Correlation between Short-Form 36 Scores and Neck Disability Index in Patients Undergoing Anterior Cervical Discectomy and Fusion

Study Design Case control study. Purpose To determine how the Neck Disability Index (NDI), a cervical spine-specific outcome, reflects health-related quality-of-life, and if NDI is correlated to the 36-item Short-Form Health Survey (SF-36) scores. Overview of Literature NDI is a useful tool for assessing health-related quality of life in patients with neck pain. Methods We used the Pearson product-moment correlation coefficient to assess the validity of all items under NDI and SF-36, and the Pearson’s correlation coefficient to assess the correlation between NDI and total SF-36 scores. The primary outcome measures were spine-specific health status- and general health status-measures after spine surgery, and these were evaluated every year for 2 years, using both NDI and SF-36 scores. Results NDI had a strong linear correlation with SF-36 and its two scales, the Physical Component Score (PCS) and the Mental Component Score (MCS), attesting to the validity of these two instruments. Among the eight subscales of SF-36, there was a strong linear correlation between NDI and PCS-physical functioning, PCS-bodily pain, and MCS-role emotional. Further, a moderate linear correlation was observed between NDI and subscales of PCS-role physical, PCS-general health, and MCS-social functioning, and between NDI and MCS-vitality and MCS-mental health. Conclusions Our findings suggest that the NDI adequately reflects the patient’s physical and mental quality of life, implying that the use of NDI to assess functional outcomes can also be ultimately used to evaluate the patient’s quality of life

OCT for non-destructive examination of the internal biological structures of mosquito specimen

The Study of mosquitoes and their behavioral analysis are of crucial importance to control the alarmingly increasing mosquito-borne diseases. Conventional imaging techniques use either dissection, exogenous contrast agents. Non-destructive imaging techniques, like x-ray and microcomputed tomography uses ionizing radiations. Hence, a non-destructive and real-time imaging technique which can obtain high resolution images to study the anatomical features of mosquito specimen can greatly aid researchers for mosquito studies. In this study, the three-dimensional imaging capabilities of optical coherence tomography (OCT) for structural analysis of Anopheles sinensis mosquitoes has been demonstrated. The anatomical features of An. sinensis head, thorax, and abdomen regions along with internal morphological structures like foregut, midgut, and hindgut were studied using OCT imaging. Two-dimensional (2D) and three-dimensional (3D) OCT images along with histology images were helpful for the anatomical analysis of the mosquito specimens. From the concurred results and by exhibiting this as an initial study, the applicability of OCT in future entomological researches related to mosquitoes and changes in its anatomical structure is demonstrated

When Bigger Is Not Greener: Ensuring the Sustainability of Power- to-Gas Hydrogen on a National Scale

As the prices of photovoltaics and wind turbines continue to decrease, more renewable electricity-generating capacity is installed globally. While this is considered an integral part of a sustainable energy future by many nations, it also poses a significant strain on current electricity grids due to the inherent output variability of renewable electricity. This work addresses the challenge of renewable electricity surplus (RES) utilization with target-scaling of centralized power-to-gas (PtG) hydrogen production. Using the Republic of Korea as a case study, due to its ambitious plan of 2030 green hydrogen production capacity of 0.97 million tons year-1, we combine predictions of future, season-averaged RES with a detailed conceptual process simulation for green H2 production via polymer electrolyte membrane (PEM) electrolysis combined with a desalination plant in six distinct scale cases (0.5-8.5 GW). It is demonstrated that at scales of 0.5 to 1.75 GW the RES is optimally utilized, and PtG hydrogen can therefore outperform conventional hydrogen production both environmentally (650-2210 Mton CO2 not emitted per year) and economically (16-30% levelized cost reduction). Beyond these scales, the PtG benefits sharply drop, and thus it is answered how much of the planned green hydrogen target can realistically be if on an industrial scale

An Ensemble-Based Approach to Anomaly Detection in Marine Engine Sensor Streams for Efficient Condition Monitoring and Analysis

This study proposes an unsupervised anomaly detection method using sensor streams from the marine engine to detect the anomalous system behavior, which may be a possible sign of system failure. Previous works on marine engine anomaly detection proposed a clustering-based or statistical control chart-based approach that is unstable according to the choice of hyperparameters, or cannot fit well to the high-dimensional dataset. As a remedy to this limitation, this study adopts an ensemble-based approach to anomaly detection. The idea is to train several anomaly detectors with varying hyperparameters in parallel and then combine its result in the anomaly detection phase. Because the anomaly is detected by the combination of different detectors, it is robust to the choice of hyperparameters without loss of accuracy. To demonstrate our methodology, an actual dataset obtained from a 200,000-ton cargo vessel from a Korean shipping company that uses two-stroke diesel engine is analyzed. As a result, anomalies were successfully detected from the high-dimensional and large-scale dataset. After detecting the anomaly, clustering analysis was conducted to the anomalous observation to examine anomaly patterns. By investigating each cluster’s feature distribution, several common patterns of abnormal behavior were successfully visualized. Although we analyzed the data from two-stroke diesel engine, our method can be applied to various types of marine engine

Feature Attribution Analysis to Quantify the Impact of Oceanographic and Maneuverability Factors on Vessel Shaft Power Using Explainable Tree-Based Model

A vessel sails above the ocean against sea resistance, such as waves, wind, and currents on the ocean surface. Concerning the energy efficiency issue in the marine ecosystem, assigning the right magnitude of shaft power to the propeller system that is needed to move the ship during its operations can be a contributive study. To provide both desired maneuverability and economic factors related to the vessel’s functionality, this research studied the shaft power utilization of a factual vessel operational data of a general cargo ship recorded during 16 months of voyage. A machine learning-based prediction model that is developed using Random Forest Regressor achieved a 0.95 coefficient of determination considering the oceanographic factors and additional maneuver settings from the noon report data as the model’s predictors. To better understand the learning process of the prediction model, this study specifically implemented the SHapley Additive exPlanations (SHAP) method to disclose the contribution of each predictor to the prediction results. The individualized attributions of each important feature affecting the prediction results are presented

Feature Attribution Analysis to Quantify the Impact of Oceanographic and Maneuverability Factors on Vessel Shaft Power Using Explainable Tree-Based Model

A vessel sails above the ocean against sea resistance, such as waves, wind, and currents on the ocean surface. Concerning the energy efficiency issue in the marine ecosystem, assigning the right magnitude of shaft power to the propeller system that is needed to move the ship during its operations can be a contributive study. To provide both desired maneuverability and economic factors related to the vessel’s functionality, this research studied the shaft power utilization of a factual vessel operational data of a general cargo ship recorded during 16 months of voyage. A machine learning-based prediction model that is developed using Random Forest Regressor achieved a 0.95 coefficient of determination considering the oceanographic factors and additional maneuver settings from the noon report data as the model’s predictors. To better understand the learning process of the prediction model, this study specifically implemented the SHapley Additive exPlanations (SHAP) method to disclose the contribution of each predictor to the prediction results. The individualized attributions of each important feature affecting the prediction results are presented

Navigating Energy Efficiency: A Multifaceted Interpretability of Fuel Oil Consumption Prediction in Cargo Container Vessel Considering the Operational and Environmental Factors

In the maritime industry, optimizing vessel fuel oil consumption is crucial for improving energy efficiency and reducing shipping emissions. However, effectively utilizing operational data to advance performance monitoring and optimization remains a challenge. An XGBoost Regressor model was developed using a comprehensive dataset, delivering strong predictive performance (R2 = 0.95, MAE = 10.78 kg/h). This predictive model considers operational (controllable) and environmental (uncontrollable) variables, offering insights into complex FOC factors. To enhance interpretability, SHAP analysis is employed, revealing ‘Average Draught (Aft and Fore)’ as the key controllable factor and emphasizing ‘Relative Wind Speed’ as the dominant uncontrollable factor impacting vessel FOC. This research extends to further analysis of the extremely high FOC point, identifying patterns in the Strait of Malacca and the South China Sea. These findings provide region-specific insights, guiding energy efficiency improvement, operational strategy refinement, and sea resistance mitigation. In summary, our study introduces a groundbreaking framework leveraging machine learning and SHAP analysis to advance FOC understanding and enhance maritime decision making, contributing significantly to energy efficiency and operational strategies—a substantial contribution to a responsible shipping performance assessment under tightening regulations

Effects of Repetitive Transcranial Magnetic Stimulation on the Primary Motor Cortex of Individuals with Fibromyalgia: A Systematic Review and Meta-Analysis

The purpose of this study was to quantify the effect of repetitive transcranial magnetic stimulation (rTMS), which is recommended for the improvement of some pain-related symptoms and for antidepressant treatment, on the primary motor cortex (M1) in patients with fibromyalgia (FM). We searched for studies comparing rTMS and sham rTMS in the M1 of FM patients. Pain intensity, quality of life, health status, and depression were compared with or without rTMS for at least 10 sessions. We searched four databases. Quality assessment and quantitative analysis were performed using RevMan 5.4. After screening, five randomized controlled trials of 170 patients with FM were included in the analysis. As a result of the meta-analysis of rTMS on the M1 of individuals with FM, high-frequency rTMS resulted in a significant improvement on quality of life (MD = −2.50; 95% CI: −3.99 to −1.01) compared with sham rTMS. On the other hand, low-frequency rTMS resulted in a significant improvement on health status (MD = 15.02; 95% CI: 5.59 to 24.45). The application of rTMS to the M1 is proposed as an adjunctive measure in the treatment of individuals with FM. Because rTMS has various effects depending on each application site, it is necessary to classify sites or set frequencies as variables

Evaluation of the Quality of N-Detect Scan ATPG Patterns on a Processor

This paper evaluates N-detect scan ATPG patterns for their impact to test quality through simulation and fallout from production on a Pentium 4 processor using 90nm manufacturing technology. An incremental ATPG flow is used to generate N-detect test patterns. The generated patterns were applied in production with flows to determine overlap in fallout to different tests. The generated N-detect test patterns are then evaluated based on different metrics. The metrics include signal states, bridge fault coverage, stuck-at fault coverage and fault detection profile. The correlation between the different metrics is studied. Data from production fallout shows the effectiveness of N-detect tests. Further, the correlation between fallout data and the different metrics is analyzed. 1

A Study on the Field Applicability of Intermittent Irrigation in Protected Cultivation Using an Automatic Irrigation System

The demand for efficient water use and automatic systems has been increasing due to the frequent drought damage to crops as a result of climate change, the shortage of water resources in rural areas, and the aging of farmers. The existing automatic irrigation systems reduce the amount of labor required for irrigation and maintain soil moisture. However, the irrigation threshold criteria are user-determined as opposed to being automated according to input objectives such as improving crop productivity and saving water. In this study, an algorithm that could automatically determine suitable soil moisture according to a database and an automatic irrigation system with intermittent irrigation for efficient water use were developed. An experiment was then conducted on the productivity of crops for protected cultivation according to the application of the system. As the frequency domain reflectometry (FDR) sensor used in this system measured the volumetric water content of the soil, the soil moisture tension corresponding with the set value was converted into the volumetric water content using a regression equation. The process of intermittent irrigation was defined by using the moisture movement modeling of Hydrus 2D to reduce water loss on the soil surface and allow moisture to penetrate the soil unobstructed. An experimental field of a tomato farm was divided into empirical manual and controlled automatic irrigation plots. A total of 97.3% of the soil moisture values in the −33 kPa-controlled automatic irrigation plot and 96.6% of the soil moisture values in the −25 kPa-controlled automatic irrigation plot were within each set range during the first cropping season. During the second cropping season, a total of 94.8% of the soil moisture values in the −33 kPa-controlled automatic irrigation plot was within the set range. Compared with the empirical manual irrigation plot, the water productivity in the first cropping season was 113.9% in the −33 kPa-controlled automatic irrigation plot and 106.3% in the −25 kPa-controlled automatic irrigation plot. In the second cropping season, the water productivity was 117.3% in the −33 kPa-controlled automatic irrigation plot. Therefore, an automatic irrigation system applied with intermittent irrigation could be critical to increasing agricultural production and improving water-use efficiency