Case Analysis Research Articles

Annular cement sheath characterization and hydraulic aperture assessment through single- and two-phase seepage tests

The integrity of well barriers is critical throughout the life cycle of oil and gas wells, with annular cement as a key component in maintaining the structural integrity of the barrier system. In this study, an annular cemented section with a well-defined microannulus was systematically constructed and characterized, and both single- and two-phase seepage tests were conducted to gain a deeper understanding of how relative permeability impacts the estimation of hydraulic aperture in potential leakage pathways. The test cell design allows modification of the microannulus gap size by changing the pressure applied inside the inner casing. This enables studying different microannulus sizes throughout the conducted tests. The single-phase seepage tests showed consistent measurements of hydraulic aperture for both gas and water, despite variations in fluid type and applied pressure, with surface roughness likely affecting the non-linear behavior of the hydraulic aperture under stress. The two-phase seepage tests demonstrated a clear relationship between breakthrough pressure and microannulus size, with the van Genuchten model providing a better fit for relative permeability and capillary pressure data. Breakthrough time experiments revealed that initial permeability estimations were significantly lower than those obtained from single-phase tests; however, once relative permeability at breakthrough was accounted for, the estimated absolute permeability values aligned closely with the single-phase experimental data. These findings offer valuable insights into the complex interactions between cement sheath integrity and gas migration, potentially contributing to developing new well integrity assessment technologies, including tracer gas below the well barrier element, as well as insights relevant for both gas migration and sustained casing pressure analysis.

HELLP Syndrome Developing at 14 Weeks of Gestation: An Extremely Rare Case Report and a Literature Review

Introduction: Hemolysis, elevated liver enzymes and low platelets (HELLP) syndrome is a disease of pregnancy that occurs very rarely before 20 weeks of gestation. We report a case of HELLP syndrome developing at 14 weeks and 2 days of gestation. Case Presentation: A 33-year-old Asian primipara at 14 weeks and 2 days of gestation visited the emergency room with a fever and headache. Initial blood pressure was 140/70 mm Hg, temperature 38.5°C, heart rate 130 beats/min with tachycardia. Her prenatal examination has been unremarkable, and fetal ultrasonography was within normal range. The laboratory results showed low platelet count with elevated liver enzymes, D-dimer, and fibrinogen but no sign of jaundice. Her WBC differential suggested a bacterial infection. Thus, we diagnosed early HELLP syndrome and immediately started conservative treatments. One day after admission, symptoms and laboratory results showed aggravation of the disease. We performed termination, followed by dilation and curettage for retained placenta. Her general condition improved rapidly after the operation. Placental biopsy showed both acute and chronic inflammation. She also had anticardiolipin antibody IgM, and after discharge, she was referred to a rheumatology specialist to address the antiphospholipid syndrome issue. Discussion: Although the triggers of HELLP syndrome are unclear, a recent inflammatory hypothesis suggests that placenta-derived inflammatory cytokines are involved. In our case, the anti-cardiolipin antibody may have triggered microangiopathy of the placenta. Our analysis of published HELLP cases revealed that, apart from the three diagnostic criteria, the most common abnormal laboratory finding was antiphospholipid antibodies. Therefore, despite its rarity, if a sign of inflammation is present in a patient, it is important to consider HELLP syndrome regardless of gestational age

Temporal Autoregressive Matrix Factorization for High-Dimensional Time Series Prediction of OSS.

Open-source software (OSS) plays an increasingly significant role in modern software development tendency, so accurate prediction of the future development of OSS has become an essential topic. The behavioral data of different open-source software are closely related to their development prospects. However, most of these behavioral data are typical high-dimensional time series data streams with noise and missing values. Hence, accurate prediction on such cluttered data requires the model to be highly scalable, which is not a property of traditional time series prediction models. To this end, we propose a temporal autoregressive matrix factorization (TAMF) framework that supports data-driven temporal learning and prediction. Specifically, we first construct a trend and period autoregressive model to extract trend and period features from OSS behavioral data, and then combine the regression model with a graph-based matrix factorization (MF) to complete the missing values by exploiting the correlations among the time series data. Finally, use the trained regression model to make predictions on the target data. This scheme ensures that TAMF can be applied to different types of high-dimensional time series data and thus has high versatility. We selected ten real developer behavior data from GitHub for case analysis. The experimental results show that TAMF has good scalability and prediction accuracy.

Exploring AI-chatbots’ capability to suggest surgical planning in ophthalmology: ChatGPT versus Google Gemini analysis of retinal detachment cases

BackgroundWe aimed to define the capability of three different publicly available large language models, Chat Generative Pretrained Transformer (ChatGPT-3.5), ChatGPT-4 and Google Gemini in analysing retinal detachment cases and suggesting...

Hierarchical Control for Partially Feasible Tasks With Arbitrary Dimensions: Stability Analysis for the Tracking Case

Hierarchical Control for Partially Feasible Tasks With Arbitrary Dimensions: Stability Analysis for the Tracking Case

What makes transitional justice possible? An analysis of the Spanish case

ABSTRACT The main purpose of this research is to identify the determinants that intervene in the transitional justice choices made in general, and those made in the Spanish case in particular. The construction of a linear regression model containing 83 transitions was developed to identify the significant explanatory factors in the transitional justice choices. The analysis enabled us to place Spain at some distance from the countries considered as successful in terms of transitional justice to classify it as an exception that is not adequately explained by the general model, while also identifying the main reasons for this.

Generating Usage-related Questions for Preference Elicitation in Conversational Recommender Systems

A key distinguishing feature of conversational recommender systems over traditional recommender systems is theirability to elicit user preferences using natural language. Currently, the predominant approach to preference elicitation is to ask questions directly about items or item attributes. Users searching for recommendations may not have deep knowledge of the available options in a given domain. As such, they might not be aware of key attributes or desirable values for them. However, in many settings, talking about the planned use of items does not present any difficulties, even for those that are new to a domain. In this article, we propose a novel approach to preference elicitation by asking implicit questions based on item usage. As one of the main contributions of this work, we develop a multi-stage data annotation protocol using crowdsourcing, to create a high-quality labeled training dataset. Another main contribution is the development of four models for the question generation task: two template-based baseline models and two neural text-to-text models. The template-based models use heuristically extracted common patterns found in the training data, while the neural models use the training data to learn to generate questions automatically. Using common metrics from machine translation for automatic evaluation, we show that our approaches are effective in generating elicitation questions, even with limited training data. We further employ human evaluation for comparing the generated questions using both pointwise and pairwise evaluation designs. We find that the human evaluation results are consistent with the automatic ones, allowing us to draw conclusions about the quality of the generated questions with certainty. Finally, we provide a detailed analysis of cases where the models show their limitations.

Compassionate Care: Listening Visits Provided by Neonatal Intensive Care Unit Nurses.

Although mothers of infants hospitalized in a neonatal intensive care unit (NICU) often experience clinically significant levels of depression symptoms, accessing mental-health treatment may be difficult. NICU mothers need emotional support that is conveniently delivered at the infant's point-of-care by a trusted professional who is knowledgeable about the medical and nursing care in the NICU. Listening Visits are an effective and accessible, nurse-delivered depression intervention, yet little is known about what mothers discuss during these sessions. This analysis of sessions recorded during the randomized controlled trial evaluation of Listening Visits in the NICU provides a glimpse into NICU mothers' concerns and experiences. This is a secondary, qualitative case analysis of the recorded Listening Visits sessions of four depressed NICU mothers as indicated by a score of 12 or above on the Edinburgh Postnatal Depression Scale. The mothers, who were all White, varied in their economic resources, educational level, availability of support, and infant illness severity. Mothers discussed similar concerns and experiences, often at analogous temporal points in the six Listening Visit sessions, as well as one common concern they voiced throughout: family and friends do not understand what it is like to have an infant in the NICU. For mildly to moderately depressed mothers of infants hospitalized in the NICU, Listening Visits provide a way for bedside nurses to deliver compassionate care by listening to mothers' concerns and experiences.

An Analysis of the Efficacy of Multilayered Repair and Reconstruction Using Combined Tissue Pedicle Flaps for Abdominal Wall Defects in Adult Bladder Exstrophy Patients.

In this study, we conducted a retrospective analysis of cases involving adult classic bladder exstrophy (CBE) accompanied by the absence of the abdominal wall. Specifically, we focused on the utilization of multilayer flaps for reconstructive purposes. In addition, we aimed to share our clinical treatment experience pertaining to similar challenges, thereby providing valuable insights to complement the surgical management of this rare disease. We conducted a retrospective analysis of 12 adult patients diagnosed with CBE who underwent initial treatment between June 2013 and January 2020. All patients underwent multilayer reconstruction to address their abdominal wall defects. This involved utilizing shallow flaps derived from the superficial fascia of the abdomen and incorporating myofascial flaps composed of the anterior sheath of the rectus abdominis and aponeurosis of the external oblique muscle. The flap sizes ranged from 9 × 11 cm to 13 × 15 cm. Abdominal wall reconstruction in the 12 patients with CBE resulted in an absence of wound dehiscence recurrence, urinary obstruction, or urinary tract infection. All patients expressed satisfaction with the aesthetic outcome of their abdominal wall based on self-evaluation. They reported a successful resumption of normal life and work activities without experiencing any restrictions. The married patients expressed contentment with their sexual function. The utilization of a multilayered reconstruction technique involving multiple flaps in adults with congenital CBE allows for successful restoration of urinary function, as well as the attainment of sufficient abdominal wall strength to support daily life and work activities, while preserving sexual function. However, it is important to approach the evaluation of surgical outcomes with caution because of the rarity of this condition and the lack of objective assessment measures.

Preservice Teachers’ Assessment Literacy Within Models-Based Practice

Purpose: Using the assessment literacy framework, the purpose of this study was to explore preservice teachers understanding and enactment of the message system during an early field experience. Method: Six PTs employed SE seasons totaling 540 min. Seven qualitative data methods were utilized. Data analysis included deductive and inductive analysis using a thematic approach. Trustworthiness included data triangulation, peer debriefing, negative case analysis, and the maintenance of an audit trail. Results: Three main themes derived from the data included: instructional decisions were driven by the SE model and informal assessment; formal assessment was driven by the structure of SE; and high assessment value but low assessment literacy. Discussion/Conclusion: The SE model drove PTs to use formal assessment, but primarily in an evaluative manner. The PTs lacked assessment literacy, felt pressure to follow the model, did not use assessment to inform teaching, and had concerns for future assessment use.

696 Optimal Management Strategy for Carotid Cavernous Aneurysm From Cost-effectiveness Perspective

INTRODUCTION: Most cavernous carotid aneurysms (CCAs) are asymptomatic benign lesions; therefore, they are frequently detected during routine clinical diagnostic processes. Most providers recommend routine surveillance instead of immediate management for CCAs given a natural history with a low risk of life-threatening complications. However, the cost-effectiveness of such care and the optimal follow-up interval remains unknown. METHODS: A decision analysis study was performed utilizing a Markov model with Monte Carlo simulations to simulate patients undergoing treatment or routine surveillance at different time intervals (half, 1-,2-,3-, 5-and 7-year intervals) for different size of CCAs (=<12 mm, 13- 24 mm, >=25 mm ). Input data for the model was extracted from the current literature. Probabilistic and deterministic sensitivity analyses were performed to evaluate the robustness of the Model. RESULTS: On base case analysis, following-up every 2 years with noninvasive imaging is the most cost-effective strategy for CCAs =< 12 mm, while annual follow up is cost-effective for large/giant (13-24 mm, >=25 mm) CCAs. The conclusions remain robust in probabilistic sensitivity and deterministic sensitivity analyses. As the risk of thrombo-embolic event gets higher and annual growth risk and annual rupture risk of CCAs increases, immediate treatment for large/giant (13- 24 mm, >=25 mm ) CCAs and annual follow up even prompt treatment for CCAs =< 12 mm is optimal. CONCLUSIONS: The most cost-effective management strategy for CCAs=< 12 mm and large/giant CCAs are following up every 2 years and following up every year, respectively. More frequent follow-up strategies or prompt preventive treatment for CCAs =< 12 mm and immediate treatment for large/giant CCAs would be more appropriate in patients with higher risk factors for cerebral ischemia, aneurysm growth and aneurysm rupture.

Automating financial reporting with natural language processing: A review and case analysis

In the evolving landscape of financial reporting, the integration of Natural Language Processing (NLP) emerges as a beacon of innovation, promising to redefine the paradigms of accuracy, efficiency, and compliance. This paper embarks on a scholarly expedition to explore the transformative potential of NLP within the realm of financial disclosures, navigating through the intricate interplay of technological advancements and regulatory frameworks. The study meticulously analyzes the application of NLP techniques in automating financial reporting, unraveling the complexities of implementation and the multifaceted challenges therein through a qualitative research design. Through a comprehensive review of the literature and empirical data, the paper illuminates the efficacy of NLP in enhancing the precision and reliability of financial reports while also delving into stakeholders' perceptions regarding its adoption. The findings reveal a significant improvement in reporting efficiency and accuracy, underscored by the strategic importance of addressing implementation hurdles and regulatory considerations. The study culminates in a set of cogent recommendations, advocating for the development of a robust framework for NLP applications in financial reporting, alongside a clarion call for ongoing research into sophisticated NLP models and scalable solutions. In essence, this paper not only charts a course for the future integration of NLP in financial reporting but also stands as a testament to the indelible impact of technological innovation on the financial industry. It beckons the academic and professional communities to forge a collaborative path towards realizing the full potential of NLP, thereby ushering in a new era of transparency and insight in financial disclosures.

A combination prediction model based on Theil coefficient and induced continuous aggregation operator for the prediction of Shanghai composite index

This paper proposes an interval combination prediction model for Shanghai composite index, utilizing the Theil coefficient and the induced continuous generalized ordered weighted logarithmic harmonic averaging (ICGOWLHA) operator. The effectiveness of the proposed model under specific weight conditions and the existence of its analytical solution are demonstrated. Shanghai composite index's case analysis demonstrates that, in terms of interval root mean squared error (IRMSE), interval mean absolute error (IMAE), interval mean absolute percentage error (IMAPE), and interval mean squared percentage error (IMSPE), the proposed model's predictive performance improvements over the best-performing single prediction model are 29.33%, 25.72%, 26.10%, and 28.86%, respectively. At the same time, the theoretical properties of the model are verified in the results of the case analysis, and the model's convergence is reflected in sensitivity analysis. Through extensive model comparisons, it is observed that the model proposed in this paper exhibits strong generalization, without specific limitations on data size or feature count. It demonstrates good aggregation prediction performance for interval data. Moreover, it is applicable to various fields, including finance, environment, and others.

Przepadek zwierzęcia w świetle orzecznictwa sądowego – analiza przypadku i postulaty de lege ferenda w celu podniesienia ochrony prawnej zwierząt

The article aims to present the functioning of the institution of animal forfeiture under the Act of August 21, 1997 on animal protection. It is a repressive-preventive institution, which is closely related to the property rights of every citizen under the Constitution of the Republic of Poland. At the beginning of the considerations, an attempt was made to define the concept of “animal forfeiture” and to indicate the legal basis for ruling on this legal measure. The types of animal forfeiture occurring under the Act are presented and the circumstances determining their ruling identified. The analysis is extended to include the presentation of cases of application of this institution taken from court rulings and supported by statistical data regarding their adjudication. An interesting aspect of the article is the presentation of resolutions of City Councils, which were declared invalid by the Provincial Administrative Court to the extent that they were inconsistent with the Animal Protection Act. Extensive legal considerations, reinforced by case law and analysis of statistics, have made it possible to present de lege ferenda postulates with regard to raising the level of protection of animal rights. One of the proposals is to combine the institution of forfeiture with a total ban on animal ownership, as well as to articulate in the law the justification for ordering the forfeiture of an animal’s carcass. The article is an attempt to present legal solutions that will enhance the legal protection of animals. As shown in the article, this measure is used relatively rarely. Statistics show that it was applied in only 21.5% of the cases analyzed. Such results may raise legitimate doubts, since most animals against which crimes have been committed are still under the care of their perpetrators, which should undoubtedly change.

Out-of-set association analysis of lung cancer drugs and symptoms based on clinical case data mining.

There are 1.8 million lung cancer deaths worldwide, accounting for 18% of global cancer deaths, including 710,000 in China, accounting for 23.8% of all cancer deaths in China. To explore the out-of-set association rules of lung cancer symptoms and drugs through text mining of traditional Chinese medicine (TCM) treatment of lung cancer, and form medical case analysis to analyze the experience of TCM syndrome differentiation in its treatment. The medical records of all patients diagnosed with lung cancer in Nanjing Chest Hospital from January to December 2018 were collected, and the out-of-set association analysis was performed using the MedCase v5.2 TCM clinical scientific research auxiliary platform based on the frequent pattern growth enhanced association analysis algorithm. In terms of TCM treatment of lung cancer, the clinical symptoms with high correlation included cough, expectoration, chest distress, and white phlegm; and the drugs with high correlation included Pinellia ternata, licorice root, white Atractylodes rhizome, and Radix Ophiopogonis; with the prescriptions based on Erchen and Maimendong decoctions. This analytical study of the medical cases of TCM treatment for lung cancer was performed using data mining techniques, and the out-of-set association rules between clinical symptoms and drugs were analyzed, including the understanding of lung cancer in TCM. Moreover, the essence of experience in drug use was gathered, providing significant scientific guidance for the clinical treatment of lung cancer.

A Study of the Subtitle Translation of Coco from the Perspective of Skopos Theory

In the context of the continuous advancement of globalization, intercultural communication is increasingly frequent. Films, as an art form, play a crucial role in promoting cultural exchange. With skopos theory as the theoretical framework, this article analyzes the subtitle translation of the film Coco. Through the analysis of specific cases, it is observed that translators adhere to the skopos rule, the coherence rule and the fidelity rule of skopos theory during the subtitle translation of Coco, and effectively address the problem of differences between target language culture and source language culture by adopting translation methods and strategies such as free translation and domestication so as to facilitate effective movie-watching experience, foster communication, and achieve cross-cultural communication.

Comparative Analysis of Pediatric Brucellosis Cases With and Without Bacteremia.

Introduction: Brucellosis, which is among the endemic regions of Turkey, is a common zoonotic disease. The gold standard in diagnosing brucellosis is culture. We aimed to compare demographic characteristics, risk factors, and clinical and laboratory variables between cases with culture positivity and undetected in culture. Materials and Methods: This single-center study was conducted between January 2007 and April 2022. Clinical and laboratory data of patients with brucella growth in blood culture and patients without growth were compared. Results: A total of 150 patients were included in the study. The median age was 10 (1-18 years). Of the patients, 66 (44%) were female and 84 (56%) were male. Forty (26.7%) of the patients were bacteremic and 110 (73.3%) were nonbacteremic. In the bacteremic group, white blood cell count, platelet, and hemoglobin counts were lower, and aspartate aminotransferase (AST) and alanine aminotransferase (ALT) values were higher. In clinical evaluation, fever, hepatomegaly, splenomegaly, and abdominal pain were more common in the bacteremic group. Conclusion: The distinction between bacteremic and nonbacteremic brucellosis can be predicted using laboratory values such as white blood cells, hemoglobin counts, platelet, ALT, and AST, and clinical findings such as fever, abdominal pain, hepatomegaly, and splenomegaly.

Development and spatial characteristics of housing support services for individuals with psychiatric disabilities in South Korea

ABSTRACT This study aims to explore the evolution of mental health housing policies and spatial characteristics for individuals with Psychiatric Disabilities (IWPDs) in South Korea. This study utilised qualitative methods, including literature review and case analysis, to investigate changes of housing policies and spatial characteristics of residential facilities and independent housing for IWPDs in South Korea. The findings indicate that current housing services limit privacy, autonomy for IWPDs and have regional differences in resource allocation. Based on the circumstance of less radical deinstitutionalisation, the Korean mental health housing policy needs a gradual approach addressing regional disparities and enhancing the residential conditions.

Analysis methods and case analysis of effect modification (3): effect modification in individual patient data Meta-analysis

This paper briefly introduces the unique advantages, overall analysis ideas and existing analysis methods of individual patient data Meta-analysis in terms of effect modification. In addition to Meta-regression and subgroup analysis, this paper also introduces the analysis methods based on part of individual patient data integrated with aggregated data and summarizes the current reporting of the above mentioned methods. In addition, the application and results interpretation of the above mentioned methods in individual patient data Meta-analysis are presented in this paper by taking "Effects of sodium-glucose cotransporter 2 inhibitors on SBP in patients with type 2 diabetes" as an example and by introducing their advantages and limitations.

Research on Coordinated Optimization of Source-Load-Storage Considering Renewable Energy and Load Similarity

Currently, the global energy revolution in the direction of green and low-carbon technologies is flourishing. The large-scale integration of renewable energy into the grid has led to significant fluctuations in the net load of the power system. To meet the energy balance requirements of the power system, the pressure on conventional power generation units to adjust and regulate has increased. The efficient utilization of the regulation capability of controllable industrial loads and energy storage can achieve the similarity between renewable energy curves and load curves, thereby reducing the peak-to-valley difference and volatility of the net load. This approach also decreases the adjustment pressure on conventional generating units. Therefore, this paper proposes a two-stage optimization scheduling strategy considering the similarity between renewable energy and load, including energy storage and industrial load participation. The combination of the Euclidean distance, which measures the similarity between the magnitude of renewable energy–load curves, and the load tracking coefficient, which measures the similarity in curve shape, is used to measure the similarity between renewable energy and load profiles. This measurement method is introduced into the source-load-storage optimal scheduling to establish a two-stage optimization model. In the first stage, the model is set up to maximize the similarity between renewable energy and the load profile and minimize the cost of energy storage and industrial load regulation to obtain the desired load curve and new energy output curve. In the second stage, the model is set up to minimize the overall operation cost by considering the costs associated with abandoning the new energy sources and shedding loads to optimize the output of conventional generator sets. Through a case analysis, it is verified that the proposed scheduling strategy can achieve the tracking of the load curve to the new energy curve, reducing the peak-to-valley difference of the net load curve by 48.52% and the fluctuation by 67.54% compared to the original curve. These improvements effectively enhance the net load curve and reduce the difficulty in regulating conventional power generation units. Furthermore, the strategy achieves the full discard of renewable energy and reduces the system operating costs by 4.19%, effectively promoting the discard of renewable energy and reducing the system operating costs.