Reasonable Method Research Articles

Diagnosa Penyakit Malaria Menggunakan Metode Case Base Reasoning (CBR) (Studi Kasus: RSUD Djoelham Kota Binjai)

The application of information technology has been widely used in medicine. This application provides convenience and smoothness in the medical world to detect symptoms of various diseases, especially malaria. Malaria is still included in the endemic diseases suffered by the community in Binjai City, the more malaria patients, of course the more doctors are needed/work to diagnose patients. Artificial intelligence is one solution and helps doctors in supporting decision making for certain diseases. Building a system to diagnose malaria using the Case-Based Reasoning (CBR) method offers various significant advantages. CBR utilizes experience and knowledge from previous cases, allowing the system to provide a more accurate diagnosis based on patterns and symptoms that have occurred in the past.

Optimal scheduling of green heating systems in buildings considering economy and exergy efficiency

Green heating in buildings is of great significance to global energy conservation and emission reduction, and the design of optimal scheduling models is the key to achieve cost reduction and efficiency improvement in heating systems. For the building heating system containing multiple heterogeneous energy sources such as solar energy, air energy and electricity, a multi-objective optimal scheduling model is established with the goal of maximizing the exergy efficiency and minimizing the economic cost of the system, taking into account the quality difference of energy in the conversion process. Regarding the nonconvex and nonlinear constraints in the scheduling model, the incremental linearization method and Big-M method are used to linearize the transformation, and obtain the multi-objective mixed-integer linear programming model. Further, the ε-constraint method is used to solve the Pareto frontier of multi-objective optimization and the technique for order preference by similarity to ideal solution (TOPSIS) method is used to make decision. Finally, the simulation study of the model is carried out, and the results show that the proposed multi-objective optimal scheduling can balance the system operation economy and exergy efficiency compared with the belief rule base and evidential reasoning (BRB-ER) method, and the system’s daily economic cost are reduced by 9.66 %, 9.94 % and 17.24 %, respectively, and the system’s average daily coefficient of performance (COP) are improved by 4.66 %, 14.05 % and 13.22 %, respectively, on three typical days, it is verified that the proposed optimal scheduling model has better economy and exergy efficiency. The proposed multi-objective optimal scheduling model introduces exergy efficiency rather than energy efficiency as one of the optimization objectives of the heating system and reasonably solves the optimization model, which can provide a new idea and reference for the multi-objective optimal scheduling of the green heating system in buildings.

Study on the seismic wave input method for earth-rock dam on overburden foundation with dynamic nonlinearity based on nonlinear artificial boundary

Accurately obtaining the seismic response of earth-rock dams on overburden foundations is crucial for seismic safety assessment and design, and a reasonable seismic input method is necessary. The seismic wave input method based on artificial boundaries is widely used in elastic foundations. However, the overburden with obvious dynamic nonlinearity is an insurmountable obstacle for the traditional method. In this research, relying on a specific earth-rock dam project, the essence of the seismic wave input method for nonlinear foundations is analyzed in detail from equivalent loads and viscous boundaries, respectively. The mechanism of inapplicability of the traditional seismic wave input method is illustrated, and an effective modified method is validated and presented. The research indicated that equivalent loads for the nonlinear dynamic response of the overburden foundation can be accurately acquired with the shear box model. The dynamic parameters of soil can be captured dynamically by the nonlinear viscous coupling boundary. The states of the lateral boundary of the overburden foundation are consistent with the free field precisely with the modified seismic wave input method by combining the shear box model and the nonlinear viscous coupling boundary. A large error will be caused by neglecting the dynamic nonlinear behavior of the overburden foundation, where the maximum deviation of peak acceleration may be over 55 % or more. A numerical model with a relatively small lateral range of overburden foundation will be obtained when the modified seismic wave input method is engaged. Moreover, the computational accuracy is enhanced remarkably, with a maximum deviation of no more than 5 %. This study provides effective theoretical support for seismic analysis and safety assessment of earth-rock dams on deep overburden foundations.

Water Contaminant Detection and Water Purification of Household Drinking Water Using a New Stacking Ensemble Model

ABSTRACTContaminated drinking water sources pose a significant health risk worldwide. Monitoring programs for drinking water quality aims to ensure safe water supply by informing management practices. Improved online monitoring of water systems is necessary as current lab‐based methods are slow and do not offer real‐time public health protection. Rapid detection and response to potential contamination events are crucial to mitigate health risks. Mark of‐purpose water treatment strategies offer a reasonable method for upgrading drinking water quality at the family level and forestalling waterborne illnesses. This study focuses on collecting household drinking water and utilizing various sensors to measure parameters such as pH, turbidity, water level, temperature, and humidity. A consistent water quality noticing system using the stacking outfit model, which solidifies Bayesian association and decision tree techniques, is proposed in this article. Bayesian network analyzes the input data attained from sensors collecting real‐time data and concludes whether the data represents the contamination event. DTs are utilized to demonstrate the connections between multivariate water boundaries utilized in the review. Afterward, a multiobjective, such as a biobjective optimization model and a nondominated genetic algorithm (NGA) are used in this work of optimization to minimize the volume of contaminated water. After the pollution in the water is identified, water decontamination processes are done given point of purpose medicines like ceramic channels and solar water disinfection (SODIS). The method outlined is executed through Python software. The findings indicate that the estimated values for PH, temperature, and turbidity are 7.3, 31.8, and 0.77, respectively. However, the proposed method is compared with the existing C‐NSGA‐II, while compared to this method, the proposed system produces improved cost functions. Consequently, suitable water treatment and supply should be considered to reduce the effects on people's health as well as to improve living conditions, respectively.

Neural-Symbolic Methods for Knowledge Graph Reasoning: A Survey

Neural symbolic knowledge graph (KG) reasoning offers a promising approach that combines the expressive power of symbolic reasoning with the learning capabilities inherent in neural networks. This survey provides a comprehensive overview of advancements, techniques, and challenges in the field of neural symbolic KG reasoning. The survey introduces the fundamental concepts of KGs and symbolic logic, followed by an exploration of three significant KG reasoning tasks: knowledge graph completion, complex query answering, and logical rule learning. For each task, we thoroughly discuss three distinct categories of methods: pure symbolic methods, pure neural approaches, and the integration of neural networks and symbolic reasoning methods known as neural-symbolic. We carefully analyze and compare the strengths and limitations of each category of methods to provide a comprehensive understanding. By synthesizing recent research contributions and identifying open research directions, this survey aims to equip researchers and practitioners with a comprehensive understanding of the state-of-the-art in neural symbolic KG reasoning, fostering future advancements in this interdisciplinary domain.

TransE-MTP: A New Representation Learning Method for Knowledge Graph Embedding with Multi-Translation Principles and TransE

The purpose of representation learning is to encode the entities and relations in a knowledge graph as low-dimensional and real-valued vectors through machine learning technology. Traditional representation learning methods like TransE, a method which models relationships by interpreting them as translations operating on the low-dimensional embeddings of a graph’s entities, are effective for learning the embeddings of knowledge bases, but struggle to effectively model complex relations like one-to-many, many-to-one, and many-to-many. To overcome the above issues, we introduce a new method for knowledge representation, reasoning, and completion based on multi-translation principles and TransE (TransE-MTP). By defining multiple translation principles (MTPs) for different relation types, such as one-to-one and complex relations like one-to-many, many-to-one, and many-to-many, and combining MTPs with a typical translating-based model for modeling multi-relational data (TransE), the proposed method, TransE-MTP, ensures that multiple optimization objectives can be targeted and optimized during training on complex relations, thereby providing superior prediction performance. We implement a prototype of TransE-MTP to demonstrate its effectiveness at link prediction and triplet classification on two prominent knowledge graph datasets: Freebase and Wordnet. Our experimental results show that the proposed method enhanced the performance of both TransE and knowledge graph embedding by translating on hyperplanes (TransH), which confirms its effectiveness and competitiveness.

The Use of Effective Core Potentials with Multiconfiguration Pair-Density Functional Theory.

The reliable and accurate prediction of chemical properties is a key goal in quantum chemistry. Transition-metal-containing complexes can often pose difficulties to quantum mechanical methods for multiple reasons, including many electron configurations contributing to the overall electronic description of the system and the large number of electrons significantly increasing the amount of computational resources required. Often, multiconfigurational electronic structure methods are employed for such systems, and the cost of these calculations can be reduced by the use of an effective core potential (ECP). In this work, we explore both theoretical considerations and performances of ECPs applied in the context of multiconfiguration pair-density functional theory (MC-PDFT). A mixed-basis set approach is used, using ECP basis sets for transition metals and all-electron basis sets for nonmetal atoms. We illustrate the effects that an ECP has on the key parameters used in the computation of MC-PDFT energies, and we explore how ECPs affect the prediction of physical observables for chemical systems. The dissociation curve for a metal dimer was explored, and ionization energies for transition metal-containing diatomic systems were computed and compared to experimental values. In general, we find that ECP approaches employed with MC-PDFT are able to predict ionization energies with improved accuracy compared to traditional Kohn-Sham density functional theory approaches.

Study on the window spill fire plume trajectory of over-ventilated intermediate-scale compartment fires

The fire plume behavior of over-ventilated compartment fire is believed to be related with the façade fire. When the fire plume is reattached to the façade wall, the combustible materials over building wall are potential to be ignited soon. To investigate the window spill fire plume trajectory of over-ventilated intermediate-scale compartment fires, a modeling of fire plume trajectory is set up by using the energy conservation equation and spread theory. The temperature history of over-ventilated compartment fire is conducted by using a 1.35 m (L) × 1.35 m (H) × 1.35 m (W) compartment with window opening aspect varied from 1.12 to 4.44 and HRR differed from 200 kW to 1000 kW. The temperature of fire plume is recorded by the thermocouple mesh and the IR camera. It shows that the modelling theory could be used to predict the shape of fire plume. It agrees well with experimental result. The heat release rate of compartment fire is found to impact little influence on the shape of fire plume regarding a fixed opening dimension. It could provide a reasonable method for fire plume trajectory prediction of the intermediate-scale enclosure fire under an over-ventilated condition.

Effects of autumn tillage with straw return on soil physical characteristics of corn fields in the eastern loess plateau

The implementation of unsuitable tillage practices has the potential to disrupt the structure integrity of the ploughed layer, as well as to influence the physical parameters of the soil. The application of a reasonable tillage method has been demonstrated to result in an improvement in the physical quality of the soil. Three autumn tillage practices have been implemented at the Dongyang Experimental Station of Shanxi Agricultural University since 2016: no-tillage with straw mulch (NTS), autumn rotary tillage with straw incorporation (RTS), and autumn plough tillage with straw incorporation (PTS). The impact of autumn tillage practices on soil physical quality in the 0–30 cm profile of spring corn fields was evaluated following the corn harvest in 2018 and 2019. The results showed that compared to the NTS treatment, the application of RTS was found to have decreased significantly by 9.6%–24.2% in soil bulk density, while it increased significantly by 12.8%–34.0% in total porosity and by 43.5%–146.4% in macroporosity at a depth of 5–10 cm. In comparison to the NTS treatment, the adoption of PTS was found to decrease significantly by 10.7%–30.5% soil bulk density, while it increased significantly by 9.9%–42.7% the total porosity and 23.1%–202.8% the macroporosity at a depth of 0–10 cm. Furthermore, the soil microporosity significantly increase of 7.5%–11.1% under the RTS treatment at the 0–5 cm soil depth and 7.7%–11.2% under the PTS treatment at the 10–20 cm soil depth. Soil physical quality index (SQI) significantly increase under the RTS and PTS treatments, with a 41.26% and 57.57% improvement, respectively, in comparison to the NTS treatment. In summary, the adoption of autumn tillage with straw return (RTS and PTS) demonstrated a reduction in soil bulk density, an increase in soil porosity, macroporosity, and a promotion of capillary porosity, and promoted the improvement of soil physical quality on the Eastern Loess Plateau when compared to no-tillage with straw mulch (NTS).

Short-term effects of restoration measures on vegetation community and soil characteristics of an Achnatherum inebrians-type degraded alpine grassland in the Eastern Qilian Mountains

The Qilian Mountains constitute an important ecological security barrier in the northwest of China, with a wealth of habitat types and biological species. They have a profound impact on the surrounding area and even the world. However, due to both natural and anthropogenic influences, Achnatherum inebrians is widely distributed in this region, seriously threatening the balance of the grass-soil-livestock ecosystem. To explore appropriate restoration measures of alpine grassland of the Eastern Qilian Mountains degraded by the presence of A. inebrians, we studied the effects of restoration measures on the A. inebrians population, the grassland vegetation community, and soil characteristics following sward ripping (SR) and swarding ripping plus reseeding excellent forage (SREF) in Tianzhu County, Gansu Province; adjacent A. inebrians-type degraded alpine grassland without any measures was the control (CK). The SR and SREF inhibited A. inebrians growth and reproduction, increased the proportion of edible forage and the community species diversity, (especially aboveground biomass and sum of sub-coverage of edible forage by as much as two and three times), and improved soil nutrients and soil quality. The radar analysis showed that SREF had the best effect on the A. inebrians-type degraded alpine grassland. Correlation analysis showed that edible forage community, plant diversity, soil moisture, and available nutrient content were the main factors driving the restoration of this degraded alpine grassland. Implementing measures tailored to local conditions could effectively inhibit the spread of A. inebrians, stimulate edible forage growth, and improve the grassland plant community and soil environment. The results indicated a reasonable method of restoration for this poisonous weed-type degraded grassland and protection of the ecological environment.

Clinical Outcomes of Dental Implant Fractures: A Case Series and Analysis of Influencing Factors.

To observe and analyse patients with dental implant fracture and explore the factors influencing the fracture with the aim of providing reference for physicians' clinical work. The clinical data of 19 patients with dental implant fracture who visited the Department of Implantology of our hospital between 2007 and 2019 were retrospectively observed and analysed, and the fracture's influencing factors (e.g. the fracted implant site, implant diameter and connection mode, upper prosthetic method and fracture site) were analysed and investigated. The fractured implants comprised 12 Straumann implants (5 fractured at the smooth dental neck and 7 atop the central screws), 5 Bego implants (4 fractured at the smooth dental neck and 1 atop the central screw), 3 Lifecore implants fractured atop the central screws and 1 Anthogyr implant fractured atop the central screw. Of the 19 patients, 6 had anterior dental implant fracture, and 13 had posterior dental implant fracture (21 fractured implants in total). Maxillary anterior dental implant fracture was observed in 6 patients (8 implants), maxillary posterior implant fracture was observed in 3 patients (3 implants), and mandibular posterior dental implant fracture was observed in 10 patients (10 implants). A good implant system design, appropriate implant diameter and reasonable upper prosthetic method are key to maintaining long-term stability of a dental implant.

Dilemmas and Countermeasures in the Criminal Law System for Offences Involving Virtual Currencies

With the rapid development of science and technology, the new asset form of virtual currency has gradually entered people's vision. However, with its wide application, crimes involving virtual currency are also increasingly rampant, ranging from theft, fraud to money laundering and illegal fundraising. These criminal acts not only damage the legitimate rights and interests of investors, but also pose a serious threat to financial order and social stability. The increasingly prominent crimes involving virtual currency pose new challenges to the traditional legal system. The anonymity and decentralization of virtual currency make criminal acts more concealed and cross-border, which undoubtedly increases the difficulty of legal supervision and crackdown. This paper uses literature analysis, case studies, logical reasoning, and other research methods to deeply explore the governance problems of crimes involving virtual currency. Aiming at the ambiguity of its legal attribute, the difficulty of value evaluation, the challenge of determining "knowingly" committing a crime, and the problem of criminal disposal, this paper proposes a series of targeted countermeasures, such as clarifying the legal attribute of virtual currency, building a standardized value evaluation system, accurately identifying "knowingly", and improving the criminal disposal procedures of virtual currency involved in the case, expecting to contribute to building a safer and more stable virtual currency market environment and providing a strong guarantee for the healthy development of the digital economy.

Academic Procrastination in Preschool Children: The Role of Positive Discipline

With the rapid development of society, the pressure of students' competition is huge. At the same time, families, schools, and society pay too much attention to students' performance, which also increases a lot of anxiety for children. In addition, some children use extremely high standards to strictly require themselves, which also brings them a lot of tension. As the age of learning moves down, preschool children, whether at home or in extracurricular institutions, also carry out many courses of study. Under such background and conditions, students begin to have bad psychology and learning habits and use procrastination to relieve stress and tension. But in the long run, this approach is not conducive to children's physical and mental development. This requires parents to correct their children's procrastination. However, it is precisely because some parents do not master reasonable educational methods to educate children and change children 's problems that the results run counter to expectations. On the basis of summarizing the causes and manifestations of academic procrastination, this paper gives the definition of positive discipline and puts forward reasonable suggestions and educational methods to parents. It aims to improve children 's problems and create a positive family environment suitable for children's growth.

Design of Radial Flux PM Synchronous Motor for EV Applications

Abstract: In recent years, with the accelerated process of urbanization, the continuous growth of urban areas and population sizes has posed higher demands on urban earthquake safety. The theory of resilient cities offers a new approach to effectively address urban earthquake safety risks. However, there is still a lack of reasonable methods for assessing and constructing urban seismic resilience both domestically and internationally. Therefore, this paper elucidates the connotation, essential characteristics, components, and evaluation indicators of a seismic-resilient city, summarizing the components of urban seismic resilience as "one fundamental, two guarantees," which include the engineering system, institutional system, and social and economic system. This paper establishes a dual-parameter urban seismic resilience assessment method based on post-earthquake losses and recovery time, proposing a "set goals, assess, improve, reassess" approach for building resilient cities. The proposed method can provide theoretical guidance and practical basis for assessing and enhancing the seismic resilience of individual infrastructures, engineering systems, urban systems, and resilience to other disasters.

A Spatial Case-Based Reasoning Method for Healthy City Assessment: A Case Study of Middle Layer Super Output Areas (MSOAs) in Birmingham, England

Assessing healthy cities is a crucial strategy for realizing the concept of “health in all policies”. However, most current quantitative assessment methods for healthy cities are predominantly city-level and often overlook intra-urban evaluations. Building on the concept of geographic spatial case-based reasoning (CBR), we present an innovative healthy city spatial case-based reasoning (HCSCBR) model. This model comprehensively integrates spatial relationships and attribute characteristics that impact urban health. We conducted experiments using a detailed multi-source dataset of health environment determinants for middle-layer super output areas (MSOAs) in Birmingham, England. The results demonstrate that our method surpasses traditional data mining techniques in classification performance, offering greater accuracy and efficiency than conventional CBR models. The flexibility of this method permits its application not only in intra-city health evaluations but also in extending to inter-city assessments. Our research concludes that the HCSCBR model significantly improves the precision and reliability of healthy city assessments by incorporating spatial relationships. Additionally, the model’s adaptability and efficiency render it a valuable tool for urban planners and public health researchers. Future research will focus on integrating the temporal dimension to further enhance and refine the healthy city evaluation model, thereby increasing its dynamism and predictive accuracy.

Enhancing 2.7 µm emission of Er3+ in bismuth glass by introducing ZnO

In recent years, 2.7 μm lasers have attracted the attention of many researchers due to their wide application in military, medical, aerospace and other fields. This study investigates the gain effect of ZnO on Er3+-doped bismuthate glasses by partially replacing GeO2 in the glass with ZnO. It is found that under 980 nm pump excitation, with the increase of ZnO, the emission of Er3+ at 1.5 μm and 2.7 μm was enhanced. This result is consistent with the changing trend of J-O parameter Ω2. All these results confirmed that the introduction of ZnO into bismuthate glasses is a reasonable and effective method to enhance the mid-infrared 2.7 μm emission in Er3+-doped bismuthate glasses. Therefore, the current research indicates that an appropriate amount of ZnO in Er3+-doped bismuthate glasses is a suitable materials for 2.7 um fiber lasers.

Unlocking authentic judicial reasoning: A Template-Based Legal Information Generation framework for judicial views

In the field of legal AI, the research on the task of generating judicial views is relatively weak. Judicial views typically include judgment information on the merits such as charges, law articles, and sentencing circumstances. There are several issues with using universal algorithms to generate text, such as a lack of professional expression required for legal documents, and missing or incorrect judgment information on the merits. Judicial views often involve judgment information on the merits, rather than summarizing and excerpting known information. This could be why the application of conventional text generation algorithms may not produce satisfactory results. In actual judicial scenarios, judges generate their views step by step. Firstly, identify multiple judgment information on the merits in the judicial views, and then connect them with legal templates to achieve the generation of judicial views. This article proposes a Template Based Legal Information Generation (TBLIG) framework that generates higher quality judicial views based on the authentic judicial reasoning methods used by judges. The method consists of two steps: The first step involves determining the judgment information on the merits in the judicial view, while the second step involves synchronously generating a legal template based on the case. We have carried out a large number of experiments on the dataset of indictments and non-indictments. Through comparison with other models and ablation experiments, we have proved the effectiveness of our proposed method.

Neurosymbolic AI for Reasoning Over Knowledge Graphs: A Survey.

Neurosymbolic artificial intelligence (AI) is an increasingly active area of research that combines symbolic reasoning methods with deep learning to leverage their complementary benefits. As knowledge graphs (KGs) are becoming a popular way to represent heterogeneous and multirelational data, methods for reasoning on graph structures have attempted to follow this neurosymbolic paradigm. Traditionally, such approaches have utilized either rule-based inference or generated representative numerical embeddings from which patterns could be extracted. However, several recent studies have attempted to bridge this dichotomy to generate models that facilitate interpretability, maintain competitive performance, and integrate expert knowledge. Therefore, we survey methods that perform neurosymbolic reasoning tasks on KGs and propose a novel taxonomy by which we can classify them. Specifically, we propose three major categories: 1) logically informed embedding approaches; 2) embedding approaches with logical constraints; and 3) rule-learning approaches. Alongside the taxonomy, we provide a tabular overview of the approaches and links to their source code, if available, for more direct comparison. Finally, we discuss the unique characteristics and limitations of these methods and then propose several prospective directions toward which this field of research could evolve.

Using Wi-Fi Probes to Evaluate the Spatio-Temporal Dynamics of Tourist Preferences in Historic Districts’ Public Spaces

Tourist preferences for public spaces in historic districts can reflect whether renovated spaces and functional structures meet tourism demands. However, conventional big data lack the spatio-temporal accuracy needed to support a refined, dynamic study of small-scale public spaces inside historic districts. This paper, therefore, proposes using a Wi-Fi probe to evaluate the spatio-temporal dynamics of tourists’ spatial preferences in historic districts. We conducted a one-week measurement in the Xiaohe Street Historic Block in Hangzhou, China. Three indicators—visit time preference, aggregation preference, and stay preference—were used to examine the dynamic change in tourists’ spatial preferences, with 15 min as the time unit and public spaces with a radius of 25 m as the spatial unit. Our research demonstrates that, compared with conventional big data, the Wi-Fi probe offers a more reasonable and accurate method to measure tourists’ spatial preferences in historic districts at a smaller time and spatial granularity. The research findings can be applied to evaluate the effectiveness of spatial regeneration and diagnose renewal-related issues in historic districts. It can also serve as a foundation for more precise planning of public spaces in historic districts, as well as the modification of functional structures.

Intramedullary Screw versus Locking Plate Fixation for Traumatic Displaced Proximal Fifth Metatarsal Fractures: A Systematic Review.

Background/Objectives: Intramedullary screw fixation (IMS) and locking plate fixation (LPF) are currently recommended treatments for proximal fifth metatarsal fractures (PFMF). However, treating comminuted or small displaced avulsion PFMF with IMS poses challenges due to complications. A novel alternative fixation method, the locking compression plate for distal ulna hook plate fixation (LPF), has been introduced recently for distal ulna fractures and has shown improved clinical results. This scoping review aims to assess whether LPF yields superior outcomes, such as postoperative AOFAS scores and rate of postoperative complications, compared to IMS in PFMF treatment. Methods: This review included randomized controlled trials (RCTs), prospective cohort studies, retrospective cohort studies, or case series involving patients with PFMF who underwent plate fixation or screw fixation. The primary outcome was the postoperative American Orthopedic Foot and Ankle Society (AOFAS) score. Studies were sourced from databases including PubMed, Embase, and Scopus, with the search conducted up to February 2024. The Systematic Review protocol was registered in the CRD PROSPERO database (CRD42024532593). Results: Ten studies were included, comprising 3 cohort studies, 1 case-control study, and 6 case series, with a total of 309 patients (158 with LPF and 142 with IMS). The postoperative AOFAS scores showed no significant difference between LPF and IMS in treating PFMF. However, LPF demonstrated efficient surgical procedures and enhanced functional outcomes. Complications were minimal in both groups, with no significant difference in the rate of postoperative complications. Conclusions: Although there was no significant difference in AOFAS scores between LPF and IMS, LPF demonstrated efficient surgical procedures and enhanced functional outcomes, making it a reasonable alternative method for PFMF. Effective shared decision-making (SDM) with patients becomes paramount in choosing the optimal surgical approach. In the surgical landscape, thoughtful deliberation, patient engagement, and adherence to biomechanical principles are crucial for achieving successful outcomes in the treatment of PFMF.