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Water contamination is a global concern as a result of growing industrialization and urbanization. Dye waste produced by numerous industries pollutes the water, so dye-based wastewater is a major alarm for animals, human health, and the environment. The elimination of these dye contaminants from water supplies is currently critical and significant due to the dyes' toxicity on humans, livestock, and plants. To remove/degrade dyes, various methods have been investigated, including extraction, absorption, membrane separation, coagulation, adsorption, biological treatment, and advanced oxidation process (AOPs). Graphitic carbon nitride (g-C3N4), a captivating formed polymer, has been transformed as an intriguing issue in AOP science because of its metal-free advantages and its high sensitivity to light. Accordingly, when AOPs are joined with g-C3N4, superb dye degradation has occurred. This paper overviewed the g-C3N4-based AOPs are discussed for dye removal. The synergist effectiveness and mechanisms behind catalytic activity of g-C3N4-based catalysts are broadly discussed. In the meantime, the impacts of pH, water temperature, dissolved oxygen, initial dye concentration, catalyst dosage, and scavengers on the reaction carried out by g-C3N4-based catalysts are illustrated.

Sharing accurate and lossless images with higher quality through digital mediums is challenging, particularly, images shared on social media platforms can serve as good carriers for sending hidden data. However, social media platforms apply severe compression when transferring images end-to-end to serve efficient network transporting bandwidth and provide enough storage space to the users. Within the scope of this research, the proposed method introduces a novel approach by combining cryptographic and steganographic techniques, providing a robust solution to protect hidden data even when subjected to repeatedly compression. The method first encrypts the secret data to be hidden using the Advanced Encryption Standard Cipher Block Chaining (AES-CBC) technique. Then, data hiding is performed on the coefficients obtained by applying Discrete Cosine Transform (DCT) to repeatedly compressed JPEG images, coefficients that are minimally affected by compression or remain unaffected are specifically selected for data hiding. Therefore, secret data is extracted with high accuracy. Experimental results show that the proposed method outperforms the state-of-the-art in achieving robust and effective data hiding techniques based on bit error rate.

This review aims to examine the existence of Pseudomonas aeruginosa (P. aeruginosa) and their antibiotic resistance genes (ARGs) in aquatic settings and the alternative treatment ways. P. aeruginosa in a various aquatic environment have been identified as contaminants with impacts on human health and the environment. P. aeruginosa resistance to multiple antibiotics, such as sulfamethoxazole, ciprofloxacin, quinolone, trimethoprim, tetracycline, vancomycin, as well as specific antibiotic resistance genes including sul1, qnrs, blaVIM, blaTEM, blaCTX, blaAIM-1, tetA, ampC, blaVIM. The development of resistance can occur naturally, through mutations, or via horizontal gene transfer facilitated by sterilizing agents. In addition, an overview of the current knowledge on inactivation of Pseudomonas aeruginosa and ARG and the mechanisms of action of various disinfection processes in water and wastewater (UV chlorine processes, catalytic oxidation, Fenton reaction, and ozonation) is given. An overview of the effects of nanotechnology and the resulting wetlands is also given.

Purpose: The head and neck cancer (HNC) literature is rife with reports of differences in planned versus actual radiation doses to the parotid gland (PG) due to changes in anatomy during the course of radiation therapy. We prospectively studied and quantified changes in planned and delivered doses due to weight loss and changes in lateral neck dimensions. Methods and Materials: Sixty patients were enrolled in this prospective non-randomized observational study. The inclusion criterion was having a newly diagnosed, histologically proven squamous cell carcinoma of HNC. Weight loss (WL) and change in lateral neck dimensions (LND) were assessed weekly, and new hybrid plans were generated using interval replanning CT scans. Dose variations were monitored and extrapolated for replanning CT scans and correlated with WL and change in LND. Results: The Pearson correlation coefficients for WL and difference in Dmean of ipsilateral and contralateral PG was 0.3292 (P = .0124) and 0.4232 (P = .0010), respectively. There was significantly higher change in the Dmean of bilateral PG (Ipsilateral(I) > contralateral(C)) in patients who experienced WL of >5%. Change in LND correlated with difference in Dmean of ipsilateral PG at 0.4829 (P = .0001) and difference in D50 at 0.4146 (P < .0013). Contralateral PG correlated with difference in Dmean at 0.5952 (P < .0001). The difference in Dmean for ipsilateral PG was 1.8535 Gy for those showing reduction in LND of >1 cm compared with 0.8596 Gy (P = .0091) for those who had ≤1 cm reduction in LND. Conclusions: Either WL of >5% or reduction in LND of >1 cm can be used as an external parameter to help select patients who might benefit most from replanning and adaptive radiation therapy.

Objective: This article presents a study comparing the responses of two AI chatbots, ChatGPT and ChatSonic, regarding inquiries about epilepsy. Overall, ChatGPT and ChatSonic are very similar in terms of their capabilities and limitations and they are the most widely used AI software. However, there are some key differences, such as their training data, supported languages, and pricing model. The study aims to assess the potential application of AI in patient counseling and decision-making regarding epilepsy treatment. Methods: The study categorized the inquiries of patients about epilepsy into two groups: patient counseling and judgment. Ten questions were formulated within these categories. Two specialized doctors evaluated the reliability and accuracy of the chatbot replies using the Global Quality Scale (GQS) and a modified version of the DISCERN score. Results: The median value for GQS of 4.5 was given by Evaluator JC, and a median value for GQS of 4.0 was given by Evaluator VV. Furthermore, a median for RS of 5.0 was given by Evaluator JC, and a median for RS of 4.0 was given by Evaluator VV. The GQS data from Evaluators JC and VV have a Spearman correlation coefficient of -0.531, indicating an inversely proportional association, and a p-value of 0.016, indicating a statistically significant relationship between the variables. However, the correlation coefficient of RS between data by Evaluator JC and Evaluator VV is 0.368 which indicates the correlation is a directly proportional relationship, with a p-value of 0.110 which is not statistically significant, does not establish a relation between the variables. Weighted Kappa was used to study the agreement between the data. With a weighted kappa value of -0.318 and a 95 %CI of -0.570, -0.065 was obtained for GQS. This can help reject the null hypothesis indicating that the values by the Evaluator JC and Evaluator VV are statistically significant and has a negative agreement. However, a weighted kappa value of 0.1327 with a 95 %CI of -0.093, 0.359 obtained for RS, fails to reject the null hypothesis indicating that the values by the Evaluator JC and Evaluator VV are not significant and no agreement exists between the Evaluators. The results of this study suggest that both ChatGPT and ChatSonic have the potential to be valuable tools for epilepsy patients and their healthcare providers. However, it is important to note that the two evaluators had better agreement on the GQS scores than on the RS scores, suggesting that the GQS may be a more reliable measure of the quality of chatbot responses. Conclusion: The findings underscore the importance of collaboration among policymakers, healthcare professionals, and AI designers to ensure appropriate and safe utilization of AI chatbots in the healthcare domain. While AI chatbots can provide valuable information, it is crucial to acknowledge their limitations, including reliance on the training data and occasional factual errors. The study highlights the need for further testing and validation of AI language models in the management of epilepsy as it concludes.

In the digital age, where information is a cornerstone for decision-making, social media's not-so-regulated environment has intensified the prevalence of fake news, with significant implications for both individuals and societies. This study employs a bibliometric analysis of a large corpus of 9678 publications spanning 2013–2022 to scrutinize the evolution of fake news research, identifying leading authors, institutions, and nations. Three thematic clusters emerge: Disinformation in social media, COVID-19-induced infodemics, and techno-scientific advancements in auto-detection. This work introduces three novel contributions: 1) a pioneering mapping of fake news research to Sustainable Development Goals (SDGs), indicating its influence on areas like health (SDG 3), peace (SDG 16), and industry (SDG 9); 2) the utilization of Prominence percentile metrics to discern critical and economically prioritized research areas, such as misinformation and object detection in deep learning; and 3) an evaluation of generative AI's role in the propagation and realism of fake news, raising pressing ethical concerns. These contributions collectively provide a comprehensive overview of the current state and future trajectories of fake news research, offering valuable insights for academia, policymakers, and industry.

The use of nanotechnology in the field of construction emerged as the utilization of nanomaterials to improve the mechanical properties of concrete. In this study, a novel form of stiffening scheme was suggested, named a catty-cornered propped concrete-filled steel tube (CFST) column. The performance of the suggested stiffened CFST column was analyzed under axial compression. The steel tube of CFST specimens was filled with normal and nanomaterial-based concrete. The three kinds of nanomaterials were utilized viz., nano-silica (NS), carbon nanotubes (CNT), and nano-titanium dioxide (NT). The results of the investigation were collected in terms of ultimate capacity, load vs strain behavior, and load vs deformation response. The ductility index (DI), secant stiffness, composite interaction, and confining effect variation were also discussed further. It was observed that the suggested stiffening scheme was able to increase the ultimate capacity of unstiffened CFST by approximately 14%. The use of nanomaterials in CFST infill concrete also resulted in an approximately 7% increase in load capacity. Further increasing the number of stiffening bars improved the ductility and stiffness of the column section. On the other hand, the inclusion of nanomaterials resulted in a decrease in the ductility index and improved the stiffness of the section. The proposed stiffening scheme resulted in better composite interaction and increased confinement. It was also concluded that the utilization of nanomaterial-based concrete as an infill in the stiffened CFST column could enhance its performance under axial compression loading.

Scrub typhus is a vector-borne infectious disease caused by Orientia tsutsugamushi and it is reportedly associated with up to 20 % of hospitalized cases of febrile illnesses. The major challenge of vaccine development is the lack of identified antigens that can induce both heterotypic and homotypic immunity including the production of antibodies, cytotoxic T lymphocyte, and helper T lymphocytes. We employed a comprehensive immunoinformatic prediction algorithm to identify immunogenic epitopes of the 56-kDa type-specific cell membrane surface antigen and surface cell antigen A of O. tsutsugamushi to select potential candidates for developing vaccines and diagnostic assays. We identified 35 linear and 29 continuous immunogenic B-cell epitopes and 51 and 27 strong-binding T-cell epitopes of major histocompatibility complex class I and class II molecules, respectively, in the conserved and variable regions of the 56-kDa type-specific surface antigen. The predicted B- and T-cell epitopes were used to develop immunogenic multi-epitope candidate vaccines and showed to elicit a broad-range of immune protection. A stable interactions between the multi-epitope vaccines and the host fibronectin protein were observed using docking and simulation methods. Molecular dynamics simulation studies demonstrated that the multi-epitope vaccine constructs and fibronectin docked models were stable during simulation time. Furthermore, the multi-epitope vaccine exhibited properties such as antigenicity, non-allergenicity and ability to induce interferon gamma production and had strong associations with their respective human leukocyte antigen alleles of world-wide population coverage. A correlation of immune simulations and the in-silico predicted immunogenic potential of multi-epitope vaccines implicate for further investigations to accelerate designing of epitope-based vaccine candidates and chimeric antigens for development of serological diagnostic assays for scrub typhus.

Renewable energy has gained enormous popularity in recent years. Both independent and grid-connected renewable energy generating capacity has increased. The potential to create green energy that is both ecologically benign and financially operative is the primary argument. To reduce the cost of maintaining a hybrid microgrid, this study provides a novel control algorithm as well as a suitable energy management system. The energy storage technologies are used in the hybrid microgrid system which consist of photovoltaic cell, fuel cell and supercapacitor. The optimal power flow in the hybrid microgrid component is developed using energy management system which consents control system to batteries, load and economic analysis with the input of electric grid. In this paper supercapacitor is which is directly connected to DC and provide fast response to load changing. DC voltage, current and hybrid microgrid is managed using the local control system. The power change in photovoltaic system is solved using energy storage system. The purpose of control method in hybrid microgrid is used to enhance the hybrid microgrid flexibility and generation of renewable resources and stored it in grid. The effectiveness and the flexibility of the proposed controller in energy management of hybrid microgrid system is simulated. The proposed energy management system provides high performance related to the existing energy management system.

Pollutants are big threat for the environment. Their imperil nature have disturbed the balance in the ecosystem and have also endangered the existence of the life on the earth. Advanced oxidation process is one of the emerging approach for the treatment of recalcitrant pollutants in nature. The process mainly involves the generation of a strong oxidant, which can easily degrade the pollutants produced from different sources. This review manifests the need of advanced oxidation process, and gives an outline of the different types of advanced oxidation process. It discusses the mechanism, advantages and disadvantages of the various forms of advanced oxidation process. Parameters for a sustainable technology such as technical and economical feasibility of the process, toxicity level and degradation effectiveness of the medium have also been discussed in detail to profess the sustainability of the process. It has potential and efficacy in removal of organic, toxic and non-biodegradable pollutants with minimum harmful effects. The future perspectives provide the room for modification and development and motivates us to overcome the present challenges and achieve better outcomes in future.

Cyclic-di-AMP (c-di-AMP) is an important secondary messenger molecule that plays a critical role in monitoring several important cellular processes, especially in several Gram-positive bacteria. In this study, we seek to unravel the physiological significance of the molecule c-di-AMP in Mycobacterium smegmatis under different conditions, using strains with altered c-di-AMP levels: c-di-AMP null mutant (ΔdisA) and a c-di-AMP over-expression mutant (Δpde). Our thorough analysis of the mutants revealed that the intracellular concentration of c-di-AMP could determine many basic phenotypes such as colony architecture, cell shape, cell size, membrane permeability etc. Additionally, it was shown to play a significant role in multiple stress adaptation pathways in the case of different DNA and membrane stresses. Our study also revealed how the biofilm phenotypes of M. smegmatis cells are altered with high intracellular c-di-AMP concentration. Next, we checked how c-di-AMP contributes to antibiotic resistance or susceptibility characteristics of M. smegmatis, which was followed by a detailed transcriptome profile analysis to reveal key genes and pathways such as translation, arginine biosynthesis, cell wall and plasma membrane are regulated by c-di-AMP in mycobacteria.

Owing to the inefficiency to hide large volume of secret data for the reversible data hiding (RDH) image steganography approaches, we propose two improved RDH based approaches, such as (1) improved dual image based least significant bit (LSB) matching with reversibility, and (2) n-rightmost bit replacement (n-RBR) and modified pixel value differencing (MPVD). The first approach extends the ability of LSB matching with reversibility using dual images. Whereas the second approach utilizes four identical cover images for secret data embedding using two phases, such as (1) n-rightmost bit replacement (n-RBR) and (2) modified pixel value differencing (MPVD). In the n-RBR phase, n bits of secret data are embedded in the pair of two neighboring pixels of the first two identical images, where 1 ≤ n ≤ 4. Correspondingly, the MPVD phase uses the third and fourth identical images for hiding the secret data. Experimental results with respect to peak signal-to-noise ratio (PSNR), embedding capacity (EC), structural similarity index (SSIM), and the comparative analysis with recently proposed state-of-art approaches exhibit the superiority of the proposed approach. Besides reversibility, the proposed approach ensures high fidelity to salt and pepper (S&P) noise, RS analysis, and pixel difference histogram (PDH) analysis.

Extrusion-based 3D printing with rapidly hardening polymeric materials is capable of building almost any conceivable structure. However, concrete, one of the most widely used materials for large-scale structural components, is generally based on inorganic binder materials like Portland cement. Unlike polymeric materials, a lack of precise control of the extent and rate of solidification of cement-based suspensions is a major issue that affects the ability to 3D-print geometrically complex structures. Here, we demonstrate a novel method for controllable-rapid solidification of concentrated mineral suspensions that contain a polymer binder system based on epoxy and thiol precursors as well as one or more mineral fillers like quartz and calcite. The thermally triggered epoxy-thiol condensation polymerization induces rapid stiffening of the hybrid suspensions (0.30 ≤ ϕ ≤ 0.60), at trigger temperatures ranging between 50 °C and 90 °C achieving average stiffening rates up to 400 Pa/s. The use of nucleophilic initiators such as 1-methylimidazole provides control over the activation temperature and curing rate, thereby helping to achieve an adjustable induction period and excellent thermal latency. By using multiple techniques, we provide guidelines to create designer compositions of mineral suspensions that utilize thermal triggers to achieve thermal latency and ultrafast stiffening – prerequisite attributes for 3D-manufacturing of topologically-optimized structural components.

Transfer learning, a machine learning technique which employs prior knowledge from solving a source problem to solve a related target problem, is utilized in this work to predict the compressive strength and modulus of elasticity of different concrete mixtures. The use of data augmentation through empirical models to estimate missing data outputs allows for the use of inductive parameter transfer-learning artificial neural network (ANN) models for fast convergence. The paper considers two distinct cases: one where the domain of the target lies somewhat outside that of the source — termed domain expansion, and another where the target output (e.g., elastic modulus) is different, but related to the source output (e.g., compressive strength) — termed domain adaptation. Transfer learning is found to be most accurate when the source dataset is more complex than the target dataset, since more features could be learned. Data augmentation and the coupling of traditional machine learning with transfer learning are demonstrated to greatly enhance the predictive capability for important concrete properties, from mixture proportions. Limited experimental data can be used to transfer-learn the properties (output) of a new dataset from a reliable source model for a related system.

The production of ordinary Portland cement (OPC), the most broadly utilized man-made material, has been scrutinized due to its contributions to global anthropogenic CO2 emissions. Thus — to mitigate CO2 emissions — mineral additives have been promulgated as partial replacements for OPC. However, additives — depending on their physiochemical characteristics — can exert varying effects on OPC’s hydration kinetics. Therefore — in regards to more complex systems — it is infeasible for semi-empirical kinetic models to reveal the underlying nonlinear composition-property (i.e., reactivity) relationships. In the past decade or so, machine learning (ML) has arisen as a promising, holistic approach to predict the properties of heterogeneous materials, even without an across-the-board comprehension of the underlying composition-properties correlations. This paper describes the use of a Random Forests (RF) model to enable high-fidelity predictions of time-dependent hydration kinetics of OPC-based systems — more specifically [OPC + mineral additive(s)] systems — using the system’s physiochemical attributes as inputs. Results show that the RF model can also be used to formulate mixture designs that satisfy user-imposed kinetics-related criteria. Lastly, the presented results can be expanded to formulate mixture designs that satisfy target kinetic criteria, even without knowledge of the underlying kinetic mechanisms.

Climate change has a detrimental impact on human health due to increasing temperature extremes. It is undoubtedly true over India, where in recent decades, a rise in the frequency and intensity of extreme events is witnessed. The present study primarily focuses on understanding the dynamics of the hot season over homogeneous regions of India. Using Canonical Correlation Analysis, five temperature homogeneous regions are defined. All regions showed a positive trend with less interannual variability, except Gangetic plains. The seasonal composites for extreme temperature years show that hot season over North India (NI) occurs mainly due to blocking high in upper atmospheres. Similarly, daily temperature anomalies for the heatwave days during hot years exhibit stationarity of such blocks centered over the region. Two global teleconnections have been found to be responsible for the NI seasonal anomaly, (i) a continuous anomalous low over Europe Granger cause anomalous high across the region, (ii) the subtropical jet stream and the polar jet stream help to maintain stationarity of anticyclonic blocks over the region. Sinking of air due to an upper atmospheric high over NI causes adiabatic warming near the surface. Southern India mainly gets heated due to warm air intrusion from northwest regions, contributing to anomalous low humidity over the southern coastal regions. This phenomenon blocks moisture transport from the adjacent ocean entering the region resulting in amplification of hotness due to moisture blocking, leading to an increased temperature anomaly.

This article contains data related to the research article entitled, “Organic radical imaging in plants: Focus on protein radicals” (Kumar et al., 2018). The data presented herein focus on reactive oxygen species (ROS) and organic radical formed within photosynthetic tissues of Arabidopsis thaliana during high light stress and includes (1) Confocal laser scanning microscopic images using 3′-p-(hydroxyphenyl) fluorescein (HPF) as specific probe for the detection of hydroxyl radical (HO•); (2) Confocal laser scanning microscopic images using Singlet Oxygen Sensor Green (SOSG) as a specific probe for the detection of singlet oxygen (1O2) and; (3) Electron paramagnetic resonance (EPR) spectroscopy using spin traps for the detection of organic radical. Keywords: Photosystem II, Reactive oxygen species, Hydroxyl radical, Singlet oxygen, Organic radical

The Application of a combination of enzymes is the best alternative to reduce the use of chemicals in the paper industry. Bacillus sp. NG-27 and Bacillus nealsonii PN-11 are known to produce thermoalkali stable xylanse (X) and mannanase (M) respectively having potential for pulp biobleaching. The Present study, reports the production of a mixture of X + M by co-culturing of strains in SSF and standardizing its application for pulp biobleaching. Production of enzymes by co-cultivation in SSF was optimized by statistical methods. Substantial increase in the yield of enzymes; 3.61 fold of xylanase and 37.71 fold of mannanase was achieved. Application of enzyme cocktail for pulp biobleaching resulted in a 45.64% reduction of kappa number with 55 IU g−1odp of enzyme dose (xylanase:mannanase; 3:1) at pH 8.0 in 1h at 65 °C along with significant increase in brightness (11%) and whiteness (75%). The Same quality of paper as made up from chemical treated pulp can be made from enzyme-treated pulp with 30% less use of chlorine. Structural analysis of enzyme-treated pulp showed dissolution of hemicellulose as indicated by pores, cracks and increased roughness all over the surface. Cocktail of X + M produced economically in a single fermentation having all the requisite characteristics for pulp biobleaching is a highly suitable candidate for application in the pulp and paper industry.

The presence of water on the surface of material is not always desired. The prolonged stay of droplet on surface can deteriorate its physical and chemical properties. Bioinspired anti-wetted surfaces can be thought of an alternative that can protect the surface by expelling the possibility of water residence on the surface for long period. These surfaces show remarkable potential due to their diverse applications such as self-cleaning, transparency, oil-water separation, anti-corrosion, anti-microbial and have gained attention due to their low cost, ease of processing, and compatibility with a variety of materials and applications. This paper consists the fundamental concept of wetting phenomena. Further it comprises the details of different techniques used for fabricating these surfaces. Review also discusses about the stability of these surfaces and ends with future prospects which gives helps in development of new and advanced super-hydrophobic/oleophobic surfaces.

The highly contagious nature of SARS-CoV-2 (COVID-19) virus has created a havoc effect all over the world in a very short period. The most effective method for precaution of this virus as suggested by WHO is use of PPEs (triple layered face mask, body suits) and social distancing. However, the frontline doctors and medical staff have high risk of exposure to the virus during treatments and removal and discarding of the PPEs. Also the PPEs are of the onetime use and wearing these PPEs in hot and humid seasons is very uncomfortable. A possible solution of this problem is if clothes are anti-bacterial and anti-viral in nature, one single-layered will be sufficient and the spread of virus will also be minimized. Considering this, we have designed a facile and durable anti-wash antimicrobial coating on cloth by aloe vera and hexadecyltrimethoxysilane (HDTMS). In lab scale study, present coating shows good chemical and thermal stability making it reusable multiple times even after repeated washing. The coated cloth reveals the excellent anti-dirt and stain resistance properties leading to complete non-adherence of dirt and stain (e.g. Color, food, ink) on it. Anti-bacterial and anti-fungi properties of the coated cloth were confirmed by doing E. coli (bacteria) and A. Niger (fungus) culture studies, respectively. This coating is imbibed with well-known anti-viral agent aloe vera which inhibits the attachment of the virus on the surface. The water-repellent nature of the coating combined with the use anti-viral agent, aloe vera makes it a potential anti-COVID-19 coating.

Background: The coronary slow flow phenomenon (CSFP) is an angiographic finding characterized by delayed distal vessel opacification in the absence of significant epicardial coronary stenosis. Although it is well-known to interventional cardiologists for approximately four decades, the etiopathogenesis still remains unclear. Aims and objectives: In this study, we aimed to determine the clinical, demographic, risk factor and angiographic profile of patients with CSFP. Methods: Clinical, demographic, risk factor and angiographic profile were recorded in all consecutive patients who had undergone coronary angiography between September 2016 and March 2017 and showed features of CSFP and a control group who showed normal coronary flow (NCF). The CSFP was diagnosed on the basis of the corrected thrombolysis in myocardial infarction frame count. Results: CSFP was significantly more prevalent in male patients. Among the traditional risk factors, there was significantly more prevalence of hypertension (31.25% versus 6.67%, p

Background: This study aimed to assess the whole-body vibration (WBV) exposure among large blast hole drill machine operators with regard to the International Organization for Standardization (ISO) recommended threshold values and its association with machine- and rock-related factors and workers' individual characteristics. Methods: The study population included 28 drill machine operators who had worked in four opencast iron ore mines in eastern India. The study protocol comprised the following: measurements of WBV exposure [frequency weighted root mean square (RMS) acceleration (m/s2)], machine-related data (manufacturer of machine, age of machine, seat height, thickness, and rest height) collected from mine management offices, measurements of rock hardness, uniaxial compressive strength and density, and workers' characteristics via face-to-face interviews. Results: More than 90% of the operators were exposed to a higher level WBV than the ISO upper limit and only 3.6% between the lower and upper limits, mainly in the vertical axis. Bivariate correlations revealed that potential predictors of total WBV exposure were: machine manufacturer (r = 0.453, p = 0.015), age of drill (r = 0.533, p = 0.003), and hardness of rock (r = 0.561, p = 0.002). The stepwise multiple regression model revealed that the potential predictors are age of operator (regression coefficient β = −0.052, standard error SE = 0.023), manufacturer (β = 1.093, SE = 0.227), rock hardness (β = 0.045, SE = 0.018), uniaxial compressive strength (β = 0.027, SE = 0.009), and density (β = –1.135, SE = 0.235). Conclusion: Prevention should include using appropriate machines to handle rock hardness, rock uniaxial compressive strength and density, and seat improvement using ergonomic approaches such as including a suspension system.