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The design of masonry structures requires accurate estimation of compressive strength (CS) of hollow concrete masonry prisms. Generally, the CS of masonry prisms is determined by destructive laboratory testing which results in time and resource wastage. Thus, this study aims to provide machine learning-based predictive models for CS of hollow concrete masonry blocks using different algorithms including Multi Expression Programming (MEP), Random Forest Regression (RFR), and Extreme Gradient Boosting (XGB) etc. A dataset of 159 experimental results was collected from published literature for this purpose. The collected dataset consisted of four input parameters including strength of masonry units ( f b ), height-to-thickness ratio (h/t), strength of mortar ( f m ), and ratio of f m / f b and only one output parameter i.e., CS. Out of all the algorithms employed in current study, only MEP and GEP expressed their output in the form of an empirical equation. The accuracy of developed models was assessed using root mean squared error (RMSE), objective function (OF), and R 2 etc. Among all algorithms assessed, XGB turned out to be the most accurate having R 2  = 0.99 and least OF value of 0.0063 followed by AdaBoost, RFR, and other algorithms. The developed XGB model was also used to conduct different explainable artificial intelligence (XAI) analysis including sensitivity and shapley analysis and the results showed that strength of masonry unit ( f b ) is the most significant variable in predicting CS. Thus, the ML-based predictive models presented in this study can be utilized practically for determining CS of hollow concrete masonry prisms without requiring expensive and time-consuming laboratory testing., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2024 The Authors.)

Nanotechnology has emerged as a promising solution for tackling antibiotic resistance in monogastric animals, providing innovative methods to enhance animal health and well-being. This review explores the novel use of nanotechnology-based nanomaterials as substitutes for antibiotics in monogastric animals. With growing global concerns about antibiotic resistance and the need for sustainable practices in animal husbandry, nanotechnology offers a compelling avenue to address these challenges. The objectives of this review are to find out the potential of nanomaterials in improving animal health while reducing reliance on conventional antibiotics. We examine various forms of nanomaterials and their roles in promoting gut health and also emphasize fresh perspectives brought by integrating nanotechnology into animal healthcare. Additionally, we delve into the mechanisms underlying the antibacterial properties of nanomaterials and their effectiveness in combating microbial resistance. By shedding light on the transformative role of nanotechnology in animal production systems. This review contributes to our understanding of how nanotechnology can provide safer and more sustainable alternatives to antibiotics., Competing Interests: The authors declare the following financial interests/personal relationships which may be considered as potential competing interests:Pavel Horky reports financial support was provided by 10.13039/501100008775Mendel University in Brno. Pavel Horky reports a relationship with Mendel University in Brno that includes: employment. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2024 Published by Elsevier Ltd.)

Understanding the precursors leading to rock fracture is crucial for ensuring safety in mining and geotechnical engineering projects. To effectively discern these precursors, a collaborative monitoring approach that integrates multiple sources of information is imperative. This paper considered a rock multi-parameter monitoring loading system, incorporating infrared radiation and acoustic emission monitoring technologies to simultaneously track the rock fracture process. The study delves into the spatiotemporal evolution patterns of infrared radiation and acoustic emission in rock under loading. Utilizing stress, cumulative acoustic emission count, and average infrared radiation temperature (AIRT), the paper establishes a comprehensive evaluation model termed "acoustic-thermal-stress" fusion information, employing principal component analysis (PCA). The research reveals that the sensitivity to rock sample damage response follows the sequence of cumulative acoustic emission count, AIRT, and stress. Furthermore, a novel method for identifying rock fracture precursors is proposed, based on the first derivative of the comprehensive evaluation model. This method addresses the limitations of single physical field information, enhancing the robustness of monitoring data. It determines the average stress level of fracture precursors to be 0.77σ max . Subsequently, the study defines the probability function of rock damage during loading and fracture, enabling the realization of probability-based warnings for rock fracture. This approach introduces a new perspective on rock fracture prediction, significantly contributing to safety monitoring and warning systems in mine safety and geotechnical engineering. The findings of this research hold paramount engineering significance, offering valuable insights for enhancing safety measures in such projects., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2024 The Authors.)

Pectinolytic enzymes are among the important group of industrial enzymes that have wide applications in different food industries. In this study, pectinase-based silica nanocarriers were synthesized using co-precipitation and cross-linking techniques. The resulting silica nanoparticles were investigated using scanning electron microscopy (SEM), energy-dispersive electron microscopy (EDEX), and X-ray diffraction (XRD) for determination of its morphology, elemental composition, and crystalline pattern. Under the optimal immobilization conditions like 1.5 % glutaraldehyde, 3000 IU/mg pectinase concentration, 90 min immobilization time and 40 °C immobilization temperature, pectinase showed maximum immobilization yield. The immobilization of pectinase onto the silica nanocarriers led to enhanced catalytic characteristics, displaying higher enzymatic activity across various temperature and pH levels compared to soluble pectinase. Moreover, the immobilization substantially improved the temperature stability of pectinase, exhibiting 100 % of its initial activity even after 120 h of pre-incubation at 50 °C. Additionally, the silica nanocarrier pectinase retained 100 % of its original activity even after being reused 10 times in a single batch of reactions. These findings indicate that the immobilization of silica nanocarriers effectively enhances pectinase's industrial capabilities, making it economically feasible for industrial use and an efficient system for various biotechnological applications., Competing Interests: The authors declare no conflict of interest., (© 2023 The Authors. Published by Elsevier Ltd.)

Physician burnout is a global concern that can lead to exhaustion, ineffectiveness, and poor health outcomes. Burnout has been linked to a variety of societal and professional variables worldwide. This cross-sectional, multi-centered study was conducted by face-to-face interviews between April 2019 and December 2021 at sixty-two (62) tertiary level hospitals to identify potential risk factors for burnout among Bangladeshi physicians, which is essential for preventing adverse impact on their well-being, improving overall quality of life, and facilitating measures to manage stress and maintain a healthy work-life balance. A simple random sampling technique in conjunction with a structured questionnaire was used to collect a total of 1434 responses, assuming 20 % of the sample as non-responsive. Univariate, bivariate, and multinomial logistic regression statistical analyses were performed to determine the risk factors and associate the level of severity. The distribution of burnout status differs significantly at distinct covariate levels, such as working place, working hour, prevalence of potential interpersonal conflicts (subsequent increase of adjusted odds ratios i.e.; 6.52, 8.82, 11.41, and 37.07 is observed for physicians having interpersonal conflicts with both co-workers & family members), job dissatisfaction, annoyed feeling while dealing with patients (adjusted odds ratios are 529.68, 518.26,983.87 and 849.57 respectively) and some other significant factors of the physicians. This study also reveals that the female physicians, physicians with age 40-49, physicians with additional liabilities, physicians with job dissatisfaction, less salary compared to the workload, less flexibility and security at the job sector as well as obese physicians are at high risk of burnout. These results are statistically significant with a p value ≤ 0.05. To reduce burnout of Bangladeshi physicians, it is necessary to address the risk factors, create supportive workplaces, maintain a healthy work-life balance, provide opportunities for self-care, and promote mental health., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2023 The Authors.)