Final Result Research Articles

Bigdata clustering and classification with improved fuzzy based deep architecture under MapReduce framework

The current state of economic, social ideas, and the advancement of cutting-edge technology are determined by the primary subjects of the contemporary information era, big data. People are immersed in a world of information, guided by the abundance of data that penetrates every element of their surroundings. Smart gadgets, the IoT, and other technologies are responsible for the data’s explosive expansion. Organisations have struggled to store data effectively throughout the past few decades. This disadvantage is related to outdated, expensive, and inadequately large storage technology. In the meanwhile, large data demands innovative storage techniques supported by strong technology. This paper proposes the bigdata clustering and classification model with improved fuzzy-based Deep Architecture under the Map Reduce framework. At first, the pre-processing phase involves data partitioning from the big dataset utilizing an improved C-Means clustering procedure. The pre-processed big data is then handled by the Map Reduce framework, which involves the mapper and reducer phases. In the mapper phase. Data normalization takes place, followed by the feature fusion approach that combines the extracted features like entropy-based features and correlation-based features. In the reduction phase, all the mappers are combined to produce an acceptable feature. Finally, a deep hybrid model, which is the combination of a DCNN and Bi-GRU is used for the classification process. The Improved score level fusion procedure is used in this case to obtain the final classification result. Moreover, the analysis of the proposed work has proved to be efficient in terms of classification accuracy, precision, recall, FNR, FPR, and other performance metrics.

MLatom Software Ecosystem for Surface Hopping Dynamics in Python with Quantum Mechanical and Machine Learning Methods.

We present an open-source MLatom@XACS software ecosystem for on-the-fly surface hopping nonadiabatic dynamics based on the Landau-Zener-Belyaev-Lebedev algorithm. The dynamics can be performed via Python API with a wide range of quantum mechanical (QM) and machine learning (ML) methods, including ab initio QM (CASSCF and ADC(2)), semiempirical QM methods (e.g., AM1, PM3, OMx, and ODMx), and many types of ML potentials (e.g., KREG, ANI, and MACE). Combinations of QM and ML methods can also be used. While the user can build their own combinations, we provide AIQM1, which is based on Δ-learning and can be used out-of-the-box. We showcase how AIQM1 reproduces the isomerization quantum yield of trans-azobenzene at a low cost. We provide example scripts that, in dozens of lines, enable the user to obtain the final population plots by simply providing the initial geometry of a molecule. Thus, those scripts perform geometry optimization, normal mode calculations, initial condition sampling, parallel trajectories propagation, population analysis, and final result plotting. Given the capabilities of MLatom to be used for training different ML models, this ecosystem can be seamlessly integrated into the protocols building ML models for nonadiabatic dynamics. In the future, a deeper and more efficient integration of MLatom with Newton-X will enable a vast range of functionalities for surface hopping dynamics, such as fewest-switches surface hopping, to facilitate similar workflows via the Python API.

A LIFEBOAT FOR FAILED NASAL RECONSTRUCTIONS: THE SUPRACLAVICULAR-SUBMENTAL SANDWICH FLAP

Many failures in total or subtotal nasal reconstructions result from an underestimation of the amount of skin required for an adequate result, especially for sufficient lining. Such planning errors usually lead to poor results, with exposure of structural grafts, infection, scar retraction, airway obstruction and finally loss of projection and shape of the reconstructed nose. Reconstruction options for cases in which previous attempts have failed are always limited, as well as in cases of trauma or burns affecting the soft tissues of the forehead and face. In such complex situations, one may employ free flaps or tissue expansion, but such resources may not be always available. We describe a technique indicated for salvage surgeries in patients whose previous nasal reconstruction have failed, allowing a generous amount of tissue transfer for the nasal region. The technique combines the use of supraclavicular and submental flaps, with simple execution, not requiring microsurgical skills or devices such as tissue expanders. Done in three stages, the described technique provides enough skin for a total nasal reconstruction. The final result is obtained after subsequent refinements, and the total number of procedures is equivalent to when more sophisticated techniques are employed, such as tissue expansion or microsurgery.

Adaptively learning visual model of surgical resection images via coarse-to-fine–grained strategy for pathological invasiveness prediction in indeterminate stage IA lung adenocarcinoma.

1563 Background: Indeterminate nodules with high risk of malignancy are suggested to receive video-assisted thoracoscopy biopsy. Frozen section assists surgeons to decide surgical strategies for stage IA lung adenocarcinoma (LUAD) intraoperatively, which is time-consuming and may misdiagnosis due to limited sampling and poor tissue quality. Methods: We prospectively enrolled stage IA LUAD patients underwent complete resection between June 2020 and September 2023 in Guangdong Provincial People's Hospital (GDPH), Affiliated Hospital of Guangdong Medical University, Meizhou People's Hospital. All the resected lung nodules were finally diagnosed as pre-invasive lesion (PIL), minimally invasive adenocarcinoma (MIA) or invasive lung adenocarcinoma (IAC) by FFPE pathological diagnosis. Images of the whole section of the lung nodules were taken by smartphones under natural lighting condition in operating theaters without shadowless surgical lights. Patients received preoperative anti-cancer therapy and with low-quality tumor section images were excluded. Images of nodules section, FS reports, FFPE diagnosis and clinical information were prospectively collected. Predictive artificial intelligence (AI) model was developed by a three-step process, which first predict the IAC and non-IAC region at coarse level, and identify fine level features through the high-risk region. The coarse and fine labels were then coordinated through risk ranking loss to reach a final predictive result. Results: We prospectively enrolled 1516 patients with 1638 indetermined lung nodules from preoperative chest CT following the inclusion and exclusion criteria, with 2438 images acquired from intraoperative nodule sections. The included patients have median age of 57 years old and 38.6% male patients. The pathological diagnosis for included lung nodules are 122 PIL nodules, 502 MIA nodules and 1014 stage IA LUAD. A multi-task artificial intelligence based on Coarse-to-Fine-Grained Strategy has been established to classify the indetermined lung nodules during the surgery. For binary classification, AI model reach an AUC of 0.86 in differentiation between IAC or non-IAC. For 3-classification level, AI model reach an AUC of 0.87 in differentiation between PIL, MIA or IAC. We further defined the IAC nodules into G1, G2 and G3 according to IASLC lung adenocarcinoma grading system. For 5-classification level, AI model reach an AUC of 0.86 in differentiation between PIL, MIA, IAC-G1, IAC-G2 and IAC-G3. Conclusions: Our AI models based on images of intraoperative surgical resection could effectively classify indetermined lung nodules, which can assist thoracic surgeons diagnose the nodules rapidly during the surging and decide the following surgical strategies. Clinical trial information: ChiCTR2300075999 .

Evaluation of social appearance concerns and satisfaction after orthognathic surgery

The main aim of orthognathic surgery is to provide a good occlusion and masticatory function and to achieve positive changes in facial aesthetics. The aim of this study was to determine the reasons behind patients' acceptance of orthognathic surgery and their expectations from this treatment, to assess whether their expectations were met, the change in their self-confidence and their satisfaction, and finally, to use the results obtained to inform future surgical procedures to increase patient satisfaction. The study was designed as a cross-sectional observational study and included 73 people treated with orthognathic surgery (surgery group) and 42 people with minimal crowding who did not require treatment (control group). The study found that the primary reason for requesting orthognathic surgery was to improve facial appearance and that patients had high expectations in this regard. Using the Social Appearance Anxiety Questionnaire and the Expectation and Satisfaction Questionnaire, 73.97 % of patients in our study said the treatment met their expectations and 90.41 % said they were satisfied with the overall results of the surgery. The study also highlighted the importance of surgeon-patient communication and the satisfaction of the patient's environment with the final result in determining patient satisfaction. In conclusion, since patients' high expectations of orthognathic surgery influence treatment success and patient satisfaction, it is important to know and manage patients' expectations before treatment and to have good patient-surgeon communication to increase patient satisfaction after surgery.

Students' Thinking Literacy Process in Mathematical Problem-Solving

This study explores and investigates the process of students’ mathematical thinking literacy in solving mathematical problems. This qualitative descriptive research used a test sheet consisting of one item adapted from the PISA mathematical problem model and interview guidelines, as the instrument. One male and two female participants were selected based on problem-solving characteristics. After that, task-based interviews were carried out to investigate the process of students’ mathematical thinking literacy. Data analysis was initiated by reviewing, reducing, and concluding all collected data to describe students’ mathematical thinking literacy in solving mathematical problems. The findings suggest that mathematical thinking literacy is initiated by understanding the problem (understanding), presenting the problem in a simpler form (specializing) to represent ideas, and creating patterns or relationships (generalizing). Based on the relationship between the problem, the subject predicts a solution (conjecturing) or determines the solution to the problem, re-checks (justifying) the problem-solving process to find a logical final result, and communicates the reasons (convincing) to convince the truth of the results obtained. The results of this study help teachers understand the process of students’ mathematical thinking literacy in solving mathematical problems so that it can be used to determine the appropriate strategy in the learning process to improve students' mathematical problem-solving abilities

A novel method for chronic obstructive pulmonary disease detection using comprehensive respiratory impedance and electrocardiogram-derived respiration

Currently, pulmonary function test is the only standard method for diagnosing COPD, but it often involves high testing costs, poor compliance, and a high dependence on patient cooperation. Other new detection methods, such as CT and FOT, also have disadvantages such as radiation exposure and high usage costs. Existing non-invasive COPD detection methods consider the impact of COPD on the lungs or heart, thus they can only roughly detect COPD in individuals, without the ability to grade COPD. Therefore, this study proposed a method for COPD detection using comprehensive respiratory impedance (CRI) and electrocardio-derived respiration (EDR) signals. The method measured both respiratory impedance and ECG synchronously, and the CRI and EDR signals were obtained through an algorithm. The effects of COPD on respiratory pattern were studied by taking CRI and EDR signal morphology as objects, from which characteristic parameters were extracted and input into the supervised classifier to fuse the information of COPD on the changes of respiratory signal morphology pattern in the heart and lungs. The final result is accurate detection of normal, mild COPD and moderate COPD. The experimental results of 46 subjects showed that the accuracy, sensitivity and specificity of CRI and EDR signal characteristics for COPD detection by different classifiers were up to 93.5 %, 90.5 % and 96.5 %, and the AUC values of different classifiers were all > 0.88. Therefore, the method can not only effectively distinguish COPD patients from healthy people, but also provide differentiated detection capabilities for different COPD clinical grades, providing strong support for the early diagnosis and treatment of COPD, and can be applied to the development of portable medical devices for the detection of COPD.

Prediction of total volatile basic nitrogen (TVB-N) in fish meal using a metal-oxide semiconductor electronic nose based on the VMD-SSA-LSTM algorithm.

The total volatile basic nitrogen (TVB-N) is the main indicator for evaluating the freshness of fish meal, and accurate detection and monitoring of TVB-N is of great significance for the health of animals and humans. Here, to realize fast and accurate identification of TVB-N, in this article, a self-developed electronic nose (e-nose) was used, and the mapping relationship between the gas sensor response characteristic information and TVB-N value was established to complete the freshness detection. The TVB-N variation curve was decomposed into seven subsequences with different frequency scales by means of variational mode decomposition (VMD). Each subsequence was modelled using different long short-term memory (LSTM) models, and finally, the final TVB-N prediction result was obtained by adding the prediction results based on different frequency components. To improve the performance of the LSTM, the sparrow search algorithm (SSA) was used to optimize the number of hidden units, learning rate and regularization coefficient of LSTM. The prediction results indicated that the high accuracy was obtained by the VMD-LSTM model optimized by SSA in predicting TVB-N. The coefficient of determination (R2), the root-mean-squared error (RMSE) and relative standard deviation (RSD) between the predicted value and the actual value of TVBN were 0.91, 0.115 and 6.39%, respectively. This method improves the performance of e-nose in detecting the freshness of fish meal and provides a reference for the quality detection of e-nose in other materials. © 2024 Society of Chemical Industry.

Antidiabetic Potential and Metabolite Profile of Leaf and Stem Extract of Castanopsis tungurrut (Blume) A.DC.

<p>Diabetes mellitus is a metabolic disorder caused by high blood sugar levels. One species in <em>the Castanopsis</em> genus is proven with a hypoglycemic effect. Therefore, the study aimed to discover the potential of <em>Castanopsis tungurrut</em> as an antidiabetic. Sample extraction, α-amylase inhibition, glucose diffusion analysis, GC-MS analysis, and molecular docking were applied in this study. Maceration of the leaf ethanol extract showed the highest yield value of 21.08%, while the stem extract was 14.04%. Leaf ethanol extract and stem ethyl acetate extract showed the highest inhibiting α-amylase activity with an inhibition value of 33.74%±1.54 and 34.45%±1.08 at 1 mg/mL concentration. The glucose entrapment assay showed that these two extracts could inhibit the diffusion of glucose in the dialysis bag. The final result was glucose concentration in dialysate for the two extracts of 0.114±0.001 mg/mL and 0.116±0.001 mg/mL which was lower than acarbose in 0.120±0.004 mg/mL. GC-MS analysis showed 6 metabolites in leaf ethanol extract and 22 metabolites in stems ethyl acetate extract from an alkane, salicylic, cinnamic, terpene, steroid, and fatty acid. Molecular docking resulting between the compounds with α-amylase enzymes complex showed γ-sitosterol and ß-bisabolene from <em>C. tungurrut</em> extract have the potential to be developed as an antidiabetic drug due to its good inhibitory activity with binding affinity values of -9.1 and -6.9 that considered better and quite close to acarbose as control of -7.7.</p>

Biomedical named entity recognition based on multi-cross attention feature fusion.

Currently, in the field of biomedical named entity recognition, CharCNN (Character-level Convolutional Neural Networks) or CharRNN (Character-level Recurrent Neural Network) is typically used independently to extract character features. However, this approach does not consider the complementary capabilities between them and only concatenates word features, ignoring the feature information during the process of word integration. Based on this, this paper proposes a method of multi-cross attention feature fusion. First, DistilBioBERT and CharCNN and CharLSTM are used to perform cross-attention word-char (word features and character features) fusion separately. Then, the two feature vectors obtained from cross-attention fusion are fused again through cross-attention to obtain the final feature vector. Subsequently, a BiLSTM is introduced with a multi-head attention mechanism to enhance the model's ability to focus on key information features and further improve model performance. Finally, the output layer is used to output the final result. Experimental results show that the proposed model achieves the best F1 values of 90.76%, 89.79%, 94.98%, 80.27% and 88.84% on NCBI-Disease, BC5CDR-Disease, BC5CDR-Chem, JNLPBA and BC2GM biomedical datasets respectively. This indicates that our model can capture richer semantic features and improve the ability to recognize entities.

DCSIAN: A novel deep cross-scale interactive attention network for fault diagnosis of aviation hydraulic pumps and generalizable applications

Channel attention (CA) has been wildly applied to enhance the diagnosis performance of multiscale convolution (MSC)-based diagnosis methods. Nevertheless, most of the existing CA modules only consider the internal local correlation among different channels within each scale feature, but ignore the global correlation among different scales, restricting further improvement. To address this issue, a novel deep cross-scale interactive attention network (DCSIAN) is developed to achieve accurate fault diagnosis for aviation hydraulic pumps under high-noise environments. Specifically, a novel cross-scale interactive attention module (CSIAM) is developed and introduced into MSC to learn complementary and rich multiscale features from original vibration signals. CSIAM adopts two cascaded submodules to focus on local channel correlation and global scale correlation simultaneously. Local channel correlation is used to adaptively measure the importance of different channel feature within each scale, while global scale correlation is used to dynamically determine the contribution of each scale feature to the final diagnostic result. In this way, the fault-related information at different scales can be fully captured and utilized. Finally, the effectiveness of DCSIAN is validated by a series of experimental comparisons on an aviation hydraulic pump dataset and a bearing dataset with various types noise.

Design of Web-based Report Card Processing Information System at MTS Negeri 4 Kebumen

Based on observations made, the processing of report card scores in MTs N 4 Kebumen is currently still manual, so a lot of time and energy is needed to carry out the task. In addition, the distribution of report cards is also still conventional because parents must come to school to take them, for this reason, a report card application is needed that can improve services to students quickly in terms of distributing report card scores while making it easier for teachers to fill in report cards. The purpose of this study is to design a web-based value processing application. In designing this application, the author uses the stages of SDLC development with the Waterfall process model. The design of this web-based report card processing information system can improve the quality of report card processing in MTs N 4 Kebumen. The programming language that the author uses is PHP and uses a MySQL database. To test the feasibility of the system, the authors used blak bock testing. The final result of this research is the creation of a web-based MTs N 4 Kebumen report card application.

The effect of main dimensions on the preliminary design of motor yachts

The design process of motor yachts mainly relies on the experience of designers, who have confidence in the knowledge acquired from designing units with similar hull-form characteristics. However, once a new concept needs to be developed, the acquired experience on a standard platform is no longer sufficient to achieve in a short time a successful design. The design of a motor yacht implies considering multiple aspects of ship hydrodynamics: resistance, propulsion, seakeeping, and manoeuvring. Such factors have been widely discussed individually on different kinds of ships, but an appropriate joint investigation of hulls like motor yachts is missing in the open literature. Therefore, the present paper intends to cover this gap, providing guidelines for the design of motor yachts in a length range between 20 and 40 meters. As a preliminary study, a series of 15 yacht hulls has been developed, starting from a reference hull form. Seakeeping, manoeuvring, and propulsive performances have been evaluated at a reference environmental condition and speeds according to the ISO 22834:2022 guidelines. Such calculations allow for developing response surfaces of the hydrodynamic properties for the series of yachts as a function of the hull's main dimensions. As a final result, the obtained responses allow for identifying the best compromise solutions for the main dimension selection of a new motor yacht in the length range of 20-40 meters.

A simple method for mechanical analysis of pressurized functionally graded material thick-walled cylinder

The complex function method is employed to establish a mechanical model for pressurized thick-wall cylinders made of functionally graded materials (FGM). This model is applicable to all radial varying modes of material properties, including both continuous and discontinuous variations in Young’s modulus and Poisson’s ratio. The main concept behind this mechanical model involves dividing the thick-walled cylinder into concentric thin cylinders, each equipped with a pair of analytical functions representing stress functions. By imposing continuity conditions between adjacent thin cylinders and considering the stress boundary conditions of the entire structure, all unknown forms of analytical functions can be determined. Subsequently, by establishing the correspondence relationship between these analytical functions and stress or displacement, it becomes possible to solve for the stress or displacement at any radial position within the thick-walled cylinder. Through comparison and verification against the numerical simulation results, it can demonstrate that as long as the differential scale is sufficiently small, high accuracy can be achieved in the final result. In other words, solutions obtained for multi-layered hollow cylinder converge toward those obtained for continuously graded thick-walled cylinder. Notably, by starting from the level of stress function and avoiding complex differential and integral equations, a linear equation system can provide information on stress and displacement distributions along the radial direction. Therefore, compared to other solving methods available, this proposed approach offers simplicity and applicability.

Comparison Between Hypnoanesthesia and Local Anesthesia in Minor Surgery

Background: Hypnoanesthesia is a state of anesthesia achieved through hypnosis techniques. Meanwhile, local anesthesia is anesthesia carried out by injecting local anesthetic drugs in or around the surgical site which causes temporary obstruction to the conduction of afferent impulses. Objective: This research was conducted to prove that there is no difference between hypnoanesthesia and local anesthesia in minor surgical procedures, with the indication of pain relief. Methods: The research subjects were patients with benign soft tissue tumors consisting of 40 people who were divided into 2 groups, namely treatment and control. Minor surgical procedures with hypnoanesthesia were performed in the treatment group, while in the control group, minor surgical procedures were performed with local anesthesia using 2% lidocaine. Pain is measured with FPS (Face Pain Scale) and monitored with a vital sign monitor. The intervention process was recorded with camcorder video. Changes in serum levels of excitatory neurotransmitters (Glutamic Acid and Substance P) and inhibitory (Beta Endorphin, Enkephalin, and Serotonin) before and after intervention were analyzed using ELISA (Enzym-Link Immunosorbent Assay) in both groups. Results: In the treatment group, it was discovered that patients did not feel pain after undergoing hypnoanesthesia intervention by providing suggestions for the relief of pain in the area where surgery would be performed. In the control group, patients also did not feel pain after local anesthetic intervention in the area to be operated on. However, the results of research and statistical tests showed that there was no significant difference in changes in Beta Endorphins, Enkephalin, and Serotonin as inhibitory neurotransmitters (p > 0.05) or Glutamic Acid and Substance P as excitatory neurotransmitters (p > 0.05) before and after the intervention in both groups of research subjects. Conclusion: the results of this study show that there is no significant difference between the treatment and control groups, they have equality in the final result, namely the relief of pain.

A Sustainable Water Resources Management Assessment Framework (SWRM-AF) for Arid and Semi-Arid Regions—Part 2: Refining the Conceptual Framework Using the Delphi Technique

In the first paper of this two-part series on the development of a sustainable water resources management assessment framework (SWRM-AF), a conceptual framework for arid and semi-arid regions was developed. The framework, rigorously selected through an extensive literature review, consisted of two main parts: components and indicators. This second paper of the series utilizes the Delphi technique as a participatory method to refine the conceptual framework, working toward a final version. This technique employs an iterative questionnaire through which 60 expert stakeholders from the Gulf Cooperation Council (GCC) countries were invited to assess 24 indicators across four components—social, economic, environmental, and infrastructure—and to assign their respective weights. However, while 40 accepted the invitation to be involved, only 33 participated in the first round. This paper identified that 97% of stakeholders in the first round were familiar with sustainability and its three pillars. The final result of the two rounds showed a preference for indicators within the social, economic, and infrastructure components to carry equal weights (i.e., four indicators within each of the three components were assigned 25% weighting each). In contrast, stakeholders identified that indicators within the environmental component should be assigned different weights. Therein, ‘Compliance of wastewater treatment plants with regulations’ and ‘Carbon dioxide emissions from desalination sector’ scored the highest and lowest weightings, accounting for 24.2% and 14.3% of the available weighting, respectively. The validation process resulted in a framework of 17 indicators. Thus, the refined and final version of SWRM-AF is presented and ready for implementation. The next stage of the research, which will keep the audience engaged, is to apply the newly developed SWRM-AF to an arid country and evaluate its effectiveness.

The Role of the Allergist in the Management of Chemotherapy-Induced Hypersensitivity Reactions

Chemotherapy-Induced Hypersensitivity Reactions are one of the main problems for oncology physicians and chemotherapy patients. The hypersensitivity reaction can either cause the cessation of the first-line treatment and it may impair the final result and impede or decrease the chances for successful treatment. Chemotherapy drugs are foreign substances that are capable to inducing the various hypersensitivity reactions from mild cutaneous symptoms to severe respiratory distress and cardiovascular collapse. The original Gell and Coomb’s classification categorizes hypersensitivity reactions into four types according to the type of immune response and the effector mechanism responsible for cell and tissue injury. The most common reactions are caused by platinum compounds, taxanes, epipodophyllotoxins, and asparaginase. The use of biologicals is increasing now, and these drugs are frequent causes of hypersensitivity reactions too. Skin testing is a method of diagnosing drug hypersensitivity, but it has not been developed enough in the field of medical oncology where a variety of the antineoplastic agents may be caused hypersensitivity reactions. Drug desensitization is the benefitial therapy in patients with chemotherapyinduced hypersensitivity reactions to chemotherapeutic drugs. This is useful for chemotherapy patients with malignancies and hematological diseases when no comparable alternative is available.

Concept of Processing and Interpretation of Spatiotemporal Vector Data

The purpose of this work is to develop an approach to complex processing and interpretation of spatio-temporal vector data. In this regard, it is necessary to analyze existing methods for visualizing vector and tensor fields and organizing a database for their storage and develop a concept which contains the main advantages of these methods and allows one to interpret spatial data represented as a tensor. The developed concept proposes an approach to field visualization based on drawing superellipse glyphs at each point in space. Axes of glyphs correspond to the rank of the visualized tensor, and attribute values are expressed by varying the color gradient in its monochrome representation. The architecture of a web-based application, which implements the storage of spatial data in a hybrid DBMS, and interpretation of the necessary data upon request is also proposed. For the physical storage of tensor information, a hybrid relational-hierarchical model is proposed, in which the representation of direct spatial data and the corresponding metadata, including those characterizing the source of information, are separated. It is advisable to present the final result and intermediate data in GeoJSON format. This format is actually currently the standard for presenting geospatial data and is supported by almost all software platforms and libraries aimed at solving problems of this kind.

Manajemen pembelajaran guru seni di SMK Negeri 5 Yogyakarta

Purpose: This research aims to explore and analyze learning management carried out by art teachers at Vocational High School (SMK) 5 Yogyakarta. The main focus of this research is to identify effective learning management strategies, which consist of planning and implementing learning carried out by art teachers at the school. Method: This research is qualitative. Subjects of this study were art teachers of ceramics crafts, wooden crafts, and metal crafts at SMK Negeri 5 Yogyakarta. Determination of the subject is done by random sampling, which consists of 3 teachers and the object of research is the effectiveness of art teachers learning process. Data was obtained using observation, interviews, and documentation, as descriptive analysis. Findings: The results of descriptive research reveal that art teacher learning at SMK Negeri 5 Yogyakarta is viewed from the aspects of learning planning and learning implementation. Learning planning activities carried out by teachers have been realized in the form of a syllabus and lesson plans. Teacher learning activities at SMK Negeri 5 Yogyakarta go through three stages, namely preliminary activities, core activities and closing activities. The learning carried out by the teacher was declared successful as proven by the students' work with good work in terms of creativity, beauty, form and completion of the final result. Evaluation of learning by teachers is realized by scoring in the form of numbers based on minimum completeness criteria.

Novel Insights into Postoperative Surveillance in Resected Pancreatic Cystic Neoplasms-A Review.

Pancreatic cystic lesions (PCL) are frequently encountered in clinical practice and some are referred to surgery due to their neoplastic risk or malignant transformation. The management of PCL involves complex decision-making, with postoperative surveillance being a key component for long-term outcomes, due to the potential for recurrence and postoperative morbidity. Unfortunately, the follow-up of resected patients is far from being optimal and there is a lack of consensus on recommendations with regard to timing and methods of surveillance. Here, we summarize the current knowledge on the postoperative surveillance of neoplastic pancreatic cysts, focusing on the mechanisms and risk factors for recurrence, the recurrence rates according to the initial indication for surgery, the final result of the surgical specimen and neoplastic risk in the remaining pancreas, as well as the postsurgical morbidity comprising pancreatic exocrine insufficiency, metabolic dysfunction and diabetes after resection, according to the type of surgery performed. We analyze postsurgical recurrence rates and morbidity profiles, as influenced by different surgical techniques, to better delineate at-risk patients, and highlight the need for tailored surveillance strategies adapted to preoperative and operative factors with an impact on outcomes.