bluuebunny/crossref_metadata_2025 huggingface dataset:
Numerical Simulation Study of Combustion under Different Excess Air Factors in a Flow Pulverized Coal Burner
Authors: Chen Lijia , Xu Yelin , Tian Shoutao , Lu Hao
Published: July 2024

The basic national condition that is dominated by coal will not alter in the foreseeable future. Coal-fired boiler is the main equipment for coal utilization, and cyclone burner is a practical type of burner. There is a cyclone formation, a primary air duct inside the center air duct, and a secondary air duct. Introducing a small stream of pulverized coal gas or oil mist stream or gas directly into the reflux zone in the center duct ignites first a stable combustion and a small fluctuation of ignition pressure. In this paper, the variation of furnace temperature for cyclone pulverized coal burner corresponding to different excess air factors and the composition of gases such as O2, CO, CO2, and NOX produced by combustion were investigated using fluent software. A single cyclone pulverized coal burner from an actual coal-fired boiler is used, and a combustion zone applicable to the study of a single pulverized coal burner is established to study the actual operation of a single pulverized coal burner at different excess air coefficients. The findings indicate that the ignition position of pulverized coal combustion advances with decreasing α (Excess Air Factors); however, the length of the produced high-temperature flame gets shorter. As the value of α decreases, the burnout in the furnace decreases and the CO emission concentration increases, with a maximum CO mole fraction of 0.38% at α = 1.2 and a maximum CO mole fraction of 3.13% at the axial position when α decreases to 0.8. The furnace’s concentration of NOX, the NOX emission level decreases significantly with decreasing α. The NOX mole mass increases gradually with increasing α, and in the bottom portion of the primary combustion zone, more NOX is produced. The concentration of NOX in the chamber changes significantly after α exceeds 1.0, and the NOX at the outlet surges from 417.25 ppm to 801.07 ppm, which is attributed to the increase in the average temperature of the chamber, which promotes the generation of thermophilic NOX. The distribution pattern of O2 mole fraction along the furnace height cross-section at different excess air factors is basically the same, with a maximum at the burner inlet and a gradual decrease in the O2 content as it enters the combustion chamber to react with the pulverized coal in a combustion reaction. The value of α = 0.8 when the air supply is obviously insufficient, the fuel cannot be fully combusted, and only a small amount of CO2 is produced.
Simulation and Experimental Verification of Pipeline Particle Deposition Based on Ellipsoidal Assumption
Authors: Niu Chenchen , Zhou Zhen , Qi Jia , Yang Xu
Published: July 2024

The problem of particle clogging in a conveying pipeline in thin-phase pneumatic transportation is essentially the effect of the particle-deposition mechanism in gas–solid two-phase flow. This paper presents a particle-deposition model of gas–solid two-phase flow based on the ellipsoid hypothesis, and a fast-calculation method of material particle-deposition efficiency in industry based on the tabular-assigned drag-correction coefficient of the particle ellipsoid-shape parameter Ar and incoming flow angle ϕ. A simulation comparison of spherical particles under the same pneumatic transport conditions and experimental verification based on the self-built particle deposition system are given. The validity of the model and the accuracy of the algorithm are verified. This provides a feasible simulation and experimental scheme for the research of pneumatic-conveying technology in the industrial field.
Development of a Value Stream Map to Optimize the Production Process in a Luxury Metal Piece Manufacturing Company
Authors: Costa Beatriz , Varejão José , Gaspar Pedro Dinis
Published: July 2024

The current market is highly competitive, and customers are increasingly demanding. In this context, organizations need to adopt tools that enhance process efficiency to ensure competitiveness. This report aims to implement Lean tools, specifically Value Stream Mapping (VSM) and complementary tools, to optimize the production process in the metal treatment industry. A case study was conducted, beginning with a brief sector and process recognition, followed by an analysis of production stages using VSM. Value-added activities, non-value-added activities, and waste were identified. The current VSM revealed a Lead Time (LT) of approximately 336 h (14 days), value-added activities (VA) of 21 h, and a process cycle efficiency (PCE) of 6.29%. Improvement actions were proposed to reduce waste and increase competitiveness. After implementation, the LT decreased to approximately 318 h (13 days), VA increased to 23 h, and process efficiency improved to 7.15%. Despite the limitations of VSM in discontinuous flows, its use increased process efficiency, demonstrating its applicability in complex industrial contexts.
The Impact of Financial Stress and Uncertainty on Green and Conventional Bonds and Stocks: A Nonlinear and Nonparametric Quantile Analysis
Authors: Mar’I Muhammad , Seraj Mehdi , Tursoy Turgut
Published: July 2024

This study aims to investigate the impact of financial stress and uncertainty on the returns of green and conventional bonds and stocks in the United States from 2010 to 2022. The research utilizes nonlinear and nonparametric analysis, which includes the quantile-on-quantile and nonparametric causality-in-quantiles approaches to examine the relationship between variables. The data analyzed using R programming language show that financial stress positively impacts the middle quantiles of both conventional and green equity, while financial uncertainty negatively impacts upper quantiles. The study also finds that financial stress has a more significant impact on all types of bonds compared to financial uncertainty, with conventional bonds being more affected. This study proposes a pyramid that classifies financial assets based on their susceptibility to financial stress, which could help investors evaluate risk levels and make better investment decisions. The study recommends that policymakers should encourage green investments by offering incentives, such as tax credits. They should also focus on enhancing the efficiency of volatile assets by implementing new investment rules and regulations.
Seasonal Variation in Total Cloud Cover and Cloud Type Characteristics in Xinjiang, China Based on FY-4A
Authors: Zeng Yong , Yang Lianmei , Tong Zepeng , Jiang Yufei , Zhou Yushu , Lu Xinyu , Abulikemu Abuduwaili , Li Jiangang
Published: July 2024

In order to deepen the knowledge of the seasonal variation in total cloud cover (TCC) in Xinjiang, China (XJ), a typical arid region, and to broaden the understanding of the seasonal variation in cloud type (CLT) in the region, we used TCC and CLT datasets from the latest generation of the geostationary satellite Fengyun 4A (FY-4A) from 2018 to 2022 to investigate the seasonal variation characteristics of TCC and CLT in XJ. Meanwhile, to verify the accuracy of TCC from FY-4A, ground observation (GROB) TCC datasets from 105 national meteorological stations (NMSs) in XJ and TCC datasets from ERA5 during the same period were used. In addition, the correlation between TCC from FY-4A and meteorological factors from ERA5 was also analyzed in this study. The TCC from FY-4A, GROB, and ERA5 can all well reflect the significant seasonal variation in TCC in XJ, with the highest (lowest) mean TCC and a distribution pattern of high in the southwest (northwest) and low in the northeast (southeast) in spring (fall) in XJ. Although the mean TCC from FY-4A in all four seasons was lower than that from GROB, the two were comparable in spring (44.09% and 47.32%) and summer (42.88% and 43.17%), while there was a significant difference between the two in fall (27.86% and 40.19%) and winter (30.58% and 46.93%) for 105 NMSs in XJ. The TCC from FY-4A was lower (higher) than that from GROB in spring and summer at most NMSs in northern (southern) XJ, while the TCC from FY-4A was lower than that from GROB for the vast majority of NMSs in fall and winter, especially in northern XJ. The seasonal variation in the spatial distribution of different CLTs (clear, water-type, supercooled-type, mixed-type, ice-type, cirrus-type, and overlap-type) from FY-4A exhibited diverse variation characteristics. Water-type (supercooled-water-type) had a high-frequency center of over 30% in the Tarim Basin (Kunlun Mountains) during summer. Mixed-type (ice-type and cirrus-type) had the highest frequency in winter (spring), while overlap-type had the highest frequency in summer. The correlation between TCC and water vapor conditions (total column vertically integrated water vapor, specific humidity at 250 hPa, 500 hPa, and 700 hPa) was positive in XJ.
Border-Enhanced Triple Attention Mechanism for High-Resolution Remote Sensing Images and Application to Land Cover Classification
Authors: Wang Guoying , Chen Jiahao , Mo Lufeng , Wu Peng , Yi Xiaomei
Published: July 2024

With the continuous development and popularization of remote sensing technology, remote sensing images have been widely used in the field of land cover classification. Since remote sensing images have complex spatial structure and texture features, it is becoming a challenging problem to accurately categorize them. Land cover classification has practical application value in various fields, such as environmental monitoring and protection, urban and rural planning and management, and climate change research. In recent years, remote sensing image classification methods based on deep learning have been rapidly developed, in which semantic segmentation technology has become one of the mainstream methods for land cover classification using remote sensing image. Traditional semantic segmentation algorithms tend to ignore the edge information, resulting in poor classification of the edge part in land cover classification, and there are numerous attention mechanisms to make improvements for these problems. In this paper, a triple attention mechanism, BETAM (Border-Enhanced Triple Attention Mechanism), for edge feature enhancement of high-resolution remote sensing images is proposed. Furthermore, a new model on the basis of the semantic segmentation network model DeeplabV3+ is also introduced, which is called DeepBETAM. The triple attention mechanism BETAM is able to capture feature dependencies in three dimensions: position, space, and channel, respectively. Through feature importance weighting, modeling of spatial relationships, and adaptive learning capabilities, the model BETAM pays more attention to edge features, thus improving the accuracy of edge detection. A remote sensing image dataset SMCD (Subject Meticulous Categorization Dataset) is constructed to verify the robustness of the attention mechanism BETAM and the model DeepBETAM. Extensive experiments were conducted on the two self-built datasets FRSID and SMCD. Experimental results showed that the mean Intersection over Union (mIoU), mean Pixel Accuracy (mPA), and mean Recall (mRecall) of DeepBETAM are 63.64%, 71.27%, and 71.31%, respectively. These metrics are superior to DeeplabV3+, DeeplabV3+(SENet), DeeplabV3+(CBAM), DeeplabV3+(SAM), DeeplabV3+(ECANet), and DeeplabV3+(CAM), which are network models that incorporate different attention mechanisms. The reason is that BETAM has better edge segmentation results and segmentation accuracy. Meanwhile, on the basis of the self-built dataset, the four main classifications of buildings, cultivated land, water bodies and vegetation were subdivided and detected, and good experimental results were obtained, which verified the robustness of the attention mechanism BETAM and the model DeepBETAM. The method has broad application prospects and can provide favorable support for research and application in the field of surface classification.
Quantifying the Relationship between Slope Spectrum Information Entropy and the Slope Length and Slope Steepness Factor in Different Types of Water-Erosion Areas in China
Authors: Xu Fujin , Zhao Weijun , Yan Tingting , Qin Wei , Zhang Guanghe , Fang Ningning , Xu Changchun
Published: July 2024

Topography critically affects the occurrence of soil erosion, and computing slope spectrum information entropy (SSIE) allows for the convenient mirroring of the patterns of macroscopic topographic variation. However, whether SSIE can be effectively utilized for the quantitative assessment of soil erosion across various types of water-erosion areas and the specific methodology for its application remain unclear. This study focused on the quantitative relationship between SSIE, the slope length and slope steepness (LS) factor within various types of water-erosion areas across different spatial scales in China using multi-source geographic information data and technical tools such as remote sensing and geographic information systems. The results revealed (1) clear consistency in the spatial patterns of SSIE and the LS factor, which both displayed a distinct three-step distribution pattern from south to north. (2) The power model (Y = A·X^B) demonstrated a superior capacity to explaining the relationship between SSIE and the LS factors compared to the linear or exponential models, as evidenced by a higher coefficient of determination (R2). R2 values of different evaluation units (second-grade water-erosion area, third-grade water-erosion area, 30 km × 30 km grid, and 15 km × 15 km grid) were 0.88, 0.88, 0.81, and 0.79, respectively. (3) Despite a range of variances across various spatial scale evaluation units and different types of water-erosion areas, no significant disparities were evident within the power model. These findings offer a new topographic factor that can be incorporated into models designed for the expedited evaluation of soil erosion rates across water-erosion areas. Information about the proximity of the SSIE to the LS factor is valuable for enhancing the practical utilization of SSIE in the quantitative evaluation of soil erosion.
L Test Subtask Segmentation for Lower-Limb Amputees Using a Random Forest Algorithm
Authors: McCreath Frangakis Alexis L. , Lemaire Edward D. , Burger Helena , Baddour Natalie
Published: July 2024

Functional mobility tests, such as the L test of functional mobility, are recommended to provide clinicians with information regarding the mobility progress of lower-limb amputees. Smartphone inertial sensors have been used to perform subtask segmentation on functional mobility tests, providing further clinically useful measures such as fall risk. However, L test subtask segmentation rule-based algorithms developed for able-bodied individuals have not produced sufficiently acceptable results when tested with lower-limb amputee data. In this paper, a random forest machine learning model was trained to segment subtasks of the L test for application to lower-limb amputees. The model was trained with 105 trials completed by able-bodied participants and 25 trials completed by lower-limb amputee participants and tested using a leave-one-out method with lower-limb amputees. This algorithm successfully classified subtasks within a one-foot strike for most lower-limb amputee participants. The algorithm produced acceptable results to enhance clinician understanding of a person’s mobility status (>85% accuracy, >75% sensitivity, >95% specificity).
Tai Chi Expertise Classification in Older Adults Using Wrist Wearables and Machine Learning
Authors: Hu Yang , Huang Mengyue , Cerna Jonathan , Kaur Rachneet , Hernandez Manuel E.
Published: July 2024

Tai Chi is a Chinese martial art that provides an adaptive and accessible exercise for older adults with varying functional capacity. While Tai Chi is widely recommended for its physical benefits, wider adoption in at-home practice presents challenges for practitioners, as limited feedback may hamper learning. This study examined the feasibility of using a wearable sensor, combined with machine learning (ML) approaches, to automatically and objectively classify Tai Chi expertise. We hypothesized that the combination of wrist acceleration profiles with ML approaches would be able to accurately classify practitioners’ Tai Chi expertise levels. Twelve older active Tai Chi practitioners were recruited for this study. The self-reported lifetime practice hours were used to identify subjects in low, medium, or highly experienced groups. Using 15 acceleration-derived features from a wearable sensor during a self-guided Tai Chi movement and 8 ML architectures, we found multiclass classification performance to range from 0.73 to 0.97 in accuracy and F1-score. Based on feature importance analysis, the top three features were found to each result in a 16–19% performance drop in accuracy. These findings suggest that wrist-wearable-based ML models may accurately classify practice-related changes in movement patterns, which may be helpful in quantifying progress in at-home exercises.
Development of a Tremor Detection Algorithm for Use in an Academic Movement Disorders Center
Authors: Saad Mark , Hefner Sofia , Donovan Suzann , Bernhard Doug , Tripathi Richa , Factor Stewart A. , Powell Jeanne M. , Kwon Hyeokhyen , Sameni Reza , Esper Christine D. , McKay J. Lucas
Published: July 2024

Tremor, defined as an “involuntary, rhythmic, oscillatory movement of a body part”, is a key feature of many neurological conditions including Parkinson’s disease and essential tremor. Clinical assessment continues to be performed by visual observation with quantification on clinical scales. Methodologies for objectively quantifying tremor are promising but remain non-standardized across centers. Our center performs full-body behavioral testing with 3D motion capture for clinical and research purposes in patients with Parkinson’s disease, essential tremor, and other conditions. The objective of this study was to assess the ability of several candidate processing pipelines to identify the presence or absence of tremor in kinematic data from patients with confirmed movement disorders and compare them to expert ratings from movement disorders specialists. We curated a database of 2272 separate kinematic data recordings from our center, each of which was contemporaneously annotated as tremor present or absent by a movement physician. We compared the ability of six separate processing pipelines to recreate clinician ratings based on F1 score, in addition to accuracy, precision, and recall. The performance across algorithms was generally comparable. The average F1 score was 0.84±0.02 (mean ± SD; range 0.81–0.87). The second highest performing algorithm (cross-validated F1=0.87) was a hybrid that used engineered features adapted from an algorithm in longstanding clinical use with a modern Support Vector Machine classifier. Taken together, our results suggest the potential to update legacy clinical decision support systems to incorporate modern machine learning classifiers to create better-performing tools.
A High-Resolution Multipath Delay Measurement Method Using KFSC-WRELAX Algorithm
Authors: Dong Yu , Zhang Zhizhong
Published: July 2024

Given the challenges associated with the low accuracy, complexity of the equipment, and poor interference resistance observed in current wireless multipath channel measurements, this study introduces a novel algorithm called KFSC-WRELAX. This algorithm integrates techniques involving pseudorandom noise (PN) sequences, Kalman filtering (KF), sliding correlation, and weighted Fourier transform combined with the RELAXation (WRELAX) algorithm. An m-sequence is employed as the probing sequence for channel detection. The effectiveness of the KFSC-WRELAX algorithm is demonstrated through both simulation experiments and corridor testing, showing that it can accurately determine the delays in various paths with robust performance at a signal-to-noise ratio (SNR) of −5 dB or higher.
Dune Morphology Classification and Dataset Construction Method Based on Unmanned Aerial Vehicle Orthoimagery
Authors: Li Ming , Yang Zekun , Yan Jiehua , Li Haoran , Ye Wangzhong
Published: July 2024

Dunes are the primary geomorphological type in deserts, and the distribution of dune morphologies is of significant importance for studying regional characteristics, formation mechanisms, and evolutionary processes. Traditional dune morphology classification methods rely on visual interpretation by humans, which is not only time-consuming and inefficient but also subjective in classification judgment. These issues have impeded the intelligent development of dune morphology classification. However, convolutional neural network (CNN) models exhibit robust feature representation capabilities for images and have achieved excellent results in image classification, providing a new method for studying dune morphology classification. Therefore, this paper summarizes five typical dune morphologies in the deserts of western Inner Mongolia, which can be used to define and describe most of the dune types in Chinese deserts. Subsequently, field surveys and the experimental collection of unmanned aerial vehicle (UAV) orthoimages for different dune types were conducted. Five different types of dune morphology datasets were constructed through manual segmentation, automatic rule segmentation, random screening, and data augmentation. Finally, the classification of dune morphologies and the exploration of dataset construction methods were conducted using the VGG16 and VGG19 CNN models. The classification results of dune morphologies were comprehensively analyzed using different evaluation metrics. The experimental results indicate that when the regular segmentation scale of UAV orthoimages is 1024 × 1024 pixels with an overlap of 100 pixels, the classification accuracy, precision, recall, and F1-Score of the VGG16 model reached 97.05%, 96.91%, 96.76%, and 96.82%, respectively. The method for constructing a dune morphology dataset from automatically segmented UAV orthoimages provides a reference value for the study of large-scale dune morphology classification.
Investigation of Flotation Bubbles Movement Behavior under the Influence of an Immersed Ultrasonic Vibration Plate
Authors: Gao Kuidong , Zong Wenchao , Zhang Zhihua , Sun Liqing , Li Lin
Published: July 2024

Ultrasonic flotation is widely used as an efficient mineral separation method. Its efficiency is related to the adhesion behavior between fine particles and flotation bubbles, which can be influenced by the bubbles’ movement behavior. This paper used two immersed ultrasonic vibration plates to generate ultrasonic action and investigated the effect of ultrasonic action on the rising process of flotation bubbles. The distribution, aggregation and fusion, velocity, and other characteristics of bubbles generated by different needle apertures were studied by experimental and simulation methods. The results showed that a 0.4 mm needle produced bubbles that were more evenly spaced and more uniform in size and shape. The ultrasonic action can make the bubbles aggregate together and reduce the bubble rise velocity, as well as prolong their time in the flotation process at the same time. It is beneficial to the sufficient collision and adhesion behavior between flotation bubbles and particles, eventually improving the efficiency of mineral flotation.
Price Cannibalization Effect on Long-Term Electricity Prices and Profitability of Renewables in the Baltic States
Authors: Kozlovas Paulius , Gudzius Saulius , Jonaitis Audrius , Konstantinaviciute Inga , Bobinaite Viktorija , Gudziute Saule , Giedraitis Gustas
Published: July 2024

This paper aims to evaluate price cannibalization effects in forecasts of long-term electricity prices and substantiate their relevance on the profitability of renewables in the Baltic States from 2024 to 2033. Statistical data analysis, literature review, scenario method, and PLEXOS modeling were applied. Five scenarios were analyzed for developing renewable energy sources (RES) and load in Lithuania. In contrast, scenarios for Estonia and Latvia were based on assumptions derived from the countries’ national RES strategies. The results showed that the increase in RES capacities will halve electricity market prices from around 130 EUR/MWh in 2024 to 58 EUR/MWh in Latvia, 60 EUR/MWh in Estonia, and 60–77 EUR/MWh in Lithuania in 2033. In time-waving, the absolute and relative price cannibalization effects of renewables were found. In 2033, the loss of revenue from solar photovoltaic (PV) generators was estimated to be 5.5–17.0 EUR/MWh in Lithuania, 7.1 EUR/MWh in Latvia, and 5.6 EUR in Estonia. The case of onshore wind demonstrated revenue losses of 10.5–22.0 EUR/MWh in Lithuania, 12.0 EUR/MWh in Latvia, and 10.0 EUR/MWh in Estonia. After 2029, revenues received by RES electricity generators could not guarantee project profitability; therefore, market flexibility options will be required. The key innovative strategy to mitigate the price cannibalization effect is the demand-side response when leveraging demand flexibility. Typically, this is achieved by sending price signals to the consumers who, if they have any, shift their demand to lower price periods. This is easily applied within HVAC systems, smart electric vehicle charging, and smart home appliance usage. Such behavior would allow the price cannibalization effect to be decreased.
Analysis of Spatiotemporal Evolution Characteristics and Influencing Factors of Vegetation Net Primary Productivity in Shendong Mining Area
Authors: Dai Linda , Wang Fei , Yan Yueguan , Bi Yinli , Zhang Yongliang , Zhang Wanqiu
Published: July 2024

Coal plays a key role in the composition of energy production and consumption in China. The mineral is a crucial resource for human economic and social development. The surface subsidence caused by coal mining has attracted disturbance and damage to the surface ecological environments of mining areas. To quantitatively study vegetation carbon sequestration in coal mining areas, this article takes the Shendong mining area as its research object and analyzes the spatiotemporal distribution characteristics and influencing factors of vegetation net primary productivity (NPP). Based on meteorological data, remote sensing images, and land use data in the mining area, this research analyzed the vegetation NPP and its relationship with meteorological factors in the Shendong mining area from 2000 to 2023. The results show that (1) in the past 24 years, the range of NPP variation in the Shendong mining area has been 0–350 gC/m2. The overall NPP shows a fluctuating upward trend. The annual average change in NPP in the mining area is bounded by the two central dividing lines, with a lower value in the central part and a gradually increasing trend in the western and eastern parts. (2) The average vegetation NPP of the Shendong mining area over 24 years was 0–250 gC/m2; after 2011, the annual average NPP of vegetation was higher than this average level, which shows that the ecological environment of the mining area is gradually improving. Coal mining in the mining area has not caused a significant impact on the overall environment of the mining area. (3) There is a significant relationship between NPP and annual cumulative rainfall, average annual temperature, and average annual Normalized Difference Vegetation Index (NDVI). Overall, it is not significantly related to mining factors. The mining factors affect local areas and have a certain correlation.
Engineering Systems with Standards and Digital Models: Development of a 15288-SysML Grid
Authors: Adams Kevin MacG. , Ibrahim Irfan , Krahn Steven
Published: July 2024

The paradigm shift that has spurred the fourth industrial revolution, in what is termed Industry 4.0, has ushered in the need to adopt digital technologies throughout the worldwide industrial base to support system design efforts. The adoption of digital technologies with a digital enterprise and the creation of cyber–physical systems are central tenets of Industry 4.0 and directly support profitable business models, improvements in efficiency, and ensure durable quality for the modern industrial base. However, the techniques for engineering systems require new, improved, digital life cycle process models if Industry 4.0—and the goals for its integrated systems—are to be realized. The development of a technique that improves the life cycles for systems within the digital enterprise is required. The 15288-SysML Grid described herein supports the Industry 4.0 paradigm and its associated digital enterprise. This is accomplished through (1) the application of a modern life cycle process model (i.e., the adapted diamond); (2) the utilization of international standards for systems; and (3) the adoption of the four fundamental aspects of system design supported by model-based systems engineering (MBSE) and the systems modeling language (SysML).
Empowerment of Digital Technology for the Resilience of the Logistics Industry: Mechanisms and Paths
Authors: Zhang Jifeng , Yang Zirui , He Bing
Published: July 2024

Digital technology, acting as an engine for industrial development, propels the rapid integration of data elements and the swift iteration of digital logistics technology, significantly enhancing logistic resilience. Exploring the impact of digital technology on the resilience of logistics helps strengthen the latter’s ability to withstand external shocks. Based on the correlation between digital technology and the resilience of the logistics industry, this study measured their levels in 275 Chinese prefecture-level cities from 2011 to 2020, showing that the former significantly improved the latter, a conclusion which remained valid after robustness tests. The mechanism analysis results showed that promoting industrial collaborative agglomeration was an important part of this process, while the threshold model analysis found that the impact on resilience had nonlinear characteristics. The heterogeneity test results showed that digital technology had a significant resilience-promoting effect in the eastern and northern coastal regions, as well as in the middle reaches of the Yellow and Yangtze Rivers. Accordingly, it is necessary to accelerate digital technology integration with the logistics industry and formulate differentiated development strategies. This study provides a theoretical foundation for exploring the relationship between digital technology and logistics resilience, as well as policy recommendations.
Efficacy of Urethral Sphincter Botulinum Toxin A Injection in Patients with Spinal Cord Injury with Dysuria: A Retrospective Cohort Study
Authors: Yu Wan-Ru , Tian Jing-Hui , Kuo Hann-Chorng
Published: July 2024

Spinal cord injury (SCI) often leads to neurogenic lower urinary tract dysfunction, causing dysuria and affecting patients’ well-being. This study aimed to evaluate the efficacy of a urethral sphincter botulinum toxin A (BoNT-A) injection in patients with SCI and dysuria. This was a retrospective study including 118 patients with SCI who underwent a urethral BoNT-A injection following a standardized protocol for refractory voiding dysfunction. The protocol involved injecting BoNT-A into the urethral sphincter under cystoscopic guidance. Patient demographics, bladder condition parameters, and treatment outcomes were analyzed. Logistic regression and receiver operating characteristic curve analyses were performed to identify predictors of treatment success. Of the 118 patients, 71 (60.1%) showed satisfactory treatment outcomes after the injection. Post-injection status, bladder management, and injection frequency varied significantly among patients with satisfactory and unsatisfactory treatment outcomes. Age, bladder compliance, intravesical pressure, and bladder contractility were indicators of satisfactory outcomes. The first sensation of bladder filling of ≤263 mL, intravesical pressure of ≤28, and bladder contractility index of ≥14 were highly correlated with satisfactory outcomes. A urethral sphincter BoNT-A injection shows promise in managing dysuria in patients with SCI. Understanding bladder condition parameters and patient demographics helps optimize patient selection for this intervention. Further studies are needed to validate these findings and refine treatment protocols.
Plasma EV-miRNAs as Potential Biomarkers of COVID-19 Vaccine Immune Response in Cancer Patients
Authors: Almeida Beatriz , Dias Tânia R. , Cruz Pedro , Sousa-Pimenta Mário , Teixeira Ana Luísa , Pereira Catarina Esteves , Costa-Silva Bruno , Oliveira Júlio , Medeiros Rui , Dias Francisca
Published: July 2024

Cancer patients, prone to severe COVID-19, face immune challenges due to their disease and treatments. Identifying biomarkers, particularly extracellular vesicle (EV)-derived microRNAs (miRNAs), is vital for comprehending their response to COVID-19 vaccination. Therefore, this study aimed to investigate specific EV-miRNAs in the plasma of cancer patients under active treatment who received the COVID-19 booster vaccine. The selected miRNAs (EV-hsa-miR-7-5p, EV-hsa-miR-15b-5p, EV-hsa-miR-24-3p, EV-hsa-miR-145- 5p, and EV-hsa-miR-223-3p) are involved in regulating SARS-CoV-2 spike protein and cytokine release, making them potential biomarkers for vaccination response. The study involved 54 cancer patients. Plasma and serum samples were collected at pre-boost vaccination, and at 3 and 6 months post-boost vaccination. Anti-spike antibody levels were measured. Additionally, RNA was extracted from EVs isolated from plasma and the expression levels of miRNAs were assessed. The results showed a significantly positive antibody response after COVID-19 boost vaccination. The expression levels of EV-hsa-miR-7-5p, EV-hsa-miR-15b-5p, EV-hsa-miR-24-3p, and EV-hsa-miR-223-3p increased significantly after 6 months of COVID-19 booster vaccination. Interestingly, an increased expression of certain EV-hsa-miRNAs was positively correlated. Bioinformatic analysis revealed that these correlated miRNAs play a critical role in regulating the targets present in antiviral responses and cytokine production. These findings suggest that EV-hsa-miR-15b-5p, EV-hsa-miR-24-3p, and EV-hsa-miR-223-3p may be crucial in immune response induced by mRNA vaccines.
Effectively Evaluating a Novel Consensus Subunit Vaccine Candidate to Prevent the H9N2 Avian Influenza Virus
Authors: Wu Qi , Wang Weihua , Zhang Xuehua , Li Ding , Mei Mei
Published: July 2024

The enormous effects of avian influenza on poultry production and the possible health risks to humans have drawn much attention to this disease. The H9N2 subtype of avian influenza virus is widely prevalent among poultry, posing a direct threat to humans through infection or by contributing internal genes to various zoonotic strains of avian influenza. Despite the widespread use of H9N2 subtype vaccines, outbreaks of the virus persist due to the rapid antigenic drift and shifts in the influenza virus. As a result, it is critical to develop a broader spectrum of H9N2 subtype avian influenza vaccines and evaluate their effectiveness. In this study, a recombinant baculovirus expressing the broad-spectrum HA protein was obtained via bioinformatics analysis and a baculovirus expression system (BES). This recombinant hemagglutinin (HA) protein displayed cross-reactivity to positive sera against several subbranch H9 subtype AIVs. An adjuvant and purified HA protein were then used to create an rHA vaccine candidate. Evaluation of the vaccine demonstrated that subcutaneous immunization of the neck with the rHA vaccine candidate stimulated a robust immune response, providing complete clinical protection against various H9N2 virus challenges. Additionally, virus shedding was more effectively inhibited by rHA than by the commercial vaccine. Thus, our findings illustrate the efficacy of the rHA vaccine candidate in shielding chickens against the H9N2 virus challenge, underscoring its potential as an alternative to conventional vaccines.
A Structured Narrative Literature Review of the Broader Value of Adult Immunisation Programmes
Authors: El Banhawi Hania , Bell Eleanor , Neri Margherita , Brassel Simon , Chowdhury Sulayman , Steuten Lotte
Published: July 2024

Vaccine-preventable diseases continue to generate a substantial burden on health, healthcare systems, and societies, which is projected to increase with population ageing. There is a need to better understand the full value of adult immunisation programmes corresponding to the broader value of vaccine frameworks that are recommended for evidence-based decision-making. This review aims to summarise and map evidence for the value of selected adult immunisation programmes (seasonal influenza, pneumococcal disease, RSV, and HZ) in ten diverse countries. We conducted a structured literature review of evidence published from 2017 to 2023. An existing framework was used to structure the assessment, developing matrices demonstrating the elements of value evidenced for each vaccine and country of focus. Our analysis showed substantial evidence base on the value of adult immunisation programmes, but the availability of evidence varied by value element and by vaccine. The impact on the quality of life of the vaccinated individual was the most evidenced value element. Mortality benefits for vaccinated individuals and cost-offsets to healthcare systems were also well-evidenced. The availability of evidence for ‘broader’ societal value elements (such as transmission value, carer productivity and impact on social equity, and antimicrobial resistance prevention) varied. No evidence was identified relating to the broader value elements of macroeconomic effects, value to other interventions, or effects on the quality of life of caregivers. Robust evidence exists to show that adult immunisation programmes generate substantial value for population health and health systems, yet some elements of broader value remain underrepresented in the academic literature. Without such evidence, the full value of immunisation programmes is underestimated, risking suboptimal policy decisions.
Waters of Contention: The GERD and Its Impact on Nile Basin Cooperation and Conflict
Authors: Almesafri Amna , Abdulsattar Sohaila , Alblooshi Alia , Al-Juboori Raed A. , Jephson Nicholas , Hilal Nidal
Published: July 2024

The Grand Ethiopian Renaissance Dam (GERD) on the Nile River has become a focal point in the fields of water diplomacy, economics, and environmental considerations in the Nile Basin. Announced during the Arab Spring in 2011, the GERD aims to address Ethiopia’s significant energy shortfall and foster regional economic growth by potentially doubling the country’s electricity output. However, this ambition has heightened tensions with downstream countries, especially Egypt and Sudan, which rely heavily on the Nile for their water needs. This paper examines the ongoing conflict surrounding the GERD, focusing on the deadlock situation despite considerable scholarly attention to its economic, political, and environmental dimensions. The analysis presented in this paper reveals the roots of contention by analyzing past treaties and the present negotiation status, illustrating the complex interplay between development goals, environmental sustainability, and regional water security. The paper advocates for a revised legal framework that moves beyond past agreements towards a more inclusive, cooperative management strategy for the Nile’s waters. It proposes the development of a new treaty between Egypt, Ethiopia, and Sudan, grounded in their 2015 Declaration of Principles agreement and inspired by successful international dispute resolutions. It also discusses the potential of neutral third-party interventions to facilitate unbiased negotiations grounded in the Graph Model for Conflict Resolution, emphasizing the importance of equitable and sustainable water governance. In essence, this research calls for a collaborative approach to resolve the GERD conflict, emphasizing the need for agreements that harmonize developmental ambitions with the water security of the Nile Basin nations.
Tolerance to and postoperative outcomes with early oral feeding following elective bowel surgery: a systematic review with meta-analysis
Authors: Mvoula Lord , Irizarry Evelyn
Published: July 2024

Purpose: Advancements in gastrointestinal surgery have directed attention toward optimizing recovery, including through the use of feeding methods that reduce prolonged postoperative hospital stays, complications, and mortality, among other undesirable outcomes. This study’s primary goals were to identify current peer-reviewed literature reporting the postoperative outcomes of elective bowel surgery and to evaluate the clinical evidence of patients’ tolerance to oral feeding following elective bowel surgery.Methods: An exhaustive literature search was conducted via PubMed and Scopus. The search results were screened for potential articles, and articles were assessed for eligibility based on prespecified eligibility criteria. The data were synthesized, and the results were reported and discussed thematically.Results: The database search yielded 1,667 articles, from which 18 randomized controlled trials were chosen for inclusion in this study. This study included 874 early oral feeding (EOF) patients, 865 traditional oral feeding patients, and 91 patients whose postoperative care was unspecified. Data synthesis was done, and meta-analyses were conducted. The results showed that EOF patients required a significantly shorter time to tolerate a solid diet and had shorter hospital stays. In addition, bowel function was restored earlier in EOF groups.Conclusion: The results show good tolerance to EOF, shorter hospitalizations, and faster restoration of bowel function, suggesting that EOF after elective bowel surgery is relatively safe. However, further studies with similar baseline conditions should be conducted to verify these results.
Efecto de los factores socioeconómicos en la inseguridad social en Ecuador
Authors: Encalada Zumba Manuel Stalin , Vega Gonzales Laura Luzmila
Published: July 2024

Los altos índices de inseguridad constituyen uno de los principales problemas en Latinoamérica, especialmente en Ecuador. El presente artículo investiga la influencia de variables como nivel de ingreso, educación, empleo y acceso a servicios en la percepción de inseguridad social en Ecuador. El objetivo consiste en analizar la correlación entre estos factores socioeconómicos y la inseguridad social en la población ecuatoriana. La metodología utilizada incluye el análisis de regresión múltiple con el software estadístico SPSS, que permite identificar las relaciones entre las variables y la inseguridad social. Se consideran dimensiones como la percepción de riesgo y la sensación de inseguridad en el hogar. Los resultados muestran una correlación significativa entre los factores socioeconómicos estudiados y la inseguridad social en Ecuador. Como conclusión, se sugiere que políticas públicas orientadas a reducir las desigualdades económicas y sociales pueden contribuir a mitigar los niveles de inseguridad percibida en el país.
Susceptible Genes and Polymorphisms Associated with Communicable and Noncommunicable Diseases
Authors: Olaniyan Mathew Folaranmi , Oladele Ayowole Abraham , Tijani Adelani , Muhibi Musa Abidemi , Olaniyan Tolulope Busayo
Published: July 2024

Background:Disease epidemiology encompasses a wide range of health conditions, divided into communicable and noncommunicable diseases.Aim and Objective:This systematic review investigates the intricate connection between genetic susceptibility and disease development within these categories. Understanding genetic factors is crucial for improving prevention, diagnosis, and treatment strategies. The central research question is as follows: Which genes are linked to susceptibility to communicable and noncommunicable diseases, and how do these genetic elements affect susceptibility? We hypothesize that an exhaustive analysis of the literature will reveal numerous genes associated with both types of diseases, revealing the complex genetic landscape influencing susceptibility.Methodology:This systematic review follows a rigorous methodology, including comprehensive search strategies, well-defined inclusion and exclusion criteria, publication bias assessment, data extraction, quality evaluation, and data synthesis, adhering to PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines to ensure transparency and ethical presentation. Several databases, including PubMed, Embase, Springer Nature, AJOL, CrossRef, Scopus, and Web of Science, were systematically searched to retrieve published articles.Findings:In communicable diseases, the genetic factors influencing susceptibility extend beyond well-established genes, warranting further investigation under conditions such as COVID-19, HIV, tuberculosis, and hepatitis B. Noncommunicable diseases, such as cardiovascular diseases, cancer, neurological disorders, and metabolic disorders, offer promising avenues for exploring additional genetic variations. Research gaps include understanding the functional impact of the identified polymorphisms, their interaction with environmental factors, and their implications for rare diseases.Conclusion:Genome-wide association studies and gene editing therapies have the potential to expand our understanding and therapeutic options for genetically based diseases. This comprehensive review contributes to the evolving landscape of genetic susceptibility and its implications for public health and personalized medicine.
Value Co-Destruction in Digital Banking Transformation: Research Propositions
Authors: De Borba Darci
Published: July 2024

Digital Transformation imposes an invisible legacy on managers: the destruction of value. The ability of technology to disseminate services can lead to irreparable losses of value for companies, resulting in the decline of economic potential and imposing a dictatorship of gratuitousness. To research how this happens and propose solutions, I analyze the trend of value co-destruction (VCD) in the Digital Bank Transformation. The ability to understand and predict such changes is important to guide the processes of planning, implementing, and evaluating business decisions. Value creation is a central concept in the business literature, as companies create value through their operations and the delivery of services and products that meet the desires of their customers. However, value can also be destroyed, causing companies to fail and significant changes in the market. Through a semi-systematic literature review, I seek the theoretical guidelines of VCD in the context of online banking. We found 112 articles related to the topic and part of the systematic analysis of these articles is available in this work. The main objective of this theoretical essay is to highlight research propositions for the analysis of VCD in the context of Digital Banking Transformation. For the main delivery, it is necessary to: 1) delimit the concepts related to Digital Transformation and VCD; 2) understand how the VCD process is configured; 3) define the mechanisms related to the VCD; and 4) gather characteristics of the financial services segment in the context of Digital Transformation. Two proposals were proposed for future research.
Hey Siri: Exploring the Effect of Voice Humanity on Virtual Assistant Acceptance
Authors: Calahorra Candao Guillermo , Martín de Hoyos María José
Published: July 2024

This work in progress presents a proposed model to measure the acceptance of the use of voiced virtual assistants in commercial contexts. The use of these devices has increased after their implementation in smartphones and with the arrival of smart speakers in any household appliance. We propose the present model emphasizing the anthropomorphic aspect of the assistant to know if it has an emphasis on its acceptance. Unlike other models of acceptance, the contribution of the work is the inclusion of anthropomorphic variables such as human presence and human like voice. The work focuses on the virtual assistant Siri.