General Data Research Articles

Effectiveness of 3D printing technology-assisted neuroendoscopic surgery for hypertensive intracerebral hemorrhage in primary hospitals

BackgroundWe aimed to investigate the clinical effectiveness of integrating 3D-printing technology with neuroendoscopy for treating hypertensive intracerebral hemorrhage (HICH). MethodsBetween November 2020 and November 2023, a total of 105 HICH cases were selected and categorized into the conventional surgery group and the neuroendoscopic surgery. General data, Glasgow Coma Scale (GCS), National institutes of health neurological impairment score (NIHSS), preoperative hematoma volume, intracerebral parenchymal and intraventricular hematoma (IVH) clearance rates, Modified Graeb Scale (mGS), postoperative rebleeding rate, intracranial infection, and Modified Rankin Scale (mRS) were collected to assess the therapeutic effectiveness. ResultsThere were no significant statistical differences in gender, age, preoperative GCS scores, NIHSS or hematoma volume between the neuroendoscopic surgery and conventional surgery group. The postoperative results indicated that the clearance rate of intracerebral parenchymal and IVH in neuroendoscopic surgery group was notably higher than the conventional surgery group. The neuroendoscopic surgery group achieved superior outcomes in terms of bone flap area, operation time, extubation time, and average hospital stay compared to the conventional surgery group (P < 0.05). Following a 3-month follow-up, there was no significant difference in the mRS and NIHSS between the two groups. However, when the patients with IVH (mGS≥10) indicated better postoperative outcomes in the neuroendoscopic surgery group (P < 0.05). ConclusionThe integration of neuroendoscopic minimally invasive surgery with 3D-printing technology for treating HICH demonstrated high efficacy for removing intracerebral and IVH. This approach incurred less trauma compared to traditional craniectomy surgery, affording shortened surgical and hospitalization durations. Consequently, it appears to be a highly efficacious method for treating HICH, particularly in cases involving IVH.

Efficacy of a Combination of Functional Exercise and Psychological Interventions in Improving Postoperative Rehabilitation and Intervention Compliance in Patients With Breast Cancer

ObjectiveThis study aimed to investigate the effect of a combination of functional exercise and psychological interventions on postoperative rehabilitation and intervention compliance in patients with breast cancer (BC). MethodsThis study involved 100 patients with BC who underwent a radical mastectomy in our hospital between April 2020 and April 2021. We assigned patients to a control group (with a functional exercise intervention for patients) and an observation group (where patients received psychological interventions based on functional exercise) using a random number table. We observed and recorded the general data, intervention compliance, range of motion (ROM) of the shoulder joint pre and postintervention, pre and postintervention quality of life scores, and anxiety and depression scores before and after the interventions. ResultsThere were no significant differences in general data between the 2 groups (P > .05). Repeated measures analysis showed no preintervention differences in compliance, shoulder ROM, quality of life, or anxiety and depression scores (P > .05). Postintervention, compliance and shoulder ROM improved in both groups, with the observation group significantly outperforming the control group (P < .05). Quality of life scores improved significantly in both groups, with higher scores in the observation group at 1 and 3 months (P < .05). Anxiety and depression scores decreased in both groups, with the observation group showing lower scores than the control group (P < .05). ConclusionCombining functional exercise with psychological interventions improves treatment compliance, psychological status, postoperative shoulder ROM, and quality of life in breast cancer patients.

Reinforcement prompting for financial synthetic data generation

The emergence of Large Language Models (LLMs) has unlocked unprecedented potential for comprehending and generating human-like text, fueling advances in the finance domain – a tool that can shape investment strategies and market predictions. Nevertheless, challenges stemming from the necessity for extensive labeled data and the imperative for data privacy remain. The generation of high-quality synthetic data emerges as a promising avenue to circumvent these issues. In this paper, we introduce a novel methodology, named “Reinforcement Prompting”, to address these challenges. Our strategy employs a policy network as a Selector to generate prompts, and an LLM as an Executor to produce financial synthetic data. This synthetic data generation process preserves data privacy and mitigates the dependency on real-world labeled datasets. We validate the effectiveness of our approach through experimental evaluations. Our results indicate that models trained on synthetic data generated via our approach exhibit competitive performance when compared to those trained on actual financial data, thereby bridging the performance gap. This research provides a novel solution to the challenges of data privacy and labeled data scarcity in financial sentiment analysis, offering considerable advancement in the field of financial machine learning.

How can bottom-up citizen science restore public trust in environmental governance and sciences? Recommendations from three case studies

Citizen science is currently at the forefront of environmental scientific research and public policy for its potential to improve environmental governance, restore epistemic trust and help address some of the most stressing environmental challenges. Although citizen science is gaining increasing popularity, there is little empirical evidence to support these claims and demonstrate how bottom-up citizen science shapes public trust in environmental governance and science. In this paper we reflect on three grassroot environmental citizen science initiatives in Cameroon, Japan, and the UK to identify and present an instrumental framework which includes trustee attributes and conditions that influence how epistemic trust is shaped, and which should inform citizen science and other participatory practices. We explain that citizen science is an approach which enables political processes through the construction of well-informed techno-scientific arguments, which expose deficit assumptions about the public’s ability to participate in knowledge co-production process. To avoid repeating the failures of the past and risk amplifying issues of public distrust further, we provide suggestions built around key trustee attributes which can be incorporated in citizen science practices and we urge that environmental policy needs to create clear policy frameworks to enable the generation of actionable data, especially when such approaches are initiated and implemented as instrumental public participation methods.

Reconstructing Prospective Intergenerational Educational Mobility in 12 Countries.

In this article, we reconstruct prospective intergenerational educational mobility and explore fertility's role in this process for women born between 1925 and 1950 in 12 European countries. We do so by combining high-quality retrospective data (Generations and Gender Survey) and low-requirement prospective datasets using an inferential method developed and advanced in prior research. Our analysis shows that the negative educational fertility gradient partly compensates for the inequality in prospective mobility rates between lower and higher educated women and is most pronounced in high-inequality contexts. However, fertility's role is small and declining and thus does not account for much of the differences in mobility rates between countries. We also explore the relative importance of sibship size effects in mediating the effect of fertility gradient, finding it negligible. Finally, we explore the correspondence between prospective and retrospective estimates in the reconstruction of prospective mobility rates and suggest why the former, when available, must be preferred.

From Novice to Mastery: Learning Curve and Efficacy Analysis of Short-Term Spinal Cord Stimulation for Diabetic Foot Ulcers

To analyze the learning curve of novices in mastering short-term Spinal cord stimulation (st-SCS) for diabetic foot, evaluating the efficacy, safety, and difficulty of this technique. A retrospective review of diabetic foot patients treated with st-SCS at our hospital was conducted. All procedures were performed by the same physician and patients were sequentially numbered according to the order of surgery. Learning curves were plotted using segmented linear regression and cumulative sum curves based on surgery duration. Patients were divided into 2 groups according to the inflection points on the learning curve: the learning group and the mastery group. Pre- and postoperative efficacy indicators were recorded and compared, along with general patient data, perioperative parameters, and incidence of complications. A total of 36 patients were included. Significant improvements were observed post-st-SCS in ulcer size (from 7.00cm2 to 4.00cm2), visual analog scale (from 7.00 to 3.00), foot temperature (from 30.06°C to 32.37°C), and Pittsburgh Sleep Quality Index (from 14.42 to 8.36) (P < 0.05). The physician could proficiently perform st-SCS after 9 cases. Surgery time was significantly shorter in the mastery group (1-9 cases) compared to the learning group (10-36 cases) (28.04 vs. 43.56min, P < 0.05). There were no significant differences between the 2 groups in baseline data, improvement in efficacy indicators, or complications (P > 0.05). St-SCS is beneficial for wound healing, pain relief, improving peripheral circulation, and improving sleep quality. Surgeons can master this simple and safe technique in about 9 cases.

Radially symmetric solutions of the ultra-relativistic Euler equations in several space dimensions

The ultra-relativistic Euler equations for an ideal gas are described in terms of the pressure, the spatial part of the dimensionless four-velocity and the particle density. Radially symmetric solutions of these equations are studied in two and three space dimensions. Of particular interest in the solutions are the formation of shock waves and a pressure blow up. For the investigation of these phenomena we develop a one-dimensional scheme using radial symmetry and integral conservation laws. We compare the numerical results with solutions of multi-dimensional high-order numerical schemes for general initial data in two space dimensions. The presented test cases and results may serve as interesting benchmark tests for multi-dimensional solvers.

The Predictive Role of Cardiac Troponin Elevation Ratio Combined With Heart Function Index Model in the Prognosis of Non-ST-Segment Elevation Myocardial Infarction Patients.

Non-ST-segment elevation myocardial infarction (NSTEMI) is a common form of coronary artery disease, and its prognosis is influenced by multiple factors. This study aimed to analyze the predictive role of the combined application of cardiac troponin and cardiac function indices in NSTEMI patients' prognosis. NSTEMI patients were screened and included in the study. Cardiac troponin elevation ratio (cardiac troponin I (cTnI)/upper limit of normal (ULN)) was measured upon admission, and cardiac function was assessed. General clinical data, laboratory parameters, Grace score, New York Heart Association (NYHA) functional class, complications, and mortality data were collected. The correlation between mortality in NSTEMI patients and clinical parameters was analyzed, and a nomogram prediction model for NSTEMI patient mortality was established. A total of 252 NSTEMI patients were included. Female gender, elevated high-sensitivity C-reactive protein (H-CRP), left ventricular ejection fraction (LVEF) < 50%, NYHA class III and IV, and cTnI/ULN elevation by 36.25-fold were significantly independently associated with mortality outcomes. Multifactorial logistic analysis indicated that these indices remained associated with mortality. A nomogram model predicting NSTEMI patient mortality was constructed using these indices, with an area under the curve (AUC) of 0.911, sensitivity of 97.5%, and specificity of 72.8%. This predictive model outperformed the Grace score (AUC = 0.840). In NSTEMI patients, a 36.25-fold increase in cTnI/ULN, coupled with NYHA class III and IV, independently predicted prognosis. We developed a nomogram model integrating cTnI/ULN and cardiac function indices, aiding clinicians in assessing risk and implementing early interventions for improved outcomes.

Treatment of failed cervical total disc replacements in a series of 53 cases and description of a management strategy.

To describe modes of failure of cervical TDR, their related treatment strategies, and to describe a management strategy for the treatment of failed cervical TDR. This retrospective study was based on a consecutive series of 53 cervical TDR patients who underwent removal or revision surgery. Chart review was conducted to collect general descriptive data, reasons for TDR removal/revision, duration from index implantation to re-operation, and the subsequent procedure performed. Among 53 patients, 36 underwent TDR removal and fusion, 16 underwent TDR removal and replacement with another TDR, and one patient's TDR was revised by repositioning. The mean duration from index surgery to removal/revision was 40.1months (range: 3days-222months). In all cases, removal/revision surgery was completed without complication. The most common reason for removal was severe osteolysis, often involving C. acnes infection, and was primarily associated with one implant type. TDR removal and fusion were performed for subsidence, device migration, treatment of symptoms arising from posterior anatomy (facet joints, etc.), approach-related complications and pain. TDR replacement was feasible for hypermobility, metal allergy, implant locked in kyphosis, and oversized implant use. In one case of TDR malpositioning, the device was successfully revised into appropriate position. After cervical TDR failure, replacing a TDR with another implant can be feasible. Reasons for revision or removal after cervical TDR surgery include biomechanical failure, implant migration, surgeon or technical error, or biological reasons. The type of failure can help the surgeon create a strategy to address these complications.

A systematic approach for data generation for intelligent fault detection and diagnosis in District Heating

This study introduces a novel systematic approach to address the challenge of labeled data scarcity for fault detection and diagnosis (FDD) in District Heating (DH) systems. To replicate real-world DH fault scenarios, we have created a controlled laboratory emulation of a generic DH substation integrated with a climate chamber. Furthermore, we present an FDD pipeline using an isolation forest and a one-class support vector machine for fault detection alongside a random forest and a support vector machine for fault diagnosis. Our research analyzed the impact of data sampling frequencies on the FDD models, revealing that shorter intervals, such as 1-min and 5-min, significantly improve FDD performance. We provide detailed information on six scenarios, including normal operation, a minor valve leak, a valve leak, a stuck valve, a high heat curve, and a temperature sensor deviation. For each scenario, we present their signature, quantifying their unique behavior and providing deeper insights into the operational implications. The signatures suggest that, while variable, faults have a consistent pattern seen in the generic DH substation. While this work contributes directly to the DH field, our methodology also extends its applicability to a broader context where labeled data is scarce.

Bridging the Data Gap in Photovoltaics with Synthetic Data Generation

Bridging the Data Gap in Photovoltaics with Synthetic Data Generation

Navigating the AI horizon in hospitality: a novel classification and future research agenda

PurposeThis study aims to evaluate Artificial Intelligence (AI) research in the hospitality industry based on the service AI framework (mechanical-thinking-feeling) and highlight prospective avenues for future inquiry in this growing domain.Design/methodology/approachThis paper conceptualizes timely concepts supported by research spanning multiple domains.FindingsThis research introduces a novel classification for the domain of AI hospitality research. This classification encompasses prediction and pattern recognition, computer vision, NLP, behavioral research, and synthetic data generation. Based on this classification, this study identifies and elaborates upon five emerging research topics, each linked to a corresponding set of research questions. These focal points encompass the realms of interpretable AI, controllable AI, AI ethics, collaborative AI, and synthetic data generation.Originality/valueThis viewpoint provides a foundational framework and a directional compass for future research in AI within the hospitality industry. It pushes the industry forward with a balanced approach to leveraging AI to augment human potential and enrich customer experiences. Both the classification and the research agenda would contribute to the body of knowledge that will guide the industry toward a future where technology and human service coalesce to create unparalleled value for all stakeholders.

Bridging the Data Gap in Photovoltaics with Synthetic Data Generation

Bridging the Data Gap in Photovoltaics with Synthetic Data Generation

A model is worth tens of thousands of examples for estimation and thousands for classification

Traditional signal processing methods relying on mathematical data generation models have been cast aside in favour of deep neural networks, which require vast amounts of data. Since the theoretical sample complexity is nearly impossible to evaluate, these amounts of examples are usually estimated with crude rules of thumb. However, these rules only suggest when the networks should work, but do not relate to the traditional methods. In particular, an interesting question is: how much data is required for neural networks to be on par or outperform, if possible, the traditional model-based methods? In this work, we empirically investigate this question in three simple examples covering estimation and classification, where the data is generated according to precisely defined mathematical models, and where well-understood optimal or state-of-the-art mathematical data-agnostic solutions are known. A first problem is deconvolving one-dimensional Gaussian signals, a second one is estimating a circle’s radius and location in random grayscale images of disks, and a third one both classifies the presence of a line and locates it when present in a binary random dot image. By training various networks, either naive custom designed or well-established ones, with various amounts of training data, we find that networks require tens of thousands of examples for estimation in comparison to the traditional methods and thousands for classification, whether the networks are trained from scratch or even with transfer-learning or finetuning.

Spatially explicit supply chain for nationwide CO2-to-fuel infrastructure: Data-driven optimization with Gaussian mixture model -based region screening and clustering

This study proposes a novel machine learning-based method for designing spatially explicit supply chains for establishing a nationwide CO2 utilization strategy. The proposed method is capable of considering not only technical feature for CO2-to-fuel conversion, but also the geographical features for facility allocation by integrating spatial data and various sociological information. To develop the proposed method, map polygonization and the ray-casting algorithm for precise spatial data generation are implemented, which identifies and screens out geographically and socially unfavorable terrains for facility allocation. The scenario evaluation method, which includes the Gaussian mixture model-based expectation–maximization algorithm and supply chain economics, such as transportation price and facility capital investment, are considered to calculate a large set of supply chain scenarios. As a case study, the proposed method is applied to determine the optimal supply chain configuration to secure economic feasibility in the Korean CO2 capture and utilization project. The results identify the hydrogen price as the most critical factor in the supply chain economy and show the potential profitability in 2043–2045. This study can promote the nationwide CO2 reduction strategy and the development of a sustainable energy supply system.

RadShap: An Explanation Tool for Highlighting the Contributions of Multiple Regions of Interest to the Prediction of Radiomic Models.

Explaining the decisions made by a radiomic model is of significant interest, as it can provide valuable insights into the information learned by complex models and foster trust in well-performing ones, thereby facilitating their clinical adoption. Promising radiomic approaches that aggregate information from multiple regions within an image currently lack suitable explanation tools that could identify the regions that most significantly influence their decisions. Here we present a model- and modality-agnostic tool (RadShap, https://github.com/ncaptier/radshap), based on Shapley values, that explains the predictions of multiregion radiomic models by highlighting the contribution of each individual region. Methods: The explanation tool leverages Shapley values to distribute the aggregative radiomic model's output among all the regions of interest of an image, highlighting their individual contribution. RadShap was validated using a retrospective cohort of 130 patients with advanced non-small cell lung cancer undergoing first-line immunotherapy. Their baseline PET scans were used to build 1,000 synthetic tasks to evaluate the degree of alignment between the tool's explanations and our data generation process. RadShap's potential was then illustrated through 2 real case studies by aggregating information from all segmented tumors: the prediction of the progression-free survival of the non-small cell lung cancer patients and the classification of the histologic tumor subtype. Results: RadShap demonstrated strong alignment with the ground truth, with a median frequency of 94% for consistently explained predictions in the synthetic tasks. In both real-case studies, the aggregative models yielded superior performance to the single-lesion models (average [±SD] time-dependent area under the receiver operating characteristic curve was 0.66 ± 0.02 for the aggregative survival model vs. 0.55 ± 0.04 for the primary tumor survival model). The tool's explanations provided relevant insights into the behavior of the aggregative models, highlighting that for the classification of the histologic subtype, the aggregative model used information beyond the biopsy site to correctly classify patients who were initially misclassified by a model focusing only on the biopsied tumor. Conclusion: RadShap aligned with ground truth explanations and provided valuable insights into radiomic models' behaviors. It is implemented as a user-friendly Python package with documentation and tutorials, facilitating its smooth integration into radiomic pipelines.

Overcoming Data Deficiency for Multi-Person Pose Estimation.

Building multi-person pose estimation (MPPE) models that can handle complex foreground and uncommon scenes is an important challenge in computer vision. Aside from designing novel models, strengthening training data is a promising direction but remains largely unexploited for the MPPE task. In this article, we systematically identify the key deficiencies of existing pose datasets that prevent the power of well-designed models from being fully exploited and propose the corresponding solutions. Specifically, we find that the traditional data augmentation techniques are inadequate in addressing the two key deficiencies, imbalanced instance complexity (IC) (evaluated by our new metric IC) and insufficient realistic scenes. To overcome these deficiencies, we propose a model-agnostic full-view data generation (Full-DG) method to enrich the training data from the perspectives of both poses and scenes. By hallucinating images with more balanced pose complexity and richer real-world scenes, Full-DG can help improve pose estimators' robustness and generalizability. In addition, we introduce a plug-and-play adaptive category-aware loss (AC-loss) to alleviate the severe pixel-level imbalance between keypoints and backgrounds (i.e., around 1:600). Full-DG together with AC-loss can be readily applied to both the bottom-up and top-down models to improve their accuracy. Notably, plugging into the representative estimators HigherHRNet and HRNet, our method achieves substantial performance gains of 1.0%-2.9% AP on the COCO benchmark, and 1.0%-5.1% AP on the CrowdPose benchmark.

Enhancing subsurface seismic profiling with distributed acoustic sensing and optimization algorithms

The distribution of shear-wave velocities in the subsurface is generally used to assess the potential for seismic liquefaction and soil amplification effects and to classify seismic sites. Newly developed distributed acoustic sensing (DAS) technology enables estimation of the shear-wave distribution as a high-density seismic observation system. This technology is characterized by low maintenance costs, high-resolution outputs, and real-time data transmission capabilities, albeit with the challenge of managing massive data generation. Rapid and efficient interpretation of data is the key to advancing application of the DAS technology. In this study, field tests were carried out to record ambient noise over a short period using DAS technology, from which the surface-wave dispersion curves were extracted. In order to reduce the influence of directional effects on the results, an unsupervised clustering method is used to select appropriate clusters to extract the Green's function. A combination of a genetic algorithm and Monte Carlo (GA-MC) simulation is proposed to invert the subsurface velocity structure. The stratigraphic profiles obtained by the GA-MC method are in agreement with the borehole profiles. Compared to other methods, the proposed optimization method not only improves the solution quality but also reduces the solution time.

Temporomandibular joint disorder among Peruvian music students of different instrumental specialties

Background Music, a profound human expression, can have physical implications for its practitioners. Musicians face various physical challenges that can impact their well-being, with craniofacial and temporomandibular pain being prevalent yet understudied. Temporomandibular joint disorder (TMJD) affects up to one-third of the population, though prevalence varies by study. While musculoskeletal issues like carpal tunnel syndrome are well-documented in musicians, craniofacial and temporomandibular pain is less explored. This study aims to determine the differences in TMJD frequency among music students of different instrumental specialties. Methods The sample was composed of students between 12 and 25 years of age from the National University of Music of Peru. A stratified random probability sample was chosen. Data were collected using a structured instrument covering general data, academic information, dental malocclusion, the Fonseca Simplified Anamnestic Index, and the Global Stress Perception Scale. Statistical analysis included Pearson's Chi-square test and a generalized linear model regression. Results The sample included 71.4% males with a median age of 23 years. Class II dental occlusion was most common (51.4%). TMJD prevalence was: 14.3% no TMJD, 60.0% mild TMJD, 22.9% moderate TMJD, and 2.9% severe TMJD. Metal wind (PR=0.04, 95%CI [0.00; 50.04], p=0.002) and woodwind players (PR=0.03, 95%CI [0.00; 0.56], p=0.018) had lower TMJD prevalence compared to plucked string and percussion players. Post-regression analysis indicated significant differences between metal wind and percussion students (p=0.030) and between percussion and woodwind students (p=0.025). These results are supported by the adjusted ratio of proportions analysis, which maintains statistical significance. Conclusion We found significant differences in TMJD prevalence among music students based on their instrumental specialty, with lower rates in metal wind and woodwind players compared to plucked string and percussion players.

Privacy Protection for Blockchain-Based Healthcare IoT Systems: A Survey

To enable precision medicine and remote patient monitoring, internet of healthcare things (IoHT) has gained significant interest as a promising technique. With the widespread use of IoHT, nonetheless, privacy infringements such as IoHT data leakage have raised serious public concerns. On the other side, blockchain and distributed ledger technologies have demonstrated great potential for enhancing trustworthiness and privacy protection for IoHT systems. In this survey, a holistic review of existing blockchain-based IoHT systems is conducted to indicate the feasibility of combining blockchain and IoHT in privacy protection. In addition, various types of privacy challenges in IoHT are identified by examining general data protection regulation (GDPR). More importantly, an associated study of cutting-edge privacy-preserving techniques for the identified IoHT privacy challenges is presented. Finally, several challenges in four promising research areas for blockchain-based IoHT systems are pointed out, with the intent of motivating researchers working in these fields to develop possible solutions.