The battery management system (BMS) can intelligently manage and maintain each battery unit while monitoring its status, thereby preventing any possible overcharge or over-discharge of the battery. In BMS research, battery state parameter collection and analysis are essential. However, traditional data collection methods require personnel to be present at the scene, leading to offline data acquisition. Therefore, this study aimed to develop a wireless BMS monitoring and alarm system based on socket connection that would enable researchers to observe the operating parameters and problem details of the battery pack from a distance. A device like this effectively raises the battery’s level of cognitive control. In the study, the researchers first designed the overall scheme of the BMS remote monitoring system, followed by building a wireless BMS monitoring and alarm system. Performance evaluations of the system were then conducted to confirm its effectiveness. A Long Short-Term Memory (LSTM) network enhanced by the Batch Normalization (BN) technique was applied to the time series data of battery parameters to solve the large accuracy inaccuracy in battery state of charge estimate. Furthermore, the Denoise Auto Encoder (DAE) algorithm was utilized to denoise the data and reduce the model’s parameter dependence. The accuracy and robustness of the estimation are improved, and the model error is gradually stabilized within 5%. With the wide application of new energy electric vehicles, batteries’ capacity, safety, health status and endurance have increasingly become the focus of attention . The batConsidering the system structure design of the battery, AeroVironment Company of the United States developed the Smart Guard system and proposed a highly integrated charging and discharging BMS . Using dIn all functions of the battery management system, SOC online estimation is at the core. Accurate online SOC estimation can provide a valuable reference for battery pack energy balance and charging and discharging strategies, ensuring the system’s stable operation and prolonging the system’s service life . CurrentThe above research on the critical technologies of BMS has extensively promoted the development of BMS. However, most traditional monitoring systems are wired, and the wiring is complex. When managing multiple batteries, personnel must debug and set them on-site, making inspection, maintenance and updating quite inconvenient. Once staff leaves the equipment, they cannot monitor and meet the timeliness and mobility requirements of the monitoring system. With the rise of the Internet of Things, cloud platforms, artificial intelligence and other technologies provide ideas for BMS innovation. The Internet of Things technology is used to complete the collection and upload of battery pack-related data, effectively realizing the intelligent collaboration of batteries . The cloThis article proposes a wireless BMS monitoring and alarm system based on Socket communication and artificial intelligence technology. The main contributions of this article are as follows:This article designed the overall scheme of the BMS remote monitoring system and built a wireless BMS monitoring and alarm system.This article proposed an LSTM network improved by the BN algorithm to the time series data of battery parameters to address the problem of significant accuracy error of battery state of charge estimation.The DAE algorithm is used to denoise and reduce the dependence of the model on parameters.Currently, the research in BMS mainly focuses on battery state estimation, battery equalization, battery thermal management and other technologies. Among them, the research on battery state primarily focuses on the estimation of battery state of charge (SOC), the prediction of battery state of health (SOH) and the prediction of remaining useful life (RUL).The online estimation of the battery state of charge is at the core of all the battery management system functions. As for the estimation of battery health status, there are many definitions of battery pack health status However, the stand-alone battery management implemented only at the device terminals can no longer meet the growing demand for BMS. The BMS implemented by combining technologies such as the Internet of Things and cloud platforms has gradually received extensive attention at home and abroad . Li et aVarious parameters of the battery are monitored. LSTM neural network is used to estimate the accuracy of the battery charge state so that researchers can remotely view the operating parameters and fault information of the battery pack, and support the subsequent analysis of parameter data, to improve the intellectual control level of the battery effectively.Currently, most algorithms for estimating battery SOC have problems such as a small amount of data and inability to represent all situations, poor model learning ability and estimation accuracy, high parameter dependence and poor robustness. This research is based on many actual battery operation state data, and after data transmission through the Internet of Things technology, the data are labeled and standardized. At the same time, we designed the SOC estimation model of the energy storage battery based on the LSTM depth learning algorithm. We introduced the BN algorithm to optimize and improve the model and used the DAE algorithm to denoise and adapt the model, thus reducing the dependence of the model on parameters and improving the accuracy and robustness of the estimation.DAE adjusts the network structure parameter values of the intermediate nodes of the encoder by performing a back-propagation algorithm on the error term, reduces the reconstruction error through continuous iteration, and finally obtains the result after feature extraction and denoising. Optimized reconstructed input samples to further improve the robustness and adaptability of the SOC prediction model. Based on the LSTM model optimized by the BN algorithm, the DAE algorithm is combined for optimization, and the overall structure of the final LSTM-DAE model is shown in Based on the LSTM-BN algorithm, this research uses the DAE algorithm to optimize and improve its network structure and obtain a better LSTM-DAE model. The BN algorithm is used to pull the input value distribution back to the standard global distribution to make the gradient larger and avoid the problem of disappearing gradients. The classification model designs a two-layer network stack LSTM structure whose batch_size is set as 128.Neither the traditional Kalman filter for SOC estimation nor the support vector machine regression method considers that SOC, as a battery state, cannot change and often depends on the previous state of charge. As a variant of the recurrent neural network, the LSTM model can effectively deal with nonlinear time series problems and have long-term memory ability. LSTM network has a forgetting gate, an input gate and an output gate, which can input valid or invalid information to its kernel state unit through the threshold.First, by converting the voltage, temperature, current and estimated SOC characteristics of the single battery in the previous state into time series characteristic data, a SOC prediction network based on the short-term memory network LSTM is shown in According to the operating state of the energy storage battery, such as voltage, current, temperature, etc., collected by BMS in real-time and superimposed with the time characteristics of a moment, an input data format conforming to the timing characteristics is constructed. The size of the matrix is 1*7. After the input data is input into the LSTM network, it first passes through a layer of the LSTM network, and the activation function is RELU. After the dropout layer, it is learned by a layer of LSTM, Then connect the 1*50 dimension fully connected neural network, and through the learning and dimension reduction of the following layers, finally obtain the 1-dimension output value, that is, the predictive value of the energy storage battery SOC.Although the method of random gradient descent has simplified the training depth network, it still has some drawbacks, such as more severe dependence on the initial set learning rate, parameter initialization, weight attenuation coefficient, dropout ratio and other parameters, so this topic uses BN  algorithm to optimize it.As shown in After the above normalization, to prevent the distribution shift of features, it is necessary to transform and reconstruct the normalized data to restore the original features. When To enhance the model’s robustness and stability under complex working conditions, this study proposes to use the Denoising AutoEncoder (DAE) to extract features and denoise the input data. This algorithm is based on the input data to denoise and extract features. The extracted features do not discard some features but fuse and represent them as high-level abstract low-dimensional features that reflect the nature of the data and have more robustness and generalization capabilities.DAE adds a certain amount of noise to the input data and then realizes the noise removal function through the degradation process in the learning process. The DAE obtains pure noise-free samples for input data with particular noise after encoding, decoding and other operations.n×l, and DAE randomly sets it to 0 according to a certain proportion, v, to obtain the degenerated input eigenvector Assume that the input sample data is x ∈ RNext, the decoder decodes the encoded feature data to obtain the optimal abstract representation of the input data and complete the decoding phase. 2 is the node offset term of the decoding layer; g(⋅) is the activation function set by the node.In the above formula, z is the reconstruction value of the input data; bX and Z are the input training data and the output reconstruction data, respectively. Then the reconstruction error function between the reconstructed data samples and the original input data is: Presently, the informatization degree of BMS monitoring and alarm system in energy management and storage is relatively low. There are high coupling degrees of informatization systems, common system flexibility and scalability, unclear information display, and lack of information on operation data. Related to analysis and utilization, it is impossible to accurately predict the SOC in real time. This research builds a wireless BMS monitoring and alarm system based on Socket communication. This effectively improves the system’s highly flexible and controllable performance and enhances the system’s real-time maintainability and intelligent prediction and early warning protection based on visualization.The BMS monitoring and alarm system designed in this study adopts the design idea of separating the front and back ends. It is divided into a business, algorithm, communication, and data layer. It has the characteristics of low coupling, high cohesion and high scalability. The corresponding library table is designed, the data storage format is standardized, and the data is intelligently analyzed and applied. Finally, it becomes possible to detect and upload the status of energy storage systems, estimate energy storage battery SOC intelligent algorithms, and visualize energy storage battery operation. Performance and functionality tests were carried out.The business layer of the BMS monitoring and alarm system is mainly responsible for realizing the interaction and request distribution among the system modules, distributing the algorithm business to the algorithm layer, and realizing the corresponding functions by interacting with the data layer modules. This study uses the Spring MVC framework to learn low-coupling system development. Spring Boot is used for automatic configuration, which assists in simplifying the steps of project construction, and uses the Tomcat server as a lightweight application server.The business layer of this system uses the Spring MVC framework to realize the construction of the business model. It implements a request-driven web framework through the MVC design pattern implemented by Java.The algorithm layer preprocesses the request message to extract valuable data features. This system uses Tornado lightweight framework to complete. This technology’s asynchronous, non-blocking IO processing method can use the HTTP protocol to pass parameters to the server, including query strings and data sent in the request body , URL regular expression interception, etc. The parameters are preprocessed and transmitted to TensorFlow Serving. The TensorFlow Serving system is well suited to serving multiple models that can dynamically change based on real-world data. It presents a solution to apply the model to actual production. After receiving the parameter data preprocessed by Tornado, this study needs to train and deploy the deep learning algorithm implemented in Chapter 3 to estimate the SOC of the energy storage battery.The TensorFlow Serving model is mainly used for deploying deep learning algorithm modules, a service system with flexibility, high performance, and use in production environments. New algorithms can be easily deployed through this service system, and multi-model services and distributed APIs can be provided simultaneously. Using the continuous integration framework based on TensorFlow Serving in the algorithm layer is mainly divided into three steps: (1) Model training: including data collection and cleaning, model training, evaluation and optimization; (2) Model online: import the model trained in the previous step into TensorFlow Serving and go online; (3) Service usage: The client communicates with the TensorFlow Serving side through gRPC and RESTful API and obtains the service.For TensorFlow serving multi-model deployment, you only need to modify the model configuration file to serve the new model. Config, add model_version_policy, place the model file of the new model in the corresponding path, and then start the corresponding service.Usually, Socket communication consists of two parts: the server side and the client side. The server and client sides can send and receive data from each other by establishing a network connection. The specific process is shown in  The Server Socket class is the primary mechanism for implementing server-side TCP/IP communication programs. When a Server Socket object is created, the program provides services on the specified port on the server side and starts listening for possible service requests from clients. When a client requests a connection and is accepted, the server program will create a Socket object to connect with the remote client to implement read-and-write communication operations. The accept of this class is used to wait and accept a connection from the client. When accept is called, the server process or thread will be blocked until it listens to a client process that makes a service request and succeeds with its connection. Then accept will return a newly created server-side Socket object.The data layer is mainly responsible for storing the real-time operation status parameters of the BMS monitoring and alarm system, alarm warning information, user logs and other file data. This research uses the MySQL database management system to save the state parameters of different single cells of the battery pack in separate tables, which increases the operating speed and flexibility of the system. At the same time, the database is mapped and configured using the MyBatis framework under the Spring framework.This research uses the MyBatis framework to automatically generate the SQL statement that meets the needs by providing the MyBatis mapping method. It can input parameters into the Prepared Statement to achieve automatic input mapping. The query result set can also be mapped to Java objects to meet the output requirements map. This section analyzes the experimental results of the SOC estimation algorithm and the wireless BMS monitoring alarm system.This study first completed the training based on the extended short-term memory network on the preprocessed data set and obtained the training model LSTM-origin. Afterward, based on batch normalization, the learning and convergence capabilities of the deep learning network were improved and optimized. The model LSTM-BN was obtained by retraining. Finally, the DAE compression autoencoder is used to optimize the generalization ability and robustness of the model, and the model LSTM-DAE is obtained by retraining. Models after training using the same training set are tested for performance metrics on the same tests.The three models—the original LSTM-origin model, the improved LSTM-BN model, and the LSTM-DAE model—can be trained and tested using the same data set, and by using the same initial and iteration round numbers, it is possible to compare the number of iterations and error reduction of each model in the training and test sets. In After the training set is finished and the test set is anticipated, This research tests and verifies the functions of the wireless BMS monitoring and alarm system to ensure the BMS’s stable, reliable and accurate operation. The test plan mainly includes the safety performance test, system working condition test and routine performance test of the battery management system.The safety performance test of the battery management system is mainly aimed at the simulation test of insulation resistance. According to GB/T 18384-2015 Safety Requirements for Electric Vehicles, the insulation resistance of the BMS system shall not be less than 500 Ω/V . This reThrough the SOC estimation test, state quantity acquisition accuracy test, and fault diagnostic test, this research evaluates the system’s operating condition performance in an effort to decrease failure rates, enhance product quality and performance, and accelerate the development cycle of BMS. Static and discharge equalization tests are the critical components of the BMS hardware’s standard performance test. After the BMS is turned on, without starting the car model, static equalization sets various initial battery voltages and waits for automatic equalization. The BMS is configured to perform discharge testing under constant working conditions thanks to discharge equalization. Due to the equalization approach, the battery management system will carry out discharge equalization. This investigation chooses twelve batteries in the same BMS port for testing. In In light of the continuous growth of electric vehicles, power batteries have gained increasing prominence. In this regard, the present research delves into the theory and methodology of battery remote monitoring and alarm. It proposes a wireless BMS monitoring and alarm system based on Socket communication. The system enables researchers to remotely view the electric vehicle battery pack’s operation parameters and fault information and subsequently analyze the parameter data. The practical application significance of the system is paramount in advancing the research of battery management systems.This study first applies the data-driven approach to enhance the estimation methodology for the battery’s state of charge in the energy storage system. The research suggests a short- and long-term memory network technique suitable for the time series data of battery characteristics to address the accuracy mistake in battery state of charge prediction. The model’s error stabilizes under 5% thanks to data annotation, data standardization, and processing of the LSTM input, forgetting, and output layers. The model is optimized in this work using the batch normalization procedure, considering the model’s strong reliance on initial set parameters, including learning rate, parameter initialization, weight attenuation coefficient, and Dropout ratio. The approach increases the training speed and improves the stability of the model.via Socket communication and data persistence module interaction of the data layer. The study performs safety performance testing, system working condition tests, and routine performance tests for the system functions to guarantee BMS’s stable, dependable, and accurate functioning.Additionally, based on the estimation method of the energy storage battery’s state of charge, the study puts a wireless BMS monitoring and alarm system into practice. The system can be divided into four layers: business, algorithm, communication, and data. The business layer is primarily in charge of implementing interaction and request distribution between different system modules, storing business logic processing, distributing algorithm services to the algorithm layer, and achieving the corresponding functions However, this study is based on a data-driven method to accurately estimate the state of battery charge accurately, mainly using voltage, current, temperature and other characteristics. Suppose other sensors can collect some electrochemical change characteristics inside the battery in the future. In that case, the change of battery SOC can be better characterized, which will improve the accurate prediction of battery SOC.10.7717/peerj-cs.1345/supp-1Supplemental Information 1Click here for additional data file.10.7717/peerj-cs.1345/supp-2Supplemental Information 2Click here for additional data file.


Background: The complex traditional treatment of inflammation diseases in oral cavity includes the prescription of antibiotic and antiseptic therapy. This systematic review aims to evaluate the effect of photodynamic therapy as a part of management of inflammatory diseases in oral cavity; Methods: The study is presented in accordance with the preferred reporting points for systematic reviews and meta-analyses (PRISMA). This systematic review was conducted using electronic databases such as Medline PubMed, Scopus and the Cochrane Central Register of Controlled Trials. All the studies in this systematic review, were randomized, the risk of bias 2 (ROB 2) were assessed; Results: Considering the inclusion and exclusion criteria, we included 10 randomized clinical trials, published up to 2023 investigating the application of photodynamic therapy as a part of management of inflammatory diseases in oral cavity. The diode laser was used in the oral cavity in the zone of inflammatory process  in nine studies or in the zone before surgical procedures in one study; Conclusion: Based on the results of clinical studies, it can be stated that photodynamic therapy shows good results for operations performed in the oral cavity and pharynx. Periodontal diseases are an important aspect of study in dentistry. It is known that periodontal microflora becomes a frequent cause of their occurrence. These bacteria are aggressive towards tooth-supporting tissues . TraditiIn this regard, of particular interest is the method of photodynamic therapy, which has been widely used in recent decades for the adjunctive treatment of various diseases of inflammatory genesis, including in dentistry.Photodynamic therapy consists of photosensitizer, light and oxygen . The basLaser radiation is widely used as a light source for the application of photodynamic therapy, which not only activates photosensitizers, but can also be used as an additional mechanical treatment of tissues . Lasers In the articles we’ve reviewed, special attention is paid to the mechanism of photodynamic therapy. As for the effectiveness, the result of this method does not depend on the spectrum of sensitivity of microorganisms to antibiotics, and photochemical exposure is able to destroy the microbial matrix and penetrate into the subsurface layers of the epithelium ,14. It iThis systematic review aims to evaluate the effect of photodynamic therapy as a part of management of inflammatory diseases in oral cavity. This topic has been repeatedly covered in various studies ,31,32,33In this review, more attention was paid to the selection and exclusion criteria. Compared to similar articles, this review covers various aspects. If we consider the criteria, the important point was the age of the patients, pregnancy or lactation. Special attention was paid to concomitant diseases, concomitant medications and the condition of the dental system. The review also draws attention to the conduct of research, namely the evaluation of the results. All this allow to narrow down the range of study and give more accurate results.This study was prepared referring to the Cochrane tool to assess the risk of bias in randomized trial, and performed according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) .This systematic review is registered in the International prospective register of systematic reviews (PROSPERO).The registration number: CRD42022348087.In this systematic review, the literature search was based on the PICO  format.Types of participants: patients not younger than 18 and not older than 80 years.Types of interventions: the use of photodynamic therapy as a part of management of inflammatory diseases in oral cavity and pharynx.Types of control: as for comparisons, we included treatment without using aPDT.Outcome: assessment parameters depend on type of inflammation disease in oral cavity.Types of studies: RCT’s.Only trials published in English were selected. Conference abstracts, reviews, commentaries and case reports were excluded.A literature search was conducted on 4 electronic databases up to 1 May 2023, Medline PubMed, Scopus, and the Cochrane.The search terms describe the PECO components:P (participants)—patients with inflammatory diseases in oral cavity or pharynx;E (exposure)—patients with inflammatory diseases in oral cavity or pharynx who were treated with adjunctive use of aPDT;C (comparison)—patients with inflammatory diseases in oral cavity or pharynx who were treated without aPDT;O (outcome)—to evaluate the effectiveness of PDT in patients with inflammatory diseases in oral cavity or pharynx based on assessment of follow criteria: microbial load, pocket depth, clinical attachment level (CAL), bleeding on probing (BOP), plaque index (PI), gingival index (GI).Exclusion and Inclusion Criteria:Randomized clinical trials;Participants are older 18 years and younger 80 years with the indication for oral surgery with/or inflammatory diseases of oral cavity and pharynx;Use of photodynamic therapy as antiseptic method before operations in oral cavity or the part of management of inflammatory diseases of oral cavity and pharynx.Inclusion criteria:Other types of research;Participants are younger than 18 years or older than 80 years old;Antibiotics taking for last month, anti-inflammatory drugs or pain-killers taking for last 5 days;Severe comorbidity proof;Pregnancy or lactation;Fungal infection;Acute viral or bacterial infections of other localization;Other conditions that are contraindications for laser use.Exclusion criteria:The filters were used: full texts, humans, clinical trials, randomized clinical trials, 11 years publication date between 2013 and 2023.The search algorithms were used for the search in the Medline PubMed.The following search of terms were used for all four data bases : «photodynamic therapy», «antimicrobial photodynamic therapy» «antibacterial», «bactericide», «oral cavity», «microflora», «photosensitizers», «randomized clinical trials».Medline/PubMed/Scopus/the Cochrane. AND  AND «oral cavity» AND «microflora» AND «photosensitizers» AND «randomized clinical trials».The date range of included articles was 2013 to 2023. We reviewed published systematic reviews and meta-analyses to ensure that the topic is relevant and that there is no duplication or plagiarism.Two groups contained three researchers  independently searched databases for the above criteria. In case of some disagreement for all group two independent researchers helped to have consensus .The date range of included articles was 2013 to 2023. Three researchers independently searched databases for the above criteria.The following data were recorded for each selected study: the source of publication , the design of the study, participants  and outcomes .Three independent authors two times performed the data extraction and collection process. Some disagreements between authors were resolved.The randomization process;Deviations from intended interventions;Missing outcome data;Measurement of the outcome; andSelection of the reported result.To exclude Bias Assessment were conducted a study with Cochrane “Risk of Bias tool” (RoB 2.0) (Higgins 2019)  method:TThe bias were analyzed independently by two authors. Also were applied the tool for each study and justifications for judgements of risk of bias . Authors resolved any disagreements in the assessment of risk of bias between each other. Each item was assessed  by authors in the risk of bias table.The study selection flow chart  34] inc inc34] iEach of the 10 studies was RCT and included studies of patients without comorbidities and not under anti-inflammatory pain killers within the last 5 days or/and not under antibiotics for the last month. The necessary moment was the absence of other methods of antiseptic in the test group of patients.Diode lasers with a wavelength from 660 nm to 940 nm were used across studies. The diode laser was used in the oral cavity in the zone of inflammatory process  in nine studies or in the zone before operations  in one study. Modes and duration were different. In studies, the authors compared a pockets depth and the number of pathogenic bacteria before and after the surgery.The periodontal probing depth (PPD) of periodontal pockets was decreased after the aPDT in eight included studies ,40,41,43All 10 studies included in systematic review were randomized, risk of bias 2 (Rub 2) was used .In recent years, photodynamic therapy (PDT) has attracted an increasing attention due to its ability to suppress the activity of drug-resistant bacteria.Bacterial infections in oral cavity are very common in clinical practice. Systemic and local antimicrobials are the main methods of treating these diseases. However, in conditions of rapid growth and drug resistance, the PDT method is universal .Antimicrobial photodynamic therapy is a modern treatment method aimed at deactivating microorganisms responsible for the occurrence and progression of oral diseases . PDT is Photodynamic therapy is a method based on the activation of exogenous photosensitizing agents using a light source .®. However, despite its effectiveness, there are certain limitations in its use—the accumulation in the body and the absorption layer of light, which is able to be scattered and absorbed by the cells of the body [If we consider photosensitizers, then several generations can be distinguished. The very first drugs appeared in the 1960s—derivatives of hematoporphyrin. These include the gold standard PDT—Photofrinthe body ,46.To overcome these shortcomings, second-generation PS were created. The wavelength range for the activation of these synthetic drugs varies from 600 to 800 nm, thereby allowing them to penetrate deeper into the tissues. However, the presence of an optimized spectrum does not ensure selectivity. Also, second-generation drugs are rapidly eliminated from the body, thereby reducing the time of photosensitization and the effectiveness of treatment ,46.Scientists achieve the greatest selectivity in relation to tissues in third-generation drugs, trying to build a receptor-target relationship. An equally important point is the expansion of light absorption—the desire for infrared absorption. Also among the new PS there are inorganic substances that have the necessary potential in biotechnology and nanotechnology .Due to the lack of protocols and conflicting results of the studies, the evidence for a PDT is yet insufficient for clinical recommendations . HoweverPDT involves the introduction of a photoactive dye that is capable of producing reactive oxygen species (ROS) when irradiated with light. So, when a dye absorbs a photon, an electron is transferred from its ground state to an electronically excited state, which returns energy along three main paths. When light energy is absorbed at the corresponding wavelength, the photosensitizer transitioned from a low-energy singlet ground state to a triplet state with higher energy .Antimicrobial photodynamic therapy can solve the problems of conventional antimicrobial therapy and can be used as an additional to the main mechanical treatment . The phoThe method of implant surface scaling (ISS) plus photodynamic therapy was assessed in some trials in the treatment of peri-implantitis and mucositis ,55,56,57In comparison to other therapies, a-PDT is less invasive and can be used for all patients, except for those with hypersensitivity to light. It can be used more frequently than antimicrobials because a-PDT does not cause bacterial resistance . MoreoveThe study of the effectiveness of antimicrobial photodynamic therapy (aPDT) was conducted by the authors of an article published in 2014. Basically, the mechanism of action of reactive oxygen species (ROS) was studied. In their work, the authors used a combination of toluidine blue O (TBO) and red LED (LED) to conduct 4 experiments. The results were as follows: the best antibacterial effect and the largest amount of reactive oxygen species were observed when using the combination of TBO and LED, while the cytotoxic effect with the additional use of LED did not have a particularly significant difference. The cytotoxicity results of aPDT were close to the cytotoxicity results of antiseptics used in dentistry. An important point of the article was that the authors drew attention to the safety of photodynamic laser therapy. This method of treatment significantly suppressed the formation of plaque, while without adversely affecting surrounding tissues and teeth .S. oralis, the authors of a randomized clinical trial in 2017 studied the antibacterial effect when exposed to other bacteria involved in the formation of plaque . The authors studied the effect of antimicrobial photodynamic therapy on the microbiological state of tissues. As a result of surgical treatment of Severe Chronic Periodontitis using aPDT in one episode with a diode laser and a phenothiazine photosensitizer, the study showed a reduction in inflammation. It is important to note that the Flap treatment has led to a decrease in the number of pathogenic periodontal microorganisms and an increase in the number of species to the favorable microflora of the oral cavity. In addition, the article stated that this treatment method can be used repeatedly due to the low risk of bacterial resistance to reactive oxygen species [Since the article examined the effects of aPDT only on  species . Thus, iColleagues noted that PDT has a high antibacterial potential. As a result, it can be used as adjunctive treatment of various diseases of the oral cavity. In addition, studies have shown that, unlike antibiotics, bacteria do not develop resistance to repeated photodynamic therapy methods. There is also a lot of evidence that PDT can destroy biofilm, further reducing the viability of bacteria and increasing sensitivity to antibiotics .The authors of the article published in 2014 were also interested in this method of additional processing. The authors, comparing the results of treatment of chronic periodontitis with the additional use of aPDT, noted that after 3 and 6 months there was a statistically significant decrease in parameters in the group after antibacterial photodynamic therapy (aPDT) in addition to scaling and root planing (SRP). The assessment was carried out according to the indicators of pocket depth and the clinical attachment levels, gingival index and gingival bleeding index, halitosis. The authors concluded that aPDT is a useful supplement to SRP in the non-surgical treatment of chronic periodontitis .In an article published in 2022, non-surgical periodontal treatment of chronic periodontitis using erbium laser and a-PDT as an adjunct to SRP was considered. According to the results of the study, the authors claim that the result of using an antibacterial photodynamic system is good. However, the additional use of the laser showed a better result . Thus, tIn a study of colleagues conducted in 2021, a diode laser was used after the usual irrigation of necrotic teeth in the first and second admission. Intra-channel laser irradiation was carried out in a pulsed mode with circular movements in order to reduce the heating of the dentin, thereby not damaging the surrounding periodontal tissues. The temperature of the channel wall immediately decreased when a laser connected to an activated fiber optical tip with a diameter of 200 microns was rapidly fed from the apical to the coronal direction. Consequently, the tissues surrounding the tooth are only slightly affected, and the peri radicular tissues are not damaged. The authors concluded that the use of a diode laser after conventional irrigation can reduce postoperative pain in single-root necrotic teeth with a PAI rating of 3 or 4 after root canal treatment carried out in two medical visits. The research results indicate that diode lasers can be used to treat root canals, providing patient comfort .Despite the large number of articles with positive results when using antimicrobial photodynamic therapy, there are also works with neutral results. In an article published in 2019, the authors used antibacterial photodynamic therapy (aPDT) and a diode laser in the non-surgical treatment of chronic periodontitis. Comparing 2 processing methods  and SRP + aPDT in combination with a diode laser), the authors concluded that there were no significant differences between the groups after 3 months. Relative attachment level, probing depths and gingival recession had similar results in the two groups. The only different parameter was the sulcus fluid flow rate. The result showed a lower value after 2 weeks in the group where additional aPDT treatment was used. The authors are inclined to believe that these results could have been obtained as a result of insufficient power, concentration, repetitions or irradiated time. Under the conditions carried out during the study, the indicators for effective reduction of the number of bacteria may not have been achieved .In an article published in 2019, the authors described and compared the long-term clinical effects of a diode laser and photodynamic therapy as additional treatments for chronic periodontitis in combination with scaling and root planning. The results of the present study did not reveal any clinical benefit after the use of diode or photodynamic laser therapy in combination with conventional SRP compared with mechanical therapy solely in terms of probing depth, CAL and bleeding during probing 6 months after treatment. There was only a tendency to a greater decrease in PD in the group of diodes for deep pockets after 3 months .The absence of a significant difference in the results of the study, which was conducted in 2018, also casts doubt on the effectiveness of aPDT treatment. In this article, the authors provided the results after a single application of the antibacterial photodynamic system as an adjunct to open flap debridement at the time of treatment of peri-implantitis. The results were monitored for 12 months after the operation. Peri-implant plaque index, bleeding on probing, pocket depth and marginal bone level were evaluated. The average value of all indicators in all groups was significant if when considering the indicators from baseline to the result after 6 months. The group in which additional photodynamic therapy was used initially had a better indicator. However, after 12 months, the results in both groups were practically the same. The results equalized by the end of the study and did not have a significant difference. The authors suggest that such a result of the study could be due to a number of factors that were not taken into account in the exclusion criteria. According to the data on the participants, about 50% of patients smoked at the time of the study, and 36% of patients were prediabetic. As stated in this article, both the first and second factors stimulate the secretion of proinflammatory cytokines that cause osteolysis of the alveolar bone. It is also important to consider that the hyperglycemia can reduce the activity of the cell mediated immunity .Among the new scientific papers written in 2023, it is important to single out a randomized clinical trial, during which the authors increased the sample of microbiological analysis, and also studied the change in the depth of gingival pockets. According to the results of the study, it was concluded that the combination of full-mouth ultrasonic subgingival debridement (FMUD) together with a diode laser and indocyanine green (ICG-aPDT) could not show high results, except for selective clinical and microbiological indicators. The authors noted in the article the high rate of pocket closure when using a diode laser. As for the results of microbiological analysis, a significant decrease was observed for two types of bacteria (Aggregatibacter actinomycetemcomitans and Parvimonas micra), but the result did not persist for a long time. In conclusion, the authors drew attention to the fact that randomized clinical trials with the possibility of changing laser parameters are still needed to confirm these results .In addition, the use of photodynamics in combination with the main treatment method shows the best treatment results ,39,40,41We did not focus on any specific inflammatory disease of the oral cavity or pharynx, while the effectiveness of photodynamic therapy in various nosology can vary greatly. In addition, we analyzed all eligible studies without examining the effectiveness of a single laser type or regimen. Thus, despite the rather strong limitations for inclusion of studies in the analysis, the results are general and can serve as a guide for subsequent targeted systematic reviews and meta-analyses.Thus, the antimicrobial photodynamic therapy can be used as adjunctive method in operations performed in the oral cavity and oropharynx for the treatment of inflammation diseases, stimulating the regeneration of tissues and microflora. Antimicrobial photodynamic therapy reduces bacterial load related to inflammation diseases and may be considered as an alternative to antibiotics. aPDT is a beneficial and promising therapeutic method, however, further high-quality RCTs focused on the standardized aPDT parameters are needed.


A Wireless Sensor Network (WSN) is a group of autonomous sensors geographically distributed for environmental monitoring and tracking purposes. Since the sensors in the WSN have limited battery capacity, the energy efficiency is considered a challenging task because of redundant data transmission and inappropriate routing paths. In this research, a Quasi-Oppositional Learning (QOL)-based African Vulture Optimization Algorithm (AVOA), referred to as QAVOA, is proposed for an effective data fusion and cluster-based routing in a WSN. The QAVOA-based Back Propagation Neural Network (BPNN) is developed to optimize the weights and threshold coefficients for removing the redundant information and decreasing the amount of transmitted data over the network. Moreover, the QAVOA-based optimal Cluster Head Node (CHN) selection and route discovery are carried out for performing reliable data transmission. An elimination of redundant data during data fusion and optimum shortest path discovery using the proposed QAVOA-BPNN is used to minimize the energy usage of the nodes, which helps to increase the life expectancy. The QAVOA-BPNN is analyzed by using the energy consumption, life expectancy, throughput, End to End Delay (EED), Packet Delivery Ratio (PDR) and Packet Loss Ratio (PLR). The existing approaches such as Cross-Layer-based Harris-Hawks-Optimization (CL-HHO) and Improved Sparrow Search using Differential Evolution (ISSDE) are used to evaluate the QAVOA-BPNN method. The life expectancy of QAVOA-BPNN for 500 nodes is 4820 rounds, which is high when compared to the CL-HHO and ISSDE. The fast development of electronic component manufacturing and wireless communication encourages WSN application using wireless sensor nodes, because of the inherent features such as strong adaptability, versatility and low-cost . SensorsEach node in the WSN is installed with limited storage, energy and processing capacities. Therefore, energy preservation is considered as an important task for improving the life expectancy, and clustering is considered as the most energy-saving approach in the WSN ,7,8. A cThe QAVOA-based CHN and route discovery are carried out for performing reliable data transmission over the network. The conventional African Vulture Optimization Algorithm (AVOA) is converted into QAVOA by incorporating QOL, which helps to enhance the population diversity for achieving the optimum solutions.Moreover, a QAVOA-optimized BPNN is used to perform an effective data fusion for eliminating the redundant information and minimizing the amount of information, which helps to minimize node’s energy usage and enhance network’s life expectancy.The contributions are as follows:The remaining paper is sorted as follows: The related works about the data fusion and cluster-based routing over the WSN are provided in this section.In general, the data fusion ,18 is acCao et al.  presenteLiu et al.  developeGavel et al.  used theLong et al.  developeLuo  presenteThis section provides the information about the different research, which was carried out based on the swarm-intelligence-based approaches.There are different optimizations such as oppositional artificial fish swarm-based clustering with improved moth flame optimization-based routing , Particl2 emissions has been designed and implemented in [A distributed Internet of Things (IoT) system for monitoring COented in . The LoRented in , a conceented in ,34, a teThe data-driven surrogate model with latent data assimilation has been shown by Sibo Cheng et al. . By combThis research performs an effective data fusion and cluster-based routing for improving the WSN performances. The BPNN is used for performing the data fusion where the weights and threshold coefficients are optimized by QAVOA. Moreover, the cluster-based routing using QAVOA is helps to choose optimum CHN and path for performing the reliable transmission. The data fusion performed by QAVOA-BPNN helps to minimize the amount of broadcasted data and eliminate the redundant data, which returns the lesser energy usage. Therefore, the lesser energy usage is used to enhance the network’s life expectancy. The flowchart for the QAVOA-BPNN is shown in The sensors are randomly positioned in the network followed by multi-objective QAVOA, which is used for searching the CHs. The clusters are created once the CHs are chosen in the network. Moreover, the QAVOA-based route discovery is accomplished for performing the data broadcasting in WSN. The detail about the CHN and route discovery are provided in the following sections.The optimum CHs from the normal nodes are chosen using the QAVOA. The conventional AVOA  is motivThe initial solutions, i.e., vultures, are fixed with a set of nominee sensors to be selected as CH. The vultures are set with a random sensor between 1 and The inputs from the initialization are given to an iterative process for identifying the optimum CHs.In general, the opposition learning is used to create the possible solutions, examine the relative solutions and choose the optimum candidate solutions for improving the capacity of searching to solve the optimization issue. Here, the QOL is developed according to the opposite number as expressed in Equation (1).The population is separated in to three classes where the fitness value is utilized for defining whether the vulture location is good or not. Therefore, the 1st group is defined as the first optimal solution for all vultures, the 2nd group is the 2nd optimal solution, and the remaining is defined as the third group. The fitness for each population is computed, once the initialization is performed for African vultures. The formula for vulture grouping is shown in Equation (3).An African vulture’s strength is denoted by the starvation rate of QAVOA. The vulture flies too far searching for the food when it is not hungry. On the other hand, the vulture does not have the strength to fly far when it is hungry. Consequently, the hungry vultures certainly become violent and surround strong vultures to obtain food instead of discovering food themselves. The exploration and development stage of vultures are varied based on the starvation rate that is expressed in Equations (5) and (6).Generally, the African vulture consumes some time for determining the position of remaining populations and then flies a higher distance for searching for food. Two different exploration approaches are designed and the strategy is defined by using The vultures are entered into the initial development stage for eliminating the imbalance among the exploration and development capacities, when the The vulture starts to think fully when The strong vulture and those expressing competition for food fly in a spiral in the air, when All vultures are full when the starvation rate is less than The vultures participate for food to avoid starvation and many flock to the same food source. Equations (13) and (14) show the vulture’s motion while the location update is performed by using Equation (15).Simultaneously, the remaining vultures are violent and fly to the leader vulture at various points to obtain the food using Equation (16).The CHN’s energy consumption is essential due to its various functions such as data collection, fusion and broadcasting over the network. CHNs with high energy are preferred for performing the reliable data transmission. The residual energy is expressed in Equation (18).Equations (19) and (20) show the neighbor node distance and sink distance that are the distance among the sensors and distance between CHN and BS. The energy consumption of the sensors is directly proportional to the distance, therefore the CHN with a lesser distance is chosen in WSN.The network has the possibility of generating huge clusters with small clusters. Equation (21) expresses the CHN balancing factor, which is used to achieve the balance among the clusters for obtaining an effective balancing of energy in WSN.A number of nodes that belong to the next hop is represented as node degree, which is expressed in Equation (22).A QAVOA considers the various fitness metrics such as residual energy These fitness metrics are utilized for selecting optimum CH between the Cluster Member Nodes (CMNs). The failure node is avoided by considering the energy that is used to avoid packet drop. The CHN with a lesser distance is obtained by considering the distance factor that helps to lessen the energy usage over the network, whereas the node degree is also used to minimize the energy . FurtherThe CMNs are assigned to an adequate CHNs, once the selection of CHN is made by using QAVOA. Equation (23) shows the potential function used for generating the clusters.After identifying the CHNs, the data fusion is performed by using QAVOA-BPNN for the data received from respective CMNs.This section shows the optimized BPNN using QAVOA to accomplish the data fusion, once CHN receives the data from CMNs. The elimination of unwanted data and the minimization of broadcasted data quantities help to minimize the energy and life expectancy. The BPNN  architecEquations (24) and (25) show the outputs of the hidden and output layers, respectively. An activation function of the hidden layer and output layer The QAVOA is adopted in BPNN for optimizing the weights and threshold coefficients for enhancing the learning capacity and local extremum. The At first, the vultures are initialized with the possible paths from the transmitter CHN to BS, whereas the dimension of each vulture is equal to the number of relay CHs of the route.The location update is accomplished by using the iterative process detailed in previous sections that was utilized to return to the optimum path based on the fitness metrics.The considered multiple fitness metrics are residual energy, sink distance and node degree. The fitness metric for QAVOA-based route discovery is expressed in Equation (32).The QAVOA-based route discovery is performed by optimizing it with distance, energy and node degree factors. The following steps depict the route discovery using QAVOA.The results and discussion of the QAVOA-BPNN are shown in this section. The design and execution of QAVOA-BPNN are carried out by using the MATLAB R2020b software. Here, the system is configured with 16 GB RAM and i5 processor. The 500 nodes are considered as randomly deployed in the area of The distribution of nodes in WSN is depicted in The performance of QAVOA-BPNN is analyzed using energy consumption, life expectancy, throughput, End to End Delay (EED), Packet Delivery Ratio (PDR) and Packet Loss Ratio (PLR). Here, the performances are analyzed with different optimization algorithms such as Particle Swarm Optimization, Whale Optimization Algorithm (WOA) and AVOA. The fitness graph for PSO, WOA, AVOA and QAVOA is shown in the The amount of energy consumed by the nodes while transmitting and receiving the data packets is defined as energy consumption, which is formulated as Equation (33).Life expectancy is the time at which all the sensors in the network lose their energy due to data transmitting over the WSN. The life expectancy comparison for QAVOA-BPNN with PSO-BPNN, WOA-BPNN and AVOA-BPNN is shown in Throughput is defined as the number of packets that are successfully transmitted between the transmitter CHN to BS. The formula for the throughput is expressed in Equation (34).EED is defined as the amount of time taken to transmit the data from the transmitter CHN to BS, which is expressed as Equation (35).The EED comparison for QAVOA-BPNN with PSO-BPNN, WOA-BPNN and AVOA-BPNN is shown in the PDR is defined as the ratio among the amount of received packets and transmitted packets at Equation (36), whereas the PLR is the ratio among lost packets and transmitted packets, which is defined in Equation (37).In this approach, Network Lifetime predicts the amount of time between sensor node locations and a dead network. The network life spans of the QAVOA-BPNN that is being described and the current technology are contrasted in The amount of energy that is still in use after network data transmission is complete is known as energy efficiency. The comparative analysis of the QAVOA-BPNN is provided in this section. The existing research such as CL-HHO  and ISSDReliable data transmission over the WSN is achieved utilizing the shortest path generating method employing QAVOA.In order to reduce energy consumption and hence lengthen the expected lifespan, duplicated data are eliminated using QAVOA-BPNN, and the shortest paths are generated.The QAVOA-BPNN method is evaluated against existing techniques like Cross-Layer-based Harris Hawks Optimization (CL-HHO) and Improved Sparrow Search Utilizing Differential Evolution (ISSDE).The 500 nodes are thought to have been distributed at random over an area of In order to eliminate redundant data and reduce the amount of broadcasted data in the WSN, optimized BPNN employing QAVOA-based data fusion is carried out in this article.The QAVOA-BPNN is analyzed in terms of energy consumption (0.47 mJ), life expectancy (482 rounds), throughput (0.85 Mbps), End to End Delay (3.4 s), Packet Delivery Ratio (97.5%) and Packet Loss Ratio (2.5) Network Lifetime (7197 rounds) and Energy Efficiency (97.2%).In this paper, optimized BPNN using QAVOA-based data fusion is performed for removing the redundant data and decreasing the amount of broadcasted data in the WSN. The BPNN performs effective data fusion by optimizing their weights and threshold coefficients, and the backward error propagation is used to minimize the error. Moreover, the optimum CHN from QAVOA is used to balance the energy consumption among the clusters. The shortest path generation using QAVOA is used to accomplish reliable data transmission over the WSN. Therefore, the redundant data elimination using QAVOA-BPNN, and the shortest path generation are used to minimize the energy utilization that leads to increase the life expectancy. From the results, it is determined that the QAVOA-BPNN achieves better performance than the CL-HHO and ISSDE. The life expectancy of QAVOA-BPNN for 500 nodes is 4820 rounds, which is high when compared to the CL-HHO and ISSDE.In the future, a novel optimization algorithm can be used for improving the performances of WSN. Furthermore, the collision aware routing protocols will be used to avoid the congestion problem in the network. Moreover, the proposed QAVOA-based approach will be extended in the future to handle/analyze larger-scale WSNs with more complex data fusion and routing requirements.


In industrial manufacturing and research laboratories, precise machining of work materials is crucial to meet the demand for fast assembly and sustainable high-quality production. Precise machining procedures play a vital role in manufacturing compatible parts that meet the production requirements. This study investigates the impact of different parameters on burr formations and slot dimensions during the micro-milling of AISI 316 material. A careful examination was conducted using scanning electron microscopy (SEM) images under varying conditions. The variables considered include cutting speed, feed rate, and depth of cut. The main finding revealed that the feed rate and depth of cut significantly influence burr formation, with lower rates and depths resulting in noticeable reductions. A higher feed rate was associated with more pronounced burr formation. Moreover, burr widths on the down-milling sides were typically wider than those on the up-milling sides due to continuous chip formation and compressive forces during down-milling. Utilizing image processing, the study further quantified burr and slot widths with high accuracy, offering a reliable method to characterize burr formation. This research presents potential ways to minimize burr formation during micro-milling processes by effectively managing machining parameters. Additmponents ,8. Additmponents . The benmponents . One of mponents . Stainlemponents . Stainlemponents ,14.–nickel stainless steels with good corrosion resistance [A common material in SLM is austenitic stainless steel is AISI 316L. AISI 316L is a family of molybdenum-added austenitic chromiumsistance . Due to sistance . Al-Mamusistance  found thsistance  comparedsistance  also stusistance  studied Micro-machining is a manufacturing method used to manufacture miniature devices and parts containing elements that range from 10 µm to a few millimeters in size . InteresImage processing is a technique that transforms the obtained image signals into a form that can be interpreted electronically (with the help of computers and software) with certain algorithms or a method used to extract some useful information from images ,35,36,37One of the main challenges encountered in micro-milling operations is the formation of burrs. These burrs are tiny in size, making the process of removing them, known as deburring, quite challenging ,42. The The adverse effects of burrs on the quality and performance of micro-parts and features cannot be neglected , compellNumerous researchers have focused on the challenge of achieving better control over burr formation in micro-milling operations. To address this issue, they have explored the application of image-processing techniques in their research. By leveraging advanced algorithms and image analysis tools, these researchers have aimed to detect and analyze burrs at a microscopic level. However, future studies need to provide more specific details and evidence regarding the outcomes and effectiveness of these image-processing techniques in improving burr control during micro-milling operations.In this study, the objective of this study is to investigate the correlation between machining parameters and burr formation in micro-milling processes of selective-laser-melted AISI 316L. The study aims to analyze the impact of cutting speed, feed rate, and depth of cut on burr formations and slot dimensions. By utilizing scanning electron microscopy (SEM) images and image processing techniques, the research aims to quantify burr and slot widths accurately. The ultimate goal is to provide insights into the influence of machining parameters on burr formation and offer potential strategies to minimize burr formation during micro-milling processes, thereby improving the quality and efficiency of industrial manufacturing. To achieve a detailed analysis, a novel algorithm is proposed to accurately detect and measure the burr and slot widths. This method not only enhances accuracy but also improves the efficiency of the process by reducing human observation errors. The expected outcomes of this study not only help in optimizing machining parameters but also provide a foundation for further research in implementing a closed-loop system for burr minimization.The experimental setup consisted of machining equipment, workpiece material, cutting tools, SEM imaging equipment, and a computer vision system. The precision micro-machining system is capable of producing high-resolution machined components suitable for machining precisely up to one µm micrometer. This system included a micro-milling machine, a workpiece holder, and a computer-controlled interface for programming machining operations. The system is utilized using high quality and has sharp innovative micro-cutting tools for 1090 µm. The workpiece material is suitable material for micro-machining, which is an additive-manufactured AISI 316L alloy. The workpiece is prepared with 30 × 10 × 3.5 mm dimensions.The Concept Laser MLAb Selective Laser Melting machine has a Yb fiber laser that can operate at up to 100 W, with the smallest laser diameter. The standard layer thickness production parameter of the company is 0.025 mm. In this study, the sample production was converted to .stl format for the SLM machine to process it by the tool path. The sample was produced by the Concept Laser MLAb Cusing SLM system and wrought AISI 316L. Additionally, during the production steps of the sample, the advised manufacturing parameters provided by the company were used. After the part manufacturing was completed, the powders on the part were first cleaned in the production cabin. The support structures of the sample were removed after the sample had been cut by using a wire erosion device .For the production of the AISI 316L sample with SLM, AISI316L that is spherical in morphology with particle size ranging from 44 to 88 µm is preferred . The cheAs shown in The image acquisition system includes a scanning electron microscope (SEM) device to capture images of the machined surface for subsequent analysis. Its illuminated area is adjusted to ensure proper lighting conditions and a stable mounting setup for consistent image acquisition. In the image processing stage, the obtained images from SEM are used to determine the burr formations and extract slot and burr characteristics to determine proper machining parameters.A graphical user interface (GUI) for the burr detection algorithm is given in The burr measurement process was performed using image processing algorithms to accomplish the measurement of irregular burr formations. The process starts with an image-capturing stage. In this stage, the digital images of the machined surfaces after micro-machining operations were acquired. The captured images cover the entire region of interest and have sufficient resolution with proper illumination for accurate analysis. The next stage includes pre-processing stages. Filtering operations, and hue saturation value (HSV) conversion with thresholding techniques were applied to enhance the image quality and remove noise. The operations were used to improve the clarity of the burr features.In the image processing stage, SEM images are accepted as input to the computer vision (CV) software. It is included by the user by referencing the location of the image source in the computer. Initially, the obtained image is filtered by applying thresholds over HSV values. In the second stage, a masking process is applied to the image for removing the background from the actual image. In the next stage, the image is divided into two imaginary divisions. The color values were summed for these two divisions, and two peak values are determined for the up- and down-milling side of the image. A chart is extracted from this procedure, and two peak values are determined for calculating the slot width. The horizontal distance between peak values is used to determine slot width. In the burr width determination steps the up- and down-milling sides of the image were evaluated separately. A threshold is applied for the two sides of the image to determine the burr width. The vertical sums above the threshold were accepted as burrs for up- and down-milling sides of the image. The horizontal distance of the furthest from the left slot border is accepted as the left burr width from starting the slot left border. The second horizontal distance in the left side of the image for the right side of the slot left border is accepted as the inside left burr of the slot. The sum of the horizontal distance of the burrs is divided by the measurement number to obtain the average burr width for the left side. The maximum burr width is determined by referencing the left side of the slot up to calculate the widest burr width, considering the thresholded data was accepted as the maximum burr width. The same procedure was applied to the right-side image to determine right-side burr characteristics.Y-axis. The Y-axis results are normalized to ensure the burr position matching step, as shown in X-axis.The workflow of the proposed algorithm for burr detection processes using SEM images is illustrated in Finally, the output of the algorithm is used for the reduction of burr formations with appropriate parameters. The extracted parameters are used as input to reduce the formation of irregular burrs during micro-machining. For this purpose, parameter optimization, design of experiments (DOE), process control, and monitoring approaches are considered for evaluation. The outcomes relate to identifying the key machining parameters such as cutting speed, feed rate, tool geometry, and machining coolant that significantly influence burr formation. In addition, the outcomes provide a solution to utilize factorial design or response surface methodology to systematically vary the machining parameters and evaluate their effects on burr formation. This enables the identification of optimal parameter combinations for minimal burr generation. In line with this, direction implementation of a real-time monitoring system to continuously measure and monitor the burr formation during micro-machining is possible. This can involve in-process sensing techniques, such as acoustic emission or force measurement, to detect the onset of burr formation. With a combination of sensing and measurement steps, it is possible to develop a closed-loop control system that adjusts the machining parameters in real-time based on the feedback from the monitoring system. Thus, a closed-loop system, including computer vision, statistical data, and parameter optimization, enables dynamic optimization and adaptive control of the machining process to minimize burr formation.SEM images of burr formations and slots after micro-milling of the AISI 316L material in different parameters are given in To further investigate the influence of feed rate and depth of cut, the researchers compared Slot-B with Slot-C. In this case, the cutting speeds were kept constant, while the feed rate and depth of the cut were reduced. The researchers aimed to determine how lower feed rates and depths of cut would affect the formation of burrs. The results showed a noticeable reduction in burr formation on both the up-milling and down-milling sides in Slot-C compared to Slot-B. This outcome suggests that decreasing the feed rate and depth of cut had a positive effect in reducing the occurrence of burrs during the machining process. When the depth of the cut decreases, it typically leads to a reduction in the amount of material being removed during each passage of the cutting tool. With a smaller depth of cut, there is less material being subjected to deformation and displacement during the cutting process . The feeExamining the transition from Slot-C to Slot-D sheds light on the impact of feed rate alone. In this experiment, the cutting speed and cutting depth remained fixed, while the feed rate was doubled. As a consequence of the increased feed rate in Slot D, the formation of burrs became more pronounced, extending widely into the interior of the slot. The presence of extensive burrs primarily drew attention in Slot D, as they were more prominent compared to Slot C. This result emphasizes the direct correlation between feed rate and burr formation, highlighting the need to carefully control this parameter in machining operations to achieve optimal results.Moreover, a noteworthy result emerged when analyzing the burr formations in greater detail. It was evident that the width of the burr formed at the down-milling side was noticeably larger than that formed at the up-milling side for slots A, B, and C. During down-milling, the chips are typically formed continuously as the tool moves through the material. This continuous chip formation, combined with the compressive forces, can contribute to the material being pushed outward, resulting in a wider burr formation on the down-milling side . In addiY-axis. The drawn-out features are examined to ascertain the parameters of the slot width and burr, based on set parameters. The width of the slot is defined as the peak values of vertical sums. Burr formations are evaluated by segregating the area of interest into the upper and lower milling sections of the image to enhance the efficiency and effectiveness of the algorithm. In the subsequent phase, categorization and identification processes are utilized to characterize the burr formation using vertical sums for the separate upper and lower milling sides. The width of the slot and burr lengths of the upper and lower milling sides for the least, most, and averaged values are utilized as features for the recognition algorithm for additional analysis. This algorithm identifies patterns and makes predictions based on these drawn-out features considering adaptable threshold parameters. During the post-processing phase, mathematical data are subjected to normalization, filtering, and thresholding operations to enhance accuracy. Finally, results are visualized and displayed in a chart window by superimposing the original image to present the outcomes.During the phase of extracting features from the SEM images, the aggregate of pixel values is drawn out from the previously processed image outputs using a vertical total of pixel values for every column. The process of extracting significant data for characterizing the formation of burrs and the determination of slot width is initiated by the image processing algorithm. The algorithm examines each pixel as a variable for every vertical line that is employed to calculate the vertical total of pixel values. The summing operation is carried out starting from the origin at the left top side of the image and continuing to the end of the columns in the The obtained burr formations from the processed images are shown for Slots A, B, C, and D in The slot boundaries, and the peaks of the burr start and end boundaries on the patterns in Slot widths c are 926—on burr formation and slot width during micro-milling of AISI 316L material. The results indicate that decreasing feed rate and depth of cut reduce burr formation, in which increased burrs are associated with down-milling, and that feed rate is directly linked to burr formation; additionally, the study proposes an effective algorithm for quick and accurate detection of slot and burr widths, with potential implications for minimizing burr formation and enhancing tool maintenance strategies.This study investigates the impact of machining parameters—cutting speed, feed rate, and depth of cut➢Burr formation during micro-milling of AISI 316L is not significantly affected by the cutting speed when the feed rate and depth of cut are kept constant.➢Decreasing the feed rate and depth of cut while keeping the cutting speed constant leads to a significant reduction in burr formation on both up-milling and down-milling sides due to a decrease in material deformation and displacement.➢The burr width on the down-milling side tends to be larger than on the up-milling side due to the continuous chip formation and compressive forces experienced during down-milling.➢Increasing the feed rate while holding other parameters constant notably enhances burr formation, suggesting the need for precise control of the feed rate for optimal machining outcomes.➢Larger burrs are more likely to occur on the down-milling side, as supported by the measurement of maximum burr widths, thus reinforcing the described burr formation mechanism.➢The proposed algorithm in the study can quickly and accurately detect both slot and burr widths for the up- and down-milling sides, surpassing potential human observational errors.➢The slot widths smaller than the tool diameter used indicate potential tool wear. This, along with the high burr density, suggests a decrease in tool performance, hinting at the need for improved machining strategies and tool maintenance.➢The study’s proposed approach could potentially minimize burr formation by providing a fast and accurate burr formation characterization that could be adapted to a closed-loop system. Future research could focus on refining this method and studying other factors that influence burr formation and tool wear.


Public transportation is a crucial component of urban transportation systems, and improving passenger sharing rates can help alleviate traffic congestion. To enhance the punctuality and supply–demand balance of dedicated buses, we propose a hierarchical multi-objective optimization model to optimize bus guidance speeds and bus operation schedules. Firstly, we present an intelligent decision-making method for bus driving speed based on the mathematical description of bus operation states and the application of the Lagrange multiplier method, which improves the overall punctuality rate of the bus line. Secondly, we propose an optimization method for bus operation schedules that respond to passenger needs by optimizing departure time intervals and station schedules for supply–demand balance. The experiments were conducted in Future Science City, Beijing, China. The results show that the bus line’s punctuality rate has increased to 90.53%, while the retention rate for platform passengers and the intersection stop rate have decreased by 36.22% and 60.93%, respectively. These findings verify the effectiveness and practicality of the proposed hierarchical multi-objective optimization model. Due to the rapid increase in traffic demands, traffic congestion has become a major problem worldwide. To combat this issue, various methods have been proposed, with bus priority being considered the most effective method. This has led to transit-oriented development (TOD)  being adThe problems related to dedicated bus lanes are also widely studied, attracting thousands of scholars worldwide. Some innovative ideas have been proposed to optimize and improve the service level of dedicated bus lanes, such as reducing per capita delays, decreasing parking rates, and increasing bus operation speeds. However, focusing solely on punctuality as a single target cannot adequately reflect the reliability and stability of buses. Combining this understanding with the literature ,3,4, we (1) Existing research on dedicated buses often separates the optimization of the punctuality service level and the supply–demand balance. However, there is a lack of correlation and collaboration between these two goals. To address this issue, a hierarchical multi-objective optimization model has been proposed to optimize the punctuality service level and achieve a balance between supply and demand for dedicated buses.(2) Many existing studies employ weighted processing to tackle the multi-objective optimization problem. However, this approach can potentially result in the main objective being influenced by other objectives. To overcome this limitation, a hierarchical multi-objective speed decision-making method utilizing the Lagrangian multiplier method is proposed. This method prioritizes ensuring the comprehensive punctuality rate of the entire bus line and subsequently optimizes stop times at intersections while achieving a balanced distribution of guidance speeds.(3) The traditional method of optimizing bus operating schedules, which solely relies on travel time, fails to consider the crucial aspect of balancing passenger demand with bus capacity. To address this issue, a bus operation schedule optimization method has been proposed to achieve a balanced relationship between bus capacity and passengers waiting at stations. This approach helps overcome inconsistencies between bus schedule settings and passenger travel demand.The structure of this paper is as follows: This part introduces the current transit priority control strategy, which mainly covers three parts: transit signal priority, speed guidance optimization, and schedule optimization. Then we summarize the current research status. In order to reduce the waiting time of buses at signal-controlled intersections and enhance bus operating efficiency, several scholars have conducted research on transit priority control, starting with intersection signal priority. Tarikul developed a dynamic priority control system that balances signal control demands and transit priority, aiming to reduce the average person delay at intersections . Qiao prScholars have also studied transit priority from the perspective of pre-signal settings, considering that it may reduce the operating efficiency of social vehicles. He aimed to reduce the average person’s delay by prioritizing bus signals through adaptive control and pre-signal settings . Liang aWith the advancement of vehicle–road cooperative technology, real-time speed guidance for bus vehicles has become possible. This development has prompted scholars to explore improvements in bus service levels through the lens of speed guidance. Khaled achieved further reductions in bus delays by implementing a strategy of early braking at red lights and extending green lights based on speed guidance while incorporating transit signal priority . Shu solScholars also studied speed guidance from the perspective of improving bus punctuality since it enhances the bus operation reliability. Takashi developed an inter-station control strategy that coordinates speed guidance and signal control based on punctuality demands for bus arrivals, leading to improved reliability of bus arrivals . Yan proThe key to improving bus punctuality and operational efficiency involves setting the bus schedule, prompting scholars to focus on its optimization. Li developed a public transport scheduling model for a microsystem that aims to minimize passenger waiting time while maximizing the number of passengers per bus. This is achieved by optimizing departure intervals and utilizing both traditional and rapid buses simultaneously . GkiotsaExisting research has made progress in optimizing bus service levels at various levels, yielding some favorable outcomes. However, most punctuality controls focus on optimizing a single section, which overlooks the optimization of bus punctuality across the entire line. As a result, there is a risk of local optimization with overall poor performance, affecting bus punctuality service levels. Furthermore, existing research often neglects the correlation between passenger travel demand and bus capacity allocation, which can lead to a mismatch between supply and demand, thereby affecting the quality of bus service.(1) All signal-controlled intersections along the route lack a bus signal priority strategy, and bus vehicles are not granted signal priority at intersections;(2) All bus stations on the route are designed as single-line bus stations, and there are no instances where multiple lines share a single bus station;(3) There is a signal-controlled intersection between every two bus stations on the line;(4) All passengers on the bus are required to swipe their cards at the front door when boarding and at the rear door when disembarking.Assumption (1) aims to enhance the algorithm’s general applicability in this paper by considering that the majority of dedicated bus lanes do not implement a bus signal priority strategy. Assumption (2) primarily seeks to minimize the impact of multiple bus lines overlapping in the analysis of this method. Assumption (3) aims to establish a standardized bus operation environment and minimize the influence of variations in the bus operation environment on the method proposed in this paper. Assumption (4) aims to standardize the information collection method for passengers boarding and alighting from the bus, ensuring consistent data quality.In order to improve the punctuality service level and supply–demand service level of dedicated buses, we analyzed the signal data, bus data, and passenger flow data, leading to the construction of a hierarchical multi-objective optimization model. From the perspective of bus operating reliability and comfort, the first layer of the multi-objective optimization model for intelligent decision-making of bus speed was developed. This layer primarily encompasses a mathematical description of bus operation, a multi-objective speed-decision-making model, and the Lagrange multiplier method for speed optimization. By considering multiple target constraints, such as the reliability of bus arrivals, non-stop operations at intersections, and smooth speed distribution, the punctuality service level of public transport has been improved. According to passenger travel needs and bus capacity allocation, the second layer of the multi-objective optimization model for the bus supply and demand balance was constructed. This layer mainly includes bus departure interval optimization and station schedule optimization. From the balanced analysis of the passenger stasis of the platform and the passenger capacity of the bus, in this paper, we realize the optimization of the bus departure time interval. Moreover, we optimize the bus station schedule by combining it with an analysis of the driving characteristics between the bus stations.The collection of traffic data is not the primary focus of this paper. Therefore, existing methods were employed to gather multi-source traffic data, including intersection signal control data, bus dwell, and travel data, as well as passenger station waiting, boarding, and alighting data.T is the bus schedule arrival time, and Considering that the majority of existing scheduled operation plans are based on minute intervals, this paper defines buses arriving within one minute of the scheduled platform time as punctual. For instance, if the scheduled arrival time is 8:05, buses arriving at the station between 8:05 and 8:06 are considered punctual.i.From analyzing The main objective of this section is to obtain the corresponding state of the bus during all stages of operation, which primarily includes the stages of bus dwell, road operation, and intersection waiting.When the bus leaves the i-th platform, the departure time can be defined as Considering the requirement for the bus to arrive punctually, when the vehicle drives away from the intersection, we need to determine a driving speed in front of the station to ensure that the vehicle arrives on time. Thus, we can construct a set The bus station departure time is mainly determined by the dwell time at the bus station and the arrival time of the bus, and we know that the bus arrival time is determined by the state of the bus at the upstream intersection. The dwell time mainly includes the predicted value i-th intersection and the upstream and downstream platform sections.Optimizing a single objective does not guarantee an overall improvement in the level of public transport services. To address this, this paper presents a hierarchical multi-objective optimization model. The model includes the following objectives: achieving the highest comprehensive punctuality rate for the bus lines as the first-level objective, minimizing the intersection stopping rate as the second-level objective, and achieving the most balanced distribution of guidance speed as the third-level objective.First, this paper uses the error of the arrival time to analyze the punctuality of the entire line; the discriminant function of punctual arrival can be determined as follows:N, and The number of punctual bus arrivals of the entire route is The traditional bus punctuality index considers only whether the arrival time falls within the specified constraint range, without considering the magnitude of the non-punctual arrival error and its distribution state. As a result, it becomes challenging to comprehensively evaluate and optimize bus punctuality control with accuracy. For this reason, this paper selects the variance of the arrival error The optimization objective for the comprehensive punctuality rate of bus lines is defined as (4). It is evident that there is a constraint that the guidance speed must satisfy at this time:K, are directly linked to the set of guidance speeds This article analyzes the bus stop times at intersections during the bus operation. The stop times, denoted as We use the round-up function to obtain the bus stop state Therefore, we can determine the objective of minimizing the stop times at intersections along the entire bus route, as well as the constraints that the guiding speed must meet:Each road section is associated with a specific guidance speed, and the guidance speeds vary along the entire bus route. However, if there is a significant difference in guidance speeds between road sections, it can lead to a poor driving experience or increase the risk of traffic accidents. To ensure a balanced distribution of bus speeds, it is essential to optimize and analyze the discrepancies in guidance speeds across each road section.We can determine the guide speeds of the route based on the above analysis; we obtain the balanced distribution speed control scheme by the variance calculation.Algorithm 1 Lagrangian multiplier method.VInput I: Guide speed of each section Output I: Optimization results 1: According to (4), determine the first-level optimization goals and constraints:3: Solve the speed combination 6: Solve the speed combination According to (4), (6) and (7), the solution to the optimal speed guidance is essentially a multi-objective hierarchical optimization problem with inequality constraints. The first-level objective is to achieve a comprehensive punctuality rate that ensures the bus arrives on time. The second-level objective is to minimize stop times at intersections, optimizing the overall bus operation service level while still meeting the requirement of timely arrivals at stations. The third-level objective focuses on achieving a balance in guide speeds, reducing speed discrepancies among road sections, and enhancing bus operation stability while maintaining punctual arrivals and non-stop intersections. Combined with the research in the literature ,32,33, tThe setting of the bus schedule involves coordinating the balance of bus service levels and bus operating costs ,35. Bus i is The primary reason for the retention of passengers who are waiting at the platform is the mismatch between the free load of the bus and the number of passengers who are waiting. Considering that there is an upper limit of It can be found from (8) that the bus departure interval is the main factor affecting the number of passengers waiting at the platform. Moreover, the number of passengers on the bus N-th bus stations are:Thus, it can be seen that the route parameters of the buses at the Therefore, from (9), we can see that the bus departure time interval is directly related to the number of people retained on the platform and the free load of the bus line. Moreover, the ideal state is Algorithm 2 Genetic algorithm.Input I: Output I: Optimal target values 1: 2: Determine the encoding method and use the real number encoding method.3: Determine the individual evaluation method; the fitness function is (10).4: Design a genetic operator, where the selection operation uses a proportional selection operator, the crossover operation uses a single point crossover operator, and the mutation operation uses a basic bit mutation operator.5: Determine the operating parameters of genetic algorithm endWe use the genetic algorithm to obtain the optimal bus departure time interval, which is introduced in Algorithm 2. It is important to note that in the actual application process, based on the findings of reference , which iThe travel time of the entire bus line is determined by the difference in the arrival time between the first and last bus. According to (1), the travel time From reference , we knowIn order to ensure that the overall bus line speed trend is stable, the average line speed was selected to reflect the overall trend; the optimized average line speed should not be lower than the speed before optimization. Therefore, its critical state can be determined to be i, i.i is adjusted to:The station schedule is based on a minimum unit of measurement of minutes. Thus, the station schedule time To test the effectiveness and practicality of the proposed method, an experimental site was selected, namely the bus line in the north and south districts of a science city in Beijing. This bus line has a total length of approximately 4.5 km and consists of 9 bus stations and 8 signal-controlled intersections. The distribution of these stations and intersections is presented in The operation time of the bus line is from 6:30–19:00, which is divided into three periods: morning peak (6:30–9:00), flat peak (9:00–16:30), and evening peak (16:30–19:00); in the morning and evening peaks, the departure time interval is 15 min, in the flat peak, the departure time interval is 30 min. Each platform is equipped with passenger flow detection equipment to monitor the arrival of passengers at the platform in real time. Each intersection signal controller is equipped with a vehicle–road collaborative information interaction device, which can realize the real-time interaction between the intersection signal timing information and the vehicle driving status information.The vehicles were equipped with card-swiping equipment, speed guidance equipment, and communication devices. These technologies enable real-time interaction with the control center and allow for the collection of passengers’ card data and GPS track data. Moreover, the terminal could push the speed guidance information in real time, which could better carry out the application and implementation of the method in this paper. Moreover, a speed priority coordination-control system of dedicated bus lanes based on passenger demand was developed. The control system structure is shown in Using the bus departures from the north district to the south district as an example, the departures in the morning peak period include 10 shifts (from the 1st shift to the 10th shift); in the flat peak period, there are 15 shifts (from the 11th shift to the 25th shift); in the evening peak period, there are 11 shifts (from the 26th shift to the 36th shift). The total number of shifts in a day is 36. As illustrated in In the optimization process of the bus comprehensive punctuality rate, this paper considers the three indicators of punctual arrival times, the balance of station arrivals, and the mean error of station arrivals to be equally important. Therefore, the weighting factors are set as The duration of the test period is 90 working days, with the first 30 working days serving as the baseline test for the state of the bus lines before optimization. The subsequent 30 working days involve testing the implementation of the speed guidance strategy under the existing schedule state. The last 30 working days focus on testing the state of the bus lines after the optimization of the bus line schedule.Under stable demand and considering the average speed of the road sections, we conducted an optimization of the bus operation schedule by analyzing the arrival and retention of the platform passenger flow over a period of 60 working days. By comparing the bus schedules before and after optimization, it can be observed that the travel time during the morning and evening peak periods has reduced from 28 min to 20 min after optimization. Similarly, the travel time during the flat peak period has decreased from 20 min to 12 min. This indicates that the optimization of the timetable has led to an improvement in the efficiency of bus operations. Additionally, the comparative analysis of schedule optimization and route passenger flow retention is depicted in In order to quantitatively evaluate the control effect of the dedicated bus coordination-control system at each station and road section in each time period, we collected information on the arrival time, travel time, stop times, and bus carrying capacity at each station for each shift. These data were collected under the demand of bus operation schedule optimization and the control objective of punctual arrival. We conducted a quantitative analysis to compare the effects of the method before and after implementation by comparing bus line travel times, intersection stop times, punctual arrival rates, and other indicators.There was a significant fluctuation in the average travel time of each shift before the system optimization, as shown in The analysis results of the average arrival punctuality rate of each shift are shown in In order to optimize punctuality and the supply–demand service level of dedicated bus lanes, this paper proposes a hierarchical multi-objective optimization model with two layers: speed optimization and schedule optimization. The speed optimization layer involves mathematical modeling of bus operations, a multi-objective speed-decision-making model, and the Lagrange multiplier method for speed optimization. The schedule optimization layer involves optimizing bus departure intervals and station schedules. By optimizing these layers, the goals of operational reliability, comfort, and supply–demand balance were achieved. Practical scenario tests show that the proposed method is effective at improving punctuality and the supply–demand balances. This article presents a fundamental and comprehensive optimization framework to tackle the challenges of weak overall effectiveness in arterial bus priority, considering multiple factors and constraints, and providing a theoretical innovative algorithm for public transportation control. The framework guarantees the punctuality rate and passenger service level of buses and can be further combined with signal priority strategies at intersections, vehicle-to-vehicle communication technology, vehicle–road coordination technology, and collaborative scheduling among vehicles in subsequent research and applications. Its universality ensures adaptability in diverse scenarios and provides reliable support for scholars, managers, and policymakers.