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The convergence of neuroscience, biotechnology, and artificial intelligence (AI) is revolutionizing our comprehension of the brain and our interactions with computers. Central to this revolution are two fundamental principles: brain-machine interfaces (BMIs) and the reverse engineering of the brain. Furthermore, these technologies have the potential to not only revolutionize healthcare and human-machine interaction but also to drive significant advancements in artificial intelligence, education, and personalized therapies for neurological disorders.Understanding Brain-Machine InterfacesBrain-machine interfaces (BMIs) are a revolutionary technology that facilitates direct interaction between the human brain and external technological equipment. Although the notion originated from the bioelectric phenomena found in the 1930s, contemporary BMIs leverage state-of-the-art developments in electrode arrays, signal processing, and AI-driven algorithms (Liu et al., 2024). Organizations such as Neuralink are currently spearheading initiatives to develop devices that enable smooth communication between the brain and machines, with the goal of enhancing human cognitive and motor capacities (Musk, 2019). BMIs have diverse applications such as (i) the neurorehabilitation for sensory-motor impairments, (ii) exoskeleton control, and (iii) cognitive state acceleration (Chen et al., 2023; Hramov, Maksimenko, and Pisarchik, 2021). The passive BMIs are increasingly recognized as effective tools for examining emotional and cognitive conditions without the need for explicit instructional input (Niso et al., 2023). In addition, these breakthroughs are offering the potential to broaden the capabilities and availability of BMIs in areas like healthcare,robotics, and personal performance improvement.Significant technological achievements in the development of BMI have played a vital role in influencing this advancement. For example, the implementation of Michigan array electrodes in the 1970s enabled scientists to accurately observe brain activity (Jarosiewicz and Morrell, 2021). Recent innovations, including microfluidic channels, bioelectronic interfaces, and three-dimensional (3D) probes, have improved the precision and security of BMI diagnostic equipment. Significant accomplishments include the development of 1,000-channel platforms for brain recording in 2019 and advancements in reliable electrophoretic recording modalities (Musk, 2019).The machine learning approaches that can be used to predict the strength of graphene nanoplatelets concrete are of interest, mainly for optimization and hyper-tuning with a graphical user interface (Alahmari and Arif, 2024). The incorporation of nanomaterials can enhance the performance and characteristics of cement composites. By adding graphene nanoplatelets (GNPs) to the composite matrix, the sustainable nature of the material can be achieved along with superior properties. On the other hand, the application of 3D printing and machine learning of graphene quantum dots (GQDs) in polymer nanocomposites are also interesting to explore. This innovation supported the development of new advanced materials by highlighting the size control and surface functionalization of GQDs to obtain the unique electronic structure, tunable bandgap with character superior in optical, mechanical, and thermal properties. This technology can be used in optoelectronics, energy storage, sensors, and biomedical devices (Dananjaya et al., 2024). Reverse Engineering the Brain: A Complex ChallengeReverse engineering the brain is a multidisciplinary undertaking that integrates neuroscience, engineering, and technology domains. This effort aims to decipher the complex biochemical and electrical pathways of the brain, therefore offering an understanding of its working mechanisms in both normal and pathological conditions. A collaborative effort between engineers and neuroscientists is undertaken to create sophisticated technology capable of observing and quantifying the electrical and anatomical activity of the brain. These technologies facilitate the analysis of brain activity at unparalleled levels of sophistication, therefore revealing novel prospects for intervention in disorders such as Alzheimer's and Parkinson's (Paulk et al., 2022).Elucidating the intricate operations of the brain has extensive consequences for the fields of medicine, education, and computing (Breier et al., 2022). Through the identification of impaired electrical circuits in Alzheimer's disease, scientists have the opportunity to create pharmaceuticals that specifically target brain signals, so potentially enhancing the efficacy of clinical interventions. Furthermore, the ramifications of reverse engineering the brain also encompass the domain of individualized learning (Dehais, Karwowski, and Ayaz, 2020). Understanding the cognitive processes of the brain would enable educators to tailor learning approaches to meet the specific needs of students, thereby improving results for individuals with diverse learning preferences. Artificial Intelligence and Cognitive ComputingReverse engineering the brain provides valuable knowledge that has the capacity to transform artificial intelligence and computing capabilities. Emulating the neural networks of the brain has the potential to facilitate the advancement of artificial intelligence systems that exhibit enhanced flexibility, adaptability, and problem-solving capacities. By emulating human cognition, these systems can potentially enhance AI's intuitiveness, capability, and efficiency.The practical implications of this knowledge span several sectors, such as robots, healthcare, and communication. Artificial intelligence (AI) systems that emulate the human brain have the ability to analyze and adjust to input in real-time, similar to the human mind. This characteristic renders them valuable for intricate and changeable jobs. The convergence of brain-inspired artificial intelligence (AI) and brain machine interface (BMI) technologies has the potential to ultimately result in smoother interactions between humans and computers, hence further eroding the distinction between biological cognition and machine intelligence. Ethical and Societal ImplicationsAlthough the technological progress in BMI and reverse engineering of the brain is certainly thrilling, it also gives rise to ethical concerns around privacy, autonomy, and the possibility of exploitation. The growing sophistication of BMIs raises significant ethical concerns about the potential to monitor, influence, and even manipulate brain function. The careful development and responsible deployment of these technologies will be crucial in order to optimize their societal advantages.The notion of technological singularity, popularised by futurists such as Ray Kurzweil, posits that while these continuous developments persist, artificial intelligence may surpass human intelligence (Kurzweil, 2005). This phenomenon prompts significant inquiries regarding the prospective developments in human-machine interactions and the attendant societal shifts that may emerge as a result of this reconfiguration. Within this framework, the advancement of BMI and the process of reverse-engineering the brain are significant technological achievements and ethical dilemmas that need to be thoughtfully addressed. ConclusionThe intersection of brain-machine interfaces and the reverse engineering of the brain marks a pivotal moment in the advancement of neuroscience and bioengineering. The aforementioned technologies possess significant promise for transformation in the fields of healthcare, artificial intelligence, and personalized education. Nevertheless, as we continue to push the boundaries of human-machine interaction, it is essential to actively consider the ethical and societal implications of these advancements.As researchers persist in investigating the enigmas of the brain and formulating increasingly advanced BMI, the capacity to augment human cognition and motor function will expand. This expedition symbolizes not just an advancement in scientific knowledge but also a significant chance to influence the future of interactions between humans and machines for the overall benefit of mankind.

Background and Aim: The dearth of new antibiotics necessitates alternative approaches for managing infections caused by resistant superbugs. This study aimed to evaluate the lytic potential of the purified bacteriophage PKp-V1 against extended-spectrum β-lactamase (ESBL) harboring hypervirulent Klebsiella pneumoniae (hvKp)-K1 recovered from veterinary specimens. Materials and Methods: A total of 50 samples were collected from various veterinary specimens to isolate K. pneumoniae, followed by antimicrobial susceptibility testing and molecular detection of various virulence and ESBL genes. Multilocus sequence typing of the isolates was performed to identify prevalent sequence types. The bacteriophages were isolated using the double-agar overlay method and characterized using transmission electron microscopy, spot tests, plaque assays, stability tests, and one-step growth curve assays. Results: Among 17 (34%) confirmed K. pneumoniae isolates, 6 (35%) were hvKp, whereas 13 (76%) isolates belonging to the K1 type were positive for the wzy (K1) virulence gene. All (100%) hvKp isolates exhibited the allelic profile of ST258. Overall, PKp-V1 exhibited an 88 % (15/17; (p ≤ 0.05) host range, among which all (100 %; p ≤ 0.01) hvKp isolates were susceptible to PKp-V1. PKp-V1 exhibited a lytic phage titer of 2.4 × 108 plaque forming unit (PFU)/mL at temperatures ranging from 25°C to 37°C. The lytic phage titers of PKp-V1 at pH = 8 and 0.5% chloroform were 2.1 × 108 PFU/mL and 7.2 × 109 PFU/mL, respectively. Conclusion: Although the incidence of ESBL-infected K. pneumoniae in veterinary settings is worrisome, PKp-V1 phages showed considerable lytic action against the host bacterium, indicating the potential of PKp-V1 as a possible alternative therapeutic option against MDR K. pneumoniae.

Abstract Green nanotechnology has significant potential for use in agriculture particularly due to their antifungal properties, ability to control fungal diseases and reduce the reliance on chemical fungicides. Biotic stresses in agriculture have caused widespread damage worldwide, and green NPs provided eco-friendly alternatives to traditional chemical treatments, which are frequently toxic and harmful to the ecosystem. Green NPs could become an important tool in modern agricultural practices and environmental remediation if appropriate research is conducted to identify cost-effective production methods as well as safe and sustainable applications. In order to understand the potential of green NPs for sustainable agriculture and identify potential risks, research is ongoing into the effectiveness in agriculture sectors. Research update on green NPs is presented in this paper using data published on science direct over the last 15 to 20 years to clarify and understand the antifungal mechanisms of green metallic NPs, carbon and graphene nanotubes, nanocomposites as well as other type of nanomaterials. These green NPs are found to be more effective against pathogens on crops and humans than conventional fungicide approaches. They are very effective against fungi that affect cereal crops, including Fusarium oxysporum, Botrytis cinerea, and Candida species, etc. The green NPs developed using green synthesis methods are both cost-effective and environmentally friendly. Moreover, research is also required to identify the best methods for applying green NPs for crop production and sustainable agriculture. Furthermore, research should be undertaken to establish the most cost-effective methods of making and deploying green nanoparticles at a large field size study where there is fungal attack that diminishes agricultural output and affects global crop production. Graphical Abstract

Abstract Management of molasses-based wastewater generated in yeast and sugar industries is a major environmental concern due to its high chemical oxygen demand and other recalcitrant substances. Several strategies have been used to reduce the inland discharge of wastewater but the results are not satisfactory due to high operating cost. However, reuse of molasses-based wastewater irrigation in agriculture has been a major interest nowadays to reduce the freshwater consumption. Thus, it is crucial to monitor the impacts of molasses-based waste water irrigation on growth, metabolism, yield and nutritional quality of crops for safer consumer’s health. In present study, carrot seeds of a local cultivar (T-29) were germinated on filter paper in Petri dishes under controlled conditions. The germinated seeds were then transplanted into pots and irrigated with three different treatments normal water (T0), diluted molasses-based wastewater (T1), and untreated molasses-based wastewater (T2), in six replicates. Results revealed that carrot irrigated with untreated molasses-based waste water had exhibited significant reductions in growth, yield, physiology, metabolism, and nutritional contents. Additionally, accumulation of Cd and Pb contents in carrot roots irrigated with untreated molasses-based waste water exceed the permissible limits suggested by WHO and their consumption may cause health risks. While, diluted molasses-based waste water irrigation positively enhanced the growth, yield of carrot plants without affecting the nutritional quality. This strategy is cost effective, appeared as most appropriate alternative mean to reduce the freshwater consumption in water deficit regions of the world.

Abstract Non-fullerene organic compounds are promising materials for advanced photovoltaic devices. The photovoltaic and electronic properties of the derivatives (TTBR and TTB1-TTB6) were determined by employing density functional theory (DFT) and time-dependent density functional theory (TD-DFT) analyses using the M06/6-311G(d,p) functional. To enhance the effectiveness of fullerene-free organic photovoltaic cells, modifications were applied to end-capped acceptors by using strong electron-withdrawing moieties. The structural tailoring showed a significant electronic impact for HOMO and LUMO for all chromophores, resulting in decreased band gaps (3.184–2.540 eV). Interestingly, all the designed derivatives exhibited broader absorption spectra in the range of 486.365–605.895 nm in dichloromethane solvent. Among all derivatives, TTB5 was observed to be the promising candidate because of its lowest energy gap (2.54 eV) and binding energy (0.494 eV) values, along with the bathochromic shift (605.895 nm). These chromophores having an A–π–A framework might be considered promising materials for efficient organic cells.

Abstract Background Sepsis poses a critical threat to hospitalized patients, particularly those in the Intensive Care Unit (ICU). Rapid identification of Sepsis is crucial for improving survival rates. Machine learning techniques offer advantages over traditional methods for predicting outcomes. This study aimed to develop a prognostic model using a Stacking-based Meta-Classifier to predict 30-day mortality risks in Sepsis-3 patients from the MIMIC-III database. Methods A cohort of 4,240 Sepsis-3 patients was analyzed, with 783 experiencing 30-day mortality and 3,457 surviving. Fifteen biomarkers were selected using feature ranking methods, including Extreme Gradient Boosting (XGBoost), Random Forest, and Extra Tree, and the Logistic Regression (LR) model was used to assess their individual predictability with a fivefold cross-validation approach for the validation of the prediction. The dataset was balanced using the SMOTE-TOMEK LINK technique, and a stacking-based meta-classifier was used for 30-day mortality prediction. The SHapley Additive explanations analysis was performed to explain the model’s prediction. Results Using the LR classifier, the model achieved an area under the curve or AUC score of 0.99. A nomogram provided clinical insights into the biomarkers' significance. The stacked meta-learner, LR classifier exhibited the best performance with 95.52% accuracy, 95.79% precision, 95.52% recall, 93.65% specificity, and a 95.60% F1-score. Conclusions In conjunction with the nomogram, the proposed stacking classifier model effectively predicted 30-day mortality in Sepsis patients. This approach holds promise for early intervention and improved outcomes in treating Sepsis cases.

Abstract Cystic fibrosis transmembrane conductance regulator (CFTR) protein is an ion channel found in numerous epithelia and controls the flow of water and salt across the epithelium. The aim of our study to find natural compounds that can improve lung function for people with cystic fibrosis (CF) caused by the p.Gly628Arg (rs397508316) mutation of CFTR protein. The sequence of CFTR protein as a target structure was retrieved from UniProt and PDB database. The ligands that included Armepavine, Osthole, Curcumin, Plumbagine, Quercetin, and one Trikafta (R*) reference drug were screened out from PubChem database. Autodock vina software carried out docking, and binding energies between the drug and the target were included using docking-score. The following tools examined binding energy, interaction, stability, toxicity, and visualize protein-ligand complexes. The compounds having binding energies of −6.4, −5.1, −6.6, −5.1, and − 6.5 kcal/mol for Armepavine, Osthole, Curcumin, Plumbagine, Quercetin, and R*-drug, respectively with mutated CFTR (Gly628Arg) structure were chosen as the most promising ligands. The ligands bind to the mutated CFTR protein structure active sites in hydrophobic bonds, hydrogen bonds, and electrostatic interactions. According to ADMET analyses, the ligands Armepavine and Quercetin also displayed good pharmacokinetic and toxicity characteristics. An MD simulation for 200 ns was also established to ensure that Armepavine and Quercetin ligands attached to the target protein favorably and dynamically, and that protein-ligand complex stability was maintained. It is concluded that Armepavine and Quercetin have stronger capacity to inhibit the effect of mutated CFTR protein through improved trafficking and restoration of original function.

Background and Aim: Trypanosomiasis, a parasitic infection caused by various Trypanosoma species, poses a significant threat to global livestock, affecting both human health and economic sectors. This study aimed to estimate the prevalence of Trypanosoma evansi in Southern Punjab, Pakistan, focusing on key ruminant species, including camels, cattle, buffaloes, goats, and sheep. Materials and Methods: A total of 240 blood samples, comprising 48 samples from each animal species (camel, cattle, buffaloes, goat, and sheep) were collected from three districts in Southern Punjab. The collected samples were subjected to thin smear microscopy, DNA extraction, and polymerase chain reaction (PCR) amplification. The molecular characterization was conducted using the TBR primer set, which targeted repeated satellite DNA regions and the cytochrome oxidase II gene of T. evansi. Results: About 22.08% (53/240) of overall samples were positive for trypanosomiasis, with prevalence rates being 23.75% (19/80), 21.25% (17/80), and 21.75% (17/80) for districts Muzaffargarh, Lodhran, and Bahawalpur, respectively. 5.83% (14/240) of samples tested for T. evansi using PCR were positive in the districts of Muzaffargarh 7.50% (6/80), Lodhran 5.00% (4/80), and Bahawalpur 5.00% (4/80). Among the animals tested, camels had the highest positivity rate. The microscopic examination confirmed infection rates of 45.83% (22/48) for camels, 18.75% (9/48) for cattle, 8.33% (4/48) for buffaloes, 18.75% (9/48) for goats, and 18.75% (9/48) for sheep (p < 0.001). PCR results did not reveal substantial differences (p < 0.05) in prevalence: camels 12.50% (6/48), cattle 6.25% (3/48), buffaloes 0% (0/48), goats 8.33% (4/48), sheep 2.08% (1/48); while distinct disparities were detected district-wise: Muzaffargarh 23.75% (19/80), Lodhran 21.25% (17/80), and Bahawalpur 21.25% (17/80). The PCR results for these districts were insignificantly different: 7.50% (6/80), 5% (4/80), and 5% (4/80). The microscopic infection rate in camels from Bahawalpur was 56.30% (9/16). The microscopic analysis in Buffaloes reported a 6.30% (1/16) infection rate, but PCR results indicated no infections (0%) in any district. A significant difference (p < 0.001) in identifying Trypanosoma species was found between positively and negatively tested animals in both microscopic and PCR methods. Conclusion: This study emphasizes the necessity of regularly using PCR-based screening for its superior sensitivity and specificity over traditional microscopy. The varying occurrence of trypanosomiasis among districts reflects the intricate nature of this diseases epidemiology in the region. Reducing economic losses from trypanosomiasis in Southern Punjab, Pakistan, requires targeted interventions, such as vector control measures and farmer education.

Abstract In the context of graph theory and chemical graph theory, this research conducts a detailed mathematical investigation of reverse topological indices as they relate to iron telluride networks, clarifying their complex interactions. Graph theory is a branch of abstract mathematics that carefully studies the connections and structural features of graphs made up of edges and vertices. These theoretical ideas are expanded upon in chemical graph theory, which models molecular architectures with atoms acting as vertices and chemical bonds as edges. By extending these concepts, this work investigates the reverse topological indices in the context of Iron Telluride networks and outlines their significant effects on chemical reactivity, molecular topology and statistical modeling. By navigating intricate mathematical formalisms and algorithmic approaches, the analysis provides profound insights into the reactivity patterns and structural dynamics of Iron Telluride compounds, enhancing our knowledge of solid-state chemistry and materials science.

Abstract In this paper, we investigate the optimal conditions to the boundaries where the unique existence of the solutions to an advection-diffusion-reaction equation is secured by applying the contraction mapping theorem from the study of fixed points. Also, we extract, traveling wave solutions of the underlying equation. To this purpose, a new extended direct algebraic method with traveling wave transformation has been used. Achieved soliton solutions are different functions which are hyperbolic, trigonometric, exponential, and some mixed trigonometric functions. These functions show the nature of solitons. Two and three-dimensional plots are drawn using different values of parameters and coefficients for the comparison and behavior of solitons as combined bright-dark, dark, and bright solitons.

Lightweight materials with better resistance to sliding wear are prominent candidates for automobile brake drums, clutch pads and cylinder block applications to facilitate fuel economy. This attempt is reserved to cater to materials with higher tribological quality needs. Less dense foamy pumice stone particles were involved in three different percentages (5, 10, and 15 wt%) to reinforce lightweight AZ31 Mg alloy. A stir-assisted squeeze casting technique was pursued to process the composite and refine the grain structure. A phase detection, elemental mapping and microstructure study were done through X-ray diffraction (XRD), energy dispersive spectroscopy (EDS), and scanning electron microscopy (SEM), respectively. An experimental dry sliding wear scrutiny was administered using a pin-on-disc apparatus by considering: (i) ambient and elevated temperature environments and (ii) three different levels of loads. The results reveal a significant drop in wear loss and a frictional coefficient for 15% pumice-loaded composite than the base alloy. Post-wear examination acknowledges the fact that the ambient temperature wear is governed by adhesive-abrasive wear and high temperature is by abrasive wear mechanisms. Worn-out scrutiny authenticates the presence of oxide layers and their role in lubrication. A comparative study with previous works upholds the novel magnesium composite is the right candidate for the mentioned automobile applications.

Permanent magnets are essential components of electromechanical devices. Majority of magnets are used in permanent magnet motors that have extensive application in relation to energy efficiency and sustainability like electric vehicles. This research is aimed for efficient manufacturing of FeCrCo permanent magnets. Electromagnets could be utilized for the generation of continuous magnetic field to use in number of manufacturing processes. A two-pole electromagnet, comprising of two solenoids each having 2200 turns of copper wire, was developed. The system was designed to produce magnetic field up to 10 kilo Gauss for spinodal decomposition of FeCrCo alloy samples under thermomagnetic treatment process. Being rare earth free alloys, FeCrCo magnet is gaining research focus as an alternative magnetic alloy for advanced applications. The electromagnetic system design was refined and confirmed by using the Finite Element Method. The experimental values, of magnetic field generated by the two-pole electromagnet setup, were well close to the simulation results. The magnetizing setup was utilized to treat the FeCrCo magnetic alloy samples simultaneously at high temperature (700 °C) and magnetic field (7 kilo Gauss). This thermo-magnetic setup helped to improve the metallurgical structures of FeCrCo to grow and develop more efficiently. Treated samples of FeCrCo alloy demonstrated enhanced magnetic properties due to effective spinodal decomposition. The improvement in magnetic properties was attributed to the elimination of retained alpha phase and formation of more alpha-1 phase.

Bioconvective non-Newtonian fluids with motile microorganisms have diverse applications in biology and medicine, offering numerous research opportunities. This article presents a comprehensive investigation into the non-similar analysis of bio-convective micropolar flow induced by a Darcy-Forchheimer exponentially stretchable sheet, considering the effects of suction/injection and thermal radiation in a three-dimensional framework. The fluid under scrutiny is a viscoelastic tangent hyperbolic nanofluid, the impact of Cattaneo-Christov heat subjected to Riga plate is considered for novelty. The role of activation energy and heat source are also computed. The governing equations of viscoelastic tangent hyperbolic nanofluids are transformed into couple of ordinary differential equations via similarity approximations. The resulting ODEs are tackled numerically via bvp4c tool of MATLAB. The impression of appropriate parameters like magnetic parameter, bio-convection parameter, viscoelasticity parameter suction/injection parameter etc. on involved profiles are computed via graphical and tabulated trends. It is observed that velocity layer f′ is dwindled for growing value of Forchheimer number Fr, while reverse relation in velocity curve is noted for developing value of mixed convection parameter δ. Micropolar fluids find biomedical engineering applications in modeling blood flow in microvessels, understanding cerebrospinal fluid dynamics, and optimization of biomedical devices and therapies and simulating drug delivery in tissues.

The current investigative exploration exemplifies the conceptualization of a novel design intelligent computing paradigm based on artificial neural networks (ANNs) by utilizing radial basis function (RBF) to analyze magnetohydrodynamic (MHD) Williamson nanofluid two-dimensional flow along a stretchable sheet under the effect of chemical reaction as well as thermal radiation in a porous medium. This newly designed technique is an amalgam of a well-known reliable global solver named genetic algorithms (GAs) and a swift convergence generated local solver named sequential quadratic programming (SQP) used in ANNs by taking RBF as a kernel function i.e. ANNs-RBF-GASQP solver. The PDEs demonstrating the current nanofluid problem flow are transformed into the system of non-linear ODEs through a relevant similarity transformation and subsequently solved using ANNs-RBF-GASQP solver to investigate thermohydraulic properties by manipulating the values of various system parameters present in the ODEs. Moreover, the simulation results show that increasing the heat source parameter leads to a significant decrease in temperature. Additionally, an increase in the porosity parameter causes a decrease in the velocity of nanofluid, as a higher value of porosity increases fluid permeability and greater resistance to flow. The efficacy of the suggested solver is scrutinized through various statistical and convergence analyses.

The dangers that Internet of Things (IoT) devices pose to large corporate corporations and smart districts have been dissected by several academics. Given the ubiquitous use of IoT and its unique characteristics, such as mobility and normalization restrictions, intelligent frameworks that can independently detect suspicious activity in privately linked IoT devices are crucial. The IoTs have led an explosion in traffic through the network, bringing information processing techniques for attack detection. The increase in traffic poses challenges in detecting attacks and differentiating traffic that is harmful. In this work, we have proposed a mechanism that uses the standard algorithms in a system that is designed to detect, track, measure and identify online traffic from organizations with malignant transmission: Random Forest (RF), gradient-boosted decision trees (GBDT), and support vector machines (SVM) gives an optimal accuracy of 80.34%,87.5%, and 88.6% while the random forest-based supervised approach is 5.5% better than the previous techniques. To facilitate comparisons between training time, prediction time, specificity, and accuracy, the proposed approach leverages the NSL KDD dataset accuracy.

The failure analysis in the industrial systems i.e. power plan, oil, and gas companies, has been reported by many researchers. One of the most reported has occurred in the Boiler system. Among the components of the boiler, problems, and failures often took place in the tube. The purposes of the recent study are to describe, classify, and review the latest update of the failure behavior of the boiler tube. The failure was formed by overheating, creep, stress corrosion, cracking, erosion, oxidation, and post-weld heat treatment (PWHT). The current works are listed based on the methods used to identify the failures, failure observations, and case studies updated from the boiler tube failure condition. The research suggests that to replace the failure tubes, analysis related to the stress induced outside and inside the tube, environment conditions, and lifetime maintenance works need to be considered.

Anchovy is one of potential commodities widely found and caught by fishermen in FMA-572 West Sumatra. Examining its exploitation rate is a part of taking control and monitor toward the fishing activities to avoid overfishing possibility. This recent study aims to calculate exploitation rate and oceanographic characteristics of anchovy fishing grounds. The data was collected from Bungus Oceanic Fishing Port, West Sumatra in 2018-2022. Meanwhile, sea surface temperature and chlorophyll data were obtained from https://marine.copernicus.eu/ and https://oceancolor.gsfc.nasa.gov 2018-2022, respectively. The results showed that the exploitation rate of anchovies in 2020 and 2022 has exceeded maximum sustainable yield categorized as overexploitation with a percentage of 130.44% and 112.84%. Anchovy catches are significantly influenced by oceanographic factors with F value 0.00000115 (α 0.05). An increase in CPUE value is align with chlorophyll increase and in contrary to sea surface temperature. The findings are expected to give an overview related to anchovy fishing activities implementation and become an input for fisheries governance that lead to sustainable fisheries management. Keywords: FMA-572 MSY Overfishing Sustainable fisheries Stolephorus sp

Electronic and structural properties of calcium clusters with a varying size range of 2–20 atoms are studied using a two-step scheme within the GW and density functional theory (DFT) with generalized gradient approximation (GGA). The GGA overestimates the binding energies, optimized geometries, electron affinities, and ionization potentials reported in the benchmark. The ground-state structure geometry and binding energy were obtained from the DFT for the ground-state structure of each cluster. The binding energy of the neutral clusters of the calcium series follows an increasing trend, except for a few stable even and odd clusters. The electronic properties of the calcium cluster were studied with an all-electron FHI-aims code. In the G0W0 calculation, the magic cluster Ca10 has relatively high ionization potential and low electron affinity. The obtained ionization potentials from the G0W0@PBE calculation showed that the larger cluster has less variation, whereas the electron affinities of the series have an increasing trend. The ionization potentials from the G0W0 benchmark for the calcium cluster series have not yet been described in the literature.

Abstract Enhanced phytoremediation offers a rapid and eco-friendly approach for cleaning agricultural soil contaminated with copper and cadmium which pose a direct threat to food scarcity and security. The current study aimed to compare the effectiveness of the two commonly used additives, IAA and EDTA, for the remediation of copper (Cu) and cadmium (Cd) contaminated soils using sunflower and maize. The plants were cultivated in pots under controlled conditions with four sets of treatments: control (0), Cu50/Cd50, Cu50/Cd50 + EDTA, and Cu50/Cd50 + IAA. The results showed that Cu50/Cd50 mg/kg drastically compromised the phytoremediation potential of both plants, as evident by reduced shoot and root length, and lower biomass. However, the augmentation of Cu50/Cd50 with EDTA or IAA improved the tested parameters. In sunflower, EDTA enhanced the accumulation of Cu and Cd by 58% and 21%, respectively, and improved plant biomass by 41%, compared to control treatment. However, IAA exhibited higher accumulation of Cu and Cd by 64% and 25%, respectively, and enhanced plant biomass by 43%. In case of maize, IAA was superior to EDTA which enhanced the accumulation of Cu and Cd by 87% and 32% respectively, and increased the plant biomass by 57%, compared to control treatment. Our findings demonstrate that foliar IAA is more effective than EDTA in enhancing the phytoremediation potential of sunflower and maize for Cu and Cd.

Abstract This work examines the (2+1)-dimensional Boiti–Leon–Pempinelli model, which finds its use in hydrodynamics. This model explains how water waves vary over time in hydrodynamics. We provide new explicit solutions to the generalized (2+1)-dimensional Boiti–Leon–Pempinelli equation by applying the Sardar sub-equation technique. This method is shown to be a reliable and practical tool for solving nonlinear wave equations. Furthermore, different types of solitary wave solutions are constructed: w-shaped, breather waved, chirped, dark, bright, kink, unique, periodic, and more. The results obtained with the variable coefficient Boiti–Leon–Pempinelli equation are stable and different from previous methods. As compared to their constant-coefficient counterparts, the variable-coefficient models are more general here. In the current work, the problem is solved using the Sardar Sub-problem Technique to produce distinct soliton solutions with parameters. Plotting these graphs of the solutions will help you better comprehend the model. The outcomes demonstrate how well the method works to solve nonlinear partial differential equations, which are common in mathematical physics.With the help of this method, we may examine a variety of solutions from significant physical perspectives.

Abstract Phenylalanine ammonia lyase (PAL) is a widely studied enzyme in plant biology due to its role in connecting primary metabolism to secondary phenylpropanoid metabolism, significantly influencing plant growth, development, and stress response. Although PAL genes have been extensively studied in various plant species but their exploration in cucumber has been limited. This study successfully identified 11 CsPAL genes in Cucumis sativus (cucumber). These CsPAL genes were categorized based on their conserved sequences revealing patterns through MEME analysis and multiple sequence alignment. Interestingly, cis-elements related to stress were found in the promoter regions of CsPAL genes, indicating their involvement in responding to abiotic stress. Furthermore, these gene’s promoters contained components associated with light, development and hormone responsiveness. This suggests that they may have roles in hormone developmental processes. MicroRNAs were identified as a key regulators for the CsPAL genes, playing a crucial role in modulating their expression. This discovery underscores the complex regulatory network involved in the plant’s response to various stress conditions. The influence of these microRNAs further highlights the complicated mechanisms that plants use to manage stress. Gene expression patterns were analyzed using RNA-seq data. The significant upregulation of CsPAL9 during HT3h (heat stress for 3 h) and the heightened upregulation of both CsPAL9 and CsPAL7 under HT6h (heat stress for 6 h) in the transcriptome study suggest a potential role for these genes in cucumber’s tolerance to heat stress. This comprehensive investigation aims to enhance our understanding of the PAL gene family’s versatility, offering valuable insights for advancements in cucumber genetics.

BACKGROUND: The burden of disease due to lead exposure continues to increase. Lead interferes with 25(OH)D hydroxylation and calcium transport, increasing osteoclastic activity and bone resorption. Pyridinoline crosslinks (PYD), as an indicator of bone damage, can be seen earlier compared to imaging changes. Therefore, it is necessary to determine the correlation between serum 25(OH)D levels and the urinary PYD/creatinine ratio in workers exposed to lead, since up to now, there are only limited studies related to it. METHODS: This cross-sectional study involved 104 workers exposed to lead, selected from parents whose children had blood lead levels above 10 µg/dL. Questionnaires and physical examination were performed to obtain characteristic data from subjects. Data regarding blood lead levels, serum 25(OH)D levels, urinary PYD levels, and urinary creatinine levels were also obtained from various laboratory methods. RESULTS: Most subjects (86.5%) had inadequate serum 25(OH)D. Median blood lead levels was 6.3 (1.2-35.5) µg/dL, chronic lead exposure index was 35.3 (1.2-535.8) years µg/dL, serum 25(OH)D levels was 22 (8-52) ng/mL, and urinary PYD/creatinine ratio was 5.3 (3.6-28.1)×10-6. There was a significant negative correlation between serum 25(OH)D levels and urinary PYD/creatinine ratio in workers exposed to lead. There was also a significant positive correlation between chronic lead exposure index and the urinary PYD/creatinine ratio. CONCLUSION: Since urinary PYD/creatinine ratio is correlated with serum chronic lead exposure index and serum 25(OH)D levels, it suggests that pyridinoline might be a potential biomarker to detect bone metabolism disorder due to the chronic lead exposure. Vitamin D adequacy is also an important factor in preventing bone metabolism disorder amidst chronic lead exposure. KEYWORDS: 25(OH)D, bone resorption, pyridinoline, lead, worker

Abstract Influenza is a highly contagious acute viral illness that affects the respiratory system, posing a significant global public health concern. Influenza B virus (IBV) causes annual seasonal epidemics. The exploration of molecular biology and reverse genetics of IBV is pivotal for understanding its replication, pathogenesis, and evolution. Reverse genetics empowers us to purposefully alter the viral genome, engineer precise genetic modifications, and unveil the secrets of virulence and resistance mechanisms. It helps us in quickly analyzing new virus strains by viral genome manipulation and the development of innovative influenza vaccines. Reverse genetics has been employed to create mutant or reassortant influenza viruses for evaluating their virulence, pathogenicity, host range, and transmissibility. Without this technique, these tasks would be difficult or impossible, making it crucial for preparing for epidemics and protecting public health. Here, we bring together the latest information on how we can manipulate the genes of the influenza B virus using reverse genetics methods, most importantly helper virus-independent techniques.

Abstract In this study, we conduct a comprehensive physical analysis of topological indices for the Iron Disulfide (FeS $$_{2}$$ 2 ) network using a curve-fitting model. Iron Disulfide is a cubic compound. In metamorphic rock, sedimentary rock, and quartz veins, it is typically found in combination with other sulfides or oxides. The numerical properties of molecular structures are referred to as topological indices. There are several different kinds of topological indices, including those that are based on distance, degree, or counting, among other factors. The real process of creating a topological index involves turning a chemical structure into a numerical value. In this paper, we calculate the iron disulfide network topological indices using the degrees of vertices in a chemical network of Iron Disulfide (FeS $$_{2}$$ 2 ). Thereafter, we discovered the physical parameters of FeS $$_{2}$$ 2 production, such as heat of formation. We then fitted curves between the thermodynamic properties and several indices. Several techniques based on rationality, linearity, and nonlinearity were used to fit curves in MATLAB. These quantitative results imply that a variety of thermodynamic characteristics of semiconducting materials may be accurately predicted by topological indices. These findings have significant ramifications as they provide the groundwork for the application of topological indices in semiconducting network design and optimization, which might result in more effective and economical material creation.