the annual effective doses from inhalation and ingestion were within WHO's safe range. Nonetheless, monitoring is advised, especially during the rainy season when contamination levels may increase.

Provenance, chemical weathering, and sedimentary environment of the aquifer sediments: Implication for arsenic enrichment in groundwater

High arsenic (As) groundwater has been widely observed in the world, but the provenance, chemical weathering, and sedimentary environment of aquifer sediments hosting high As groundwater remain poorly understood. The geochemical and isotopic characteristics of high-resolution aquifer sediments were investigated to unravel the roles of provenance, chemical weathering, and sedimentary environment of the aquifer sediments. The average values of rare earth elements (REEs) showed the enrichment of light REEs and flat heavy REEs in the aquifer and the Yellow River sediments. The chemical index of alteration reflected incipient and moderate weathering (41 to 71). The δ13CSOM (−27.4‰ ± 4.3‰) and C/N values (8.1 ± 5.8) indicated aquatic environment of sedimentation. Shallow aquifer sediments mostly had relatively high ΣREEs contents and negative Eu anomalies, demonstrating that the aquifer sediments were mainly derived from the Yellow River (up to 94.3% ± 5%). Deep aquifer sediments had relatively low ΣREEs contents and positive Eu anomalies, which originated from the Yinshan Mountains (up to 72.2% ± 20.4%) principally with igneous and metamorphic rocks. Moreover, recycling of the Yellow River sediments from Northeastern Tibetan Plateau enhanced the silicate weathering and increased contents of Fe/Mn oxides and As. The δ13CSOM and C/N values evidenced that organisms of paleo-lake or paleo-Yellow River were responsible for abundant sedimentary organic matter. Concentrations of Fe, Mn, and As were high in shallow groundwater from the plain. The As enrichment in the shallow groundwater was jointly controlled by the provenance and chemical weathering of the aquifer sediments (14%), sedimentary environment (10%), reductive dissolution (35%) of Fe/Mn oxides, As desorption under elevated pH (20%), and competitive adsorption of HCO3- (21%). This study systematically reveals the roles of provenance, chemical weathering, and sedimentary environment of the aquifer sediments and highlights joint contributions of geochemical processes and geochemical conditions to As enrichment in groundwater.

Uncovering PPAR-γ agonists: An integrated computational approach driven by machine learning

Peroxisome proliferator-activated receptor gamma (PPAR-γ) serves as a nuclear receptor with a pivotal function in governing diverse facets of metabolic processes. In diabetes, the prime physiological role of PPAR-γ is to enhance insulin sensitivity and regulate glucose metabolism. Although PPAR-γ agonists such as Thiazolidinediones are effective in addressing diabetes complications, it is vital to be mindful that they are associated with substantial side effects that could potentially give rise to health challenges. The recent surge in the discovery of selective modulators of PPAR-γ inspired us to formulate an integrated computational strategy by leveraging the promising capabilities of both machine learning and in silico drug design approaches. In pursuit of our objectives, the initial stage of our work involved constructing an advanced machine learning classification model, which was trained utilizing chemical information and physicochemical descriptors obtained from known PPAR-γ modulators. The subsequent application of machine learning-based virtual screening, using a library of 31,750 compounds, allowed us to identify 68 compounds having suitable characteristics for further investigation. A total of four compounds were identified and the most favorable configurations were complemented with docking scores ranging from −8.0 to −9.1 kcal/mol. Additionally, the compounds engaged in hydrogen bond interactions with essential conserved residues including His323, Leu330, Phe363, His449 and Tyr473 that describe the ligand binding site. The stability indices investigated herein for instance root-mean-square fluctuations in the backbone atoms indicated higher mobility in the region of orthosteric site in the presence of agonist with the deviation peaks in the range of 0.07–0.69 nm, signifying moderate conformational changes. The deviations at global level revealed that the average values lie in the range of 0.25–0.32 nm. In conclusion, our identified hits particularly, CHEMBL-3185642 and CHEMBL-3554847 presented outstanding results and highlighted the stable conformation within the orthosteric site of PPAR-γ to positively modulate the activity.

Structural changes in spinal cord following optic neuritis: Insights from quantitative spinal MRI

ObjectivesPrevious studies have demonstrated that optic neuritis (ON) affects brain plasticity. However, whether ON affects the spinal cord remains unclear. We aimed to investigate the spinal cord changes in ON and their associations with disability. MethodsA total of 101 ON patients, and 41 healthy controls (HC) were retrospectively recruited. High-resolution imaging was conducted using a Magnetization Prepared Rapid Acquisition Gradient-Echo (MP-RAGE) sequence for T1-weighted images and an echo planar imaging (EPI) sequence for Diffusion Tensor Imaging (DTI) data collection. Additionally, patients' disability and cognitive impairment were evaluated using the Expanded Disability Status Scale (EDSS) and the Paced Auditory Serial Addition Test (PASAT), respectively. The quantitative spinal MRI was employed to examine the cross-sectional area (CSA) and diffusion indicators, with a specific focus on calculating the average values across the C2-C7 cervical spinal cord segments. CSA, fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD), and radial diffusivity (RD) were compared between groups. Correlation analyses were performed between CSA, diffusion indicators, and clinical variables. ResultsNo significant differences were found in CSA between ON patients and HCs. MD (p = 0.007) and RD (p = 0.018) were increased in ON patients compared with HCs, and AD was decreased in ON (p = 0.013). The AD values of the ON patients were significantly positively correlated with PASAT scores (r = 0.37, p < 0.001). ConclusionsThis study provided imaging evidence for DTI abnormalities in patients with ON. Spinal cord DTI can improve our knowledge of the path physiology of ON, and clinical progression.

Adaptive filter of frequency bands based coordinate attention network for EEG-based motor imagery classification.

In the brain-computer interface (BCI), motor imagery (MI) could be defined as the Electroencephalogram (EEG) signals through imagined movements, and ultimately enabling individuals to control external devices. However, the low signal-to-noise ratio, multiple channels and non-linearity are the essential challenges of accurate MI classification. To tackle these issues, we investigate the role of adaptive frequency bands selection and spatial-temporal feature learning in decoding motor imagery. We propose an Adaptive Filter of Frequency Bands based Coordinate Attention Network (AFFB-CAN) to improve the performance of MI classification. Specifically, we design the AFFB to adaptively obtain the upper and lower limits of frequency bands in order to alleviate information loss caused by manual selection. Next, we propose the CAN-based network to emphasize the key brain regions and temporal segments. And we design a multi-scale module to enhance temporal context learning. The conducted experiments on the BCI Competition IV-2a and 2b datasets reveal that our approach achieves an outstanding average accuracy, kappa values, and Macro F1-Score with 0.7825, 0.7104, and 0.7486 respectively. Similarly, for the BCI Competition IV-2b dataset, the average accuracy, kappa values, and F1-Score obtained are 0.8879, 0.7427, and 0.8734, respectively. The proposed AFFB-CAN method improves the performance of MI classification. In addition, our study confirms previous findings that motor imagery is mainly associated with µ and β rhythms. Moreover, we also find that γ rhythms also play an important role in MI classification.

Rigorous Reliability Testing of an Encapsulated Thermal Pyrolytic Graphite (TPG) Heat Spreader for Passive Thermal Management Applications

Continued microelectronic miniaturization has resulted in higher power density, increasing the need for high performance thermal management. One possible solution for board-level thermal management is to fully encapsulate a high thermal conductivity material, such as Thermal Pyrolytic Graphite (TPG; ~1700 W/mK) in a metal (such as 6061 aluminum), producing an efficient passive heat spreader for thermal dissipation. The metal shell provides sufficient mechanical strength at a small detriment to the overall thermal conductivity. Reliability and consistent performance over time are critical for thermal management systems, especially in Defense and Aerospace applications. MIL-STD-883H provides test parameters for reliability involving exposure to mechanical shock (Method 2002.5), mechanical vibration (Method 2005.2), and thermal shock (Method 1011.9), with traditional evaluation criteria involving microelectronic device performance and/or a visual examination. For this work, the reliability of aluminum encapsulated TPG test coupons was evaluated with more demanding criteria: a visual examination, X-ray inspection, acoustic microscopy, and thermal conductivity performance testing, both before and after the previously discussed MIL-STD-883H exposures. The encapsulated coupons showed excellent robustness; the thermal conductivity properties remained constant with an average value around 965 W/mK, and the non-destructive imaging revealed excellent bond quality for the metal encapsulation, with no evidence of delamination. This more stringent evaluation criteria after MIL-STD-883H testing provides a better way to evaluate the reliability of passive thermal management systems. The study results also demonstrate the metal encapsulated TPG heat spreader as a superior thermal management solution with high performance and high reliability for demanding applications.

Annual consumption and types of synthetic nitrogen fertilizers: Ammonia emission indicators for mitigation strategies in the European Union

Anthropogenic ammonia (NH3) emissions are primarily derived from agricultural activities. NH3 emissions from synthetic nitrogen (N) fertilizers are associated with the annual consumption of various N fertilizers. In this study, the annual consumption of different types of synthetic N fertilizers in the EU countries, derived from the database of the International Fertilizer Association, was analyzed, and the implementation of mitigation measures for NH3 emissions at the national level was assessed. NH3 emissions were estimated using the emission factors of different N fertilizers in cool, temperate and hot climates, and low- and high-pH soils, as the European Environment Agency proposed. The results revealed that the annual consumption of various synthetic N fertilizers (average values from 2016 to 2020) varied markedly in different EU countries. France, Germany, Poland, Spain, and the United Kingdom had the largest annual consumption of synthetic N fertilizers. NH3 emissions from urea-based fertilizers are the highest in most EU countries. The annual NH3 emissions were associated with the type of synthetic N fertilizer consumed. For example, although the annual synthetic N fertilizer consumption in Germany was 38% and 41.5% higher than those in Spain and Italy, the annual NH3 emissions in Germany were 63% and 20% lower than those in Spain and Italy, respectively, due to the high proportion of urea-based fertilizers with the highest emission factor among the synthetic N fertilizers in Spain and Italy. Thus, mitigation strategies should be tailored to specific types of N fertilizers with high emission factors and proportions to minimize NH3 emissions. Since the NH3 emissions from urea-based fertilizers are the highest in the EU countries, especially in the top five countries, we recommend the addition of urease inhibitors to urea fertilizers as one important strategy for mitigating NH3 emissions.

A discussion on carbon and oxygen isotopes of secondary carbonates in loess-paleosol sequences

Although carbon and oxygen isotopic compositions of carbonates are valuable for Quaternary climate reconstruction due to their sensitivity to paleoclimate changes, a comprehensive understanding regarding the formation, factors influencing isotopic variations, and age uncertainties of secondary carbonates in loess deposits remains elusive. This study investigates the microstructure and stable isotopes of secondary carbonates, specifically calcified root cells (CRC) and needle-fiber calcite (NFC), found within the loess deposits of the last glacial period in the northwestern region of the Chinese Loess Plateau. The average δ13C values of soil total organic matter, CRC, NFC, and total inorganic carbonates in the YuanBao sections are −22.1‰, −14.9‰, −7.0‰, −4.6‰, respectively. Radiocarbon dating results reveal that both CRC and NFC were not of synsedimentary origin. The pronounced negativity of the carbon isotopic composition and the microstructures offer insights that CRC formation is closely tied to C3 plants. The synchronous depletion of δ13CCRC and δ18OCRC values during the transition period from the last glacial to the Holocene, underscores the sensitivity of isotopic composition of CRC to local environmental changes, making it a potential candidate for paleo-climate studies. Additionally, CRC formed during the glacial period provides a close approximation to the true sedimentation age. The radiocarbon dating of NFC highlights a significant fluctuation in carbon source translocation depth, resulting in a pronounced temporal and spatial decoupling between NFC formation and the surrounding deposits. Consequently, values of δ13CNFC and δ18ONFC seem to exhibit no significant trend across the limited depth range in this research. We propose that, when using NFC for paleoclimate reconstruction, careful consideration of variations in translocation depths of carbon sources is imperative to prevent misestimations. NFC could potentially be more fitting for broader temporal-scale paleoclimate reconstructions rather than high-resolution investigations.

UJI EFEK EKSTRAK ETANOL UMBI TALAS HISTOPATOLOGI GINJAL TIKUS PUTIH JANTAN (Rattus norvegicus) DIINDUKSI STREPTOZOTOCIN

Diabetes mellitus is a systemic disease or metabolic syndrome caused by chronic disorders, especially in the body's carbohydrate, fat, and protein metabolism system. This study aims to determine the content of secondary metabolites of alkaloids, flavonoids, saponins, and tannins in the ethanolic extract of taro tubers and determine the effect and effective dose of the extract on the regeneration of kidney organ cells in streptozotocin-induced rats. Rats were divided into five groups: normal control, negative control, and extract treatment groups at doses of 100, 200, and 300 mg/kg BW that were performed for 28 days. This study observed changes that occurred in the renal tubular cells of white male rats induced by streptozotocin in the form of the level of damage with a value calculated based on the percentage (%) in each part of the tubule that was injured or damaged. The results showed that the taro tuber extract contained positive secondary metabolites of alkaloids, flavonoids, saponins, and tannins. Administration of ethanol extract of taro tubers at a dose of 300 mg/kg BW can affect the regeneration of kidney tubule cells in STZ-induced rats, with an average damage value of 1 out of a maximum of 4.

Epilepsy EEG signals classification based on sparse principal component logistic regression model

Epilepsy is a chronic brain disease caused by excessive discharge of brain neurons. Long-term recurrent seizures bring a lot of trouble to patients and their families. Prediction of different stages of epilepsy is of great significance. We extract pearson correlation coefficients (PCC) between channels in different frequency bands as features of EEG signals for epilepsy stages prediction. However, the features are of large feature dimension and serious multi-collinearity. To eliminate these adverse influence, the combination of traditional dimension reduction method principal component analysis (PCA) and logistic regression method with regularization term is proposed to avoid over-fitting and achieve the feature sparsity. The experiments are conducted on the widely used CHB-MIT dataset using different regularization terms L 1 and L 2 , respectively. The proposed method identifies various stages of epilepsy quickly and efficiently, and it presents the best average accuracy of 94.86%, average precision of 96.71%, average recall of 93.48%, average kappa value of 0.90 and average Matthews correlation coefficient (MCC) value of 0.90 for all patients.

Sphingomicrobium clamense sp. nov., Isolated from Sediment of Clam Island Beach in China.

A Gram-stain-negative, non-flagellated, aerobic, ovoid or rod-shaped bacterium with motility, designated B8T, was isolated from the sediment of Clam Island beach, Liaoning province, China. The optimum growth of strain B8T occurred at 35 oC, pH 7.0, and in the presence of 4.0-5.0% (w/v) NaCl. Phylogenetic analysis based on 16S rRNA gene sequences showed that strain B8T formed a distinct lineage within the genus Sphingomicrobium and was closely related to Sphingomicrobium nitratireducens O-35T (98.3% sequence similarity), Sphingomicrobium aestuariivivum KCTC 42286T (96.9%), and Sphingomicrobium astaxanthinifaciens JCM 18551T (96.5%). The digital DNA-DNA hybridization and average nucleotide identity values between strain B8T and closely related strains were lower than 21.0% and 78.0%, much lower than the cutoff values of 70.0% and 95.0%, respectively, for bacterial species delineation. The dominant respiratory quinone of strain B8T was ubiquinone-10. The major fatty acids were Sum In Feature 8 (C18:1ω7c and/or C18:1ω6c), Sum In Feature 3 (C16 : 1ω7c and/or C16 : 1ω6c), C17:1ω6c, C18:1 2-OH, and C16:0. The major polar lipids were diphosphatidylglycerol, phosphatidylglycerol, phosphatidylethanolamine, sphingoglycolipid, glycolipids, and four unknown polar lipids. The DNA G + C content of strain B8T was 63.9%. Based on the phenotypic, phylogenetic, and chemotaxonomic analyses, strain B8T is considered a new species of Sphingomicrobium, for which the name Sphingomicrobium clamense sp. nov. is proposed. The type strain is B8T (= CGMCC 1.19486T = KCTC 92052T).

Event shape engineering via Glauber MC model

In our exploration of event shape engineering, the Glauber model served as a foundational tool for under- standing the anisotropic geometry of the Quark-Gluon Plasma (QGP). Utilizing the TGlauberMC-3.2 model within ROOT, we systematically analyzed one million events. From the 2 &amp; Npart plot, our data revealed an average maximum dN value of 12.00 with associated parameters: 2 = 0.91, 2 =2.74, 3 =0.91 and Npart = 14.00. These findings illuminate the distinct configurations that yield the most pronounced anisotropic geometries of the QGP, providing insights into optimizing event shape configurations.