bluuebunny/crossref_metadata_2025 huggingface dataset:
Controlling inter-particle distances in crowds of motile, cognitive, active particles
Authors: Negi Rajendra Singh , Iyer Priyanka , Gompper Gerhard
Published: April 2024

Distance control in many-particle systems is a fundamental problem in nature. This becomes particularly relevant in systems of active agents, which can sense their environment and react by adjusting their direction of motion. We employ agent-based simulations to investigate the complex interplay between agent activity, characterized by Péclet number $$\hbox{Pe}$$
Pe
, reorientation maneuverability $$\Omega$$
Ω
, vision angle $$\theta$$
θ
and vision range $$R_0$$

R
0

, and agent density, which determines agent distancing and dynamics. We focus on semi-dense crowds, where the vision range is much larger than the particle size. The minimal distance to the nearest neighbors, exposure time, and persistence of orientation direction are analyzed to characterize the behavior. With increasing particle speed at fixed maneuverability, particles approach each other more closely, and exhibit shorter exposure times. The temporal persistence of motion decreases with increasing $$\hbox{Pe}$$
Pe
, reflecting the impact of activity and maneuverability on direction changes. For a vision angle $$\theta =\pi /4$$

θ
=
π
/
4

, we observe the emergence of flocking aggregates with a band-like structure, somewhat reminiscent of the bands in the Vicsek model. Additionally, for vision angles $$\theta \ge \pi /2$$

θ
≥
π
/
2

, several quantities are found to display a universal scaling behavior with scaling variable $$\hbox{Pe}^{3/2}/\Omega$$

Pe

3
/
2

/
Ω

. Our results are in good agreement with recent experiments of pedestrians in confined spaces.
Financial big data management and intelligence based on computer intelligent algorithm
Authors: Liu Jia , Fu Shuai
Published: April 2024

With the acceleration of China’s economic integration process, enterprises have gained greater advantages in the fierce market competition, and gradually formed the trend of grouping and large-scale. However, as the scale of the company increases, the establishment of a branch also causes many problems. For example, in order to obtain more benefits, the business performance of the company can generate false growth, resulting in financial and operational risks. This paper analyzed the current situation and needs of enterprise financial control from two aspects of theory and practice, combined with specific engineering projects, taking ZH Group as an example, according to the actual situation of the enterprise. The article first introduces the basic situation of the enterprise; Then, the financial control strategy was designed, and different modules were designed to achieve financial control; Afterwards, use a reverse neural network to evaluate the effectiveness of financial management and risk warning; Relying on particle swarm optimization algorithm to seek the optimal solution and applying it to financial management and risk warning, in order to improve the level of introspection and risk management in decision-making. Finally, the value of computer intelligence algorithms in financial big data management is evaluated by constructing a financial risk indicator system. Through the analysis of enterprise financial management, the total asset turnover rate of ZH Group decreased by 0.39 times in 5 years. After 5 years of adjustment of the company’s business, the company’s overall operational capabilities still needed to be improved, and the company’s comprehensive business capabilities also still needed to be improved. Therefore, the application of intelligent algorithms for financial control is very necessary.
Effects of aquaculture effluents on the slender sea pen Virgularia mirabilis
Authors: Taormina Bastien , Kutti Tina , Olsen Siri Aaserud , Sævik Pål Næverlid , Hannisdal Rita , Husa Vivian , Legrand Erwann
Published: April 2024

This study aims to assess in situ the impact of effluents originating from an Atlantic salmon (Salmo salar) farm on a nearby slender sea pen (Virgularia mirabilis) field. We evidenced (1) the presence and persistence of emamectin residues (i.e. a common chemotherapeutants used for treating ectoparasites in salmons) in V. mirabilis tissue 56 days after treatment and (2) lethal and sublethal responses of V. mirabilis to effluents discharged by the salmon farm. Particularly, sea pens near the fish farm exhibited significant overproduction of mucus, contraction of polyps’ tentacles, and disappearance of associated fauna. Furthermore, sea pens located directly underneath the farm showed substantial tissue necrosis and, in the most severe case, complete tissue loss and mortality. Our results suggest that lethal damages on sea pens occur directly below the farm, and that sublethal effects are visible up to 500 m from the farm. However, the presence of V. mirabilis below the studied farm, which has been active for more than twenty years, suggests that V. mirabilis population possesses the capacity to recover from the impacts of the farm, thereby preventing the complete disappearance from the area. In this context, it would be particularly interesting to run a temporal survey following the health state of V. mirabilis during an entire production cycle to have a more precise overview of fish farm impacts on this species, including during and after the post-production fallowing period.
Joint Bayesian estimation of cell dependence and gene associations in spatially resolved transcriptomic data
Authors: Chakrabarti Arhit , Ni Yang , Mallick Bani K.
Published: April 2024

Recent technologies such as spatial transcriptomics, enable the measurement of gene expressions at the single-cell level along with the spatial locations of these cells in the tissue. Spatial clustering of the cells provides valuable insights into the understanding of the functional organization of the tissue. However, most such clustering methods involve some dimension reduction that leads to a loss of the inherent dependency structure among genes at any spatial location in the tissue. This destroys valuable insights of gene co-expression patterns apart from possibly impacting spatial clustering performance. In spatial transcriptomics, the matrix-variate gene expression data, along with spatial coordinates of the single cells, provides information on both gene expression dependencies and cell spatial dependencies through its row and column covariances. In this work, we propose a joint Bayesian approach to simultaneously estimate these gene and spatial cell correlations. These estimates provide data summaries for downstream analyses. We illustrate our method with simulations and analysis of several real spatial transcriptomic datasets. Our work elucidates gene co-expression networks as well as clear spatial clustering patterns of the cells. Furthermore, our analysis reveals that downstream spatial-differential analysis may aid in the discovery of unknown cell types from known marker genes.
A nanowell platform to identify, sort and expand high antibody-producing cells
Authors: Abali Fikri , Schasfoort Richard , Nijland Sanne , Wittenberns Jelle , Tibbe Arjan. G. J. , den Hartog Marcel , Boon Louis , Terstappen Leon W. M. M.
Published: April 2024

Increased use of therapeutic monoclonal antibodies and the relatively high manufacturing costs fuel the need for more efficient production methods. Here we introduce a novel, fast, robust, and safe isolation platform for screening and isolating antibody-producing cell lines using a nanowell chip and an innovative single-cell isolation method. An anti-Her2 antibody producing CHO cell pool was used as a model. The platform; (1) Assures the single-cell origin of the production clone, (2) Detects the antibody production of individual cells and (3) Isolates and expands the individual cells based on their antibody production. Using the nanowell platform we demonstrated an 1.8–4.5 increase in anti-Her2 production by CHO cells that were screened and isolated with the nanowell platform compared to CHO cells that were not screened. This increase was also shown in Fed-Batch cultures where selected high production clones showed titers of 19–100 mg/L on harvest day, while the low producer cells did not show any detectable anti-Her2 IgG production. The screening of thousands of single cells is performed under sterile conditions and the individual cells were cultured in buffers and reagents without animal components. The time required from seeding a single cell and measuring the antibody production to fully expanded clones with increased Her-2 production was 4–6 weeks.
Predicting FFAR4 agonists using structure-based machine learning approach based on molecular fingerprints
Authors: Sherwani Zaid Anis , Tariq Syeda Sumayya , Mushtaq Mamona , Siddiqui Ali Raza , Nur-e-Alam Mohammad , Ahmed Aftab , Ul-Haq Zaheer
Published: April 2024

Free Fatty Acid Receptor 4 (FFAR4), a G-protein-coupled receptor, is responsible for triggering intracellular signaling pathways that regulate various physiological processes. FFAR4 agonists are associated with enhancing insulin release and mitigating the atherogenic, obesogenic, pro-carcinogenic, and pro-diabetogenic effects, normally associated with the free fatty acids bound to FFAR4. In this research, molecular structure-based machine-learning techniques were employed to evaluate compounds as potential agonists for FFAR4. Molecular structures were encoded into bit arrays, serving as molecular fingerprints, which were subsequently analyzed using the Bayesian network algorithm to identify patterns for screening the data. The shortlisted hits obtained via machine learning protocols were further validated by Molecular Docking and via ADME and Toxicity predictions. The shortlisted compounds were then subjected to MD Simulations of the membrane-bound FFAR4-ligand complexes for 100 ns each. Molecular analyses, encompassing binding interactions, RMSD, RMSF, RoG, PCA, and FEL, were conducted to scrutinize the protein–ligand complexes at the inter-atomic level. The analyses revealed significant interactions of the shortlisted compounds with the crucial residues of FFAR4 previously documented. FFAR4 as part of the complexes demonstrated consistent RMSDs, ranging from 3.57 to 3.64, with minimal residue fluctuations 5.27 to 6.03 nm, suggesting stable complexes. The gyration values fluctuated between 22.8 to 23.5 nm, indicating structural compactness and orderliness across the studied systems. Additionally, distinct conformational motions were observed in each complex, with energy contours shifting to broader energy basins throughout the simulation, suggesting thermodynamically stable protein–ligand complexes. The two compounds CHEMBL2012662 and CHEMBL64616 are presented as potential FFAR4 agonists, based on these insights and in-depth analyses. Collectively, these findings advance our comprehension of FFAR4’s functions and mechanisms, highlighting these compounds as potential FFAR4 agonists worthy of further exploration as innovative treatments for metabolic and immune-related conditions.
Optimizing the releasing strategy used for the biological control of the sugarcane borer Diatraea saccharalis by Trichogramma galloi with computer modeling and simulation
Authors: Gomes Garcia Adriano , Wajnberg Eric , Parra José Roberto Postali
Published: April 2024

One of the challenges in augmentative biological control programs is the definition of releasing strategy for natural enemies, especially when macro-organisms are involved. Important information about the density of insects to be released and frequency of releases usually requires a great number of experiments, which implies time and space that are not always readily available. In order to provide science-based responses for these questions, computational models offer an in silico option to simulate different biocontrol agent releasing scenarios. This allows decision-makers to focus their efforts to more feasible options. The major insect pest in sugarcane crops is the sugarcane borer Diatraea saccharalis, which can be managed using the egg parasitoid Trichogramma galloi. The current strategy consists in releasing 50,000 insects per hectare for each release, in three weekly releases. Here, we present a simulation model to check whether this releasing strategy is optimal against the sugarcane borer. A sensitive analysis revealed that the population of the pest is more affected by the number of releases rather than by the density of parasitoids released. Only the number of releases demonstrated an ability to drive the population curve of the pest towards a negative growth. For example, releasing a total of 600,000 insects per hectare in three releases led to a lower pest control efficacy that releasing only 250,000 insects per hectare in five releases. A higher number of releases covers a wider range of time, increasing the likelihood of releasing parasitoids at the correct time given that the egg stage is short. Based on these results, it is suggested that, if modifications to the releasing strategy are desired, increasing the number of releases from 3 to 5 at weekly intervals is most likely preferable.
Quantitative analysis and stochastic modeling of osteophyte formation and growth process on human vertebrae based on radiographs: a follow-up study
Authors: Wu Tong , Wang Changxi , Li Kang
Published: April 2024

Osteophytes are frequently observed in elderly people and most commonly appear at the anterior edge of the cervical and lumbar vertebrae body. The anterior osteophytes keep developing and will lead to neck/back pain over time. In clinical practice, the accurate measurement of the anterior osteophyte length and the understanding of the temporal progression of anterior osteophyte growth are of vital importance to clinicians for effective treatment planning. This study proposes a new measuring method using the osteophyte ratio index to quantify anterior osteophyte length based on lateral radiographs. Moreover, we develop a continuous stochastic degradation model with time-related functions to characterize the anterior osteophyte formation and growth process on cervical and lumbar vertebrae over time. Follow-up data of anterior osteophytes up to 9 years are obtained for measurement and model validation. The agreement test indicates excellent reproducibility for our measuring method. The proposed model accurately fits the osteophyte growth paths. The model predicts the mean time to onset of pain and obtained survival function of the degenerative vertebrae. This research opens the door to future quantification and mathematical modeling of the anterior osteophyte growth on human cervical and lumbar vertebrae. The measured follow-up data is shared for future studies.
Urinary metabolomic profiling of a cohort of Colombian patients with systemic lupus erythematosus
Authors: Rojo-Sánchez Alejandra , Carmona-Martes Ada , Díaz-Olmos Yirys , Santamaría-Torres Mary , Cala Mónica P. , Orozco-Acosta Erick , Aroca-Martínez Gustavo , Pacheco-Londoño Leonardo , Navarro-Quiroz Elkin , Pacheco-Lugo Lisandro A.
Published: April 2024

Systemic lupus erythematosus (SLE) is an autoimmune and multisystem disease with a high public health impact. Lupus nephritis (LN), commonly known as renal involvement in SLE, is associated with a poorer prognosis and increased rates of morbidity and mortality in patients with SLE. Identifying new urinary biomarkers that can be used for LN prognosis or diagnosis is essential and is part of current active research. In this study, we applied an untargeted metabolomics approach involving liquid and gas chromatography coupled with mass spectrometry to urine samples collected from 17 individuals with SLE and no kidney damage, 23 individuals with LN, and 10 clinically healthy controls (HCs) to identify differential metabolic profiles for SLE and LN. The data analysis revealed a differentially abundant metabolite expression profile for each study group, and those metabolites may act as potential differential biomarkers of SLE and LN. The differential metabolic pathways found between the LN and SLE patients with no kidney involvement included primary bile acid biosynthesis, branched-chain amino acid synthesis and degradation, pantothenate and coenzyme A biosynthesis, lysine degradation, and tryptophan metabolism. Receiver operating characteristic curve analysis revealed that monopalmitin, glycolic acid, and glutamic acid allowed for the differentiation of individuals with SLE and no kidney involvement and individuals with LN considering high confidence levels. While the results offer promise, it is important to recognize the significant influence of medications and other external factors on metabolomics studies. This impact has the potential to obscure differences in metabolic profiles, presenting a considerable challenge in the identification of disease biomarkers. Therefore, experimental validation should be conducted with a larger sample size to explore the diagnostic potential of the metabolites found as well as to examine how treatment and disease activity influence the identified chemical compounds. This will be crucial for refining the accuracy and effectiveness of using urine metabolomics for diagnosing and monitoring lupus and lupus nephritis.
Identification of type VI secretion system effector-immunity pairs using structural bioinformatics
Authors: Geller Alexander M , Shalom Maor , Zlotkin David , Blum Noam , Levy Asaf
Published: April 2024

The type VI secretion system (T6SS) is an important mediator of microbe–microbe and microbe–host interactions. Gram-negative bacteria use the T6SS to inject T6SS effectors (T6Es), which are usually proteins with toxic activity, into neighboring cells. Antibacterial effectors have cognate immunity proteins that neutralize self-intoxication. Here, we applied novel structural bioinformatic tools to perform systematic discovery and functional annotation of T6Es and their cognate immunity proteins from a dataset of 17,920 T6SS-encoding bacterial genomes. Using structural clustering, we identified 517 putative T6E families, outperforming sequence-based clustering. We developed a logistic regression model to reliably quantify protein–protein interaction of new T6E-immunity pairs, yielding candidate immunity proteins for 231 out of the 517 T6E families. We used sensitive structure-based annotation which yielded functional annotations for 51% of the T6E families, again outperforming sequence-based annotation. Next, we validated four novel T6E-immunity pairs using basic experiments in E. coli. In particular, we showed that the Pfam domain DUF3289 is a homolog of Colicin M and that DUF943 acts as its cognate immunity protein. Furthermore, we discovered a novel T6E that is a structural homolog of SleB, a lytic transglycosylase, and identified a specific glutamate that acts as its putative catalytic residue. Overall, this study applies novel structural bioinformatic tools to T6E-immunity pair discovery, and provides an extensive database of annotated T6E-immunity pairs.
A single-step high-throughput bioassay for quantifying Fc-containing recombinant proteins based on non-classical calculation of fluorescence polarization
Authors: Zheng Yujuan , Chen Ganjun , Liu Guojian , Rana Gul E. , Wang Chunhe
Published: April 2024

A single-step and high-throughput bioassay based on non-classical calculation of fluorescence polarization for quantifying Fc-containing recombinant proteins in the cell culture supernatant.
Self-assembled tetrazine cryptophane for ion pair recognition and guest release by cage disassembly
Authors: Miton Louise , Antonetti Elise , Poujade Marie , Dutasta Jean-Pierre , Nava Paola , Martinez Alexandre , Cotelle Yoann
Published: April 2024

A cryptophane is able to release guests by the addition of thiols thanks to the dynamic nucleophilic substitution of tetrazines.
Magnetic Pd–Fe nanoparticles for sustainable Suzuki–Miyaura cross-coupling reactions
Authors: Zhu Zhuangli , Liang Sanqi , Sun Huaming , Zhang Weiqiang , Yang Jianming , Gao Ziwei
Published: April 2024

The development of palladium-catalyzed cross-coupling reactions has been revolutionized by bimetallic co-catalysis and magnetic separation techniques, which address the dual challenges of reducing the reliance on palladium (Pd) and advancing sustainable chemistry practices.
Molecular two-point recognition of fructosyl valine and fructosyl glycyl histidine in water by fluorescent Zn(ii)-terpyridine complexes bearing boronic acids
Authors: Salomón-Flores María K. , Valdes-García Josue , Viviano-Posadas Alejandro O. , Martínez-Otero Diego , Barroso-Flores Joaquín , Bazany-Rodríguez Iván J. , Dorazco-González Alejandro
Published: April 2024

We report novel cationic Zn-terpyridine complexes bearing a N-quinolinium nucleus covalently linked to strongly acidic phenylboronic acids for the fluorescent recognition of fructosyl valine and fructosyl glycyl histidine in water.
Aggregation-induced enhanced fluorescence emission of chiral Zn(ii) complexes coordinated by Schiff-base type binaphthyl ligands
Authors: Tauchi Daiki , Kanno Katsuya , Hasegawa Masashi , Mazaki Yasuhiro , Tsubaki Kazunori , Sugiura Ken-ichi , Shiga Takuya , Mori Seiji , Nishikawa Hiroyuki
Published: April 2024

A pair of chiral Zn(ii) complexes coordinated by Schiff-base type binaphthyl ligands exhibiting aggregation-induced enhanced emission and circularly polarized luminescence have been synthesized and characterized.
Toward understanding CO oxidation on high-entropy alloy electrocatalysts
Authors: Salinas-Quezada María Paula , Pedersen Jack K. , Sebastián-Pascual Paula , Chorkendorff Ib , Biswas Krishanu , Rossmeisl Jan , Escudero-Escribano María
Published: April 2024

Extended high-entropy alloy electrocatalysts as a platform to investigate electrocatalytic CO oxidation and surface structure–property relations.
Heterogeneous CNF/MoO3 nanofluidic membranes with tunable surface plasmon resonances for solar-osmotic energy conversion
Authors: Zheng Mengmeng , Liu Pei , Yan Pengfei , Zhou Teng , Lin Xiangbin , Li Xin , Wen Liping , Xu Qun
Published: April 2024

This work first reveals the regulation and mechanism of surface plasmon resonance on ion transport in nanofluidic membranes and explores the potential of solar-osmotic energy conversion based on cellulose nanofiber/molybdenum oxide heterogeneous nanofluidic membranes.
KHMDS mediated ring-opening/reconstruction of anthranils with arylacetonitriles: synthesis of multisubstituted 2-aminoquinoline N-oxides
Authors: Jat Ram Singh , Singh Gautami , Bhanuchandra M.
Published: April 2024

A convenient and atom-economical protocol for the construction of multisubstituted 2-aminoquinoline N-oxides using readily available starting materials anthranil and arylacetonitrile has been reported.
Highly selective, sensitive, and visible detection of fluoride ions through transforming their roles from reactants to catalysts in the silicon corrosion reaction
Authors: Hu Xinyue , Zhang Bingchang , Shi Yihao , An Xingda , Yu Jia , He Le , Zhang Xiaohong
Published: April 2024

Transforming fluoride ions from reactants to catalysts in the silicon corrosion reaction enables their detection at high selectivity and sensitivity.
Green-synthesized porous g-C3N4@ZnHCC nanocomposite for the on–off fluorescence detection and elimination of toxic chromium(vi) metal ions
Authors: Yadav Jyoti , Rani Manviri , Shanker Uma
Published: April 2024

Chromium(vi) is a significant environmental pollutant because of its highly hazardous and cancer-causing properties.
Facile synthesis of diiodoheteroindenes and understanding their Sonogashira cross-coupling selectivity for the construction of unsymmetrical enediynes
Authors: Ponomarev Alexander V. , Danilkina Natalia A. , Okuneva Julia S. , Vidyakina Aleksandra A. , Khmelevskaya Ekaterina A. , Bunev Alexander S. , Rumyantsev Andrey M. , Govdi Anastasia I. , Suarez Thomas , Alabugin Igor V. , Balova Irina A.
Published: April 2024

Iodocyclization of Ar-iodoacetylenes yields 2,3-diiodoheteroindene substrates for C2-regioselective Sonogashira cross-couplings. The high selectivity observed for diiodobenzothiophene allows the one-pot synthesis of unsymmetrical enediynes.
Recent developments of polymeric delivery systems in gene therapeutics
Authors: Li Yijia , Tian Ruizhen , Xu Jiayun , Zou Yingping , Wang Tingting , Liu Junqiu
Published: April 2024

A brief overview of main strategies to improve the gene transfection efficiency of PCVs recently and their applications in certain diseases.
Advancements in application of chitosan and cyclodextrins in biomedicine and pharmaceutics: recent progress and future trends
Authors: Bahavarnia Farnaz , Hasanzadeh Mohammad , Bahavarnia Parinaz , Shadjou Nasrin
Published: April 2024

The global community is faced with numerous health concerns such as cancer, cardiovascular and neurological diseases, diabetes, joint pain, osteoporosis, among others.
Investigation of mechanical behavior of glass fiber reinforced extruded polystyrene core material composites
Authors: Yavuz İbrahim , Şi̇mşi̇r Ercan , Şenol Barış
Published: April 2024

Layered composites are composite materials created by combining different layers of materials.
Synthesis of novel phthalazine-based derivatives with potent cytotoxicity against HCT-116 cells through apoptosis and VEGFR2 inhibition
Authors: El Sayed Donia , El Rayes Samir M. , Soliman Hamdy A. , AlBalaa Imad Eddin , Alturki Mansour S. , Al Khzem Abdulaziz Hassan , Alsharif Mohammed Abdullah , Nafie Mohamed S.
Published: April 2024

A novel phthalazine derivative exhibited potent cytotoxicity against HCT-116 cells as VEGFR2 inhibitor and apoptosis cell death.
Fast response/recovery and sub-ppm ammonia gas sensors based on a novel V2CTx@MoS2 composite
Authors: Luo Manyu , Huang Xingpeng , Xiong Deshou , Cai Sijin , Li Shuang , Jia Zhenhong , Gao Zhixian
Published: April 2024

The V2CTx@MoS2-2 composite sensor prepared in this work can realize rapid detection of sub-ppm ammonia at room temperature with good anti-interference ability and long-term stability.
Advanced polyanionic cathode materials for aqueous zinc-ion batteries: from crystal structures, reaction mechanisms, design strategies to future perspectives
Authors: Jin Can , Shen Qiuyu , Zhang Long , Li Shengwei , Qu Xuanhui , Jiao Lifang , Liu Yongchang
Published: April 2024

This review summarizes the latest research progress in polyanionic cathodes for aqueous Zn-ion batteries from the perspectives of crystal structure, reaction mechanism, and design strategy, finally highlights potential challenges and future outlook.
New ultrafast scintillators with core valence luminescence: Cs2MgCl4 and Cs3MgCl5
Authors: Rutstrom Daniel , Stand Luis , Windsor Dylan , Xu Haixuan , Kapusta Maciej , Melcher Charles L. , Zhuravleva Mariya
Published: April 2024

Single crystals of Cs2MgCl4 and Cs3MgCl5 are grown by the Bridgman method and revealed as promising scintillator materials.
Additive manufacturing of LiCoO2 electrodes via vat photopolymerization for lithium ion batteries
Authors: Martinez Ana C. , Aranzola Ana P. , Schiaffino Eva , MacDonald Eric , Maurel Alexis
Published: April 2024

3D printed LiCoO2 cathodes for lithium-ion batteries were obtained via vat photopolimerization additive manufacturing. The best electrochemical performance was obtained after performing thermal post-processing.
An effective screening of COVID‐19 pneumonia by employing chest X‐ray segmentation and attention‐based ensembled classification
Authors: Sayeed Abu , Khansur Nasif Osman , Srizon Azmain Yakin , Faruk Md. Farukuzzaman , Alyami Salem A. , Azad AKM , Moni Mohammad Ali
Published: April 2024

Quick and accurate diagnosis of COVID‐19 is crucial in preventing its transmission. Chest X‐ray (CXR) imaging is often used for diagnosis, however, even experienced radiologists may misinterpret the results, necessitating computer‐aided diagnosis. Deep learning has yielded favourable results previously, but overfitting, excessive variance, and generalization errors may occur due to noise and limited datasets. Ensemble learning can improve predictions by using robust techniques. Therefore, this study, proposes two‐fold strategy that combines advanced and robust algorithms, including DenseNet201, EfficientNetB7, and Xception, to achieve faster and more accurate COVID‐19 detection. Segmented lung images were generated from CXR images using the residual U‐Net model, and two attention‐based ensemble neural networks were used for classification. The COVID‐19 radiography dataset was used to evaluate the proposed approach, which achieved an accuracy of 98.21%, 93.4%, and 89.06% for two, three, and four classes respectively which outperformed previous studies by a significant margin considering COVID, viral pneumonia, and lung opacity simultaneously. Despite the similarity in CXR images of COVID, pneumonia, and lung opacity, the proposed approach achieved 89.06% accuracy, demonstrating its ability to recognize distinguishable features. The developed algorithm is expected to have applications in clinics for diagnosing different diseases using X‐ray images.
Joint waveform design and resource allocation strategy for cognitive radar target situation awareness
Authors: Song Yuxiao , Tian Biao , Wang Rongqing , Xu Shiyou , Chen Zengping
Published: April 2024

Traditional radar systems use fixed patterns and constant electromagnetic wave transmission to illuminate targets, but they often do not effectively use prior information about targets and consume significant radar resources. Cognitive radar has emerged as a way to improve resource efficiency and address these shortcomings. A joint waveform design and resource allocation strategy for cognitive radar that incorporates target situational awareness is proposed. This method integrates the interacting multiple model algorithm and the Unscented Kalman Particle Filter to achieve target situation awareness as prior knowledge. By combining the target attitude and the frequency response function of the target radar cross section at different time points in the prior knowledge, a joint beam control and power allocation strategy is formulated and transformed into an optimization problem. In addition, a cognitive pulse‐to‐pulse frequency agile waveform design method is proposed to support multiple target tracking under complex motion models. Simulation experiments demonstrate the effectiveness of this approach in obtaining accurate target situation information, achieving beam control, and optimizing power allocation. The designed waveforms can enhance radar target detection performance and improve low probability of intercept characteristics by adjusting the pulse repetition interval. This method has significant technical value.
AMICO-COSMOS galaxy cluster and group catalogue up to z = 2: Sample properties and X-ray counterparts
Authors: Toni G. , Maturi M. , Finoguenov A. , Moscardini L. , Castignani G.
Published: April 2024

Aims. We present the results of a new galaxy cluster search in the COSMOS field obtained using the Adaptive Matched Identifier of Clustered Objects (AMICO). Our aim was to produce a new cluster and group catalogue up to z = 2 by performing an innovative application of AMICO with respect to previous successful applications to wide-field surveys in terms of depth (down to r < 26.7), small area covered (∼1.69 deg2 of unmasked effective area), and redshift extent. This sample and the comparative analysis we performed with the X-rays, allowed for the calibration of mass-proxy scaling relations up to z = 2 and down to less than 1013 M⊙, and constitutes the basis for the refinement of the cluster model for future applications of AMICO, such as the analysis of upcoming Euclid data.

Methods. The AMICO algorithm is based on an optimal linear matched filter and detects clusters in photometric galaxy catalogues using galaxy location, photometric redshift and, in the simplest case, one galaxy property. We chose to use a single magnitude as the galaxy property, avoiding the explicit use of galaxy colour for the selection of clusters. We used three different magnitudes by performing three independent runs in the r-, Y-, and H-bands using both COSMOS2020 and COSMOS2015 galaxy catalogues. We created a composite visibility mask and cluster models for the signal to detect, and we estimated the noise directly from the data.

Results. We performed a matching of the catalogues resulting from the three runs and merged them to produce a final catalogue that contains 1269 and 666 candidate clusters and groups with S/N > 3.0 and > 3.5, respectively. A total of 490 candidates are detected in all three runs. Most of the detections unmatched between runs have S/N < 3.5, which can be set as a threshold for selecting a more robust sample. We assigned X-ray properties to our detections by matching the catalogue with a public X-ray selected group sample and by estimating, for unmatched detections, the X-ray properties at the location of AMICO candidates using Chandra+XMM-Newton data. There are in total 622 candidate clusters and groups with an X-ray flux estimate. This large sample of candidates with X-ray properties allowed the calibration of the scaling relations between two AMICO mass-proxies (richness and cluster amplitude) and X-ray mass and the study of their redshift dependence for the selection of the most stable photometric bands.
Differentiated Paths of Regional Green Development Driven by Industrial Enterprise Innovation
Authors: Sheng Qinya , Guo Wen , Zhao Meiling
Published: April 2024

A fundamental shift in the regional development pattern is crucial to achieving a comprehensive green transformation in China. Currently, innovation-driven green development is a significant strategic option for regional development. As the main body of innovation and the basic unit of regional composition, enterprises have a profound impact on the development of regional economy, society, ecology, and other aspects. However, considering China’s vast territory and significant regional differences in natural environment and industrial structure, it’s necessary to further explore the specific impact paths of regional green development driven by enterprise innovation. Therefore, taking industrial enterprises as an example, based on the panel data of 30 provinces in China from 2016 to 2020, this study verifies the impact of industrial enterprise innovation on the regional green development level by constructing a parallel multiple mediating effect model and dividing the economy into eastern, central, and western regions to discuss the specific impact paths. The results show that industrial enterprise innovation has a significant positive effect on regional green development level, via different influencing paths in each region: (1) The eastern region improves the regional green development level by narrowing the urban-rural income gap; (2) The central region improves the regional green development level by reducing resource dependence; and (3) The western region raises the regional green development level by improving the rationalization of industrial structure.
Retraction Note: Public regulation effectiveness assessment of Russian regional labor markets using hierarchical clustering method
Authors: Gurieva Lira , Dzhioev Aleksandr
Published: April 2024

It has been commonly decided to retract this article on the grounds that one of the authors, Lira Gurieva, has used material without due knowledge of all parties involved in the results obtained within this research article. The author apologizes for the inadvertent misappropriation of the data.

The Authors, the Publisher and Editor approved to the retraction.
Renoprotective effects of Cermai Leaves (Phyllanthus acidus(L.) Skeels) as a candidate for antidiabetic in silico
Authors: Purnama Erlix Rakhmad , Kuswanti Nur , Qomariyah Nur , Khaleyla Firas , Aziz R. Ahmad Zainul
Published: April 2024

Renal complications are a major concern in individuals with diabetes, necessitating the search for effective antidiabetic agents with renoprotective properties.Phyllanthus acidus(L.) Skeels, commonly known as Cermai leaves, have been recognized for their medicinal properties, including potential antidiabetic effects. This in silico study aimed to investigate the renoprotective effects of Phyllanthus acidus as an antidiabetic agent by targeting the insulin-like growth factor-1 (IGF-1) (PDB code 1GZR) and angiotensin-converting enzyme 2 (ACE2) (PDB code 6M0J) receptors. The prediction of phytochemical properties was made based on the five laws of Lipinski as seen from the values of absorption, distribution, and metabolism with Swiss ADME. The in-silico method was conducted with the assistance of computer devices, databases like Protein Data Bank (PDB) and PubChem, and software like PyRx and Discovery Studio. The results showed the compounds kaempferol and cafestol meet the five laws of Lipinski. The compound kaempferol has the lowest binding affinity, with a value of -5.8 kcal/mol on IGF-1 and -6.1 kcal/mol on ACE2 and cafestol, -5.6 kcal/mol on IGF-1 and -5.8 kcal/mol on ACE2. It’s compared to the native ligand value -3.9 kcal/mol on IGF-1 and -5.2 kcal/mol on ACE2. Based on the results, it is known that this compound has a high potential to be a drug candidate for renoprotective.
Potency of active compounds extract of soursop leaves (Annona muricata) as a candidate for cervical cancer drug in Silico
Authors: Afnani Moh. Royhan , Emilia Nur Fidia , Damayanti Ayu Elysia , Rabbani Calista Naya , Purnama Erlix Rakhmad
Published: April 2024

Cervical cancer is a cancer caused by an infection derived from the Human Papillomavirus (HPV) that proliferates in the cervical mucosa. Cervical cancer ranks among the two highest incidences experienced by women, with 23.4% and an average death rate of 13.9%. Vascular endothelial growth factor (VEGF) is an important protein that plays a role in the pathology of angiogenesis in tumor growth and metastasis. Various efforts have been made to minimize mortality from cervical cancer, but they have caused side effects. Indonesia is rich in a diversity of flora that has the potential to be used as a natural medicine, for example, soursop leaves (Annona muricata). The study aimed to predict the compound extract of soursop leaves as a candidate drug for cervical cancer based on a silico approach. The prediction of phytochemical properties is made based on the five laws of Lipinski as seen from the values of absorption, distribution, and metabolism with Swiss ADME. The in-silico method was conducted with the assistance of computer devices, databases, and software like Autodock, PyRx, PyMol, and Discovery Studio. The target proteins used are VEGFR1 (PDB: 3HNG) and VEGFR-2 (PDB: 3VHE). The results showed that kaempferol, sativene, and thiazole-2,4(3H,5H)-dione, 5-benzylideno-3-[(ethylphenylamino)methyl] meet the five laws of Lipinski. The compound kaempferol has the lowest binding affinity, with a value of -8.5 kcal/mol on VEGFR-1 and -10.2 kcal/mol on VEGFR-2. On the other side, sunitib interactions with VEGFR-1 were -10.0 kcal/mol and -9.4 kcal/mol, respectively. Based on the results, can be concluded that kaempferol has a high potential to be a drug candidate for cervical cancer.
Wonorejo mangrove indigenous bacteria: An insight into their potential as plastic-degrading agents
Authors: Lisdiana Lisa , Trimulyono Guntur , Amaliah Afrida , Rifda  , Nafidiastri Farah Aisyah
Published: April 2024

The use of bioplastic, such as polycaprolactone, to substitute conventional plastic remains a problem to solve. The bioplastic degradation time is still relatively low when compared to the rate of plastic consumption by the public. Therefore, exploration of indigenous bacteria with plastic-degrading potential is needed. This study aims to reveal the potential of indigenous bacteria isolated from Wonorejo Mangrove as plastic-degrading bacteria based on their growth in selective media and biofilm formation. Bacterial isolates obtained from water bodies and sediments of Wonorejo’s mangrove were inoculated on minimum salt media with the addition of 0.25% polycaprolactone as the sole carbon source and then incubated for four weeks to determine the bacterial growth based on its total number. The total number of bacteria was calculated by the direct counting method using a hemocytometer. The results indicated a slight decrease in the number of cells for each isolate. Isolate T1A.1 obtained from mangrove water samples encountered a decrease in the total number of bacteria by 2 times the initial number. Meanwhile, isolate T2.1, which was isolated from mangrove sediments, was decreased by 1.4 times from the initial number. However, the enumeration did not cover the cells that formed the biofilm, which was observed in this study. Based on the ability of the isolates to live in the minimum media and the biofilm formation indicated their potential as plastic-degrading agents, specifically for polycaprolactone. Identification and further studies of both isolates are needed to get a better insight into their potential as polycaprolactone-degrading agents.
Plant taxonomy: Cooperation, innovation, productivity
Authors: van Welzen Peter C. , Putri Eva Kristinawati , Bijmoer Roxali
Published: April 2024

Floristic taxonomic research, looking for constant differences between species, is a very time-consuming and slow process. The flora of the Malay Archipelago is estimated to have 45,000 vascular plant species and the Flora Malesiana project that describes them has completed c. 1/3rd in 75 years. Presently, the flora is heavily threatened because of land use and climate change. We have to speed up the flora writing to understand what will be threatened and how to counter the threats. Innovation using Next Generation Sequencing of DNA to find the species in combination with Image Recognition might help to increase speed. Another aspect is cooperation, more international scientists should be involved, also students and citizens via citizen science projects. Citizens can help to gather information (observations and collecting). Once the flora is better known, monitoring of the flora by regular inventories (done by non-scientists in the Netherlands) helps to see which plants are threatened or become plagues and causes can then be investigated. Monitoring will help to change energy consumption (no more fossil energy), agriculture, industry, and living: improve our climate, and save the planet.
Scientific literacy in physics learning: A bibliometric analysis from 1977 until 2023 and its impact on quality education
Authors: Alhusni Hanan Zaki , Habibbulloh Muhammad , Lestari Nurita Apridiana , Realita Arie , Jatmiko Budi , Deta Utama Alan
Published: April 2024

This study focuses on keyword mapping and exploring research trend topics of scientific literacy in physics education for future research opportunities. The study aims to analyze bibliometrics literature on scientific literacy in physics education and publication trends indexed by the Scopus database. The method used is using bibliometrics analysis in the Scopus database. According to the data, this research shows that scientific literacy publications from 1977-2023 are 287 research papers. The most dominant keywords are scientific literacy skills, physics teacher, and science education. The top author in scientific literacy is Prahani BK. The most published articles in scientific literacy are in the Journal of Physics Conference Series. Scientific literacy can serve as a strong foundation for achieving the SDG for quality education, as it enables individuals and society to understand, encourage, and support efforts to improve the quality of education for all. In conclusion, scientific literacy is still possible to analyze and link with other topics, so scientific literacy for education physics will make the world of education more diverse. There are many future study opportunities, from results analysis to literacy science.
Numerical simulation study on the drag reduction characteristics of grooves-microbubbles coupling surfaces
Authors: Li Tianjian , Yang Tianlong , Dong Qin , Sun Xizhi , Wu Tao , Xu Ziqi
Published: April 2024

Surface drag reduction technologies can significantly reduce the resistance during ship navigation, enhancing speed, efficiency and adaptability under various operating conditions. This paper uses numerical simulation technology to analyze the drag reduction characteristics of grooved and grooves-microbubbles coupling surface, focusing on the effects of groove width, gas flow rate, and liquid flow velocity on the drag reduction performance. The research results indicate that the grooved surface is suitable for full surface drag reduction at velocity below 3 m/s with a maximum drag reduction rate of 4.02%. Microbubbles can greatly improve the drag reduction effect of the grooved surface, and the drag reduction effect of the coupling surface gradually increases with the gas flow rate increases. The maximum drag reduction rate can reach 89.86% at the gas inlet velocity of 1 m/s. The liquid flow velocity has a significant impact on the drag reduction. In both the groove model and the coupling model, the drag reduction rate initially rises and then declines with the liquid flow velocity increases.
VAEFL: Integrating variational autoencoders for privacy preservation and performance retention in federated learning
Authors: Li Zhixin , Liu Yicun , Li Jiale , Ye Guangnan , Chai Hongfeng , Lu Zhihui , Wu Jie
Published: April 2024

Federated Learning (FL) heralds a paradigm shift in the training of artificial intelligence (AI) models by fostering collaborative model training while safeguarding client data privacy. In sectors where data sensitivity and AI model security are of paramount importance, such as fintech and biomedicine, maintaining the utility of models without compromising privacy is crucial with the growing application of AI technologies. Therefore, the adoption of FL is attracting significant attention. However, traditional FL methods are susceptible to Deep Leakage from Gradients (DLG) attacks, and typical defensive strategies in current research, such as secure multi-party computation and differential privacy, often lead to excessive computational costs or significant decreases in model accuracy. To address DLG attacks in FL, this study introduces VAEFL, an innovative FL framework that incorporates Variational Autoencoders (VAEs) to enhance privacy protection without undermining the predictive prowess of the models. VAEFL strategically partitions the model into a private encoder and a public decoder. The private encoder, remaining local, transmutes sensitive data into a latent space fortified for privacy, while the public decoder and classifier, through collaborative training across clients, learn to derive precise predictions from the encoded data. This bifurcation ensures that sensitive data attributes are not disclosed, circumventing gradient leakage attacks and simultaneously allowing the global model to benefit from the diverse knowledge of client datasets. Comprehensive experiments demonstrate that VAEFL not only surpasses standard FL benchmarks in privacy preservation but also maintains competitive performance in predictive tasks. VAEFL thus establishes a novel equilibrium between data privacy and model utility, offering a secure and efficient FL approach for the sensitive application of FL in the financial domain.
Mit Krafttraining zum Fußballstar – ein systematischer Aufbau für Kinder und
Jugendliche
Authors: Hartmann Patrick
Published: April 2024

Timo ist 12 Jahre alt und begeisterter Fußballer. Kürzlich hat er eine Dokumentation über seinen
Lieblingsverein gesehen und dabei erfahren, dass seine Vorbilder weit mehr tun, als nur Fußball zu
spielen. Es wurde gezeigt, wie die Profis auch abseits des Fußballplatzes trainieren, unter anderem mit
Gewichten im Kraftraum. Und das alles, um die nächste Meisterschaft zu gewinnen. Er war fasziniert.
Neuausrichtung der Therapie für hochfunktionale Jugendliche mit
Zerebralparese
Authors: Minghetti Alice , Keller Martin , Gysin Ramon , Roth Ralf
Published: April 2024

Ein innovativer Ansatz für funktionelles Training bei Jugendlichen mit Zerebralparese: wie
wissenschaftliche Erkenntnisse umgesetzt in praktische Anwendungen erstaunliche Fortschritte
ermöglichen.