Data Reduction Methods Research Articles

A framework for optimizing environmental covariates to support model interpretability in digital soil mapping

A common practice in digital soil mapping (DSM) is to incorporate many environmental covariates into a machine-learning algorithm to predict the spatial patterns of soil attributes. Variance inflation factor (VIF), principal component analysis (PCA), and recursive feature elimination (RFE) are three statistical methods that can be used to reduce the number of covariates. This study aims 1) to compare VIF and PCA approaches; 2) to identify an approach to determine the minimum number of covariates in DSM to ensure model parsimony using RFE after using VIF; and 3) to examine methods to interpret the impact of covariates on the variability of the predicted soil properties. The study area was the province of British Columbia (BC), Canada. This study used legacy data for four soil properties to make digital soil maps: soil organic carbon (SOC%), pH, clay%, and coarse fragment (CF%). Seven models were made for each soil property to determine the influence on validation results by using a different number of covariates produced by various methods on validation results. The results showed that the number of covariates could be reduced from 70 to 4 to 12 with only a little or no difference in concordance correlation coefficient (CCC) validation results. The CCC results of pH models using 70 and 7 covariates were both 0.74, and for other soil properties, this difference was negligible. The validation results obtained from PCA models showed that the performance of PCA in reducing the number of covariates was not as effective as when using VIF. Moreover, this study showed that covariates related to precipitation were the most important for modeling SOC%, soil pH, and clay%. Topographic covariates were the most influential covariates for modeling soil CF%. This study emphasizes the potential benefits of combining various data reduction methods to achieve optimal outcomes and generate the most parsimonious and interpretable models.

Kajian Dampak Tiktok Pada Siswa Sekolah Dasar: Kelebihan, Kekurangan, Dan Implikasi Pendidikan

TikTok app is a video-based online social media application with interesting special effects designed to interest many people, especially children. These elementary school kids love to play, and the playful nature of children makes them interested in the tiktok app and its features. The purpose of the study was to assess the advantages, disadvantages and implication of the tiktok application for elementary school students in Krikilan 1, Kec. Kab, Kalijambe Sragen, Prov. Southeast Java qualitative research, also known as field research , requires interviews and observation of respondents or actors. Children, class teachers, and parents of SD Krikilan 1 were used as research subjects. Utilizing observation and interview methods for data collection, data reduction, data presentation, and conclusion drawing are the three parts of the data analysis method used. According to research findings, the most popular TikTok app content for children aged 6 to 8 years is about challenges, food, cuisine, even student idols such as Ibnu wardani and Fuji. The average use of the tiktok application ranges from 30 minutes to 2 hours. The tiktok app has both positive and negative impacts on children who are 6 to 8 years old. The bad result is that children lose interest in learning, while the positive result is that it can foster and develop creativity and help children develop their skills in video editing.

Transiting exoplanets with the Mid-InfraRed Instrument on board JWST: From simulations to observations

Context. The James Webb Space Telescope (JWST) has now started its exploration of exoplanetary worlds. In particular, the Mid-InfraRed Instrument (MIRI) with its Low-Resolution Spectrometer (LRS) carries out transit, eclipse, and phase-curve spectroscopy of exoplanetary atmospheres with an unprecedented precision in a so far almost uncharted wavelength range. Aims. The precision and significance in the detection of molecules in exoplanetary atmospheres relies on a thorough understanding of the instrument itself and on accurate data reduction methods. This paper aims to provide a clear description of the instrumental systematics that affect observations of transiting exoplanets through the use of simulations. Methods. We carried out realistic simulations of transiting-exoplanet observations with the MIRI LRS instrument that included the model of the exoplanet system, the optical path of the telescope, the MIRI detector performances, and instrumental systematics and drifts that could alter the atmospheric features we are meant to detect in the data. After we introduce our pipeline, we show its performance on the transit of L 168-9b, a super-Earth-sized exoplanet observed during the commissioning of the MIRI instrument. Results. This paper provides a better understanding of the data themselves and of the best practices in terms of reduction and analysis through comparisons between simulations and real data. We show that simulations validate the current data-analysis methods. Simulations also highlight instrumental effects that impact the accuracy of our current spectral extraction techniques. These simulations are proven to be essential in the preparation of JWST observation programs and help us to assess the detectability of various atmospheric and surface scenarios.

Towards Real-Time Machine Learning-Based Signal/Background Selection in the CMS Detector Using Quantized Neural Networks and Input Data Reduction

The Large Hadron Collider (LHC) is being prepared for an extensive upgrade to boost its particle discovery potential. The new phase, High Luminosity LHC, will operate at a factor-of-five-increased luminosity (the number proportional to the rate of collisions). Consequently, such an increase in luminosity will result in enormous quantities of generated data that cannot be transmitted or stored with the currently available resources and time. However, the vast majority of the generated data consist of uninteresting data or pile-up data containing few interesting events or electromagnetic showers. High-Luminosity LHC detectors, including the Compact Muon Solenoid (CMS), will thus have to rely on innovative approaches like the proposed one to select interesting collision data. In charge of data reduction/selection at the early stages of data streaming is a level 1 trigger (L1T), a real-time event selection system. The final step of the L1T is a global trigger, which uses sub-system algorithms to make a final decision about signal acceptance/rejection within a decision time of around 12 microseconds. For one of these sub-system L1T algorithms, we propose using quantized neural network models deployed in targeted L1T devices, namely, field-programmable gate arrays (FPGA), as a classifier between electromagnetic and pile-up/quantum chromodynamics showers. The developed quantized neural network operates in an end-to-end manner using raw detector data to speed up the classification process. The proposed data reduction methods further decrease model size while retaining accuracy. The proposed approach was tested with simulated data (since the detector is still in the production stage) and took less than 1 microsecond, achieving real-time signal–background classification with a classification accuracy of 97.37% for 2-bit-only quantization and 97.44% for quantization augmented with the data reduction approach (compared to 98.61% for the full-precision, standard network).

Review of Islamic Business Ethics on Buying Practices of Round Coconut

This research aims to find out how the practice of buying and selling coconuts is made and reviewing Islamic business ethics in the practice of buying and selling round coconuts in Muara Sabak Ilir Village, Muara Sabak Timur District, East Tanjung Jabung Regency. The type of research that the author conducted was field research, using qualitative research methods and data collection techniques in the form of observation, interviews and documentation. Then for data analysis, the author uses data reduction methods, data presentation, and drawing conclusions. Based on cases in the field related to the practice of buying and selling round coconuts in Muara Sabak Ilir Village, Muara Sabak Timur District, East Tanjung Jabung Regency, there are two weighing methods that are often carried out, namely per kilo weighing and per seed weighing. In weighing each seed, the size of the coconut will be calculated equally, so it can be said that buying and selling in this kind of weighing contains an element of uncertainty, because there is a lack of transparency regarding the weight of coconuts in buying and selling transactions, which can cause losses for sellers (farmers). From the results of this research, it can be seen that from a review of business ethics in the practice of buying and selling round coconuts in Muara Sabak Ilir Village, Muara Sabak Timur District, East Tanjung Jabung Regency, it is still not in accordance with the principles of Islamic business ethics, because there are differences between farmers who have debt and those who have no debt. Where when coconut farmers have debt they cannot choose which weighing method to use when buying and selling coconuts, because the choice of weighing scale is determined unilaterally by the coconut buyer (toke). Then as long as the seller (farmer) still has a debt with the coconut buyer (toke), they cannot sell the coconut to another buyer (toke).

Data reduction and processing for photon science detectors

New detectors in photon science experiments produce rapidly-growing volumes of data. For detector developers, this poses two challenges; firstly, raw data streams from detectors must be converted to meaningful images at ever-higher rates, and secondly, there is an increasing need for data reduction relatively early in the data processing chain. An overview of data correction and reduction is presented, with an emphasis on how different data reduction methods apply to different experiments in photon science. These methods can be implemented in different hardware (e.g., CPU, GPU or FPGA) and in different stages of a detector’s data acquisition chain; the strengths and weaknesses of these different approaches are discussed.

Targeting Efficient Features of Urate Oxidase to Increase Its Solubility.

With the demand for mass production of protein drugs, solubility has become a serious issue. Extrinsic and intrinsic factors both affect this property. A homotetrameric cofactor-free urate oxidase (UOX) is not sufficiently soluble. To engineer UOX for optimum solubility, it is important to identify the most effective factor that influences solubility. The most effective feature to target for protein engineering was determined by measuring various solubility-related factors of UOX. A large library of homologous sequences was obtained from the databases. The data was reduced to six enzymes from different organisms. On the basis of various sequence- and structure-derived elements, the most and the least soluble enzymes were defined. To determine the best protein engineering target for modification, features of the most and least soluble enzymes were compared. Metabacillus fastidiosus UOX was the most soluble enzyme, while Agrobacterium globiformis UOX was the least soluble. According to the comparison-constant method, positive surface patches caused by arginine residue distribution are appropriate targets for modification. Two Arg to Ala mutations were introduced to the least soluble enzyme to test this hypothesis. These mutations significantly enhanced the mutant's solubility. While different algorithms produced conflicting results, it was difficult to determine which proteins were most and least soluble. Solubility prediction requires multiple algorithms based on these controversies. Protein surfaces should be investigated regionally rather than globally, and both sequence and structural data should be considered. Several other biotechnological products could be engineered using the data reduction and comparison-constant methods used in this study.

HDRA: A Haybird Data Reduction and Routing Algorithm

Presently, wireless sensor networks (WSNs) are emerging as a vibrant field of research due to various challenging aspects such as energy consumption, routing strategies, effectiveness, among others. Despite unresolved issues within WSNs, a substantial array of applications has already been developed. For any application design, a primary objective is to optimize the WSN in terms of its lifecycle and functionality. Recent studies on data reduction methods have shown that sensor nodes often transmit data directly (single hop) to the base station (BS). However, a significant concern is that most existing multi-hop routing protocols do not address data reduction before forwarding data to the BS. Consequently, this study introduces a Hybrid Data Reduction and Routing Algorithm (HDRA). The principal aim of HDRA is to prolong the lifespan of cluster-based WSNs. It strives to decrease the packet transmission by sensor nodes, especially when there's minimal change in sensor readings. The findings indicate that HDRA outperforms the LEACH protocol in terms of energy efficiency in sensor networks, irrespective of network type (T, H, or TH) or deployment scenarios (200x200m or 400x400m). Overall, the proposed algorithm enhances network performance by conserving energy and extending network lifespan.

INFLUENCE OF PLY ORIENTATION ON INTERLAMINAR FRACTURE TOUGHNESS OF CARBON/EPOXY COMPOSITES

Delamination is a concern for the structural integrity of fiber-reinforced polymer composite structures. The decisive material parameter for delamination is fracture toughness (G<sub>Ic</sub>). The main objective of this study was to investigate the in-plane, quasi-static fracture toughness (both initiation and propagation) of two laminate composites. Both composites contain carbon fibers in an epoxy matrix. The tests were performed using double cantilever beam specimens with different ply orientations according to ASTM standards. The effect of ply orientation was evaluated using data reduction methods to obtain fracture toughness. The experiments revealed that the ply sequence changed the crack propagation mechanism, increasing the initiation and propagation fracture toughness values for 90/90 layup during mode I interlaminar tests, by 80% and 69%, respectively. In addition, the mode I fracture toughness increased by 40% after 50 mm crack extension.

(Invited) Considerations in Sample Environment and Data Processing for Operando Cell Studies by Neutron Depth Profiling

Neutron depth profiling (NDP) is a nominally non-destructive analytical technique used to measure select isotopes (6Li, 14N, 10B, etc...) in a diversity of materials. NDP can be conducted on operating Li-ion cells and be used to provide researchers real-time information about cell formation, conductance, and resilience. The increased use of NDP for electrochemical studies within the past decade has inspired the development of new sample preparation and data reduction strategies. Select topics that have been studied in the U.S. have included issues of neutron attenuation and scattering due to the addition of a protective capping layer over a cell, the possible need for matrix matched standards for complex materials, and the streamlining of the NDP data reduction procedure. Several challenges remain, however, including improving NDP data acquisition and reduction methods for detectable, low-Z isotopes such as 14N and 35Cl and improving upon the NDP data reduction and processing procedures so that they are more accessible to non-instrument specialists. All these topics will be discussed in this presentation.

Characterization of Transport Pathways in Electrolyzers Using Neutron and X-Ray Tomography

Electrolysis is a critical component to realizing a true green hydrogen economy. The performance and efficiency of an electrolyzer is directly tied to the multiphase transport that occurs within the porous transport layer (PTL). The PTLs must allow sufficient water transport to the catalyst layer to maintain the reaction while removing the product gases. PTLs are typically created from titanium foams or sintered from titanium powders or fibers. The resultant structure is heterogenous and requires 3D imaging to properly capture the pore structure and transport pathways. The use of titanium for the PTL can make it difficult to track the multiphase flow using X-rays alone as the X-ray energy required to penetrate 1 cm or more reduces the contrast between water and oxygen without the use of contrast agents that could affect the reaction. Neutrons provide a penetrating probe capable of imaging through titanium while having extreme sensitivity to water. Combing neutrons and high energy X-rays provides an easy way to identify all components in the PTL, i.e., the solid structure, oxygen, and water. The National Institute of Standards and Technology provides the Neutron Imaging Facility to the larger research community through the Center for Neutron Research facility user program. This instrument provides the ability to perform simultaneous neutron and X-ray tomography. Moving from 2D radiography to 3D tomography is critical to fully understand the influence of the PTL pore structure on the multiphase transport. This talk will give an overview of the instrument capabilities, showcase past success for neutron imaging of electrolyzers, and provide current progress on development of hardware and data reduction methods for simultaneous neutron and X-ray tomography of electrolyzers.

Four-Dimensional History Matching Using ES-MDA and Flow-Based Distance-to-Front Measurement

The use of 4D seismic data in history matching has been a topic of great interest in the hydrocarbon industry as it can provide important information regarding changes in subsurfaces caused by fluid substitution and other factors where well data is not available. However, the high dimensionality and uncertainty associated with seismic data make its integration into the history-matching process a challenging task. Methods for adequate data reduction have been proposed in the past, but most address 4D information mismatch from a purely mathematical or image distance-based standpoint. In this study, we propose a quantitative and flow-based approach for integrating 4D seismic data into the history-matching process. By introducing a novel distance parametrization technique for measuring front mismatch information using streamlines, we address the problem from a flow-based standpoint; at the same time, we maintain the amount of necessary front data at a reduced and manageable amount. The proposed method is tested, and its results are compared on a synthetic case against another traditional method based on the Hausdorff distance. The effectiveness of the method is also demonstrated on a semi-synthetic model based on a real-case scenario, where the standard Hausdorff methodology could not be applied due to high data dimensionality.

Survey of burning rate measurements in small solid rocket motors

Burning rate plays a crucial role in determining the performance of solid rocket motors (SRMs). In the traditional approach for solid propellant propulsion, technical activities regarding burning rates are developed at three different operational levels: (i) Strand burners or laboratory-scale devices in general; (ii) Small-scale motors (SSMs); And (iii) full-size or end-item rocket motors. While strand burners are extensively used for propellant development (formulation exploration, ingredients screening, performance verification, and production control) and relatively little is done experimentally at the full-size motor level (being large-scale experimentation too expensive and dangerous), a lot is usually carried out at SSM level to obtain burning rate information under motor operating conditions. In the introductory part of this work, burning rate fundamentals are recalled and burning rate measurement devices are quickly summarized. Then, a survey of subscale motors is conducted and several automated procedures to deduce burning rate from SSM testing are analyzed. Data reduction methods commonly used by leading European companies are based on the thickness-over-time (TOT) definition. Attention is dedicated to procedures used in Italy and France for quality control of the European space launchers (solid propellant boosters of Ariane family and core solid rocket motors of VEGA family). In addition, automated data reduction methods based on mass balance (MB) and often used in USA are investigated. Specific features and general trends of the tested industrial procedures are pointed out. Since for any tests the actual burning rates are unknown, results can only be compared based on the statistical quality of the deduced ballistic data. Mainly reproducibility, ease of application, and suitability for automated computer implementation are of interest to industrial users. The effects of test variability, input data quality, and data reduction methods on result reproducibility are discussed with reference to fire tests of the successful Ariane-5 solid boosters. The systematic analysis of industrial data conducted in this work suggests that improving the actual mix reproducibility and quality of experimental data is more important than perfecting the current data reduction methods. Moreover, the international trends suggest that the fundamental TOT procedures are being replaced by MB procedures or advanced TOT procedures with burning times evaluated using pressure integrals.

INTERNALISASI NILAI-NILAI KARAKTER MELALUI PENDEKATAN BUDAYA KEAGAMAAN DALAM MENCEGAH PERILAKU BULLYING PESERTA DIDIK DI MADRASAH IBTIDAIYAH KAB. PARIGI MOUTONG

Abstract
 The aim of this research is to determine the forms of internalization of character values ​​through a religious culture approach in preventing bullying behavior among students at Madrasah Ibtidaiyah Parigi Moutong Regency as well as the impact of internalizing character values ​​through a religious culture approach in preventing bullying behavior among students in Madrasah Ibtidaiyah Parigi Moutong Regency. The type of research used in this research is descriptive qualitative. The subjects of this research are teachers and students. Data collection used observation, interview and documentation techniques which were analyzed using data reduction, data presentation and data verification methods. Forms of internalizing Character values ​​towards Bullying Behavior among Students at Madrasah Ibtidaiyah Parigi Moutong Regency firstly, instilling religious character values ​​through extra-curricular activities, through getting used to good behavior and through advice, secondly instilling the character values ​​of tolerance, instilling character values Social care. The impact of internalizing character values ​​on bullying behavior among students at Madrasah Ibtidaiyah, Parigi Moutong Regency has a positive impact. This can be seen from changes in behavior for the better among students as well as reducing and preventing bullying behavior in the school.

Improved secure PCA and LDA algorithms for intelligent computing in IoT‐to‐cloud setting

AbstractThe rapid development of new technologies such as artificial intelligence and big data analysis requires the simultaneous development of cloud computing technology. The application of IoT‐to‐cloud setting has been fully applied in various industry sectors, such as sensor‐cloud system which is composed of wireless sensor network and cloud computing technology. With the increasing amount and types of collected data, companies need to reduce the dimension of massive data in cloud servers for obtaining data analysis reports rapidly. Due to frequent cloud server data leaks, companies must adequately protect the privacy of some confidential data. To this end, we designed a dimension reduction method for ciphertext data in the sensor‐cloud system based on the CKKS encryption scheme, principal component analysis (PCA) and linear discriminant analysis (LDA) dimension reduction algorithm. As data cannot be directly calculated using traditional PCA and LDA algorithm after encryption, we add some interactive operations and iterative calculations to replace some steps in traditional algorithms. Finally, we select the classification dataset IRIS which is commonly used in machine learning, and screen out the best encryption and calculation parameters, and efficiently realize the dimension reduction method of ciphertext data through a large number of experiments.

Metode Pembagian Harta Bersama Pasca Cerai Mati Di Desa Pandanarum Kecamatan Sutojayan Blitar

Joint assets are assets acquired during a legal marriage. Husband and wife have the same rights and obligations over joint property. In the practice of implementing law in the community,especially regarding the distribution of joint assets, there are variations/diversities in accordance with the customary provisions and habits of each region. In this regard, this research is focused on examining the method of sharing joint assets after divorce in Pandanarum Village, Blitar Regency. This research uses a qualitative method with a phenomenological approach, phenomenology is a type of qualitative research that looks at and looks more closely and in detail about individual explanations and understanding of their experiences. Data is collected by data reduction methods, data display, and verification or drawing conclusions. The results of this study indicate that in the practice of the Pandanarum village community, marriages that break up due to death then all assets become inheritance assets. if the husband dies first, then all assets become inheritance and are distributed to heirs based on fiqh provisions. The wife in this case only gets 1/8 if there are children and ¼ if there are no children. Meanwhile, if the wife dies first, usually the assets will not be divided until the husband dies.
 Keywords : Distribution of Wealth, death divorce

Penerapan Program ODOJ (One Day One Juz) dalam Upaya Peningkatan Minat dan Kemampuan Membaca Al-Quran pada Anak di Perumahan Jatiwangi

Allah revealed the Qur’an as the holy book of Muslims SWT to the prophet Muhammad through the angel Gabriel which is used as a guide for human life. namely By reading the Qur'an regularly, Allah has promised a double reward for people who are always close to the Qur'an. Sometimes there are many Muslims who do not know the importance of reading the Qur'an apart from getting a reward as well as a cure for all problems. This study aims to find out how much interest children have in reading the Qur'an and how to add motivation to children in reading the Qur'an. Because sometimes humans need motivation or encouragement to be istiqomah in doing good. This is where the ODOJ community plays an important role in building the motivation of its members. As Muslims we are obliged to carry out the commands of Allah SWT. Starting from the habit of reciting the Koran which decreased during Ramadan among Muslims and less and less interacting with the Qur’an due to increased busyness and routine. In order to obtain relevant and quality data and explanations in this scientific work, the author uses a form of Islamic education approach which is very useful for discovering the nature and meaning contained in the educational process itself and the data is obtained from the field where the educational process takes place in the context of its environment. Data collection techniques were carried out through observation, interviews and documentation. The method that the author uses in analyzing the data obtained is by using the methods of data collection, data reduction, data presentation, data verification and drawing conclusions. This study concludes that the application of the ODOJ (One Day One Juz) program is very helpful in increasing the interest and ability to read the Koran in children. With the ODOJ program, children have the responsibility to finish reading the Qur’an regularly so that teenagers will automatically take the time to read the Qur’an.

Real-time FPGA-based laser absorption spectroscopy using on-chip machine learning for 10 kHz intra-cycle emissions sensing towards adaptive reciprocating engines

Fast emissions sensing is needed to enable rapid optimization on-the-fly for increasingly adaptive engine architectures to improve performance over a wide range of loads and to offer fuel flexibility towards a low-carbon energy future. In this work, a real-time laser absorption spectroscopy technique is developed for 10 kHz on-line measurements of engine exhaust gas temperature and carbon monoxide. Latency due to data reduction is significantly shortened through a machine learning approach to spectral analysis and minimization of post-processing complexity for implementation on a high-bandwidth field-programmable gate array (FPGA). The data reduction method is tested on cycle-resolved laser-absorption thermochemistry measurements in reciprocating piston engine exhaust. The sensor employs a quantum cascade laser to spectrally-resolve two fundamental rovibrational absorption lines of carbon monoxide near 4.9 μm at a rate of 10 kHz. The recorded signals are passed to an FPGA to infer CO concentration and gas temperature through either a pre-trained ridge regression or artificial neural network model. Models are constructed to limit complexity with the aim of minimizing resource utilization and latency on the FPGA. Experimental data are used to evaluate the prediction accuracy of the models, with the neural network achieving RMS errors in CO mole fraction and gas temperature of 0.0390% and 15.0 K, respectively, compared to spectral fitting of the absorption lineshapes. The data reduction latencies are measured through hardware-in-the-loop demonstration, achieving a 10 kHz throughput and 25 μs latency. Computational time of the end-to-end data reduction process on the FPGA is measured at 300 ns and 600 ns for the ridge regression and neural network, respectively. The data reduction methods presented in this work expand the utility of laser absorption spectroscopy for low-latency sensors that match the timescales of combustion and increase the potential for real-time sensing and control to minimize engine exhaust emissions and maximize performance.

Autonomous Internet of Things (IoT) Data Reduction Based on Adaptive Threshold

With the development of intelligent IoT applications, vast amounts of data are generated by various volume sensors. These sensor data need to be reduced at the sensor and then reconstructed later to save bandwidth and energy. As the reduced data increase, the reconstructed data become less accurate. Usually, the trade-off between reduction rate and reconstruction accuracy is controlled by the reduction threshold, which is calculated by experiments based on historical data. Considering the dynamic nature of IoT, a fixed threshold cannot balance the reduction rate with the reconstruction accuracy adaptively. Aiming to dynamically balance the reduction rate with the reconstruction accuracy, an autonomous IoT data reduction method based on an adaptive threshold is proposed. During data reduction, concept drift detection is performed to capture IoT dynamic changes and trigger threshold adjustment. During data reconstruction, a data trend is added to improve reconstruction accuracy. The effectiveness of the proposed method is demonstrated by comparing the proposed method with the basic Kalman filtering algorithm, LMS algorithm, and PIP algorithm on stationary and nonstationary datasets. Compared with not applying the adaptive threshold, on average, there is an 11.7% improvement in accuracy for the same reduction rate or a 17.3% improvement in reduction rate for the same accuracy.

Fracture energy and cohesive law analysis of adhesives using a recently developed SCB joint: The influence of joint geometry and mode mixity

In a prior study, the current authors proposed the utilization of semi-circular bend (SCB) specimens for analyzing the fracture energy of adhesive materials. However, similar to standard fracture test specimens, the geometry and size of the joint can impact the measured fracture energy when using SCB samples. Therefore, the objective of this research is to investigate the influence of adhesive layer thickness, substrate thickness, and disk radius of SCB specimens on cohesive law parameters and fracture energy of the adhesive. The study examines the fracture energy under different loading conditions, including pure mode I, pure mode II, and two mixed-mode conditions. By employing an inverse data reduction method, the cohesive law parameters of the tested adhesive are determined for various joint geometries. The results demonstrate that the disk radius does not affect the cohesive law of the joint. Furthermore, it is observed that bondline thicknesses of 0.2 mm and 0.4 mm lead to similar cohesive laws, whereas an adhesive thickness of 1 mm results in lower initial stiffness but higher fracture energy and damage initiation traction. Moreover, the study reveals that an increase in substrate thickness reduces the final fracture separation, indicating a more brittle fracture behavior. This can be attributed to a smaller fracture process zone caused by the plane strain load distribution in the adhesive layer.