bluuebunny/crossref_metadata_2025 huggingface dataset:
Two $$\beta $$-ensemble realization of $$\beta $$-deformed WLZZ models
Authors: Mironov A. , Oreshina A. , Popolitov A.
Published: July 2024

We consider a two $$\beta $$
β
-ensemble realization of the series of $$\beta $$
β
-deformed WLZZ matrix models. We demonstrate that such a realization involves $$\beta $$
β
-deformed Harish-Chandra–Itzykson–Zuber integrals, one of them providing a coupling to the external field. We also construct Ward identities in the corresponding two $$\beta $$
β
-ensemble model, which requires a set of identities for partition function of the one $$\beta $$
β
-ensemble in the external field, and a set of identities for the $$\beta $$
β
-deformed Itzykson-Zuber integral. These both sets of identities are formulated in terms of the Dunkl operators.
Saturation and absorption effects on the transmission properties of Kerr-type nonlinear layers
Authors: Betancur-Yepes C. A. , Mazo-Vásquez J. D. , Reyes-Gómez E.
Published: July 2024

The effects of absorption and saturation on soliton states in Kerr-type nonlinear layers are theoretically investigated. In addition to the usual gray and bright soliton structures observed in nonlinear slabs, a flat soliton, i.e., a particular soliton excitation with electric field amplitude independent of the position within the layer, is researched in cases of self-defocusing and self-focusing nonlinearities. Effects caused by the combination of absorption and saturation, such as the shift and extinction of the flat soliton peak, the decrease in the amplitude of the electric field within the nonlinear layer, and the suppression of the multistable behavior of the transmission coefficient in the vicinity of the soliton peaks, are discussed. The present theoretical results are compared and found in good quantitative agreement with previous experimental measurements.

Graphical Abstract
Crystal channeling investigations at medical synchrotron regimes
Authors: Rossi Roberto , Andreazza Attilio , Bauce Matteo , Carbone Antonio , D’Auria Saverio , Garattini Marco , Hall Geoff , Lari Tommaso , Mereghetti Alessio , Pesaresi Mark , Pullia Marco , Valente Paolo , Variola Alessandro , Zanzottera Riccardo , Scandale Walter
Published: July 2024

An experimental setup to investigate ion channeling in the hundreds MeV/u energy range is described. A short bent crystal, aligned to the incoming beam by an angular actuator, should deflect ions into a single plane pixel detector used to identify channeled and unchanneled particles. In order to enhance the footprint of channeling on the recorded signals, the incoming particle emittance is tailored on the crystalline plane acceptance by means of massive collimators. Numerical simulations of low energy carbon ions are performed with FLUKA to fully understand the measurement conditions demonstrate the feasibility of the test, to be performed in the experimental room of the National Center for Oncological Hadrontherapy accelerator complex in Pavia, Italy.
Intelligent Monitoring System for Highway Roadbed Based on Combination Neural Network Algorithm
Authors: Lei Bijun , Li Rui , Luo Zhixu
Published: July 2024

To solve the problem of the frequent occurrence of roadbed faults, we studied the highway roadbed intelligent monitoring system based on a combined neural network algorithm. Based on the embedded system, with a variety of sensors, we completed the construction of the roadbed monitoring system. In the selection of the data processing algorithm model, the combined neural network algorithm based on an artificial immune algorithm and probabilistic neural network (PNN) is selected. The accurate acquisition of data characteristics is realized by data preprocessing, data smoothing and data fitting. Through experimental verification, the accuracy of the research model in identifying roadbed settlements has been improved by about 5% compared to traditional models. Furthermore, the processing time of the model has been shortened by about 19.5%, proving the effectiveness of the model. In terms of fault identification, compared with other classic models, the final recognition accuracy of this model reached 96.7%, far exceeding the comparison model. This provides new ideas for the monitoring and protection of roadbed faults.
CLOUD-EDGE CONTINUUM FRAMEWORK FOR ADMISSION DATA MANAGEMENT USING DEEP LEARNING MODEL
Authors: ALASHJAEE ABDULLAH M. , ALJEBREEN MOHAMMED , ALFRAIHI HESSA , HASSINE SIWAR BEN HAJ , ALGHUSHAIRY OMAR , ALGHAMDI BANDAR M. , ALALLAH FOUAD SHOIE
Published: July 2024

The surge in applications necessitates a more intelligent and automated Higher Education Admission Management System (HEAMS). This research proposes a novel Deep Learning (DL)-based HEAMS utilizing a Cloud-Edge Architecture. The first step collects applicant data like transcripts, test scores, essays, and recommendations. Edge devices perform initial cleaning and preprocessing on these data to ensure quality and privacy. These preprocessed data using normalization and feature extraction using the Latent Dirichlet Allocation (LDA) are then transferred to the cloud where DL models, such as Convolutional Neural Networks (CNNs) for essays or Recurrent Neural Networks (RNNs) for transcripts, are trained. These models learn complex patterns from historical labeled data (admitted/not admitted) to predict an applicant’s success probability. During application evaluation, new data are fed through the trained models on the edge, generating probabilities for predefined classifications — high-potential, moderate, or low-potential. The cloud receives these probabilities and combines them with predefined admission criteria like minimum GPA. This combined analysis leads to a final classification using Novel Lite Convolutional Neural Network with Hybrid Leader-based Optimization (Lite CNN-HLO) for each applicant — admitted, waitlisted, or rejected and admission management system by refining admission decisions for admitted, waitlisted, and rejected applicants based on institutional priorities and constraints. The system not only generates classifications but also provides detailed model score breakdowns for transparency. This Cloud-Edge HEAMS offers improved efficiency, reduced workload for admissions staff, and potentially fairer decisions by mitigating bias through data-driven analysis.
Nonlinear evolution equations in dispersive media: Analyzing the semilinear dispersive-fisher model
Authors: Altuijri Reem , Abdel-Aty Abdel-Haleem , Nisar Kottakkaran Sooppy , Khater Mostafa M. A.
Published: July 2024

The nonlinear Semilinear Dispersive-Fisher (SDF) model is an important equation describing wave dynamics in dispersive media influenced by nonlinear and diffusive effects. This study enhances both analytical and numerical methods to solve the SDF model and validate the solutions. We utilize the Modified Khater (MKhat) and Unified (UF) methods for analytical solutions, and He’s Variational Iteration (HVI) scheme for numerical approximations. Our investigation connects the SDF model to other nonlinear equations like the Korteweg–de Vries (KdV) and Fisher–Kolmogorov (FK) equations, demonstrating the consistency between analytical and numerical solutions. This research contributes to the understanding and modeling of wave phenomena in dispersive media with nonlinear and diffusive dynamics, offering refined analytical and numerical techniques specific to the SDF model. Key contributions include introducing the MKhat and UF methods for analytical solutions and employing the HVI scheme for numerical approximations, which are less represented in current literature. This work is positioned in mathematical physics, focusing on nonlinear evolution equations.
Influence of scalar field parameter on the stability of lower-dimensional thin-shell wormholes
Authors: Fatima G. , Javed F. , Mustafa G. , Tchier Fairouz
Published: July 2024

This paper focuses on the examination of stable and unstable geometrical structures of thin-shell wormholes constructed from Henneaux–Martinez–Troncoso–Zanelli black holes through the Visser cut-and-paste approach. We use the Israel thin-shell formalism for evaluating the stress–energy tensor components of the matter distribution near the wormhole throat. Equations of state, specifically the phantom-like and generalized Chaplygin gas model for exotic matter, are used to conduct an extensive investigation into the stability of the thin-shell. The radial perturbation around the equilibrium throat radius is also considered to explore the stable configuration for specific values of physical parameters. Our results show that the stable zones of these thin-shell wormholes increase as the scalar field parameter [Formula: see text] approaches −1. This study gives light on the behavior and dynamics of these wormholes throw light on their potential stability and lead the way for additional theoretical physics research.
Relation between the minimal Lorentz surfaces in ℝ24 and ℝ13
Authors: Kanchev Krasimir , Kassabov Ognian , Milousheva Velichka
Published: July 2024

In this paper, we give Weierstrass-type representation formulas for the null curves and for the minimal Lorentz surfaces in the Minkowski 3-space [Formula: see text] using real-valued functions. Applying the Weierstrass-type representations for the null curves, we find a correspondence between the null curves in [Formula: see text] and the pairs of null curves in [Formula: see text]. Based on this correspondence, we obtain a relation between the minimal Lorentz surfaces in [Formula: see text] and the pairs of minimal Lorentz surfaces in [Formula: see text]. As an application, we give examples showing how we can obtain a minimal Lorentz surface in [Formula: see text] using a pair of minimal Lorentz surfaces in [Formula: see text].
Online Spatial-Temporal EV Charging Scheduling with Incentive Promotion
Authors: Ting Lo Pang-Yun , Wang Huan-Yang , Jhang Jhe-Yun , Chuang Kun-Ta
Published: July 2024

The growing adoption of electric vehicles (EVs) has resulted in an increased demand for public EV charging infrastructure. Currently, the collaboration between these stations has become vital for efficient charging scheduling and cost reduction. However, most existing scheduling methods primarily focus on recommending charging stations without considering users’ charging preferences. Adopting these strategies may require considerable modifications to how people charge their EVs, which could lead to a reluctance to follow the scheduling plan from charging services in real-world situations. To address these challenges, we propose the POSKID framework in this article. It focuses on spatial-temporal charging scheduling, aiming to recommend a feasible charging arrangement, including a charging station and a charging time slot, to each EV user while minimizing overall operating costs and ensuring users’ charging satisfaction. The framework adopts an online charging mechanism that provides recommendations without prior knowledge of future electricity information or charging requests. To enhance users’ willingness to accept the recommendations, POSKID incorporates an incentive strategy and a novel embedding method combined with Bayesian personalized analysis. These techniques reveal users’ implicit charging preferences, enhancing the success probability of the charging scheduling task. Furthermore, POSKID integrates an online candidate arrangement selection and an explore-exploit strategy to improve the charging arrangement recommendations based on users’ feedback. Experimental results using real-world datasets validate the effectiveness of POSKID in optimizing charging management, surpassing other strategies. The results demonstrate that POSKID benefits each charging station while ensuring user charging satisfaction.
Molecular Mechanisms for Iron Uptake and Homeostasis in Marine Eukaryotic Phytoplankton
Authors: Lampe Robert H. , Coale Tyler H. , McQuaid Jeffrey B. , Allen Andrew E.
Published: July 2024

The micronutrient iron is essential for phytoplankton growth due to its central role in a wide variety of key metabolic processes including photosynthesis and nitrate assimilation. As a result of scarce bioavailable iron in seawater, marine primary productivity is often iron-limited with future iron supplies remaining uncertain. Although evolutionary constraints resulted in high cellular iron requirements, phytoplankton evolved diverse mechanisms that enable uptake of multiple forms of iron, storage of iron over short and long timescales, and modulation of their iron requirement under stress. Genomics continues to increase our understanding of iron-related proteins that are homologous to those characterized in other model organisms, while recently, molecular and cell biology have been revealing unique genes and processes with connections to iron acquisition or use. Moreover, there are an increasing number of examples showing the interplay between iron uptake and extracellular processes such as boundary layer chemistry and microbial interactions.
The Role of Transdisciplinary Approaches in Environmental Economics
Authors: Mehta Shefali V. , Polasky Stephen , Tsakakis Elena
Published: July 2024

Environmental and resource challenges continue to grow in magnitude and complexity. Addressing these challenges requires expansive thinking to formulate and implement effective solutions. Transdisciplinary research, considered the pinnacle of integration across disciplines, offers a powerful approach for addressing complex questions. Three key themes emerge from transdisciplinary research in the context of environmental economics: (a) There are degrees of transdisciplinarity that contribute to new emergent approaches and on-the-ground application through behavior and practice changes; (b) there are many examples of environmental economics research that exhibit characteristics of transdisciplinary research, but aspects of transdisciplinarity could be expanded in many cases; and (c) there are various barriers (cross-disciplinary, individual, and systemic) facing economists in the application of a transdisciplinary approach. While transdisciplinary research comes with its own barriers, environmental economists could generate large potential gains in formulating and implementing solutions by exploring the transdisciplinary mindset and approach.
The role of FGF19 in metabolic regulation: insights from preclinical models to clinical trials
Authors: Carvalho Marcela Botelho de , Jorge Gabriel Machado Chavez Passos , Zanardo Luiza Wolf , Hamada Leticia Miho , Izabel Larissa dos Santos , Santoro Sergio , Magdalon Juliana
Published: July 2024

Fibroblast growth factor 19 (FGF19) is a hormone synthesized in enterocytes in response to bile acids. This review explores the pivotal role of FGF19 in metabolism, addressing the urgent global health concern of obesity and its associated pathologies, notably type 2 diabetes. The intriguing inverse correlation between FGF19 and body mass or visceral adiposity, as well as its rapid increase following bariatric surgery, emphasizes its potential as a therapeutic target. This article meticulously examines the impact of FGF19 on metabolism by gathering evidence primarily derived from studies conducted in animal models or cell lines, using both FGF19 treatment and genetic modifications. Overall, these studies demonstrate that FGF19 has antidiabetic and antiobesogenic effects. A thorough examination across metabolic tissues, including the liver, adipose tissue, skeletal muscle, and the central nervous system, is conducted, unraveling the intricate interplay of FGF19 across diverse organs. Moreover, we provide a comprehensive overview of clinical trials involving an FGF19 analog called aldafermin, emphasizing promising results in diseases such as nonalcoholic steatohepatitis and diabetes. Therefore, we aim to foster a deeper understanding of FGF19 role and encourage further exploration of its clinical applications, thereby advancing the field and offering innovative approaches to address the escalating global health challenge of obesity and related metabolic conditions.
Renal ischemia/reperfusion induces prominent progressive kidney disease in diabetic mice
Authors: de Ponte Mariana Charleaux , Cardoso Vanessa Gerolde , Costa-Pessoa Juliana Martins da , Lopes-Gonçalves Guilherme , Pereira Beatriz Maria Veloso , Thieme Karina , Oliveira-Souza Maria
Published: July 2024

This study demonstrated that early diabetes potentiates progressive kidney disease after ischemia/reperfusion (IR)-induced acute kidney injury, mainly by exacerbating pro-inflammatory and sustaining fibrotic responses and altering renal glucose metabolism. Thus, these findings will contribute to the therapeutic support of patients with type 1 diabetes with eventual renal IR intervention in clinical practice.
Influence of spatial orientation training in a centrifuge on the ability of fighter pilots to assess the bank angle during flight without visual references
Authors: Brink Andreas , Keramidas Michail E. , Bergsten Eddie , Eiken Ola
Published: July 2024

Spatial disorientation is a major problem in aviation. When performing coordinated flight turns without external visual cues (e.g., flying in clouds or darkness), the pilot underestimates the aircraft bank angle because the vestibular system provides unreliable information of roll tilt. The present study demonstrates that it is possible to, in a long-arm centrifuge, train a pilot’s ability to perceive large but not discrete-to-moderate roll-angular displacements.
Improved Pedestrian Location Method for the Indoor Environment Based on MIMU and sEMG Sensors
Authors: Ling Xu , Yun Xu , Funeng Wang
Published: July 2024

In order to guarantee the pedestrian location accuracy for the indoor environment, an improved pedestrian location method based on the improved Mahony complementary filter algorithm is proposed by the data fusion of microinertial measurement unit (MIMU) and sEMG sensors, which are installed on the lower limbs. First, the pedestrian location method for the indoor environment is introduced by the data fusion of MIMU and surface electromyography; and then, BP neural network is applied to adjust the parameters in real time for PI controller of Mahony complementary filter algorithm, which can obtain the current heading angle for the location. Finally, the indoor environment experiments for pedestrian location are carried out. The heading angle estimating experiment result shows that the indoor heading angle for the pedestrian can be exactly calculated by the improved Mahony complementary filter algorithm. The indoor location experiment shows that the average relative location error is 2.34 m, and the average relative location error rate is 2.27%.
The Riemann Problem for the Blood Flow Model with Body Force Term
Authors: Sheng Wancheng , Xu Shufang
Published: July 2024

In this paper, we study the Riemann problem for the blood flow model with body force term. The Riemann solutions involve the rarefaction waves, the shock waves, and the collapsed state. Due to the effect of body force term, the solutions are not self‐similar. By a transformation, the system can be transformed into the nonreducible blood flow model. The existence of the simple wave solutions to the nonreducible blood flow model is obtained by the method of characteristic decomposition. All Riemann solutions are constructed for any given Riemann initial data. The simple wave and shock wave connected to two constant states on both sides are not straight lines, which are the key innovation and different from the results for the reducible system.
Design of Rocket‐Borne Data Transmission System for Sounding Rocket
Authors: Xu Ziyu , Deng Yongsheng , Xie Yifang
Published: July 2024

The design of a rocket‐borne data transmission system is presented in this article. This data transmission system is used for Meridian Space Weather Monitoring Project II sounding rocket. The major function of this data transmission system is to transmit payload data and rocket telemetry data to ground station. The data transmission system achieves power greater than 4 W (36 dBm). The amplitude unbalance is 1.60% (0.14 dB) and the phase unbalance is 1.74°. Total power consumption of the data transmission system is less than 24 W (28 V power supply). The radiation performance of the data transmission antenna is good. Based on the actual condition of solid rocket, the relevant heat dissipation and vibration reduction measures are designed under the demand of miniaturization. The data transmission system has been tested by performance test, environmental simulation test, system integration test, and other tests; the test results show that it has good working performance, good stability, and high reliability.
Overcoming Double Jeopardy: Successful Orthotopic Heart Transplant in a Recipient With Bacterial and Fungal Infections
Authors: Munthananuchat Paopat , Naratreekoon Bundit , Kantathut Narongrit , Samankatiwat Piya , Trirattanapikul Akeatit , Yingchoncharoen Teerapat
Published: July 2024

Although active infection is generally a contraindication before an orthotopic heart transplant, a 16‐year‐old man diagnosed with dilated cardiomyopathy successfully underwent an orthotopic heart transplant despite having active probable invasive pulmonary aspergillosis and bacterial pneumonia in the presence of septic and cardiogenic shock.
Novel Crushing Technique for Measuring δ18O and δ2H Values of Fluid Inclusions (H2O) in Quartz Mineral Veins Using Cavity Ring‐Down Spectroscopy
Authors: Huseynov Akbar Aydin Oglu , van der Lubbe (Jeroen) H. J. L. , Verdegaal-Warmerdam S. J. A. , Postma Onno , Schröder Julian , Vonhof Hubert
Published: July 2024

Fluid inclusions in mineralized fracture infillings (i.e., veins) could preserve information about subsurface fluids like temperature and salinity. The isotopic composition of water in these fluid inclusions could provide direct evidence of the provenance of these mineral‐forming fluids. So far, the isotope compositions of fluid inclusions have been mainly derived from carbonate veins and other precipitates, like speleothems. The aim of this study is to analyse the δ18O and δ2H isotopic compositions of aqueous fluid inclusions of quartz veins using a cavity ring‐down spectroscopy (CRDS) analyser in combination with a moisturized nitrogen background and mechanical crusher. For this study, we analysed δ18O and δ2H values of fluid inclusions in quartz veins from three north‐western European locations formed during the Variscan orogeny. Prior to crushing, the fluid‐rich quartz fraction was separated from the pure quartz fraction, from other mineral phases and host rock by using conventional heavy liquids and magnet separation. Raman spectrometry detected some rare occurrences of hydrocarbon, methane, and nitrogen in the fluid inclusions. The samples were sequentially crushed to elucidate the potential impact of different fluid inclusion assemblages (FIA) on the δ18O and δ2H values. The results from single and sequential mechanical crushing, together with interlaboratory comparisons, exhibit reliable and consistent isotopic patterns across locations with high precision (for δ18O: 1σ SD < 0.8‰; for δ2H: 1σ SD < 1.5‰). The obtained data occur in three different clusters for three study zones, providing evidence for the presence of meteoric‐derived fluids in the fold‐and‐thrust belts of the Variscan orogeny. These findings demonstrate that the CRDS approach can be successfully applied to quartz minerals, investigating fluid pathways within the upper crust and the formation of these secondary minerals.
ABCA1 Deletion Does Not Affect Aqueous Humor Outflow Function in Mice
Authors: Qin Bo , Hu Chunchun , Zhang Youjia , Chen Yuhong , Lei Yuan
Published: July 2024

Background. ATP binding cassette transporter A1 (ABCA1) is a candidate gene within a POAG susceptibility locus by GWAS analysis, and it is involved in IOP modulation via the Cav1/eNOS/NO signaling pathway. We aim to examine the phenotype of ABCA1 deletion in the ABCA1 gene knockout (Abca1−/−) mice. Methods. The anterior segments of Abca1−/− eyes were imaged by slit‐lamp microscopy and anterior segment OCT. IOPs were measured by rebound tonometry. By perfusing enucleated eyes at various pressures, the aqueous humor outflow facility was determined. The mRNA expressions of ABCA1, Cav1, and eNOS were measured by RT‐qPCR. The protein expressions were analyzed by western blot and immunofluorescence staining. Results. There was no significant difference in the anterior segment morphology of Abca1−/− mice. IOP and aqueous humor outflow facility did not change in Abca1−/− mice compared with wild‐type mice. mRNA and protein expressions of ABCA1 were significantly lower in the outflow tissue of Abca1−/− eyes. The expressions of Cav1 and eNOS were both significantly upregulated in the outflow tissue of Abca1−/− eyes. Conclusion. ABCA1 deletion does not affect IOP and aqueous humor outflow function but the Cav1/eNOS/NO pathway is changed in Abca1−/− mice. The function of ABCA1 in aqueous humor outflow still requires further research.
Nurses’ Workplace Social Capital and Sustainable Development: An Integrative Review of Empirical Studies
Authors: Xu Jia-Min , Cao Ming-Guo , Gao Qian-Cheng , Lu Yi-Xuan , Stark Azadeh T.
Published: July 2024

Aim. The purpose of our review was to assess the role of nurses’ workplace social capital in meeting the Sustainable Development Goals (SDGs) of the United Nations (UN). Background. In 2015, the 2030 Agenda for Sustainable Development with 17 universal goals was adopted by members of the UN. Although nurses have been acknowledged as important contributors to sustainable development, they still have difficulties in connecting their work to the SDGs. Nurses’ workplace social capital is an important concept in nursing management due to its constructive consequences. However, the potential association between nurses’ workplace social capital and the SDGs has not been evaluated. Evaluation. We conducted an integrative review, following the methodology of Whittemore and Knafl. Seven databases, Medline, CINAHL, Web of Science, Cochrane Library, Embase, PsycINFO, and Scopus with no restriction on publication year, were searched in May 2023 to identify statistically significant empirical evidence. Only peer‐reviewed research papers published in English language journals were considered. We applied the Mixed Methods Appraisal Tool to evaluate the quality of the selected articles. We categorized outcomes of nurses’ workplace social capital into themes and connected them to the SDGs through repeated comparisons and discussions. Key Issues. Twenty‐nine of 2,188 retrieved articles were included in the final data analysis. Twenty‐three outcomes of nurses’ workplace social capital were identified, and three themes were abstracted. Nurses’ workplace social capital is positively associated with SDG 3 (good health and well‐being), SDG 8 (decent work and economic growth), and SDG 17 (partnerships for the goals). Conclusion. Findings of our integrative review shed light on the importance of nurses’ workplace social capital and the role of nurses in achieving the global movement for sustainable development. Implication for Nursing Management. Investment in nursing workforce and nurses’ workplace social capital can further strengthen the position of nurses to support and deliver the SDGs.
Study on the Potential Probiotics Isolated from Marine Aquaculture System and Evaluation for Aquaculture Application
Authors: Gao Yingli , Qiang Lu , Wu Ni , Tan Ruiming , Sun Yungui , Li Zeyu , Shen Xin , Cai Yuefeng
Published: July 2024

In recent years, more attention has been paid to the application of host‐associated microorganisms as an appropriate source of probiotics in aquaculture. These probiotics promoted effective colonization and application and provided better benefits than other sources. A total of 32 isolates were collected from the water, shrimp, and sediment of the cultured pond of Litopenaeus vannamei. Probiotic effects were comprehensively evaluated by extracellular enzyme activity, virulence genes detection, bacteriostatic activity, drug susceptibility test, tolerance to adverse environment, and challenge experiment. Six of these isolates exhibited bacteriostatic activity against pathogens, such as Vibrio cholerae, V. owensii, V. fluvialis, V. campbellii, V. rotiferianus, Photobacterium damselae, V. vulnificus, and V. parahaemolyticus. Using 16S rRNA gene sequencing analysis, isolates were identified as Bacillus subtilis (P8, P29, and P30), B. velezensis (P12), Prestia flexa (P21), and Cytobacillus firmus (P23). They were tolerant to high temperature, low acid (pH 2.0), bile salt (2%), and the simulated gastrointestinal environment, secreting phospholipase, amylase, protease, gelatinase, and lipase. They were sensitive to only two or fewer drugs among 27 antibacterial drugs and did not carry any of the tested virulence genes. Biosafety in vivo evaluations showed that they were safe for Ctenopharyngodon idella (P12 ＞ P23 ＝ P29 ＞ P8 ＞ P30 ＝ P21). The results suggest that P8, P12, P23, P21, P29, and P30 have high probiotic potential and can be used as good probiotics in aquaculture and the development of ecological preparations.
Study of the Acute Toxicity of Scorpion Leiurus macroctenus Venom in Rats
Authors: Gunas Valery , Maievskyi Oleksandr , Raksha Nataliia , Vovk Tetiana , Savchuk Oleksiy , Shchypanskyi Serhii , Gunas Igor
Published: July 2024

Background. The expansion of the territory of human habitation leads to inevitable interference in the natural range of distribution of one or another species of animals, some of which may be dangerous for human life. Scorpions—the Arachnida class and order Scorpiones—can be considered as such typical representatives. Scorpions of the Buthidae family pose a particular danger to humans. However, LD50 has not yet been defined for many species of this family, in particular, new representatives of the genus Leiurus. Leiurus macroctenus is a newly described species of scorpion distributed in Oman, and the toxicity of its venom is still unknown. Estimating the LD50 of the venom is the first and most important step in creating the antivenom and understanding the medical significance of the researched animal species. The purpose of this study was to determine the lethal dose (LD100), the maximum tolerated dose (LD0), and the average lethal dose (LD50) in rats when using Leiurus macroctenus scorpion venom. Methods and Results. 15 sexually mature scorpions were used in the study, which were kept in the same conditions and milked by a common method (electric milking). For the study, 60 male rats were used, which were injected intramuscularly with 0.5 ml of venom solution with a gradual increase in the dose (5 groups, 10 rats in each), and 10 rats were injected intramuscularly with physiological solution as control group. LD calculations were done using probit analysis method in the modification of the method by V.B. Prozorovsky. The LD0 of Leiurus macroctenus scorpion venom under the conditions of intramuscular injection was 0.02 mg/kg, LD100 was 0.13 mg/kg, and LD50 was 0.08 ± 0.01 mg/kg. Conclusions. The analysis of scientific publications and other sources of information gives reason to believe that Leiurus macroctenus has one of the highest values of LD50 not only among scorpions but also among all arthropods in the world. All these point to the significant clinical importance of this species of scorpion and require further research that will concern the toxic effect of its venom on various organ systems. Determining the LD50 of the venom for new scorpion species is crucial for creating effective antivenoms and understanding the medical implications of envenomation by this species.
Data from Origins of Second Malignancies in Children and Mutational Footprint of Chemotherapy in Normal Tissues
Authors: Sánchez-Guixé Mònica , Muiños Ferran , Pinheiro-Santin Morena , González-Huici Víctor , Rodriguez-Hernandez Carlos J. , Avgustinova Alexandra , Lavarino Cinzia , González-Pérez Abel , Mora Jaume , López-Bigas Núria
Published: July 2024

Abstract
Pediatric cancers are rare diseases, and children without known germline predisposing conditions who develop a second malignancy during developmental ages are extremely rare. We present four such clinical cases and, through whole-genome and error-correcting ultra-deep duplex sequencing of tumor and normal samples, we explored the origin of the second malignancy in four children, uncovering different routes of development. The exposure to cytotoxic therapies was linked to the emergence of a secondary acute myeloid leukemia. A common somatic mutation acquired early during embryonic development was the driver of two solid malignancies in another child. In two cases, the two tumors developed from completely independent clones diverging during embryogenesis. Importantly, we demonstrate that platinum-based therapies contributed at least one order of magnitude more mutations per day of exposure than aging to normal tissues in these children.
Significance:
Using whole-genome and error-correcting ultra-deep duplex sequencing, we uncover different origins for second neoplasms in four children. We also uncover the presence of platinum-related mutations across 10 normal tissues of exposed individuals, highlighting the impact that the use of cytotoxic therapies may have on cancer survivors.

See related commentary by Pacyna and Nangalia, p. 900.

This article is featured in Selected Articles from This Issue, p. 897
Supplementary Figure 4 from Exploration of Immune-Modulatory Effects of Amivantamab in Combination with Pembrolizumab in Lung and Head and Neck Squamous Cell Carcinoma
Authors: Lim Sun M. , Kang Seong-san , Kim Dong K. , Lee Soo-Hwan , Synn Chun-Bong , Baek Sujeong , Yang Seung M. , Han Yu J. , Kim Mi H. , Han Heekyung , Na Kwangmin , Kim Young T. , Yun Mi R. , Kim Jae H. , Byeon Youngseon , Kim Young S. , Lee Jii B. , Hong Min H. , Curtin Joshua C. , Patel Bharvin , Bergiers Isabelle , Pyo Kyoung-Ho , Cho Byoung C.
Published: July 2024

This figure shows the single cell RNA sequencing analysis of EGFR high tumor subcluster in the humanized LUSC PDX model.
Supplementary Figure 2 from Exploration of Immune-Modulatory Effects of Amivantamab in Combination with Pembrolizumab in Lung and Head and Neck Squamous Cell Carcinoma
Authors: Lim Sun M. , Kang Seong-san , Kim Dong K. , Lee Soo-Hwan , Synn Chun-Bong , Baek Sujeong , Yang Seung M. , Han Yu J. , Kim Mi H. , Han Heekyung , Na Kwangmin , Kim Young T. , Yun Mi R. , Kim Jae H. , Byeon Youngseon , Kim Young S. , Lee Jii B. , Hong Min H. , Curtin Joshua C. , Patel Bharvin , Bergiers Isabelle , Pyo Kyoung-Ho , Cho Byoung C.
Published: July 2024

Figure S2 shows the gating strategy for T panel in immune profiling analysis.
Artificial intelligence in the United Arab Emirates public sector: a systematic literature review
Authors: Akhoirshieda Modafar Shaker , Naim Ku Khalif Ku Muhammad , Awang Suryanti
Published: July 2024

This systematic literature review examines United Arab Emirates (UAE) public sector artificial intelligence (AI) use, impact, and challenges. Using the preferred reporting items for systematic reviews and meta-analyses (PRISMA) protocol, 20 relevant Scopus articles were selected for the study. Data from selected articles were used to analyse AI's use, benefits, and drawbacks in the UAE's public sector. Quality assessment was done throughout the review process. The results showed that AI is being used more in the UAE's public sector to improve efficiency, cost savings, decision-making, and service delivery. The review also found data, privacy, security, technical, infrastructure, AI, and user challenges. Publication bias and the lack of AI studies in the UAE's public sector limit the study. The findings have major implications for policy and practice, emphasising the need for AI strategies and UAE-specific solutions.
Insight of recent artificial intelligence-based strategy to effectively screen COVID-19
Authors: Cheluvaraju Girish Shyadanahalli , Shivasubramanya Jayasri Basavapatna
Published: July 2024

The recent era of pandemic by corona virus disease (COVID-19) has witnessed a faster evolution of various technological solution to thwart the life-threating situation. The most important step was to select a faster mode of screening COVID-19 using chest x-ray (CXR) which could be actually ten folds faster than conventional invasive screening methods. However, the method of determining the presence of COVID-19 from CXR is critically challenging owing to the dynamic and complex nature of disease. Such problem is attempted to be solved by harnessing the potential of artificial intelligence (AI). Hence, this paper contributes towards discussion of most recent and current implementation strategies formulated by AI models towards diagnosing COVID-19. The study outcome of this paper yields an interesting learning outcome to show that AI models’ adoption is increasing in faster pace and yet challenges do exist till date. The outcome of study will assist in better adoption of AI models towards screening COVID-19.
A survey of detecting leaf diseases using machine learning and deep learning in various crops
Authors: T Thilagraj , Kareem Abdul , Kumara Varuna , Udesh Naik Utkrishna , Poojary Sanjana , R Bharath
Published: July 2024

For agricultural productivity and food security to be guaranteed, early detection and treatment of illnesses are crucial. Machine learning (ML) and deep learning (DL) approaches can be used to precisely and successfully identify plant leaf diseases. A heterogeneous dataset comprising photos of both healthy and diseased leaves such as bacterial blights, fungal infections, and viral manifestations provides the foundation for the model building and training. Accuracy, precision, recall, and F1-score are the measures used to assess the model's performance. ML techniques are helpful in the identification and extraction of pertinent information from plant leaf pictures, whereas DL techniques in general, and convolutional neural networks (CNN), in particular, are remarkable at learning complex hierarchical representations. Therefore, DL architectures like CNN are utilized in conjunction with ML approaches like support vector machines (SVM), decision trees, and random forests to extract complicated patterns and attributes from leaf pictures. This research provides an extensive analysis of the performance and application of DL and ML approaches recently applied to the early identification of leaf diseases in different crops.
Artificial intelligence in land use prediction modeling: a review
Authors: Utami Westi , Sugiyanto Catur , Rahardjo Noorhadi
Published: July 2024

This study aims to review methods of artificial intelligence (AI) in land use modelling. Data were extracted from journals in the Scopus and Google Scholar databases using the preferred reporting items for systematic reviews and meta-analyses (PRISMA) method. The review demonstrates that modelling land use predictions is a complex matter that involves land use maps and driving forces. AI technology can support land use forecasting by interpreting land use data, analyzing drivers, and modeling. However, AI has limitations in terms of broad contextual understanding and algorithmic errors. To anticipate this, it is necessary to select the appropriate image resolution and interpretation method in accordance with digital data segmentation. It is also recommended to use spatial regression methods to determine the driving forces that affect land use. Hybrid models such as multilayer perceptron neural network Markov chain (MLPNN-MC), random forest algorithm (RFA), and cellular automata (CA)-Markov chain (MC) are recommended for modelling. The selection of a model should be based on the data's characteristics and tested for accuracy. The use of AI for land use prediction modelling is expected to provide accurate predictions that can be used as a basis for land use policy.
Framework for contextual consulting practices in adherence for decentralized data-driven decision making
Authors: Pandey Vijay Kumar , Rathore Neeraj , Bhosale Narayan P.
Published: July 2024

With the rising adoption of technological advancement and industry-based automation standards, the area of consulting firms is gradually evolving to keep up this pace towards incorporating sophisticated analytical operation for facilitating value-added consulting services. Review of existing practices of consulting firm shows increasing adoption of analytical process which leads to complex form of operation towards knowledge discovery of consulting data. Hence, this manuscript introduces a framework of contextual consulting practices where the core idea is to incorporate a baseline structure of knowledge discovery associated with consulting data in adherence of industry 4.0 automation standards. The framework takes the input of streamed consulting diversified data governed by a template-based entry-points where the consulting data is subjected to series of transformation operation that not only preprocess the consulting data but also optimizes the data to enhance its data quality. The study model is implemented in MATLAB considering an extensive analytical framework towards data-driven decision making and decentralization to exhibit proposed model to offer better analytical performance in contrast to existing study models.
Design and implementation of a driving safety assistant system based on driver behavior
Authors: Salbi Adil , Gadi Mohamed Amine , Bouganssa Tarik , Eloudrhiri Hassani Abdelhadi , Lasfar Abdelali
Published: July 2024

These days, road accidents are one of Morocco's biggest problems. Fatigue, drowsiness, and driver behavior are among the primary causes.This research aims to develop an embedded system by image processing and computer vision to ensure driving safety by monitoring driver behavior and assist drivers to awaken from micro-sleep or fatigue due to long driving hours and various other reasons. Indeed, the driver inattention, drowsiness or driver fatigue can be detected. The suggested method is designed to support drivers if needed, based on the vehicle velocity. Once the driver crosses a certain speed limit, the program starts face detection and analyzing this data to determine whether the driver is tired, sleepy, or inattentive. This activates different alarm depending on the criticality level. It can sound a voice alert to help him wake up and drive more cautiously. The system is based on AI algorithms in image processing based on OpenCV libraries and the Python language to capture the movements of the driver's eyes and head when starting the automobile. Every algorithm is run on a Raspberry-Pi 4 card, and numerous experimentation series have demonstrated overall credible performance with success accuracy of over 93% in EAR and MAR calculations.
Chatbot with ChatGPT technology for mental wellbeing and emotional management
Authors: Andrade Arenas Laberiano , Yactayo-Arias Cesar
Published: July 2024

There is a growing concern among the world's population about mental health in work, academics, and other contexts where stress, anxiety, and depression are common problems that negatively impact mental health. This study evaluates a chatbot powered by ChatGPT, offering a novel perspective on emotional intervention and mental well-being. It highlights the urgency of this approach in a context where mental health is critical, providing value by combining advanced technologies with emotional management. A multi-faceted approach was implemented to evaluate both usability and technical performance. The usability of the chatbot was evaluated by users using the System Usability Scale (SUS), while the technical performance was evaluated by experts. The active participation of 15 users provided a detailed perspective, resulting in an average usability of 83, reflecting a positive experience in interacting with the system. At the same time, five experts, through technical metrics, assigned an average technical performance of 4.28, indicating solid operational effectiveness. In conclusion, although more research is needed to customize and optimize chatbots over the long term, this approach holds promise for addressing mental health issues in a variety of settings and represents the integration of artificial intelligence to the benefit of those seeking help managing emotional disorders.
Innovative credit card fraud detection: A hybrid model combining artificial neural networks and support vector machines
Authors: Ndama Oussama , Bensassi Ismail , En-naimi El Mokhtar
Published: July 2024

In recent years, escalating fraudulent activities have led to significant financial losses across industries, intensifying the critical challenge of fraud detection. This study introduces a novel hybrid model that combines artificial neural networks (ANN) with support vector machines (SVM) to construct a robust additive model for fraud detection. Emphasizing the Synthetic Minority Over-sampling Technique (SMOTE), our investigation addresses the imbalanced nature of fraud versus non-fraud transactions. The clear novelty of our research lies in the seamless integration of these two powerful tools, offering a comprehensive and effective solution to the challenges posed by credit card fraud detection. Furthermore, our study stands out by emphasizing the collaborative synergy between ANN and SVM, particularly through the integration of multiple kernels, which improves the adaptability and accuracy of the proposed hybrid model. We conducted a thorough examination of 284,807 anonymized transactions, placing special emphasis on comparing the hybrid approach's performance and showcasing its superiority over traditional methodologies in the realm of fraud detection.
The cooperative algorithm with auxiliary objectives for the truck and trailer routing problem
Authors: Pérez-Rodríguez Ricardo , Urbán-Rivero Luis Eduardo
Published: July 2024

In this paper, a cooperative algorithm with auxiliary objectives is proposed to resolve the truck and trailer routing problem. In this proposal, each member of the population does not represent a complete solution as in almost any evolutionary algorithm. In addition, for each member, an aptitude is not possible to compute based only on its codification, because the member has only partial information of the solution. All the members of the population have partial information of the solution. Therefore, these members need to cooperate to obtain an aptitude for the entire population. This way of computing fitness is clearly a gap in the literature, and must be investigated. Moreover, the multi-objectivization approach incorporates an important feature to the proposed algorithm in order to improve its performance, i.e., the multi-objectivization approach permits to identify the best trips using the auxiliary objectives. Enough experimental results are shown that the cooperative algorithm is competitive against other current evolutionary algorithms. There no exist statistically significant difference between the cooperative algorithm and the others.