Industrial Problems Research Articles

Characterization of Drupe Traits in Sixty-Six Teak Candidate Plus Trees (CPTs) in Gujarat Forests of India for Enhanced Seed Collection and Fruit Production

The most valuable timber of the world i.e., teak was recognized as king of wood due to its wood quality such as durability, physical and aesthetic wood property for multiple uses. It is distributed throughout India and Southeast Asia and known for center of teak genetic diversity. It has a high demand in international market and national market due to multifarious uses. However, the natural population of Teak is decreasing in India due to anthropogenic activities, deforestation, climate change and other environmental factors. In addition, the poor seed yield per tree and extremely low seed germination rates are significant problems for the teak plantation industry and production of quality seed. Nevertheless, there are huge gap between demand and supply of industrial timber in India. This can be fulfilled by the teak plantation within agroforestry systems and degraded forest lands. Gujarat forest has natural teak populations which falls in the conjunction point of Western Ghats of India. Hence, sixty-six CPTs of teak were screened out for quality seed collection and reproduction from natural teak populations. Fruits of teak were collected from various CPTs with wide range of geographic locations in Gujarat forests of India. The drupe and stone morphometric traits were studied to capture the phenotypic and genotypic variations; to select better traits on the basis of repeatability coefficient; and to look geographic location effect on various fruit characters and inter-character correlations for quality seed collection and fruit production. Present result showed significant differences (p≤ 0.0001) in all the studied fruit traits among 66 CPTs of Tectona grandis. Drupe were lengthiest in GJNBD 0467 (12.36 mm), broadest in the GJNBD 0467 (14.75 mm) and heaviest in the GJAH 1056 (0.73 g) CPT, whereas smallest in GJNBD 1122 (8.3 mm), narrowest in GJNBD 1122 (9.91 mm) and lightest in GJNBD 1122 (0.32 g) CPT. Stone were longest in GJNBD 0467 (8.71 mm), thickest in GJAH 1056 (10.01 mm) and heaviest in GJAH 1056 (0.51 g) CPT, while shortest in GJNBD 1122 (6.06 mm), thinnest stone in GJNBD 1122 (7.56 mm) and lightest in GJNBD 0772 (0.22 g) CPT. Overall, GJNBD 0467, GJAH 1056, GJNBD 0950, GJAH 0844 and GJNBD 0470 CPTs were superior for studied traits as compared to others. CPT repeatability coefficient was higher for drupe characters as compared to stone parameters, where selection can be made for drupe length for future tree improvement programme. The genetic improvement via CPT selection is better option for maintaining genetic diversity of Teak, its conservation and management. Latitude showed strong negative correlation with drupe length, stone length and drupe mass. Longitude was negatively correlated with stone length and width. Thus, the geographical parameter has an impact on the seed formation, evolution and plant fitness. The strong correlations were found among seed morphometric characters which influenced to seed development and creating fitness interaction with continuous changing environment.

Guest Editorial: Quantum Computing: A Beacon of Transformation for the Oil and Gas Industry

This past June, the United Nations proclaimed 2025 as the International Year of Quantum Science and Technology to recognize the potential of quantum science to drive innovations in sustainable development and global communications. It is also a recognition of how far the theoretical concept of quantum computing has come to become a reality, and the race to build a powerful quantum computer is accelerating every year. According to a recent report from Global Quantum Intelligence, the impact of quantum tech across the oil and gas industry could equate to $2.6 trillion by 2035. As the first chief data scientist in the oil and gas industry, I am acutely aware of the myriad challenges that our industry faces. I firmly believe that quantum computing and related quantum technologies are on the brink of revolutionizing our industry in ways we have yet to fully comprehend. The quantum revolution demands that the oil and gas and the broader energy industry rethink their approach to exponential technology and sustainability. The industry's history of adopting emerging technologies, as evidenced by the slow uptake of digital transformation, underscores the urgency of this shift. Even 30 years after the introduction of the digital oilfield concept, the industry lags in data quality, addressing data silos, and data democratization, as compared with other complex and highly regulated industries. The urgency of an energy transition towards a sustainable future necessitates the oil and gas industry to confront its complex challenges, which are beyond the scope of current computing power. Quantum computing, with its unparalleled computational power, emerges as one of the most promising technologies to optimize and accelerate solutions to the industry's intricate problems. As we discuss quantum computing in detail, we will explore how related quantum interconnected areas—quantum sensing, quantum networking, and quantum simulations—will enable unique applications for the oil and gas industry. In simple words, quantum computing is the field of computing that uses the principles of quantum mechanics to solve complex problems that are intractable for classical computers. The three main principles of quantum mechanics that are the cornerstone of quantum computing are superposition, entanglement, and quantum interference. Unlike traditional computing, where a bit (0 or 1) is the basic unit, a qubit is the unit of quantum computing. A qubit can exist in a state of 0, 1, or both simultaneously due to superposition, enabling parallel computations and the exploration of multiple solutions at once. Entanglement allows for correlated states, enabling operations on multiple qubits simultaneously for enhanced computational power. Quantum interference allows quantum states to interfere constructively or destructively with each other, amplifying correct solutions and suppressing incorrect ones, thereby empowering efficient computations. Thus, a significant quantum advantage is gained by using quantum computing for certain computationally challenging problems compared with traditional computing. Quantum computing will be performed on quantum computers, which are developed today using various technologies such as photonics, light particles, trapped ions, neutral atom traps, superconducting qubits, and controlling nitrogen vacancy centers in imperfect diamonds.

A novel many-objective symbiotic organism search algorithm for industrial engineering problems

A novel many-objective symbiotic organism search algorithm for industrial engineering problems

Study on the Effectiveness of Okra as an Environmentally Friendly and Economical Lubricant for Drilling Fluid

With the gradual improvement and implementation of unconventional wells drilling and environmental regulations, there is an urgent need for high-performance and more environmentally friendly lubricants for water-based drilling fluids (WD). Developing green oilfield chemicals from natural products is a shortcut. In this work, Abelmoschus esculentus (L.) Moench/okra has been studied as the lubricant in WD. The green drilling fluid lubricant developed demonstrates excellent lubrication performance, as well as good filtration loss reduction and inhibition of bentonite hydration expansion. The results show that with the addition of 2.5% okra slurry to water-based drilling fluid, the coefficient of friction decreased by 51.68%, the apparent viscosity (AV) increased by 51.32%, the plastic viscosity (PV) increased by 42.99%, and the fluid loss decreased by 39.88%. Moreover, through TGA, SEM, FT-IR, particle distribution tests, and contact angle tests, the lubrication mechanism of okra slurry was discussed. Finally, the economic feasibility of using okra as an environmentally friendly lubricant for drilling fluids was analyzed. This work combines agricultural products with industrial production, which not only solves industrial problems but also enhances the added value of agricultural products, providing a reference for the coordinated development of industry and agriculture.

Microservices Patterns Recommendation based on Information Retrieval

Software developers encounter recurring problems during software development, which can be solved using proven solutions known as design patterns. Microservices architecture, a Service-Oriented Architecture (SOA) variant, presents common communication, deployment, and service definition challenges. However, selecting the appropriate design pattern from a vast pool of patterns to solve a problem is difficult for novice and experienced developers. This paper proposes a recommendation tool based on Information Retrieval (IR) to assist developers in choosing the suitable microservices pattern to solve a given problem. The tool leverages textual descriptions given by developers to provide relevant indications of microservices patterns. It has been evaluated using both toy and industrial problems, demonstrating promising results. The results showed that the tool was able to solve 60% of toy design problems, indicating that it can provide valuable and accurate recommendations. Furthermore, tests with industrial problems revealed that over 70% of the recommended patterns helped to address the problems at hand. Interviews with developers who work in the software industry corroborated the recommendation tool’s effectiveness and practicality.

Retrospective analysis of prevalence and risk factors of bovine lameness in dairy farms in South Africa

Lameness is a crippling problem in the dairy industry, posing a significant welfare challenge and leading to premature culling. The study aimed to determine the seasonal prevalence of lameness in two different provinces of South Africa. The data was collected in two local municipalities (Mpofana and Raymond Mhlaba). However, the computer software systems of four randomly selected dairy farms were used to collect retrospective secondary data for lameness and predisposing factors (2018–2022). The data's targeted parameters include breed, parity, weight, stage of lactation, diseases diagnosed (lameness), age, and milk production trends. The highest prevalence was observed in uMpofana municipality (58.8%) compared to Raymond Mhlaba municipality (41.2%). Among cattle above nine years of age, the prevalence of lameness was the highest (36%), compared to 7-8-year-old (24%), 4-6-year-old (21.7%), and 1-3-year-old (14.3%) cattle. Additionally, the highest prevalence of lameness was observed during the summer season, followed by spring, winter, and autumn. The strongest association is kept for the right front leg (p<0.0001), followed by the left front leg (p<0.001), right back leg (p<0.001), and left back leg (p<0.001). In conclusion, this study provides baseline information on several key factors influencing the prevalence of lameness in dairy cattle in various farms in uMpofana municipality and Raymond Mhlaba municipality. The study findings could be used for farm managerial decision-making and epidemiological interventions. Given the high prevalence of lameness in dairy farms in uMpofana municipality, there is a need for more focused research aimed at identifying the specific causes of lameness in these localities.

Research on Adsorption of Heavy Metal Ions by Polysaccharide Hydrogels

AbstractIn recent years, various industrial and agricultural problems have been highlighted, and the pollution of heavy metal ions to water bodies has become a considerable serious phenomenon. Adsorption method is an efficient, low‐cost, and easy to implement method for removing heavy metal ions, which does not produce secondary pollution, and has been widely used in water treatment. Polysaccharides are natural polymers, and their hydroxyl, carboxyl, and amine groups provide feasibility for the synthesis of hydrogels with various structures. The synthesized polysaccharide hydrogel has the characteristics of high adsorption capacity, specific adsorption, nontoxic, and so on. In this review, the kinds of polysaccharide hydrogels and different synthesis methods were discussed first. Then, the adsorption mechanism and different adsorption modes of polysaccharide hydrogels were reviewed. Finally, the effects of different environmental factors on the adsorption capacity of polysaccharide hydrogel were studied. The future research on the adsorption of heavy metal ions by polysaccharide hydrogels was proposed.

Stress Can Induce Bovine Alpha-Herpesvirus 1 (BoHV-1) Reactivation from Latency

Bovine alpha-herpesvirus 1 (BoHV-1) is a significant problem for the cattle industry, in part because the virus establishes latency, and stressful stimuli increase the incidence of reactivation from latency. Sensory neurons in trigeminal ganglia and unknown cells in pharyngeal tonsils are importantsites for latency. Reactivation from latency can lead to reproductive problems in pregnant cows, virus transmission to young calves, suppression of immune responses, and bacterial pneumonia. BoHV-1 is also a significant cofactor in bovine respiratory disease (BRD). Stress, as mimicked by the synthetic corticosteroid dexamethasone, reproducibly initiates reactivation from latency. Stress-mediated activation of the glucocorticoid receptor (GR) stimulates viral replication and transactivation of viral promoters that drive the expression of infected cell protein 0 (bICP0) and bICP4. Notably, GR and Krüppel-like factor 15 (KLF15) form a feed-forward transcription loop that cooperatively transactivates immediate early transcription unit 1 (IEtu1 promoter). Two pioneer transcription factors, GR and KLF4, cooperatively transactivate the bICP0 early promoter. Pioneer transcription factors bind silent viral heterochromatin, remodel chromatin, and activate gene expression. Thus, wepredict that these novel transcription factors mediate early stages of BoHV-1 reactivation from latency.

The hydromechanical coupled numerical method in pseudo-3D axisymmetric domain with cracks extension and coalescence applies to the decompression failure problem

The stress-strain state of the saturated porous media determines the behavior of fracturing, which defines the efficiency of developing tight oil, shale, coalbed, and thermal energy fields. Therefore, reliable hydromechanical coupled simulations with destruction reconstruction are critical.The proposed innovative simulator has a strong interrelation between fluid flow and rock deformation of porous media and realizes a fully coupled pseudo-transient numerical method by high-performance computing (HPC) tools. To increase the detail of the results in the problem, a finite difference numerical algorithm was implemented in the axisymmetric cylindrical domain, which reduces from three to two dimensions without loss of precision. Highly efficient parallelization using CUDA on the GPU computes meshes of up to one billion cells, allowing the simulation of a total core sample to sub-micrometer resolution in an appropriate time. The algorithm has been validated to find the exact solution to the cylinder problem. The proposed model accounts for cracks propagation with their coalescence within a single computational static grid, which keeps timing close to the continuous model.This comprehensive implementation enables solving industrial problems, such as modeling core sample damage during rapid decompression. High-resolution simulations help reconstruct fracture propagation, analyze the initial stress state, and identify critical damage factors. The comparison with the exact solution to the cylinder problem confirmed the reliability of the algorithm. The calculation results show a strong dependence of decompression failure on the coalescence and elongation of cracks, influenced significantly by the rock's cohesion. Microcracks length and distribution play a decisive role in the decompressive destruction behavior of the rock sample. For the first time, the simulations demonstrated the decompressive destruction of a core sample during an uncontrolled, rapid core retrieval operation.

Gene Expression Analysis for Drought Tolerance in Early Stage of Potato Plant Development

Drought has increasingly affected the yield of Solanum tuberosum L. (potato) every year over the last decade, posing serious economic problems for the global agricultural industry. Therefore, it is important to research drought tolerance in plants and obtain more robust varieties of crops. The aim of the present work was to study the expression of drought-upregulated genes in drought-tolerant and drought-sensitive varieties of potato. Bioreactors were used to identify whether each variety was drought-tolerant or drought-sensitive; then, expression analysis was performed according to the morphological characteristics of the plantlets in two different media: Murashige and Skoog (MS) medium and MS medium with 20% PEG-6000 to simulate osmotic stress. Based on the quantitative parameters of six initial varieties, two varieties were selected (Gala and Aksor) for further gene expression analysis. The expression of genes commonly upregulated in drought (ER24, TAS14, DREB147315, PP2C, 102605413 and NF-YC4) was higher in the drought-tolerant variety than in the sensitive one. Therefore, the expression of these genes can be used to determine the drought tolerance of a potato variety in vitro in the early plant development stage. Moreover, comparative analysis showed that some of the targeted genes used to identify drought tolerance in this study are conserved across different plant species.

Research on intelligent control of fresh air introduction based on electric vehicle air conditioning system

The high power consumption of air conditioning systems is an important reason for the significant reduction in EVs (electric vehicles) range. Reducing its energy consumption has become an urgent industry problem to be solved. In order to reduce the energy consumption of the air conditioning system, this paper proposes an intelligent control strategy for the proportion of fresh air in the cabin of EVs, taking into account the impact of factors such as the number of passengers in the cabin, ambient temperature, and humidity on demand for fresh air, and analyzes the energy-saving effect of the intelligent control strategy through simulation. The analysis found that the minimum fresh air demand in the passenger compartment is greatly affected by the number of passengers in the cabin. The developed intelligent control strategy can significantly reduce the energy consumption of air conditioning systems. The energy-saving effect in northern China is significantly better than in southern China. The actual driving range of refrigeration and heating modes can be improved by 50 and 126 km, respectively.

Controllable mechanism of hazardous jarosite transformation into recyclable hematite in the leaching solution of secondary zinc oxide powder

The controlled synthesis of recyclable hematite in the leaching solution of secondary zinc oxide powder is an urgent problem in the chemical industry and metallurgy fields. In this paper, the effects of the temperature, agitation speed, and seed addition on the contents of iron, sulfur, potassium, sodium, and zinc in the iron removal residues, as well as the iron concentration in the supernatant after iron removal were systematically studied. In addition to the temperature control already reported, we found that the agitation speed can also control the transformation between jarosite and hematite phases. The content of jarosite in the residues can be effectively controlled by adjusting the agitation speed, thereby significantly improving the quality of hematite product, as indicated by SEM-EDS and XRD results. Under temperature of 185 °C, an agitation speed of 500 rpm, and a seed addition of 15 g/L, the iron, sulfur and zinc contents in the filter residues and the iron concentration in the supernatant were 59%, 3.22%, 0.92%, and 4.182 g/L, respectively. Kinetic studies show that the rapid oxidation kinetics is not conducive to the formation of high-quality hematite products. These results can directly guide the process of transformation of harmful jarosite into recyclable hematite from the leaching solution of secondary zinc oxide powder, which is of great practical significance for the controlled and waste-free removal of iron from aqueous solutions in the chemical industry and metallurgy fields.

6-DOFs Robot Placement Based on the Multi-Criteria Procedure for Industrial Applications

Robot acceptance is rapidly increasing in many different industrial applications. The advancement of production systems and machines requires addressing the productivity complexity and flexibility of current manufacturing processes in quasi-real time. Nowadays, robot placement is still achieved via industrial practices based on the expertise of the workers and technicians, with the adoption of offline expensive software that demands time-consuming simulations, detailed time-and-motion mapping activities, and high competencies. Current challenges have been addressed mainly via path planning or robot-to-workpiece location optimization. Numerous solutions, from analytical to physical-based and data-driven formulation, have been discussed in the literature to solve these challenges. In this context, the machine learning approach has proven its superior performance. Nevertheless, the industrial environment is complex to model, generating extra training effort and making the learning procedure, in some cases, inefficient. The industrial problems concern workstation productivity; path-constrained minimal-time motions, considering the actuator’s torque limits; followed by robot vibration and the reduction in its accuracy and lifetime. This paper presents a procedure to find the robot base location for a prescribed task within the robot’s workspace, complying with multiple criteria. The proposed hybrid procedure includes analytical, physical-based, and data-driven modeling to solve the optimization problem. The contribution of the algorithm, for a given user-defined task, is the search for the best robot base location that enables the target points, maximizing the manipulability, avoiding singularities, and minimizing energy consumption. Firstly, the established method was verified using an anthropomorphic robot that considers different levels of a priori kinematics and system dynamics knowledge. The feasibility of the proposed method was evaluated through various simulations for small- and medium-sized robots. Then, a commercial offline program was compared, considering three scenarios and fourteen robots demonstrating an energy reduction in the 7.6–13.2% range. Moreover, the unknown joint dependency in real robot applications was investigated. From 11 robot positions for each active joint, a direct kinematic was appraised with an automatic DH scheme that generates the 3D workspace with an RMSE lower than 65.0 µm. Then, the inverse kinematic was computed using an ANN technique tuned with a genetic algorithm showing an RMSE in an S-shape task close to 702.0 µm. Finally, three experimental campaigns were performed with a set of tasks, repetitions, end-effector velocity, and payloads. The energy consumption reduction was observed in the 12.7–22.9% range. Consequently, the proposed procedure supports the reduction in workstation setup time and energy saving during industrial operations.

Market Demand, Policy Environment, and Future Trends: Analysis of New Energy Vehicle Industry in China

Under the complex and ever-changing global economic situation, the sales of new energy vehicles in China are facing many development opportunities and practical challenges. Market demand and policy environment impact the development of China's new energy vehicle industry. This research takes the new energy vehicle industry in China as the research object, adopts a combination of literature review and case analysis methods by combing and analyzing relevant literature, combined with practical cases, deeply discusses the development status and problems of China's new energy vehicle industry, and proposes improvement suggestions from two dimensions: market demand and policy environment. Research shows that insufficient market demand, insufficient technological innovation, and an unstable policy environment are the main factors that restrict industrial development. To solve these problems, measures need to be taken, such as increasing government support, enhancing technological innovation capabilities, and promoting reforms and innovations in marketing and other aspects, aiming to inspire China's new energy vehicle industry to go global.

An analysis and solution of ill-conditioning in physics-informed neural networks

Physics-informed neural networks (PINNs) have recently emerged as a novel and popular approach for solving forward and inverse problems involving partial differential equations (PDEs). However, ensuring stable training and obtaining accurate results remain challenging in many scenarios, often attributed to the ill-conditioning of PINNs. Despite this, a deeper analysis is still lacking, which hampers progress and application of PINNs in complex engineering problems. Drawing inspiration from the ill-conditioning analysis in traditional numerical methods, we establish a strong connection between the ill-conditioning of PINNs and the Jacobian matrix of the PDE system. Specifically, for any given PDE system, we construct a controlled system that allows for the adjustment of the Jacobian matrix's condition number while retaining the same solution as the original system. Our numerical experiments show that as the condition number of the Jacobian matrix decreases, PINNs exhibit faster convergence and higher accuracy. Building upon this principle and the extension of controlled systems, we propose a general approach to mitigate the ill-conditioning in PINNs, leading to successful simulations of three-dimensional flow around the M6 wing at a Reynolds number of 5,000. To the best of our knowledge, this is the first time that PINNs have successfully simulated such complex systems, offering a promising new technique for addressing industrial complexity problems. Our findings also provide valuable insights to guide the future development of PINNs.

Solving industrial fault diagnosis problems with quantum computers

In this article, we investigate in how far quantum computers can be leveraged to solve NP-complete fault diagnosis problems within the area of industrial cyber-physical systems. Therefore, two approaches are proposed which exploit quantum computing to solve diagnosis problems: The first method employs Grover’s algorithm, and the second is based on the Quantum Approximate Optimization Algorithm. To show the industrial application, we present an integrated approach to learn the diagnosis model from process data, check whether the model is suitable, and use it for diagnosis. The result is a method for quantum industrial fault diagnosis. For this approach, the diagnostic capabilities and the runtime have been evaluated on an IBM Falcon processor using three publicly available benchmarks from the process industry. Further, the scaling between quantum computers and classical PCs has been analyzed.

ОСОБЛИВОСТІ АУДИТУ ДОХОДІВ, ВИТРАТ І ФІНАНСОВИХ РЕЗУЛЬТАТІВ ВІТЧИЗНЯНОГО СІЛЬСЬКОГОСПОДАРСЬКОГО ПІДПРИЄМСТВА В УМОВАХ ВОЄННОГО СТАНУ

The article is devoted to the study of the audit of income, expenses, and financial results of agricultural enterprises in Ukraine during the martial law. The main problems of the domestic agricultural industry, caused by the specifics of the industry, are identified: deterioration of economic conditions, economic difficulties, competition problems, infrastructural limitations, personnel problems, difficulty in obtaining permits and certification, limited access to modern technologies. It is substantiated that the problems of the organization of accounting and management of agricultural enterprises are represented by insufficient automation of accounting and control processes, insufficient investment attractiveness of these enterprises in reporting, the complexity of accounting for supply processes, production and formation of costs, the complexity of obtaining loans, the complexity of tax accounting. The identified problems of the functioning of domestic enterprises in the conditions of martial law: the threat of security for workers and resources, the complication and increase in the price of the supply and implementation process, the loss of qualified labor, economic difficulties, the instability of legislation, the cancellation of benefits. It was established that the state of war in Ukraine causes additional problems for auditing: changes in the conditions of audit activity and audit risks, the complication of forecasting and assessment of financial results, the need to assess the impact on financial reporting of changes in accounting policies and management strategies, the need for additional audit procedures to determine and settlement of new risks. The work analyzes the peculiarities of the audit process of financial reporting of an agricultural enterprise, its income, expenses, and results of activity. The study showed that the main factor influencing the quality of the audit is the use of effective audit procedures. The work presents directions for optimizing the audit due to its digitalization, identifies problems that may arise, and suggests ways to solve them.

Development of a Triplex qPCR Assay Based on the TaqMan Probe for the Detection of Haemophilus parasuis, Streptococcus suis Serotype 2 and Pasteurella multocida

Porcine respiratory disease is a significant economic problem for the global swine industry. Haemophilus parasuis (H. parasuis), Streptococcus suis (S. suis), and Pasteurella multocida (P. multocida) are three important pathogenic bacteria of the swine respiratory tract. Notably, the three pathogens not only frequently manifest as mixed infections, but their striking clinical similarities also present difficulties for pig populations in terms of disease prevention and treatment. Thus, we developed a triplex real-time quantitative polymerase chain reaction (qPCR) assay based on a TaqMan probe for the detection of H. parasuis, S. suis serotype 2, and P. multocida. Primers and probes were designed to target the conserved regions of the H. parasuis OmpP2 gene, the S. suis serotype 2 gdh gene, and the P. multocida Kmt1 gene. By optimizing the reaction system and conditions, a triplex qPCR method for simultaneous detection of H. parasuis, S. suis serotype 2, and P. multocida was successfully established. The amplification efficiencies of the standard curves for all three pathogens were found to be highly similar, with values of 102.105% for H. parasuis, 105.297% for S. suis serotype 2, and 104.829% for P. multocida, and all R2 values achieving 0.999. The specificity analysis results showed that the triplex qPCR method had a strong specificity. The sensitivity test results indicated that the limit of detection can reach 50 copies/μL for all three pathogens. Both intra- and inter-assay coefficients of variation for repeatability were below 1%. This triplex qPCR method was shown to have good specificity, sensitivity, and reproducibility. Finally, the triplex qPCR method established in this study was compared with the nested PCR as recommended by the Chinese national standard (GB/T34750-2017) for H. parasuis, the PCR as recommended by the Chinese national standard (GB/T 19915.9-2005) for S. suis serotype 2, and the PCR as recommended by the Chinese agricultural industry standard (NY/T 564-2016) for P. multocida by detecting the same clinical samples. Both methods are reasonably consistent, while the triplex qPCR assay was more sensitive. In summary, triplex qPCR serves not only as a rapid and accurate detection and early prevention method for these pathogens but also constitutes a robust tool for microbial quality control in specific pathogen-free pigs.

Sophorolipid: An Effective Biomolecule for Targeting Microbial Biofilms.

Biofilms are microbial aggregates encased in a matrix that is attached to biological or nonbiological surfaces and constitute serious problems in food, medical, and marine industries and can have major negative effects on both health and the economy. Biofilm's complex microbial community provides a resistant environment that is difficult to eradicate and is extremely resilient to antibiotics and sanitizers. There are various conventional techniques for combating biofilms, including, chemical removal, physical or mechanical removal, use of antibiotics and disinfectants to destroy biofilm producing organisms. In contrast to free living planktonic cells, biofilms are very resistant to these methods. Hence, new strategies that differ from traditional approaches are urgently required. Microbial world offers a wide range of effective "green" compounds such as biosurfactants. They outperform synthetic surfactants in terms of biodegradability, superior stabilization, and reduced toxicity concerns. They also have better antiadhesive and anti-biofilm capabilities which can be used to treat biofilm-related problems. Sophorolipids (SLs) are a major type of biosurfactants that have gained immense interest in the healthcare industries because of their antiadhesive and anti-biofilm properties. Sophorolipids may therefore prove to be attractive substances that can be used in biomedical applications as adjuvant to other antibiotics against some infections through growth inhibition and/or biofilm disruption.

Prediction of vibro-acoustic sound emissions based on mapped structural dynamics

In the product design of shell-like housing structures for vibrating bodies, acoustic emission plays a significant role in usability and customer comfort. Consequently, the prediction of the emitted sound, e.g., in the form of the radiated sound power, is desired in the earliest possible stage of the development cycle. In industry, performing extensive acoustic testing using microphone arrays or laser scanning vibrometry is often time-consuming or infeasible, as it requires an acoustically treated measurement chamber and specific measurement equipment. On the other hand, accelerometer data is more straightforward to obtain due to the lower cost of the sensors and relaxed constraints on the measurement chamber compared to acoustic measurements. Hence, this contribution utilizes available surface velocity data in an acoustic simulation of the free-field Helmholtz problem. In doing so, it compares a conventional element-based and a mesh-less data-driven mapping technique to investigate the reduction of the number of provided sensor positions for the radiation problem. The open-source boundary element framework NiHu is used to give accurate predictions of acoustic emissions over a wide frequency range. The approach is applied to an industrial problem and validated against microphone measurements.