Conventional Analysis Methods Research Articles

Image-processing-based ultrasonic velocimetry development with high applicability to flows in microparticle dispersion

This study presents the development of a novel methodology, image-processing-based ultrasonic velocimetry (IPUV). For evaluating flow velocity, we present a new perspective utilizing image processing for spatiotemporal echo images instead of conventional ultrasonic analysis methods such as pulse compression and quadrature detection. In addition to traditional tracer particle size O(100 μm), IPUV has a high applicability for measuring the velocity of microparticle dispersion of the particle size O(1 μm) and dilute concentration O(0.001 wt.%). Velocimetry utilizing microparticles has significant merit: Velocity measurements can be conducted under conditions with less influence on the flow and higher traceability to fluid flow than the size conventionally used as a tracer particle. The accuracy and effectiveness of velocity analysis based on the IPUV principle are verified numerically and experimentally. In the experiments, IPUV was applied to Couette flow and stirring flow. In the former, the validity of the velocity profiling by IPUV was confirmed compared to the theoretical profiles of Couette flow. In the latter, IPUV measured velocity profiles in the stirring flows with different dispersions [spherical particle O(100 μm) and mica O(1 μm)]. Differences in the ability of dispersed particles to follow turbulent fluctuations in stirring water flow appeared in the frequency spectrum of IPUV velocity profiles.

Experimental study of Cryogenic jet injection using centrifugal nozzles at supercritical pressure

The new generation of large-thrust rockets commonly employ cryogenic liquids such as liquid hydrogen (LH2), liquid oxygen (LOX), and methane as propellants. Conventional analytical methods established under subcritical (specifically, low-pressure) conditions are inadequate for addressing the atomization issues under trans-critical and supercritical conditions. Therefore, conducting experimental research on atomization of cryogenic fluids under supercritical conditions holds significant practical value. This paper employs liquid nitrogen (LN2) as the working fluid and focuses on conducting injection atomization experiments on LN2 under supercritical pressure conditions. The research involves the design and construction of an experimental system for supercritical cryogenic fluid injection, with the aim of measuring and studying the spray characteristics of centrifugal nozzles under various conditions. Three major categories of experiments were designed for low-temperature LN2 injection: subcritical nitrogen into subcritical environment (BB), subcritical nitrogen into supercritical environment (BP), and supercritical nitrogen into supercritical environment (PP). The experimental results ultimately reveal that for jets in BB, both the spray cone angle and the breakup length increase with higher injection pressure drop. In the case of BP, the spray cone angle decreases while the penetration distance increases with higher pressure drop. As for the jets in PP, the spray cone angle increases and the penetration distance decreases with higher injection pressure drop. Across different geometric parameters of the centrifugal nozzle, it was observed that with an increase in the geometric parameter, the rate of decrease in the spray cone angle with respect to pressure drop amplifies, and the penetration distance exhibits an increasing trend as the geometric parameter grows.

Less interference fluorescence analytical strategy: Bridging substance-triggered ratiometric sensor with convenient preparation and application

High interference and narrow application range are key of bottleneck of recent fluorescence analysis methods, which limit their wide application in the sensing field. Therefore, to overcome these disadvantages, a ratiometric fluorescence sensing system utilizing berberine (BER) and silver nanoclusters protected by dihydrolipoic acid (DHLA-AgNCs) was constructed for the first time in this work, to achieve determination of BER and daunorubicin (Dau). BER aqueous solution (non-planar conformation) has no fluorescence emission. When it was mixed with DHLA-AgNCs, the conformation of BER became planar, producing fluorescence emission at 515 nm besides the fluorescence emission peak of DHLA-AgNCs at 653 nm. With the increase of BER concentration added in system, the fluorescence intensity of BER (planar conformation) at 515 nm increased obviously and the fluorescence intensity of DHLA-AgNCs decreased slightly. Therefore, the dual emission fluorescence sensing system was constructed based on a fluorescence substance and non fluorescence substance, to achieve determination of BER. Meanwhile, based on the bridging effect of BER and fluorescence resonance energy transfer effect from Dau, the altering of two peaks intensity was utilized to achieve determination of Dau. Thus, this dual emission sensing system can not only be used for fluorescence analysis of BER and its analogues, but also based on the bridging effect of BER, allowing the determination of Dau and its analogues that could not be directly measured with silver nanoclusters, expanding the application range of traditional dual emission detection systems. Meanwhile, this system has strong anti-interference ability and low toxicity to the human body and less pollution to the sample and environment. This provides a new direction and universal research strategy for the construction of new fluorescence sensing systems in the future for the analysis of target substances that cannot be directly detected with conventional fluorescence analysis methods.

Environmentally safe chemometric evaluation and data analysis for the kinetic investigation of mirabegron stability

The physicochemical characteristics of different pharmaceuticals may change with transportation, storage, and usage which directly affect their efficacy, quality, and other features. It is crucial to investigate the kinetic action of pharmaceutical compounds to produce predictive data that aid in the computation of their shelf lives. Several conventional analytical methods introduce unwanted dangerous risks, like employing unsafe solvents, that harm both the analyst and the environment. This created the motive for analysts to apply green approaches that consider using safe chemicals and producing as little waste as possible. In this study, powerful green assessment methods were investigated to study the kinetic process of Mirabegron (MIR) in different hydrolytic conditions giving its main degradation product. First, the optimal solvent was identified for the given kinetic procedures with the aid of Principal Component Analysis. Then two advanced chemometric methods were applied to estimate the kinetic parameters of the MIR degradation process namely: Multivariate Curve Resolution-Alternating Least Squares and Artificial Neural Networks. By data manipulation, both algorithms could provide an explanation of the MIR kinetic process in different hydrolytic conditions. Moreover, modified National Environmental Methods Index (NEMI), eco-scale, Hexagon algorithm, Complementary Green Analytical Procedure Index (Complex GAPI), and analytical greenness metric for sample preparation (AGREEprep) were adopted to ensure the greenness of the proposed study. Eventually, the presented investigation could successfully present an eco-friendly chemometric approach for monitoring the different kinetic processes of pharmaceutical compounds with minimum effort and solvent consumption and maximum time saving and analyst safety compared to other traditional methods.

Enhancing Interpretability in Drill Bit Wear Analysis through Explainable Artificial Intelligence: A Grad-CAM Approach

This study introduces a novel method for analyzing vibration data related to drill bit failure. Our approach combines explainable artificial intelligence (XAI) with convolutional neural networks (CNNs). Conventional signal analysis methods, such as fast Fourier transform (FFT) and wavelet transform (WT), require extensive knowledge of drilling equipment specifications, which limits their adaptability to different conditions. In contrast, our method leverages XAI algorithms applied to CNNs to directly identify fault signatures from vibration signals. The signals are transformed into their frequency components and then employed as inputs to a CNN model, which is trained to detect patterns indicative of drill bit failure. XAI algorithms are then employed to generate attention maps, highlighting regions of interest in the CNN. By scrutinizing these maps, engineers can identify critical frequencies associated with drill bit failure, providing valuable insights for maintenance and optimization. This method offers a transparent and interpretable framework for analyzing vibration data, enabling informed decision-making and proactive maintenance strategies to enhance drilling efficiency and minimize downtime. The integration of XAI with CNNs facilitates a deeper understanding of the root causes of drill bit failure and improves overall drilling performance.

Explainable Anomaly Detection of Synthetic Medical IoT Traffic Using Machine Learning

In the context of the Internet of Things (IoT), particularly within medical facilities, the detection and categorization of Internet traffic remain significant challenges. While conventional methods for IoT traffic analysis can be applied, obtaining suitable medical traffic data is challenging due to the stringent privacy constraints associated with the health domain. To address this, this study proposes a network traffic simulation approach using an open-source tool called IoT Flock, which supports both CoAP and MQTT protocols. The tool is used to create a synthetic dataset, to simulate IoT traffic originating from various smart devices in different hospital rooms. The study shows a complete anomaly detection analysis of IoT-Flock-generated traffic, both normal and malicious, by leveraging and comparing traditional machine learning techniques, deep learning models with multiple hidden layers, and explainable artificial intelligence techniques. The results are very promising. For the binary classification, for example, the obtained accuracy is close to 100%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$100\\%$$\\end{document} in the case of the CoAP protocol. Good results are also obtained when the multinomial classification is performed, observing that CoAP packets are classified better than MQTT packets, even if the identification of the different MQTT packets reaches very high metrics for the most of the considered algorithms. Moreover, the obtained classification rules are also meaningful in the considered IoT context. The results indicate that IoT-Flock synthetic data can effectively be used to train and test machine and deep learning models for detecting abnormal IoT traffic in medical scenarios. This research attempts also to bridge the gap between IoT security and healthcare, providing useful insights into securing medical IoT networks in general.

Single-cell SERS imaging of dual cell membrane receptors expression influenced by extracellular matrix stiffness

Membrane receptors perform a diverse range of cellular functions, accounting for more than half of all drug targets. The mechanical microenvironment regulates cell behaviors and phenotype. However, conventional analysis methods of membrane receptors often ignore the effects of the extracellular matrix stiffness, failing to reveal the heterogeneity of cell membrane receptors expression. Herein, we developed an in-situ surface-enhanced Raman scattering (SERS) imaging method to visualize single-cell membrane receptors on substrates with different stiffness. Two SERS substrates, Au@4-mercaptobenzonitrile@Ag@Sgc8c and Au@4-pethynylaniline@Ag@SYL3c, were employed to specifically target protein tyrosine kinase-7 (PTK7) and epithelial cell adhesion molecule (EpCAM), respectively. The polyacrylamide (PA) gels with tunable stiffness (2.5–25 kPa) were constructed to mimic extracellular matrix. The simultaneous SERS imaging of dual membrane receptors on single cancer cells on substrates with different stiffness was achieved. Our findings reveal decreased expression of PTK7 and EpCAM on cells cultured on stiffer substrates and higher migration ability of the cells. The results elucidate the heterogeneity of membrane receptors expression of cells cultured on the substrates with different stiffness. This single-cell analysis method offers an in-situ platform for investigating the impacts of extracellular matrix stiffness on the expression of membrane receptors, providing insights into the role of cell membrane receptors in cancer metastasis.

IoT Based Soil pH Detection and Crop Recommendation System

Agricultural productivity hinges on soil fertility, influenced by key factors like nitrogen, phosphorus, potassium, pH level, and soil moisture. Yet, achieving optimal crop growth is challenging due to limited farmer knowledge and difficulties in determining precise fertilizer quantities. Conventional soil analysis methods involve manual sampling andcostly lab tests, which are subjective. To address this, aproposed solution integrates IoT-enabled soil nutrient monitoring with machine learning algorithms for croprecommendations. Sensors collect data on crucial parameters like nitrogen, phosphorus, and soil temperature, transmitting it to a cloud-based database. Machine learning analyzes this data to suggest ideal crops, minimizing fertilizer use, reducing labor, and enhancing overall productivity. This innovative approach streamlines crop selection, minimizing unnecessary inputs while maximizing yields. By harnessing IoT and machine learning, farmers gain valuable insights into soil health, enabling precise fertilization and crop selection. This not only boosts agricultural productivity but also contributes to economic growth by fostering sustainable practices andincreased yields.

Enhanced Detection of Estrogen-like Compounds by Genetically Engineered Yeast Sensor Strains.

The release of endocrine-disrupting compounds (EDCs) to the environment poses a health hazard to both humans and wildlife. EDCs can activate or inhibit endogenous endocrine functions by binding hormone receptors, leading to potentially adverse effects. Conventional analytical methods can detect EDCs at a high sensitivity and precision, but are blind to the biological activity of the detected compounds. To overcome this limitation, yeast-based bioassays have previously been developed as a pre-screening method, providing an effect-based overview of hormonal-disruptive activity within the sample prior to the application of analytical methods. These yeast biosensors express human endocrine-specific receptors, co-transfected with the relevant response element fused to the specific fluorescent protein reporter gene. We describe several molecular manipulations of the sensor/reporter circuit in a Saccharomyces cerevisiae bioreporter strain that have yielded an enhanced detection of estrogenic-like compounds. Improved responses were displayed both in liquid culture (96-well plate format) as well as in conjunction with sample separation using high-performance thin-layer chromatography (HPTLC). The latter approach allows for an assessment of the biological effect of individual sample components without the need for their chemical identification at the screening stage.

Dealing with soft variables and data scarcity: lessons learnt from quantification in a participatory system dynamics modelling process

AbstractSystem dynamics (SD) models are commonly used for structuring complex problems to support decision‐making. They are used to investigate areas in which limited knowledge is available, describing nonlinear relationships and including intangible elements. Although this explorative nature is one of the key advantages, it also represents a challenge for quantifying the intangible, i.e. more qualitative aspects of an SD model, especially when it is not possible to apply conventional analytical methods due to data scarcity. Procedures to obtain and analyse information using participatory approaches are limited. First, this article outlines existing quantification methods and related open questions when dealing with soft variables and data scarcity. Secondly, it summarises the quantification process developed during a participatory SD process, describing how we dealt with data scarcity and soft variables. Lastly, we suggest a quantification framework in relation to data availability and level of stakeholder engagement. © 2024 The Authors. System Dynamics Review published by John Wiley & Sons Ltd on behalf of System Dynamics Society.

Improved dynamic analysis for plane elasticity problems with a boundary meshfree method and distributed sources

In this paper, we present an improved dynamic analysis method to address plane elasticity problems. Distributed sources are employed in a boundary-free mesh-based approach known as Boundary Distributed Source (BDS). This method, which builds upon a modified Method of Fundamental Solutions (MFS), aims to eliminate singularities without the reliance on fictitious boundaries. Distributed sources are strategically positioned directly on the real boundary, centered over disks. The method's efficiency and simplicity are highlighted by its ability to operate without the need for precise data concerning adjacent points. The dual reciprocity method (DRM) is utilized to approximate the non-homogeneous inertial term. Furthermore, the present formulation investigates several factors that influence the convergence of the proposed method, including the number of collocation points, internal points, the radius of the circular disk, and the time step size. Based on the numerical results, it is evident that the method exhibits remarkable accuracy and efficiency when applied to plane elasticity problems including forced, harmonic, and free analyses. As compared to conventional numerical methods for dynamic analysis, the method outperforms in both accuracy and convergence rate.

Comprehensive performance domain tolerance analysis methodology for freeform imaging spectrometers

In recent years, attention has been directed towards cost-effective and compact freeform Schwarzschild imaging spectrometers with plane gratings. The utilization of tolerance analysis serves as a potent approach to facilitate the development of prototypes. Conventional tolerance analysis methods often rely solely on the modulation transfer function (MTF) criterion. However, for a spectrometer system, factors such as the keystone/smile distortion and spectral resolution performance also require consideration. In this study, a tailored comprehensive performance domain tolerance analysis methodology for freeform imaging spectrometers was developed, considering vital aspects such as the MTF, keystone/smile distortion, and spectral resolution. Through this approach, meticulous tolerance analysis was conducted for a freeform Schwarzschild imaging spectrometer, providing valuable insights for the prototype machining and assembly processes. Emphasis was placed on the necessity of precise control over the tilt and decenter between the first and third mirrors, whereas the other fabrication and assembly tolerances adhered to the standard requirements. Finally, an alignment computer-generated hologram (CGH) was employed for the preassembly of the first and third mirrors, enabling successful prototype development. The congruence observed between the measured results and tolerance analysis outcomes demonstrates the effectiveness of the proposed method.

Process Approach in Memory Assessment of Patients With Alzheimer's Disease and Mild Cognitive Impairment:A Review

The process approach,a set of analytical methods used in neuropsychology,quantifies the word-list learning tests and conventional analytical methods and fully reflects the memory profile of the subject.Therefore,it is widely used in the memory assessment of patients with Alzheimer's disease(AD)and mild cognitive impairment(MCI).The common indices of process approach,such as learning slope,semantic clustering,serial position effects,discriminability,and response bias,are key components of memory assessment.This article reviews the application of common indices of process approach in memory assessment of AD and MCI patients and discusses the shortcomings and future research directions of process approach.

Quantitative and rapid detection of nanoplastics labeled by luminescent metal phenolic networks using surface-enhanced Raman scattering

The escalating prevalence of nanoplastics contamination in environmental ecosystems has emerged as a significant health hazard. Conventional analytical methods are suboptimal, hindered by their inefficiency in analyzing nanoplastics at low concentrations and their time-intensive processes. In this context, we have developed an innovative approach that employs luminescent metal–phenolic networks (L-MPNs) coupled with surface-enhanced Raman spectroscopy (SERS) to separate and label nanoplastics, enabling rapid, sensitive and quantitative detection. Our strategy utilizes L-MPNs composed of zirconium ions, tannic acid, and rhodamine B to uniformly label nanoplastics across a spectrum of sizes (50–500 nm) and types (e.g., polystyrene, polymethyl methacrylate, polylactic acid). Rhodamine B (RhB) functions as a Raman reporter within these L-MPNs-based SERS tags, providing the requisite sensitivity for trace measurement of nanoplastics. Moreover, the labeling with L-MPNs aids in the efficient separation of nanoplastics from liquid media. Utilizing a portable Raman instrument, our methodology offers cost-effective, swift, and field-deployable detection capabilities, with excellent sensitivity in nanoplastic analysis and a detection threshold as low as 0.1 μg/mL. Overall, this study proposes a highly promising strategy for the robust and sensitive analysis of a broad spectrum of particle analytes, underscored by the effective labeling performance of L-MPNs when coupled with SERS techniques.

A novel approach to data analysis for lithium-ion batteries using higher-order spectral analysis

The usage of lithium-ion batteries has become widespread across various applications, such as electronic devices, electric vehicles, and integrated renewable energy systems. Hence, there is a pressing need to enhance analytical techniques to optimize their performance and prolong their operational lifespan. This study introduces an innovative methodology that utilizes higher-order spectral analysis (HOSA) for evaluating data collected from lithium-ion batteries. The objective is to capture the inherent nonlinear and non-stationary behaviors displayed by battery data. The proposed approach mainly uses Bispectrum and Bicoherence to establish hypothesis tests for nonlinearity and non-Gaussianity of data. Through a customized algorithm, the higher-order spectrum of these refined data is calculated, uncovering intricate characteristics that may go unnoticed by conventional analytical methods. These findings have a wide range of potential applications, including battery modeling, fault detection, state-of-charge estimation, state-of-health estimation and the enhancement of battery performance. Finally, an experiment is conducted to validate the effectiveness of the method.

The Applications of qNMR in Drug Quality Control

NMR is well known as one of the most important methods for elucidating the structure of organic compounds. Furthermore, it has recently been recognized as a powerful tool for quantitative analysis. The quantitative NMR (qNMR) has become an official analytical method described in detail in the Japanese Pharmacopoeia. And today, it is widely applied in drug development. The qNMR method offers many new advantages over traditional and conventional quantitative analysis methods. For example, this method requires only a few milligrams of the analyte and allows absolute quantitation of the analyte without using a qualified reference standard as a control sample. Then, it can be easily applied to most chemicals without expending significant time and resources on method development. In addition, residual solvent can be determined using qNMR methods. The peak area of an NMR spectrum is directly proportional to the number of protons contributing to the resonance. Based on this principle, the residual solvent can be determined by counting the signal corresponding to the residual solvent in the sample solution. We have applied qNMR as an alternative to GC. Thus, qNMR is an innovative and promising analytical technique that is expected to make significant progress in the future. Recently, the analytical research and quality control departments have been working together to expand this technology to a wide range of areas in the pharmaceutical industry.

An improved laser flash method for thermal conductivity measurement of molten salts

Thermal conductivity measurement of high-temperature heat transfer fluids provides a crucial basis for designing utility-scale thermal systems. Molten salts are promising heat transfer and thermal storage fluids in high-temperature thermal energy storage systems, while the molten salt thermal conductivity obtained in existing studies exhibits large deviations due to the high experimental complexity and unstandardized test procedures. In this work, we improve the conventional laser flash analysis method by proposing a theoretical heat transfer model for multi-layer heat conduction and providing a near-optimal molten salt container design. With water as a test sample, the relative error of thermal conductivity measurement using the improved method is 6.3%. The thermal conductivity of Solar Salt from 250 to 400 ​°C, and of Hitec Salt from 160 to 250 ​°C are measured and compared with the previous work. Both results show that the thermal conductivity increases with the temperature rising. This work will promote the technology standardization for accurately acquiring the thermal conductivity of molten salts or other similar high-temperature heat transfer fluids.

The Effect of the COVID-19 Pandemic on the Factors Influencing Iranian Medical Residents' Specialty Selection: A Qualitative Study.

Medical students' specialty selection influences the composition of the physician workforce and the effectiveness of health systems. Therefore, the identification of factors that influence the choice of specialty is critical for an evidence-based health policy. This study aimed to investigate the effect of the Coronavirus Disease 2019 (COVID-19) pandemic on the determinants of specialty choice among Iranian medical residents. In early 2022, this qualitative study was conducted among Iranian medical residents in seven provinces, including Tehran, Isfahan, Fars, Khorasan Razavi, Kerman, Kermanshah, and Khuzestan. The participants were selected using a purposeful sampling method. Data were collected using 74 semi-structured in-depth face-to-face interviews. Finally, a thematic content analysis (conventional content analysis) method was applied for data synthesis. The participant's mean age was 28.7±2.5 years, and more than 52% (N=39) were men. Following data synthesis, 10 sub-themes and four main themes were identified, including educational aspects affected by the pandemic, career-related hazards, personal and professional lifestyles affected by the disease, and experiences and beliefs regarding the pandemic. The COVID-19 pandemic has had a significant impact on medical students' educational, professional, and personal aspects of specialty choices. This study demonstrated how the disease affected the choice of specialty. Therefore, the findings could be used for developing national health policy and planning.

Comparing laser-induced breakdown spectroscopy and visible near-infrared spectroscopy for predicting soil properties: A pan-European study

Soil organic carbon (SOC), texture, clay/organic carbon (OC) ratio, and extractable phosphorus are among the key soil descriptors representing the physical, chemical, and biological properties. However, analyzing these soil properties using conventional methods of analysis is time-consuming and often involves the use of hazardous chemicals. Therefore, scaling these methods to analyze more samples covering large geographical distributions within a reasonable turn-around time has become a challenge. In this study, two rapid techniques, namely laser-induced breakdown spectroscopy (LIBS) and visible and near-infrared spectroscopy (vis-NIRS), were compared for the prediction of SOC, texture, extractable phosphorus, and clay/OC ratio. A total of 896 samples collected from 23 European countries from the Land Use/Cover Area frame Survey (LUCAS) database were used to represent a wide distribution in terms of soil texture and geographical coverage. The data was manually split into calibration and validation samples while ensuring a representative geographical distribution in the calibration and validation set for the selected samples. Partial least square regression (PLSR) models were developed for both LIBS and vis-NIRS and compared based on prediction accuracy. LIBS outperformed vis-NIRS in predicting SOC, sand content, and clay/OC ratio, with root mean square error of prediction (RMSEP) values of 1.79 %, 12 %, and 7.69, respectively. Conversely, vis-NIRS yielded slightly higher RMSEP values for SOC, sand content, and clay/OC ratio predictions at 1.88 %, 15 %, and 8.13, respectively. Both LIBS and vis-NIRS performed similarly in predicting clay content, with an RMSEP of 6 %. However, there was a low predictive ability for silt and phosphorus concentration, as shown by the weak correlations (R2 < 0.5) between the predicted and reference soil properties. There was no significant difference between LIBS and vis-NIRS predictions for clay, silt, and SOC content (p > 0.05). However, there was a significant difference between the predicted sand content (p = 0.011) and clay/OC ratio (p < 0.001). The overall better performance of LIBS highlights its potential for application in a wide range of soils, especially for soils covering large geographical distributions and varying soil properties. Further, both techniques present opportunities for rapid determination of soil properties important for evaluating soil health on continental scales.

Dissatisfaction and Protest of First-Year Obstetrics and Gynecology Residents and Related Factors: A Qualitative Study

Background: Physical and emotional stress during the residency period can have lasting negative effects on residents’ health as well as patient care. Such stress can cause emotional and psychological burnout, decreased physician productivity, impaired job performance, and poor social relationships. First-year residents have more burnout and stress due to many factors. This study aimed to investigate the reasons for the protest of this group of residents. Methods: This qualitative study was conducted with the participation of residents and professors of obstetrics and gynecology in one of the teaching hospitals of Tehran University of Medical Sciences in spring of 2021. Data were collected through individual face-to-face interviews and group discussions. The conventional content analysis method was used to analyze the data. To ensure the accuracy of the qualitative data, the criteria proposed by Lincoln and Guba were considered. Results: A total of 14 participants were interviewed and 16 people participated in focus group discussion sessions. From the text of the interviews, two themes were extracted. Participants’ experiences showed unbearable pressure, domination, and anonymity as stressors in first-year obstetrics and gynecology residents. Conclusion: The results showed that the main reason for the protest of the first-year residents was the wrong behavior of the second-year residents and the lack of supervision over these behaviors. Launching a kindness campaign was a suggested solution in this research that could help improve communication between residents and the educational atmosphere.