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Editorial: venom collection

Correction: Stem cell factor restrains endoplasmic reticulum stress-associated apoptosis through c-Kit receptor activation of JAK2/STAT3 axis in hippocampal neuronal cells

Author Correction: Induction of mitochondria-mediated apoptosis and suppression of tumor growth in zebrafish xenograft model by cyclic dipeptides identified from Exiguobacterium acetylicum

Correction: Gender effects on outcomes of psychosomatic rehabilitation are reduced

An efficient binary salp swarm algorithm for user selection in multiuser MIMO antenna systems

Abstract The past ten years have seen notable research activity and significant advancements in multiuser multiple-input multiple-output (MU-MIMO) antennas. An MU-MIMO antenna system must accommodate many subscribers without additional bandwidth or energy. User scheduling becomes a critical strategy to take advantage of multiuser heterogeneity and acquire maximum gain in systems where the total number of recipients exceeds the number of transmitting antennas. Due to their high computational cost, many user selection methods currently in use, such as greedy algorithms and exhaustive search are unsuitable for MU-MIMO systems. A suitable scheduling mechanism is essential for the various users in an MU-MIMO system to utilise bandwidth and enhance the system’s total rate effectively. In this article, we proposed a user and antenna scheduling with a population-based meta-heuristic approach, namely the binary salp swarm algorithm (binary SSA), to increase the system sum rate with low computing complexity. We specifically used a population-based meta-heuristics optimisation technique to simulate the user scheduling problem in MU-MIMO systems, characterising complicated issues with binary decisions. Additionally, binary SSA significantly outperforms existing population-based models, such as the binary bat algorithm (binary BA), PSO, SSA, FPA and binary flower pollination algorithm (binary FPA), regarding system throughput/sum rate. The proposed binary SSA technique also effectively achieves a system sum rate compared to a random search scheme and other existing suboptimal scheduling methods. Compared to binary BA and binary FPA approaches, the binary SSA has a higher convergence rate and superior searching capabilities. The simulation outcomes show the proposed binary SSA-based scheduling scheme delivers noticeable performance benefits.

The “multiple exposure effect” (MEE): How multiple exposures to similarly biased online content can cause increasingly larger shifts in opinions and voting preferences

In three randomized, controlled experiments performed on simulations of three popular online platforms – Google search, X/Twitter, and Alexa – with a total of 1,488 undecided, eligible US voters, we asked whether multiple exposures to similarly biased content on those platforms could shift opinions and voting preferences more than a single exposure could. All participants were first shown brief biographies of two political candidates, then asked about their voting preferences, then exposed to biased content on one of our three simulated platforms, and then asked again about their voting preferences. In all experiments, participants in different groups saw biased content favoring one candidate, his or her opponent, or neither. In all the experiments, our primary dependent variable was Vote Manipulation Power (VMP), the percentage increase in the number of participants inclined to vote for one candidate after having viewed content favoring that candidate. In Experiment 1 (on our Google simulator), the VMP increased with successive searches from 14.3% to 20.2% to 22.6%. In Experiment 2 (on our X/Twitter simulator), the VMP increased with successive exposures to biased tweets from 49.7% to 61.8% to 69.1%. In Experiment 3 (on our Alexa simulator), the VMP increased with successive exposures to biased replies from 72.1% to 91.2% to 98.6%. Corresponding shifts were also generally found for how much participants reported liking and trusting the candidates and for participants’ overall impression of the candidates. Because multiple exposures to similarly biased content might be common on the internet, we conclude that our previous reports about the possible impact of biased content – always based on single exposures – might have underestimated its possible impact. Findings in our new experiments exemplify what we call the “multiple exposure effect” (MEE).

Sustainable alkali activated binders from a blend of biomass ash and iron sludge precursor

Spin density wave and van Hove singularity in the kagome metal CeTi3Bi4

Abstract Kagome metals with van Hove singularities near the Fermi level can host intriguing quantum phenomena such as chiral loop currents, electronic nematicity, and unconventional superconductivity. However, to our best knowledge, unconventional magnetic states driven by van Hove singularities–like spin-density waves–have not been observed experimentally in kagome metals. Here, we report the magnetic and electronic structure of the layered kagome metal CeTi3Bi4, where Ti kagome electronic structure interacts with a magnetic sublattice of Ce3+ J eff = 1/2 moments. Neutron diffraction reveals an incommensurate spin-density wave ground state of the Ce3+ moments, coexisting with commensurate antiferromagnetic order across most of the temperature-field phase diagram. The commensurate component is preferentially suppressed by thermal fluctuations and magnetic field, yielding a rich phase diagram involving an intermediate single-Q spin-density wave phase. First-principles calculations and angle-resolved photoemission spectroscopy identify van Hove singularities near the Fermi level, with the observed magnetic propagation vectors connecting their high density of states, strongly suggesting a van Hove singularity-assisted spin-density wave. These findings establish kagome metals LnTi3Bi4 as a model platform where the characteristic electronic structure of the kagome lattice plays a pivotal role in magnetic order.

First identification of ORF virus causing contagious ecthyma in Morocco (MOR20): Genomic, phylogenetic, and sequence variants analyses for vaccine design

The ORF virus induces a zoonotic contagious ecthyma disease, affecting small ruminants such as sheep and goats. ORF virus has not been identified in Morocco, and there is no vaccination protocol against contagious ecthyma. In this study, we analyzed the genome sequence of a new strain isolated in Morocco (MOR20) from a flock of sheep showing suspicious signs of Sheepox virus infection. ORFV MOR20 strain was isolated after 2 initial blind passages on Heart cells. The cytopathic effect was characterized by aggregation, swelling and detachment of cells, appearing 4 days after infection. The virus was harvested on day 6 pi with a titer of 107.2 TCID/ml. ORFV MOR20 was sequenced using the Illumina NovaSeq 6000 platform. After employing several bioinformatics tools, we identified that ORFV MOR20 shares 98.59% similarity with the TVL strain virus, which is used in a commercial live vaccine. Additionally, We aligned 33 ORFV genomic sequences with MOR20 sequences and visualized the pairwise comparisons using a Heat Map. ORFV was classified into two genetic groups: those isolated from sheep and those from goats. This was confirmed by a phylogenetic tree. Furthermore, we analyzed genetic variants identified in the MOR20 strain in comparison with ORFV TVL strain and found 636 sequence variants. Some genes, such as ORFV086, ORFV112, and ORFV132, have a particularly high number of sequence variants. All in all, ORFV MOR20 isolate represents a promising candidate for further studies aimed at developing a standardized vaccine against contagious ecthyma.

Electrical double layer induced zeta potential effect in a disk cone system with surface catalyzed reaction

Gas–liquid two-phase bubble flow spinning for hydrovoltaic flexible electronics

Optimization of drilling processes in panel furniture manufacturing: A case study

The drilling process is a crucial component in the production of panel furniture enterprises; simultaneously, it is also the most complex process. And the furniture enterprise’s transition to intelligent manufacturing lacks effective process optimization. Therefore, this study focuses on optimizing the drilling process in panel furniture. Initially, an analysis of cabinet structures was conducted, followed by data collection on drilling patterns. Based on this data and insights from hole distribution patterns, a novel COING (Coordinate Information Grid) data analysis method was proposed. Subsequently, the application of this method at the Company W, combined with the ARM (Association Rule Mining) method, revealed inconsistencies in drilling process parameters. After proposing and validating solutions in the Company W’s workshop, the findings demonstrated a 14.0% reduction in drilling occurrences and a 3.87% enhancement in drilling efficiency. This study demonstrates the optimization of drilling processes in panel furniture manufacturing.

The analysis of artificial intelligence knowledge graphs for online music learning platform under deep learning

Stability of the fcc phase in shocked nickel up to 332 GPa

HCAP: Hybrid cyber attack prediction model for securing healthcare applications

The rapid development and integration of interconnected healthcare devices and communication networks within the Internet of Medical Things (IoMT) have significantly enhanced healthcare services. However, this growth has also introduced new vulnerabilities, increasing the risk of cybersecurity attacks. These attacks threaten the confidentiality, integrity, and availability of sensitive healthcare data, raising concerns about the reliability of IoMT infrastructure. Addressing these challenges requires advanced cybersecurity measures to protect the dynamic IoMT ecosystem from evolving threats. This research focuses on enhancing cyberattack prediction and prevention in IoMT environments through innovative Machine-learning techniques to improve healthcare data security and resilience. However, the existing model’s efficiency depends on the diversity of data, which leads to computational complexity issues. In addition, the conventional model faces overfitting issues in training data, which causes prediction inaccuracies. Thus, the research introduces the hybridized cyber attack prediction model (HCAP) and analyzes various IoMT data source information to address the limitations of dataset availability issues. The gathered information is processed with the help of Principal Component-Recursive Feature Elimination (PC-RFE), which eliminates the irrelevant features. The extracted features are fed into the lion-optimization technique to fine-tune the hyperparameters of the recurrent neural networks, enhancing the model’s ability to efficiently predict cybersecurity threats with a maximum recognition rate in IoMT environments. The recurrent networks, specifically Long Short Term Memory (LSTM), process data from healthcare devices, identifying abnormal patterns that indicate potential cyberattacks over time. The created system was implemented using Python, and various metrics, including false positive and false negative rates, accuracy, precision, recall, and computational efficiency, were used for evaluation. The results demonstrated that the proposed HCAP model achieved 98% accuracy in detecting cyberattacks and outperformed existing models, reducing the false positive rate by 25%. The false negative rate by 20% and a 30% improvement in computational efficiency enhances the reliability of IoMT threat detection in healthcare applications.

Surface modification of MXene using cationic CTAB surfactant for adsorptive elimination of cefazolin antibiotic from water

Abstract The increasing prevalence of pharmaceutical contaminants in aquatic ecosystems has raised significant environmental concerns, necessitating the development of efficient removal strategies. In this study, Ti3C2Tx MXene was synthesized and modified with cetyltrimethylammonium bromide (CTAB) to enhance its adsorption performance for cefazolin (CFZ), a widely used cephalosporin antibiotic. The structural and physicochemical properties of the modified MXene were comprehensively characterized using FESEM, EDS, FTIR, XRD, BET, and zeta potential analyses. Adsorption experiments were conducted under various operational conditions, including pH, contact time, adsorbent dosage, and initial CFZ concentration. The results revealed that CTAB modification significantly improved the adsorption capacity by increasing interlayer spacing and enhancing the accessibility of active adsorption sites. The optimized adsorbent (MC-0.9) exhibited a maximum CFZ removal efficiency of 96.3% and an adsorption capacity of 481.5 mg/g under optimal conditions: an adsorbent dosage of 0.1 g/L, a solution pH of 5, a contact time of 60 min, and an initial CFZ concentration of 50 mg/L. Kinetic and isotherm modeling indicated that the batch adsorption process followed the pseudo-second-order kinetic model and fitted well with the Langmuir isotherm, suggesting monolayer adsorption. Additionally, the presence of co-existing anions adversely affected adsorption efficiency, following the order CO3 2− > Cl− > SO4 2 − > NO3 −. The adsorption mechanism was primarily governed by electrostatic interactions, π–cation interactions, and hydrogen bonding. Furthermore, the CTAB-modified MXene demonstrated robust recyclability, maintaining high efficiency over four consecutive cycles, highlighting its potential as a promising adsorbent for the removal of pharmaceutical pollutants from wastewater.

Exploration of crystal chemical space using text-guided generative artificial intelligence

Abstract The vastness of chemical space presents a long-standing challenge for the exploration of new compounds with pre-determined properties. In materials science, crystal structure prediction has become a mature tool for mapping from composition to structure based on global optimisation techniques. Generative artificial intelligence now offers the means to efficiently navigate larger regions of crystal chemical space informed by structure-property datasets of materials. Here, we introduce a model, named Chemeleon, designed to generate chemical compositions and crystal structures by learning from both textual descriptions and three-dimensional structural data. The model employs denoising diffusion techniques for compound generation using textual inputs aligned with structural data via cross-modal contrastive learning. The potential of this approach is demonstrated for multi-component compound generation, including the Zn-Ti-O ternary space, and the prediction of stable phases in the Li-P-S-Cl quaternary space of relevance to solid-state batteries.

The relationships among nurses’ spiritual health, sleep quality, and stress and the factors influencing stress during the late global COVID-19 pandemic: A cross- sectional study

Background COVID-19 has had a significant impact on healthcare workers. Although several studies have looked at the pandemic’s physical and mental effects on nurses, little has been done to investigate their spiritual health and its relationship to stress and sleep quality during the late pandemic. Purpose This study sought to fill a knowledge gap in the literature about the relationships between nurses’ reported stress, sleep quality, and spiritual health during the late COVID-19 epidemic. Methods A cross-sectional study using purposive sampling was performed out in a medical center in Taiwan. A total of 376 nurses participated. The Perceived Stress Scale, Pittsburgh Sleep Quality Index, and Spiritual Health Scale-Short Form were used for assessing nurses’ stress levels, sleep quality, and spiritual health. Results The results showed that the mean perceived stress score was 1.80 ± 0.50 (out of 4), the sleep quality score was 8.17 ± 3.29 (out of 21), and the mean spiritual health score was 3.66 ± 0.59 points (out of 5). Although 77.1% of the nurses in this study experienced sleep disorders (PSQI > 5), they had better sleep quality during the COVID-19 pandemic than those in other countries. Perceived stress, sleep quality, and spiritual health were significantly correlated. Nurses with support from their friends and family and hospital during the COVID-19 pandemic had lower perceived stress and higher sleep quality and spiritual health scores than their peers (p < .05). Age, work experience, sleep quality, and spiritual health were predictors of perceived stress in nurses during the late COVID-19 pandemic (F = 20.19, p < .001) and could explain 30.6% of the variation. Conclusions Spiritual health is correlated with the nurses’ stress levels. Despite providing extrinsic support, we encourage nursing management to pay attention to nurses’ spiritual needs and implement psychological education programs to help frontline nurses navigate ever-changing and discerning healthcare environments.

Tumor microenvironment modulation by SERPINE1 increases radioimmunotherapy in murine model of gastric cancer

Transcriptome analysis of archived tumors by Visium, GeoMx DSP, and Chromium reveals patient heterogeneity

Abstract Recent advancements in probe-based, full-transcriptome technologies for FFPE tissues, such as Visium CytAssist, Chromium Flex, and GeoMx DSP, enable analysis of archival samples, facilitating the generation of data from extensive cohorts. However, these methods can be labor-intensive and costly, requiring informed selection based on research objectives. We compare these methods on FFPE tumor samples in Breast, NSCLC and DLBCL showing 1) good-quality, highly reproducible data from all methods; 2) GeoMx data containing cell mixtures despite marker-based preselection; 3) Visium and Chromium outperform GeoMx in discovering tumor heterogeneity and potential drug targets. We recommend the use of Visium and Chromium for high-throughput and discovery projects, while the manually more challenging GeoMx platform with targeted regions remains valuable for specialized questions.