Manual Process Research Articles

Optimizing Swim Pose Estimation and Performance Analysis Using AI and Predictive Analytics

Swim position estimation and performance analysis are critical for improving training outcomes and minimizing injury rates among swimmers of all skill levels. Traditional approaches rely heavily on manual video processing, which is timeconsuming and prone to human error, as well as biomechanical evaluations. Recent advances in artificial intelligence (AI) and predictive analytics have created new prospects for automating and improving these operations. The goal of this project is to create an artificial intelligence-powered swim position evaluation system that utilizes machine learning and computer vision techniques. The device monitors swimmers' activities in real time and gives exact feedback on technique, posture, and stroke efficiency. Predictive analytics improves this technique by predicting future trends and potential areas for development based on performance indicators and historical data. To provide precise, non-invasive human movement tracking in water, the proposed system employs biomechanical studies, deep learning models, and 3D pose estimation techniques

Determinants Of Artificial Intelligence Adoption In Accounting Among Malaysia Small And Medium-Sized Enterprises

Malaysia Small and Medium-Sized Enterprises (MSMEs) are still in the early phases of adopting artificial intelligence (AI) to transform manual or conventional processes into ones that are supported by AI technology. Most MSMEs have yet to fully grasp the essence of digitalization as a general movement in corporate strategy and work processes to improve decision-making, boost efficiency, and discover untapped possibilities. The purpose of this research is to examine the determinants of AI adoption in accounting among MSMEs. The independent variables are perceived ease of use, management support, organizational readiness, government support, and external pressure. The dependent variable is AI adoption in accounting among MSMEs. This research applied a quantitative method by using a questionnaire consisting of 24 questions. The study used a convenience sampling method, with a sample size of 200 respondents from MSMEs. A total of 150 samples are valid. The collected data was analyzed using SPSS. The collected information was assessed using demographic descriptive analysis, data descriptive analysis, normality test, validity analysis, reliability test, correlation analysis, and regression analysis. The findings indicated that the adoption of AI in accounting among MSMEs is positively influenced by perceived ease of use, management support, organizational readiness, government support, and external pressure. The results and suggestions will be advantageous for SMEs looking to adopt AI in their accounting functions. Additionally, this research proposes a methodology and framework to assist SMEs owners and management in comprehending the determinants that drive significant AI adoption in accounting.

Foot Reflexology for Managing Menopausal Symptoms in Women: A Systematic Review and Meta-Analysis

Background: Menopause can cause a variety of unpleasant symptoms, such as hot flashes, night sweats, mood swings, and sleep disturbances. Although hormone replacement therapy is frequently utilized to help manage these issues, long-term use of this treatment approach carries certain potential risks. Objective: This study aims to examine the effects of foot reflexology, a type of massage therapy that focuses on applying pressure to specific reflex points on the feet, on menopausal symptoms (primary outcome) and anxiety, fatigue, and depression (secondary outcomes). Methods: A systematic electronic search for relevant studies was conducted in PubMed, Scopus, Web of Science, the Cochrane Library, SID, Magiran, and Google Scholar until September 21, 2023. In addition to the electronic database searches, manual searches were also performed. This manual search process included scanning the reference lists of the included studies and handsearching key journals in the topic area. The risk of bias was assessed via the RoB 2.0 and ROBIN-I tools. Heterogeneity was evaluated via the I² statistic, and in cases of high heterogeneity, a random effects model was used instead of a fixed effects model. Subgroup analyses were performed for the primary outcome on the basis of menopausal symptoms (vasomotor/psychosocial/physical/sexual). The certainty of evidence was assessed via the GRADE approach. Results: A total of 186 articles were retrieved from database searches, and an additional 9 articles were identified through manual searches. Ultimately, 8 studies were included in the systematic review, and 6 studies were included in the meta-analysis. The meta-analysis revealed that foot reflexology had a significant effect on menopausal symptoms, with an average reduction of 3.41 points on the associated measurement scale (95% confidence interval (CI): -4.81 to -2.01; low-certainty evidence). It also significantly reduced fatigue, with an average reduction of 5.84 points (95% CI: - 11.61 to -0.06; very low-certainty evidence). However, there was no significant impact on anxiety (SMD: -0.89, 95% CI: -1.96--0.18; very low-certainty evidence) or depression (SMD: -0.73, 95% CI: -1.80--0.34; very low-certainty evidence) compared with the control group. Conclusion: The present study demonstrated that the use of foot reflexology as a nonhormonal and noninvasive intervention led to a significant reduction in menopausal symptoms and fatigue among women. Given the limited number of studies available in this area and the low to very low-level certainty of the evidence for the desired outcomes, further research with appropriate methodologies to ensure accurate conclusions in this field is strongly recommended.

An automatic hybrid mesh generation method for combustion fluid dynamics simulations

PurposeAutomatic mesh generation is still a challenge problem for combustion fluid dynamics simulations because of the high-quality requirement and complexity of geometries. This paper aims to find an efficient automatic analysis model creation and mesh generation method to save the time for pre-processing in numerical simulations.Design/methodology/approachBased on the previous work, we explore effective model processing and mesh generation methods from practical engineering applications. Considering the automation and high quality requirement, we construct an automatic mesh generation procedure for combustion fluid dynamics problems.FindingsThe numerical results show that the procedure we proposed in this paper can automatically generate high quality mesh for combustion fluid dynamics simulations. The strategy of fluid model construction is time-saving and with high-precision. The mesh generation method in our procedure is automatic and efficient.Practical implicationsThe procedure proposed in this paper is applicable to the practical engineering application model, such as aircraft simulation, aeroengine simulation and so on. The procedure has been integrated into an numerical simulation software.Originality/valueThe method proposed in this paper has very practical application value. It can be used in the practical application and saves a lot of manual processing time for numerical stimulation specialists.

Inovasi Sistem Kontrol Elektronik Berbasis Arduino untuk Proses Penekanan Adonan Kue Tradisional

The aging of Traditional production of Onthok Yuyu, a local delicacy, encountered challenges in efficiency and consistency due to its manual process, which affected the product's uniformity and overall productivity. This study aimed to design and implement an automated dough-cutting system using an Arduino Uno-based controller combined with Pulse Width Modulation (PWM) to address these issues. The system used an Arduino Uno to manage motor speed and cutting precision, utilizing a programmable keypad for setting time intervals and controlling dough compression and cutting stages. The experimental setup included a DC motor to drive the cutting blades, a conveyor motor, and a display for real-time process monitoring.Key findings revealed that the automated system achieved consistent cutting lengths with minimal variance, improving both the efficiency and reliability of the production process compared to traditional methods. The system effectively managed motor speed within specified parameters, resulting in uniform dough portions while reducing manual labor. Additionally, the study showed that the Arduino-controlled setup maintained operational stability under various loads, making it adaptable for small-scale production.This research demonstrated the potential for Arduino-based automation to enhance production quality in traditional food processing. It offers implications for similar small and medium-sized enterprises (SMEs) aiming to modernize processes, reduce dependency on manual labor, and achieve more consistent output. Future studies are recommended to explore further optimization in system scalability and adaptability for diverse product types

Quality assessment of artisanal palm oil from smallholders in the department of Man, western region of Côte d'Ivoire

In the western region of Côte d'Ivoire, more particularly from the department of Man, artisanal crude palm oil enjoys a reputation for quality among consumers and is the subject of a developed trade. To guarantee consumers quality palm oil, it is crucial to register this product as a Geographical Indication artisanal palm oil. This study aims to evaluate the physicochemical properties of this artisanal palm oil to better understand his quality. A descriptive survey of palm oil processing was conducted among 200 smallholders in 10 localities in the department of Man, Western region of Côte d'Ivoire. Then, samples were collected from the producers and were taken to the laboratory for analysis. The physicochemical properties of CPO samples were determined using standard analytical method. The results showed that manual processing and screw hand press methods were used for palm oil extraction. About 58% of the processors have 11-20 years’ experience in oil palm processing and have no formal education (48.2%). Concerning the physicochemical quality of artisanal palm oil producing, the lipid oxidation, carotenoids contents, DOBI, slip melting point, iodine and saponification indexes were relatively conformed to the standard while FFA, moisture and impurities content were different. These results demonstrate a need for monitoring of artisanal palm oil production by smallholders in the sector who should adopt good agricultural practices.

Rancangan Aplikasi Kasir Toko Kelontong Berbasis Website Menggunakan Metode Waterfall

Digitalization in the business world has become an urgent need to enhance efficiency andproductivity, particularly for grocery stores that still rely on manual methods for managingtransactions and inventory. Dependence on manual processes often leads to various issues, such asinaccurate record-keeping, difficulties in tracking inventory, and suboptimal customer service. Toaddress these challenges, this study develops a web-based cashier application designed to automatetransactions, inventory management, and sales reporting. The application is expected to facilitatestore owners in managing daily operations while improving data accuracy and customersatisfaction. The development of the application follows the Waterfall method, a structured andlinear software development approach. This method involves stages of requirements analysis,system design, implementation, testing, and maintenance. The outcome of this study is a webbasedcashier application capable of addressing the challenges faced by grocery stores, withfeatures that support operational efficiency and flexibility in monitoring business activities. Thisapplication also offers long-term benefits in enhancing the competitiveness of grocery stores in thedigital era.

The golden batch-driven root cause analysis for anomalies in bioreactor fermentation process

Bioreactors are essential for the production of biopharmaceuticals and bioproducts, requiring continuous monitoring to ensure quality assurance. Manual processes in manufacturing plants often lead to anomalies such as out-of-trend and out-of-spec incidents, necessitating extensive root cause analysis that typically takes 2–8 weeks. This paper introduces an innovative methodology that uses the golden batch profile as a benchmark to identify deviations and root causes in subsequent industrial batches. The methodology involves normalizing the data and calculating the variances of a specified batch from the golden batch profile. By examining the contribution of each critical process parameter to these variances, the study highlights their importance in root cause analysis. The application of this methodology to the IndPenSim dataset demonstrated its effectiveness by significantly reducing false positives and negatives compared to traditional PCA-based methods. Emphasis on the deviations of critical quality attributes and critical process parameters from the specified batch compared to the golden batch profile offers valuable insights into industrial process analysis. This approach not only enhances anomaly detection accuracy but also improves the efficiency and reliability of biopharmaceutical and bioproduct manufacturing processes.

Automatic Scheduling Method for Customs Inspection Vehicle Relocation Based on Automotive Electronic Identification and Biometric Recognition

This study presents an innovative automatic scheduling method for the relocation of customs inspection vehicles, leveraging Vehicle Electronic Identification (EVI) and biometric recognition technologies. With the expansion of global trade, customs authorities face increasing pressure to enhance logistics efficiency. Traditional vehicle scheduling often relies on manual processes and simplistic algorithms, resulting in prolonged waiting times and inefficient resource allocation. This research addresses these challenges by integrating EVI and biometric systems into a comprehensive framework aimed at improving vehicle scheduling. The proposed method utilizes genetic algorithms and intelligent optimization techniques to dynamically allocate resources and prioritize vehicle movements based on real-time data. EVI technology facilitates rapid identification of vehicles entering customs facilities, while biometric recognition ensures that only authorized personnel can operate specific vehicles. This dual-layered approach enhances security and streamlines the inspection process, significantly reducing delays. A thorough analysis of the existing literature on customs vehicle scheduling identifies key limitations in current methodologies. The automatic scheduling algorithm is detailed, encompassing vehicle prioritization criteria, dynamic path planning, and real-time driver assignment. The genetic algorithm framework allows for adaptive responses to varying operational conditions. Extensive simulations using real-world data from customs operations validate the effectiveness of the proposed method. Results indicate a significant reduction in vehicle waiting times—up to 30%—and an increase in resource utilization rates by approximately 25%. These findings demonstrate the potential of integrating EVI and biometric technologies to transform customs logistics management. Additionally, a comparison against state-of-the-art scheduling algorithms, such as NSGA-II and MOEA/D, reveals superior efficiency and adaptability. This research not only addresses pressing challenges faced by customs authorities but also contributes to optimizing logistics operations more broadly. In conclusion, the automatic scheduling method presented represents a significant advancement in customs logistics, providing a robust solution for managing complex vehicle scheduling scenarios. Future research directions will focus on refining the algorithm to handle peak traffic periods and exploring predictive analytics for enhanced scheduling optimization. Advancements in the intersection of technology and logistics aim to support more efficient and secure customs operations globally.

Promoting audit transparency in higher education through an internal academic quality audit application

This study aims to design and develop a web-based application for Internal Quality Audit at Universitas Ibnu Sina, replacing the conventional audit process which is currently in use. The previous manual audit process resulted in inefficiencies and increased the risk of errors. Key challenges identified include limitations in human resources, time, and funding, as well as the low effectiveness of the audit process. In order to address these issues, the proposed information system facilitates the distribution of online checklists and document uploads, thereby expediting the verification and reporting audit results. This research adopts the System Development Life Cycle (SDLC) approach in its development, utilizing methods that include use case diagrams, activity diagrams, sequence diagrams, and database design. These methods enhance interaction and workflow within the audit team. Testing results indicate that the system functions to the expected specifications. Furthermore, this study demonstrates that implementing a web-based system can accelerate reporting, improve the structure and documentation of audits, and support efforts to enhance academic quality at the university. Implementing this system is expected to increase efficiency and accuracy in the academic quality audit process at Universitas Ibnu Sina.

Perancangan Aplikasi Sistem Absensi Guru Dan Karyawan Berbasis Web Sekolah Menengah Kejuruan

The development of information technology is quite extensive and global and has had a changing impact on all aspects ofhuman life. In the field of education, information technology is very necessary in order to support the performance ofeducation so that it can be useful in building an intelligent and competitive generation, one of which is at Global PersadaVocational School. Based on observations, Global Persada Vocational School does not yet have a teacher and employeeattendance system for processing teacher absence data. The obstacles that are often encountered in manual processing ofabsentee data are that it is very slow, ineffective, and makes it difficult for school principals to manage and check data onteachers who are present, and is even prone to losing absenteeism recap data. The aim of this research is to design andbuild a Web-based attendance system for processing teacher and employee attendance data for Global PersadaVocational School to make it easier to process teacher and employee attendance data. In building the teacher andemployee attendance system, the author used the SDLC method with the Waterfall model in designing the system andproduced a system with a simple interface that can help present accurate and relevant value data processing information.

Mobile Platform for Building Applications at the Integrated Services Office Medan City

The advancement of computer technology has developed rapidly and has been applied in various aspects of life. At that time, computers were considered one of the most efficient tools in supporting technological development. At the Office of Investment and One-Stop Integrated Services (OI-OIS) in Medan City, the process of applying for Building Permits (BP) was still conducted manually and had not been computerized, resulting in slow and inefficient licensing processes. Applicants were required to fill out forms manually and deliver physical documents to the village and sub-district offices, which extended processing times. Furthermore, application reports were often piled up, hindering prompt and accurate handling. The lack of efficiency in managing applicant data further exacerbated the situation and slowed down the completion of applications. Therefore, an integrated digital system was needed to expedite and facilitate the submission and management of IMB applications. This research aimed to develop an Android-based Information System for Building Permit Applications at DPMPTSP in Medan City using the R&D (Research and Development) method. It was hoped that this application would make it easier for staff to manage IMB application data. The research results indicated that the development of the Android-based Information System for Building Permit Applications successfully addressed the issues encountered in the previously manual IMB application process. This application expedited and simplified the filling out and submission of applications, eliminating the need for applicants to deliver physical documents. Additionally, the management of applicant data became more efficient and computerized, reducing the backlog of reports and speeding up the verification process.

Impact of an enterprise controlled substance management system on labor and inventory costs.

In an effort to expedite the publication of articles, AJHP is posting manuscripts online as soon as possible after acceptance. Accepted manuscripts have been peer-reviewed and copyedited, but are posted online before technical formatting and author proofing. These manuscripts are not the final version of record and will be replaced with the final article (formatted per AJHP style and proofed by the authors) at a later time. To evaluate the impact of an enterprise controlled substance (ECS) management system with integration of analytics software on labor requirements and inventory cost within a health-system pharmacy. A prospective, pre-post observational study was designed to assess the impact of implementing a solution that connects disparate systems with the integration of analytics software. Three study modules were implemented over approximately 18 months. The intervention consisted of implementation of new CS vaults, a centralized server, an automated central medication inventory system, and inventory optimization analytics software. The number of transactions and time spent on CS reports were compared before and after implementation to determine labor and inventory efficiencies. Both of the study facilities had a decrease in CS daily stockouts and an increase in inventory turns compared to baseline, while the total number of transactions (vends) at the central vault and decentralized dispensing cabinets increased. The addition of analytics allowed for establishment of informed changes to periodic automated replenishment levels. Additionally, both facilities saw a reduction in the number of expired medications, and there was subsequently a reduction in the total reverse distributor costs. Finally, both facilities had a reduction in the amount of time spent on manual tasks associated with reconciling and managing discrepancies. An inventory management system integrated with an advanced analytics tool provided a reduction in the time spent on receiving, storing, and reconciling CS records while the number of transactions increased. The ECS solution enhanced the visibility of the chain of custody while closing the loop between reporting and receiving inventory, eliminating or reducing the frequency of manual processes.

Transformation of Traditional Corporate Tax Planning into AI-Driven Corporate Tax Planning

Corporate tax planning has traditionally relied on manual processes and expert judgment to minimize tax liabilities. However, the rise of artificial intelligence (AI) presents new opportunities to transform these practices through automation, real-time analysis, and predictive capabilities. To conduct this research, a comprehensive review of academic literature, case studies, and existing AI tools in tax planning was carried out. The proposed AI-based tax planning model was developed based on these findings and validated through simulations and expert feedback. The study also examined the process of integrating AI systems with existing corporate tax structures to assess the feasibility and effectiveness of implementation. Results show that AI-driven corporate tax planning significantly enhances accuracy and efficiency compared to traditional methods. The AI model provides real-time analysis and predictive insights, enabling businesses to optimize tax strategies while ensuring compliance with evolving regulations. Tax authorities also benefit from AI implementation, gaining more transparent and equitable tax structures, along with stronger supervision capabilities. The proposed model can be developed into customized software tailored to individual companies, seamlessly integrating with existing corporate systems. These findings indicate that AI has the potential to revolutionize corporate tax planning by streamlining tax management and optimizing outcomes. For businesses, AI enables personalized tax strategies that adapt to specific data and regulations, improving both compliance and long-term tax optimization.

The Impact of International Classification of Disease-Triggered Prescription Support on Telemedicine: Observational Analysis of Efficiency and Guideline Adherence.

Integrating decision support systems into telemedicine may optimize consultation efficiency and adherence to clinical guidelines; however, the extent of such effects remains underexplored. This study aims to evaluate the use of ICD (International Classification of Disease)-coded prescription decision support systems (PDSSs) and the effects of these systems on consultation duration and guideline adherence during telemedicine encounters. In this retrospective, single-center, observational study conducted from October 2021 to March 2022, adult patients who sought urgent digital care via direct-to-consumer video consultations were included. Physicians had access to current guidelines and could use an ICD-triggered PDSS (which was introduced in January 2022 after a preliminary test in the preceding month) for 26 guideline-based conditions. This study analyzed the impact of implementing automated prescription systems and compared these systems to manual prescription processes in terms of consultation duration and guideline adherence. This study included 10,485 telemedicine encounters involving 9644 patients, with 12,346 prescriptions issued by 290 physicians. Automated prescriptions were used in 5022 (40.67%) of the consultations following system integration. Before introducing decision support, 4497 (36.42%) prescriptions were issued, which increased to 7849 (63.57%) postimplementation. The physician's average consultation time decreased significantly to 9.5 (SD 5.5) minutes from 11.2 (SD 5.9) minutes after PDSS implementation (P<.001). Of the 12,346 prescriptions, 8683 (70.34%) were aligned with disease-specific international guidelines tailored for telemedicine encounters. Primary medication adherence in accordance with existing guidelines was significantly greater in the decision support group than in the manual group (n=4697, 93.53% vs n=1389, 49.14%; P<.001). Most of the physicians adopted the PDSS, and the results demonstrated the use of the ICD-code system in reducing consultation times and increasing guideline adherence. These systems appear to be valuable for enhancing the efficiency and quality of telemedicine consultations by supporting evidence-based clinical decision-making.

Automated construction site layout design system for prefabricated buildings using transformer based conditional GAN

Construction site layout plans (CSLP) are crucial for efficient prefabricated construction project management. Traditional manual design process is costly and time-consuming, while optimization methods heavily depend on expert knowledge. Recent advancements in deep generative models present promising alternatives. However, their application to the generation of prefabricated construction site layouts is hindered by several challenges, including limited datasets, significant overlap between facilities, and the necessity to generate layouts based on fixed facilities with specific attributes such as minimal transportation costs. These challenges constrain the efficacy and applicability of the generated layouts. To address these issues, this study introduces an innovative automated generative design system for prefabricated construction site layouts, leveraging a novel Transformer-based conditional generative adversarial network (GAN). The data preparation module of the system collects and augments layout data for training. The CSLGAN module is designed to generate layouts that conform to spatial constraints and desired attributes, with minimal facility overlap. Furthermore, this study establishes benchmarks in terms of model capacity and specialized performance metrics. Extensive experiments demonstrate the effectiveness of the proposed system in automated construction site layout generation.

Research on Network Security Protection Technology Based on P2AEDR in New Low-Voltage Control Scenarios for Power IoT and Other Blockchain-Based IoT Architectures.

In the construction of new power systems, the traditional network security protection mainly based on boundary protection belongs to static defense and still relies mainly on manual processing in vulnerability repair, threat response, etc. It is difficult to adapt to the security protection needs in large-scale distributed new energy, third-party aggregation platforms, and flexible interaction scenarios with power grid enterprise systems. It is necessary to conduct research on dynamic security protection models for IoT and other Blockchain-based IoT architectures. This article proposes a network security comprehensive protection model P2AEDR based on different interaction modes of cloud-edge interaction and cloud-cloud interaction. Through continuous trust evaluation, dynamic access control, and other technologies, it strengthens the internal defense capabilities of power grid business, shifting from static protection as the core mode to a real-time intelligent perception and automated response mode, and ultimately achieving the goal of dynamic defense, meeting the security protection needs of large-scale controlled terminal access and third-party aggregation platforms. Meanwhile, this article proposes a dynamic trust evaluation algorithm based on deep learning, which protects the secure access and use of various resources in a more refined learning approach based on the interaction information monitored in the system. Through experimental verification of the dynamic trust evaluation algorithm, it is shown that the proposed model has good trust evaluation performance. Therefore, this research is beneficial for trustworthy Power IoT and other Blockchain-based IoT architectures.

A Rapid Analysis Method for Determination of Hydrocarbon Types in Aviation Turbine Fuel by Gas Chromatography-Mass Spectrometry.

A rapid analysis method for determination of hydrocarbon types in aviation turbine fuel was investigated in this study. A kind of reversible adsorption material packed as an unsaturated trap was used to separate saturated hydrocarbons and unsaturated hydrocarbons in the GC-MS system. No manual process or organic reagent was needed during the entire analysis process. The contents of 13 kinds of hydrocarbons, including paraffins, monocycloparaffins, dicycloparaffins, tricycloparaffins, monoolefins, cycloolefins, alkylbenzenes, CnH2n-8 aromatics, CnH2n-10 aromatics, naphthalenes, CnH2n-14 aromatics, CnH2n-16 aromatics, and CnH2n-18 aromatics were calculated by the characteristic mass fragments detected by MS with the modified matrices of ASTM D2425. The overall analysis time was less than 10 min. The precision of this test method has been conducted with 8 laboratories attended and 16 samples analyzed. The performance of this new method was demonstrated by comparing the results tested by ASTM D1319, ASTM D2425, and ASTM D6379. This rapid analysis method can provide hydrocarbon compositions of aviation turbine fuels or other liquid hydrocarbon samples within the same distillation range.

Saudi Learner Translation Corpus: The design and compilation of an English-Arabic learner translation corpus.

This article introduces the Saudi Learner Translation Corpus (SauLTC), an innovative multi-version English-Arabic parallel corpus featuring part-of-speech tagging. We describe the corpus parameters and compilation process and explain how textual processing and sentence alignment are conducted. The participants include 366 student translators, 48 instructors, and 23 alignment verifiers. The corpus provides access to two target versions of every ST to allow the detection of the changes in the translation and revision processes from the initial to the final draft. The translations were collected over three years, yielding 5,160,386 tokens. The metadata of 23 sentence alignment verifiers were added to the analysis as a unique variable to investigate individual differences in the manual verification process. This unidirectional corpus can be used to identify student translators' strategies and errors in translation and analyze the efficacy of instructors' feedback. Furthermore, it is accessible via an application and a website. It provides translation teachers and researchers with a database that can help develop corpus-based and corpus-driven teaching materials.

Large language model-based optical network log analysis using LLaMA2 with instruction tuning

The optical network encompasses numerous devices and links, generating a significant volume of logs. Analyzing these logs is significant for network optimization, failure diagnosis, and health monitoring. However, the large-scale and diverse formats of optical network logs present several challenges, including the high cost and difficulty of manual processing, insufficient semantic understanding in existing analysis methods, and the strict requirements for data security and privacy. Generative artificial intelligence (GAI) with powerful language understanding and generation capabilities has the potential to address these challenges. Large language models (LLMs) as a concrete realization of GAI are well-suited for analyzing DCI logs, replacing human experts and enhancing accuracy. Additionally, LLMs enable intelligent interactions with network administrators, automating tasks and improving operational efficiency. Moreover, fine-tuning with open-source LLMs protects data privacy and enhances log analysis accuracy. Therefore, we introduce LLMs and propose a log analysis method with instruction tuning using LLaMA2 for log parsing, anomaly detection and classification, anomaly analysis, and report generation. Real log data extracted from the field-deployed network was used to design and construct instruction tuning datasets. We utilized the dataset for instruction tuning and demonstrated and evaluated the effectiveness of the proposed scheme. The results indicate that this scheme improves the performance of log analysis tasks, especially a 14% improvement in exact match rate for log parsing, a 13% improvement in F1-score for anomaly detection and classification, and a 23% improvement in usability for anomaly analysis, compared with the best baselines.