Processing Path Research Articles

Robot path planning based on IAPF-A-Star algorithm

The traditional A-Star algorithm has the problem of performing much invalid traversal work of nodes near concave obstacles when dealing with path planning tasks in the presence of concave obstacles. To solve this problem, a fusion algorithm (called IAPF-A-Star here) based on improved artificial potential field (IAPF) and A-Star is proposed. In IAPF-A-Star, to better measure the distances between the nodes and the target node, the potential energy of the artificial potential field is used to represent the node’s proximity to the target node, and an iterative correction method is used to improve the potential field in order to eliminate the local low-potential energy that may exist in the artificial potential field. The improved potential field effectively avoids the occurrence of local low-potential fields and makes it possible to measure the position of nodes in relation to their target node by means of the potential field. Finally, the obtained path is converted to a curve path by using a cubic spline curve. In addition, to solve the problem that the curve path generated by the cubic spline curve may collide with obstacles, a path optimization strategy based on an adaptive cubic spline curve is proposed to make the final path smooth and collision avoidance, shorter and closer to the actual situation. Simulation experiments show that the proposed IAPF-A-Star algorithm in this paper can effectively avoid the problem of additional local energy consumption caused by concave obstacles when processing path planning tasks, and the traversal of invalid nodes facing different complex concave obstacles is effectively avoided.

Cognitive-Behavioural Social Skills Training: Mediation of Treatment Outcomes in a Randomized Controlled Trial for Youth at Risk of Psychosis: L'entraînement aux compétences sociales cognitivo-comportementales : variables médiatrices des résultats thérapeutiques dans le cadre d'un essai clinique randomisé pour les jeunes présentant un risque de psychose.

Currently, there are no effective treatments for functional outcomes (i.e., role and social) and negative symptoms for youth at clinical high-risk (CHR) for psychosis. Investigations into possible mechanisms that may contribute to the improvement of functioning and negative symptoms are needed in CHR research to help inform psychosocial treatments. The present study examined whether functioning and negative symptoms were mediated by asocial beliefs, defeatist beliefs, self-efficacy, maladaptive schemas, anxiety, depression, social cognition, or attenuated psychotic symptoms (APS) in a large clinical trial. CHR participants (n = 203; 104 females; 99 males) were recruited as part of a three-site randomized control trial comparing group cognitive-behavioural social skills training (CBSST) versus a supportive therapy group. Mediation analyses were conducted to test the relationships between treatment group, mediators (asocial beliefs, defeatist beliefs, self-efficacy, maladaptive schemas, anxiety, depression, social cognition, and APS), and outcome (social and role functioning, and negative symptoms). The mediation analyses employed conditional process path analysis via ordinary least squares regression. At the end of treatment, but not 12-month follow-up, more severe APS were found to mediate the impact of treatment on negative symptoms, and social and role functioning. The greater the severity of APS, the less likely that CBSST would result in improvement in negative symptoms and social and role functioning. Many of the other variables showed significant associations with social (less for role) functioning and negative symptoms but did not mediate the effect of treatment on these outcomes at the end of treatment or 12-month follow-up. There were no significant mediators except for APS at the end of treatment. Since more severe APS may result in participants being unable to fully participate in therapy and thus limit their gains, clinical implications may include offering some individual therapy to prepare these young people to benefit from the group treatment.

A Production-Logistics prediction method integrating Spatial-Temporal features in flexible production workshop for buffer allocation problem

To meet the demands of personalized manufacturing, characterized by customized production with varying batch sizes, logistics equipment such as Automated Guided Vehicles (AGVs) play a critical role in the manufacturing process. However, the distribution of multiple batches is influenced by various factors, with buffer zone capacity allocation emerging as a key challenge. Optimizing buffer zone allocation necessitates a thorough consideration of both spatial characteristics (e.g., shop floor layout and workpiece pathways) and temporal characteristics (e.g., the sequence of material distribution) to enhance resource allocation, reduce bottlenecks, and improve efficiency. This research proposes a novel logistics prediction method for flexible production plants, utilizing a graph attention network that integrates spatial–temporal features. The method first applies a multi-head attention mechanism to capture significant temporal information. Then, a graph convolutional network is employed to model the workshop layout topology and workpiece processing paths, thereby extracting the spatial features of logistics. This spatial information is sequentially processed through a gated recurrent unit and the multi-head attention mechanism to capture the dynamic temporal features of logistics. The proposed model is ultimately employed to predict production logistics in a flexible manufacturing workshop. The experimental results of the MA-T-GCN (Multi-head Attention Temporal Graph Convolution Network) model on production logistics prediction demonstrate an improvement over the best-performing baseline methods on standard benchmark metrics under varying experimental conditions.

Z-Scheme BiVO4/g-C3N4 Photocatalyst-With or Without an Electron Mediator?

The hybrid system BiVO4/g-C3N4 is a prospective photocatalyst because of the favorable mutual alignment of the energy bands of both semiconductors. However, the path of the photocatalytic process is still unclear because of contradictory information in the literature on whether the mechanism of charge carrier separation at the BiVO4/g-C3N4 interface is band-to-band or Z-scheme. In this work, we clarified this issue by comparative photocatalytic studies with the use of systems without a mediator and with different kinds of mediators including Au nanoparticles, fullerene derivatives, and the Fe3+/Fe2+ redox couple. Additionally, the charge transfer dynamics at the BiVO4/g-C3N4 and BiVO4/mediator/g-C3N4 interfaces were investigated by time-resolved photoluminescence (TRPL) measurements, while the influence of the mediator on the surface recombination of the charge carriers was verified by intensity-modulated photocurrent spectroscopy (IMPS). We proved that the charge carrier separation at the BiVO4/g-C3N4 interface occurs according to the mechanism typical for a heterojunction of type II, while the incorporation of the mediator between BiVO4 and g-C3N4 leads to the Z-scheme mechanism. Moreover, a very strong synergetic effect on caffeine (CAF) degradation rate was found for the system BiVO4/Au/g-C3N4 in the presence of Fe3+ ions in the CAF solution.

Математична модель камери згоряння газотурбінного двигуна, який працює на етанолі

Ethanol is one of the most promising alternative fuels for Ukraine. To convert existing projects of power and cogeneration plants based on gas turbine engines (GTEs) operating on petroleum products and natural gas to ethanol, as well as for their design and performance calculation, it is necessary to have a mathematical model of the working process of GTE combustor. The object of the study is the working process of the GTE combustor fueling on ethanol. The subject of the study is a mathematical model of the working process of GTE combustor fueling on ethanol. The work aims to improve a mathematical model of the working process of a GTE combustor fueling on ethanol by changing the algorithm for calculation of the fuel air ratio, considering thermal dissociation, and a correctly formulated equivalent chemical reaction path of the combustion process. To achieve the aim, the following tasks were solved: based on the use of experimental values of specific isobaric heat capacities of combustion products, which are a function of temperature and pressure, a mathematical model of the working process of the GTE combustor was improved ("simplified" mathematical model); based on the solution of the system of equations of chemical thermodynamics, a mathematical model of the working process of the GTE combustor was developed ("complex" mathematical model); the results of calculation by "simplified" mathematical model of the working process of the GTE combustor were compared with the "complex" one. The following results were obtained: the difference in the calculation of the combustion products' thermodynamic parameters between the developed mathematical models was less than 1.2% for the three modes of the General Electric CF6-80A engine. Conclusion: the "simplified" mathematical model of the working process of the GTE combustor fueling on ethanol was improved. A feature of the model is the implicit consideration of the effect of thermal dissociation and correctly formulated equivalent chemical reaction path of the combustion process by using experimental values of specific isobaric heat capacities of combustion products. This will improve the accuracy of fuel air ratio calculation and other thermodynamic parameters of GTE combustor mathematical models fueling on ethanol, without significantly complicating the model algorithm.

Modeling conformational changes in alginic acid oligomers induced by external forces

In this study, the mechanism and nature of mechanical force-induced conformational transitions of alginate oligomers with different ratios of β-d-mannuronic acid (M unit) and α-l-guluronic acid (G unit) units were investigated. The influence of the type of glycosidic linkage in either homo- or heterooligomers on the nature of conformational transitions was also considered. For this purpose, two different theoretical methods were used: quantum mechanics (QM) at the DFT level with the EGO (Enforced Geometry Optimization) approach previously tested also for other saccharide systems, and molecular dynamics (MD) simulations within hybrid interaction potentials, which take into account both the ab initio (QM) level of theory and classical molecular mechanics (MM) force fields. This allowed to characterize in detail the structural and energetic properties of the conformational transition occurring upon the influence of external, mechanical forces (e.g. ring conformations at the path of ring-inversion process as well as the energies corresponding to initial, final and intermediate states). The results indicate qualitatively various responses against the applied force, depending on the G:M ratio, which have their source in differing topologies of glycosidic linkage in either G or M units. This is of potential relevance for determining the content of naturally heterogeneous alginate chains by the AFM experimental studies. The effects of explicit solvent and non-zero temperature are not of primarily importance in the context of determined stretching properties.

Atomically Dispersed Cobalt Anchored on Hollow Tubular Carbon Nitride Mediates Direct Electron Transfer and Oxygen-Related Active Species Path for Activation of Permonosulfate.

Atomically dispersed catalysts anchored on nitrogen-rich substrates present promising application potential for the persulfate-based advanced oxidation process. Nevertheless, efficient activation efficiency and a clear activated mechanism of persulfate remain challenging in carbon nitride-based single-atom catalysts (SACs). To these, combined with the regulation strategy of metal-ligand section and carrier's architecture, an atomically dispersed Co single-atom catalyst anchored on regular hollow tubular carbon nitride (Co/TCN SAC) herein was devised and utilized to activate permonosulfate. As a result, Co/TCN SACs show excellent catalytic performance for the degradation of common antibiotics. Combined with X-ray absorption fine structure and theory calculation, it is confirmed that superficially anchored CoO3 sites of the Co2N2O2-CoO3 unit are the catalytic active center for peroxymonosulfate (PMS) activation. The electrochemical test and in situ electron paramagnetic resonance results demonstrate radical (SO4•- and •OH) and nonradical (electron transfer process and 1O2) paths contributing to the superior catalytic performance. In addition, the catalyst exhibits high reaction efficiency and structural stability considering water quality parameters. Finally, a continuous and efficient device was operated on a laboratory scale, which exhibited satisfactory efficiency in continuously removing electron-rich antibiotics such as tetracycline. This work reveals the atomic-level modulation of cobalt atomic sites on hollow tubular carbon nitride and their structure-activity relationship with persulfate activation.

Increasing the speed of digital devices using the ASMD-FSMD method using non-blocking operators

The ASMD-FSMD method for designing digital devices is considered, which consists of constructing a block diagram of an algorithmic state machine with a data path (ASMD), which describes the behavior of the device, and creating project code in Verilog in the form of a finite state machine with a data processing path. (finite state machine with datapath – FSMD). One of the directions for the development of the ASMD-FSMD methodology is the use of features of the hardware description language (HDL). A hypothesis has been put forward: in the ASMD-FSMD technique, it is possible to apply several non-blocking assignment operators to the same variable in one synchronization cycle, which will lead to an increase in device performance. The hypothesis put forward was investigated in the design of synchronous multipliers that implement the classical multiplication algorithms c and d. Experimental studies have confirmed the validity of the put forward hypothesis, while the speed of the multipliers increases two to three times, and the cost of implementation in most cases decreases compared to the traditional approach.

Wind speed effect on infrared-image-based coal and gangue recognition with liquid intervention in LTCC

The commitment to achieving carbon neutrality by 2060 makes the clean transition of fossil energy production inevitable in China. As the primary fossil energy source, China produced approximately 4.66 billion tons of raw coal in 2023. Among them, in the fields of mining engineering and environmental science, the accurate recognition of gangue (one of the environmental pollutants produced by coal combustion is black-gray solid waste) from coal is crucial because it is a key step in reducing resource waste and environmental pollution. Hence, coal and gangue automatic recognition with high accuracy is very important for intelligent longwall top coal caving (LTCC) mining. A novel approach of “liquid intervention + infrared detection” is introduced to enhance the recognition accuracy when the exterior color of coal and gangue is similar. This study focuses on the infrared image recognition of coal and gangue after liquid intervention under varying wind speed conditions, and optimizes the image processing path. The effect of varying wind speed conditions on the recognition accuracy of coal and gangue after liquid intervention was analyzed, and the optimal wind speed range was determined. The morphological evolution characteristics of droplets under varying wind field conditions were studied by COMSOL two-phase flow interface. The results show that with the increase of wind speed, the surface contour of coal is gradually clear, and the overall recognition accuracy is also improved. When the high wind speed is 1.55 m/s, the recognition accuracy reaches 99.89%, and the recognition accuracy of other wind speed regions is also above 90% (within 7s). In addition, the numerical simulation can maintain the volume fraction of the droplet in a short time under the constant flow wind flow field, thus inhibiting the phenomenon that the recognition accuracy decreases with time. The simulation results are in good agreement with the physical experimental phenomena, which proves the reliability of the numerical simulation. This study is of great significance to the recognition of coal gangue types with low identification characteristics in LTCC, so as to ensure the primary economic benefits, ecological environment safety and sustainable development of coal mines.

Robust Characterization of Photonic Integrated Circuits

AbstractPhotonic integrated circuits (PICs) offer ultra‐broad optical bandwidths that enable unprecedented data throughputs for signal processing applications. Dynamic reconfigurability enables compensation of fabrication flaws and fluctuating external environments, tuning for adaptive equalization and training of optical neural networks. The initial step in PIC reconfiguration entails measuring its dynamic performance, often described by its frequency response. While measuring the amplitude response is straightforward, e.g. using a tunable laser and optical power meter, measuring the phase response presents challenges due to various factors, including phase variations in test connections and instrumentation limitations. To address these challenges, a universal and robust characterization technique is proposed, which uses an on‐chip reference path coupled to the signal processing core (SPC), with a delay gap larger than the total delay across the signal processing paths. A Fourier transform of the chip's power response reveals the SPC's impulse response. The method is more robust against low reference‐path power and imprecise delays. Experiments using a finite‐impulse‐response (FIR) structure demonstrate rapid SPC training, overcoming thermal crosstalk and device imperfections. This approach offers a promising solution for PIC characterization, facilitating expedited physical parameter training for advanced applications in communications and optical neural networks.

Bounded Rational Decision Networks with Belief Propagation.

Complex information processing systems that are capable of a wide variety of tasks, such as the human brain, are composed of specialized units that collaborate and communicate with each other. An important property of such information processing networks is locality: there is no single global unit controlling the modules, but information is exchanged locally. Here, we consider a decision-theoretic approach to study networks of bounded rational decision makers that are allowed to specialize and communicate with each other. In contrast to previous work that has focused on feedforward communication between decision-making agents, we consider cyclical information processing paths allowing for back-and-forth communication. We adapt message-passing algorithms to suit this purpose, essentially allowing for local information flow between units and thus enabling circular dependency structures. We provide examples that show how repeated communication can increase performance given that each unit's information processing capability is limited and that decision-making systems with too few or too many connections and feedback loops achieve suboptimal utility.

Non-planar 5-axis directional additive manufacturing of plastics: Machine, process, and tool path

Additive manufacturing (AM) of plastics is a rising research and application area for light weighting. The conventional way of AM is conducted layer-by-layer, whereas recently non-planar AM is proposed with several advantages such as better use of the material through decreased weight-to-strength ratio, reuse of support material and increased conformity for AM of complex shapes. As for all the tool path-based manufacturing processes, generation of the tool path, selection of process parameters and the machine tool configuration significantly affects the product quality. In this study, non-planar AM of plastics is handled in a holistic manner covering tool path, process, and machine perspectives. Tool path generation for non-planar and directional AM is discussed in accordance with the CAD-CAM software, where the tool path is generated using a novel approach which works with the bead directions obtained from FEM based mechanical analysis. The effects of critical process parameters on the geometrical conformity for non-planar AM are discussed through visual inspection. The proposed cluster-based tool path planning is successfully shown demonstrated from the geometrical perspective.

Microstructure and mechanical properties of 2195 Al-Li alloy via friction stir additive manufacturing with different stirring paths

Friction stir additive manufacturing (FSAM) is increasingly becoming a feasible method for aluminum alloy structure due to the unique forming characteristics. In this study, two different stirring paths were explored for 2195 Al-Li alloy and the microstructure evolution and mechanical properties were ‌investigated. An ultrafine grained microstructure was formed in the FSAMed zone with an average grain size ranging from 1 to 5μm. The microstructure in the FSAMed zone at the bottom exhibited a consistent direction trend and a significant increase in dislocation density. Different stirring paths had minimal impact on the phase distribution. Frictional heating led to the re-dissolution and re-precipitation of precipitates, resulting in T1, θ', and δ'/β' were distributed within the grains. The samples of intersecting stirring path showed similar mechanical properties in different directions, while the UTS of LD was 52.56 MPa higher than TD of the reciprocating path samples. The microhardness range for the intersecting stirring path ranged from 87.0 HV0.2 to 126.6 HV0.2, while the reciprocating stirring path exhibited a microhardness range of 83.8 HV0.2 to 132.4 HV0.2. In summary, this study provides valuable insights into designing processing paths for FSAM of 2195 Al-Li alloy.

Assessment of Measured Mixing Time in a Water Model of Eccentric Gas-Stirred Ladle with a Low Gas Flow Rate: Tendency of Salt Solution Tracer Dispersions

The measurement of mixing time in a water model of soft-stirring steelmaking ladles is practically facing a problem of bad repeatability. This uncertainty severely affects both the understandings of transport phenomenon in ladles and the measurement accuracy. Scaled down by a ratio of 1:4, a water model based on an industrial 260-ton ladle is used. This paper studies the transport process paths and mixing time of salt solution tracers in the water model of eccentric gas-stirred ladles with a low gas flow rate. After a large number of repeated experiments, the different transport paths of the tracer and the error of the mixing time in each transport path are discussed and compared with the numerical simulation results. The results of a large number of repeated experiments on the water model show that there are five transport paths for the tracer in the ladle. The tracer of the first path is mainly transported by the left-side main circulation flow, which is identical to the numerical simulation results. The tracer of the second and third paths are also mainly transported by the left-side circulation flow, but bifurcations occur when the tracer in the middle area is transported downward. In the third path, the portion and intensity of the tracer transferring to the right side from the central region is higher than in the second path. The fourth path is that the tracer is transported downward from the left, middle, and right sides with a similar intensity at the same time. While the tracer in the fifth path is mainly transported on the right side, and the tracer forms a clockwise circulation flow on the right side. The mixing times from the first transport path to the fifth transport path are 158.3 s, 149.7 s, 171.7 s, 134 s and 95.7 s, respectively, among which the third transport path and the fifth transport path are the maximum and minimum values among all transport paths. The error between the mixing time and the averaged mixing time at each monitoring point in the five transport paths of the tracer is between −34.7% and 40.9%. Furthermore, the error of the averaged mixing time of each path and the path-based average value is between 5.5% and 32.6%.

Metastable Liquid–Liquid Phase Separation and Aging Lead to Strong Processing Path Dependence in Mini‐Spidroin Solutions

AbstractRecombinant silk proteins provide a route toward sustainable and biocompatible materials. For making such materials, the assembly process from dilute protein into a functional material is central. The assembly mechanism in engineered materials is by necessity different from the natural ones—this poses challenges but also opens opportunities for scaling up and for developing novel properties. The phase behavior of a mini‐spidroin, NT‐2Rep‐CT is studied, which is a widely studied variant of recombinant silk. NT‐2Rep‐CT can be triggered to assemble by lowering the pH, but even at high pH—considered as storage conditions—it can be in various states, such as forming condensates, clusters, gels, and soluble protein. It is shown how its assembly phases evolve through both metastable and dynamically arrested states. The observed behavior of silk protein solutions is highly complex, and elements thereof from phase diagrams associated with polymers, colloidal systems, and globular proteins are found. Based on the characterization of cluster formation and structural intermediates, a minimalist phase diagram is proposed for NT‐2Rep‐CT and argues that the understanding and insight into silk assembly via its phase behavior, and especially the arrested states, is central for designing recombinant silk proteins and their processing for materials applications.

Separation and utilization of iron, cerium, and other rare earth elements from rare earth waste by chlorination

Due to the lack of effective screening systems in the rare earth waste recycling industry, the composition of rare earth elements in rare earth waste is complex and difficult to separate. In response to such problems, by studying the reaction behavior between various elements in rare earth waste and cobalt chloride, we propose a process path for the separation and recovery of iron, cerium and other rare earth elements using cobalt chloride roasting. The experiments on simulated wastes show that the leaching rates of the Nd, Sm, Gd, Pr can reach 98.31%, 94.5%, 93.87% and 72.05% under the optimal process conditions, respectively. Ce and iron remain in the leaching residue in the form of CeO2 and CoFe2O4, respectively. And through a simple magnetic separation process, CeO2 and CoFe2O4 can be enriched in non-magnetic leaching residue and magnetic leaching residue, respectively. The cerium content in the leaching residue composed of cobalt ferrite is only 1.95%. Therefore, this method is beneficial to the separation and high-value utilization of iron, cerium, and other rare earth elements in the waste system. The research results can provide theoretical reference for the low-cost and high-value recovery of rare earth secondary resources.

Efficient and Secure Management of Medical Data Sharing Based on Blockchain Technology

In the current landscape of medical data management, processing data across diverse institutions and maximizing their value are paramount. However, traditional methods lack a secure and efficient mechanism for end-to-end traceability and supervision, posing challenges in distributed scenarios lacking mutual trust. Leveraging blockchain’s decentralized, tamper-proof, and traceable features, this paper introduces a blockchain-based medical data management platform. This platform enables full-process management of raw data, operational behaviors, intermediate data, and final data, meeting the needs of trusted storage and supervision of data. We propose two methods, namely, naive method and DAG-based method, to realize forward tracking and backward tracing of medical data stored on the blockchain, respectively. We validated and analyzed the storage and query performance of the medical data management platform on real medical data, and we also conducted experimental analyses on the efficiency of the proposed traceability algorithm under different data scales and processing path lengths. The results demonstrate that our platform and traceability methods effectively meet the management needs of medical data distributed across institutions.

Simulation on lightning erosion protection performance of CFRP laminates with metal cluster structure

The tufting technology has the advantages of high flexibility and high economy. Research has found that metal tufting structures can significantly improve the mechanical and electrical properties of carbon fiber composites, and have great prospects in engineering applications. In order to investigate the damage characteristics of carbon fiber CFRP laminates with metal tufted structures during lightning strikes and the influence of structural parameters on lightning protection effectiveness, an electric thermal coupling model of CFRP laminates with metal tufted structures was established. Comparing the simulation and experimental results under the same load condition, it was found that the two were consistent in terms of damage appearance, damage area, and damage propagation trend, proving the effectiveness of the model. The transverse heat transfer process and current conduction path of CFRP laminates with metal cluster structure were discussed. The research results indicate that the ablation surface area of CFRP laminates with metal tufted structures is reduced to 8.06% of the reference component. Wire spacing had an effect on the area and shape of the area of the high potential region, and the wire spacing had the greatest impact on the ablation area of lightning strikes.

A cooperative learning-aware dynamic hierarchical hyper-heuristic for distributed heterogeneous mixed no-wait flow-shop scheduling

The distributed heterogeneous mixed scheduling mode in the manufacturing systems emphasizes the cooperation between factories for the entire production cycle, which poses enormous challenges to the processing and assignment of jobs. Discrepancies in the processing environment and types of machines of each factory during various production stages cause diverse processing paths and scheduling. The distributed heterogeneous mixed no-wait flow-shop scheduling problem with sequence-dependent setup time (DHMNWFSP-SDST), abstracted from the industrial scenarios, is addressed in this paper. The mathematical model of DHMNWFSP-SDST is established. A cooperative learning-aware dynamic hierarchical hyper-heuristic (CLDHH) is proposed to solve the DHMNWFSP-SDST. In CLDHH, a cooperative initialization method is developed to promote diversity and quality of solutions. A hierarchical hyper-heuristic framework with reinforcement learning (RL) is designed to select the algorithm component automatically. Estimation of Distribution Algorithm (EDA) guides the upper-layer RL to select four neighborhood structures. A dynamic adaptive neighborhood switching constructs the lower-layer RL to accelerate exploitation with the dominant sub-neighborhoods. An elite-guided hybrid path relinking achieves local enhancement. The experimental results of CLDHH and six state-of-the-art algorithms on instances indicate that the proposed CLDHH is superior to the state-of-the-art algorithms in solution quality, robustness, and efficiency.

Foot-and-mouth disease knowledge and attitude as determinants of preventative behaviors among dairy farmers in a Thai endemic setting based on structural equation modeling

Thailand has been struggling to eliminate foot-and-mouth disease (FMD) for many years. To combat FMD, dairy farmers are advised to implement biosecurity measures and vaccinate their cattle regularly. Despite taking these precautions, the disease persists, with outbreaks occurring frequently in certain regions. This study aims to determine the knowledge, attitude, and practices (KAP) involved in FMD surveillance and prevention among dairy farmers in endemic areas and identify a causal relationship between them. A cross-sectional survey was conducted from April to May 2023 in 109 dairy farms using questionnaires for data collection. Multiple linear regression analysis and structural equation modeling (SEM) were applied to examine the KAP predictors with process path analysis conducted among significant factors. The results indicated that most dairy farmers demonstrated moderate level of knowledge, attitude, and practice toward FMD surveillance and control for 66.06%, 50.46%, and 54.13% of respondents, respectively. Additionally, a positive relationship was found between knowledge and attitude (β = 1.68, p < 0.05) and between attitude and practices (β = 0.55, p < 0.05). Additionally, educational background had a positive effect on knowledge of FMD (β = 2.54, p < 0.05). Accessibility to FMD information positively enhanced the farmers’ knowledge and attitude (β = 0.89, 1.79, respectively; p < 0.05) toward preventive practices. The results of this study suggest that continuous and regular FMD surveillance and prevention training should be provided for dairy farmers. Furthermore, practically understandable media and appropriately qualified personnel are required to encourage dairy farmers in this area to improve their knowledge and attitude, which would positively impact the quality of FMD preventive practices.