bluuebunny/crossref_metadata_2025 huggingface dataset:
Correlates of Professional Breastfeeding Perceived Role, Perceived Influence, and Confidence in Providing Lactation Support Among Registered Dietitians and Registered Nurses
Authors: Dinour Lauren M. , Shefchik Melanie , Uguna Andrea
Published: October 2024

Lactation support from health professionals is a crucial social determinant of health. Registered dietitians (RDs) and registered nurses (RNs) can play an important role in supporting breastfeeding, yet most have minimal formal lactation education and training. This study seeks to explore the correlates of professional breastfeeding perceived role, perceived influence, and confidence in providing lactation support among RDs and RNs. Using a cross-sectional design, a convenience sample of 111 credentialed providers completed an online survey assessing their breastfeeding-related training, experiences, knowledge, attitudes, and practices. We also examined perceptions of social support, intentions for future breastfeeding, professional roles, and confidence in lactation support. We tested for relationships between variables using chi-square, Fisher’s exact, and Mann–Whitney U tests and identified which correlates were associated with the dependent variables using bivariate logistic regression. Respondents who were breastfed as infants were 3.4 times more likely to agree that it is their responsibility to highlight health problems associated with infant formula when giving breastfeeding advice (p = 0.009). Those agreeing that people around them support breastfeeding were 8.0 times more likely to believe they can influence a woman’s breastfeeding decision and duration (p = 0.05). Respondents working in maternal/child/family health were 14.3 times more likely to feel confident in their ability to provide lactation counseling, guidance, and recommendations (p = 0.03). Educational institutions and employers should provide all RDs and RNs with opportunities for personal reflection and lactation training while strengthening breastfeeding social norms.
The 3rd Educational Course of the European Burns Association (EBA)
Authors: Depetris Nadia , de Jong Alette E. E. , Schiestl Clemens , Siemers Frank , Meirte Jill , Vuola Jyrki , Cabral Luís , Van Zuijlen Paul , Almeland Stian
Published: October 2024

Abstracts of the plenary sessions, workshops, and poster presentations of the 3rd EBA Educational Course in Porto, Portugal, 17–18 October 2024.
Outcomes of Open Knee Joint Injuries in Trauma Patients at Tibebe-Ghion Specialized Hospital, Bahir-Dar, Ethiopia
Authors: Shumye Getachew Wuhib , Wolle Melatwork Assefa , Shumye Mekuriaw Wuhib , Zeru Leul Mekonnen
Published: October 2024

Background: Open knee joint injury is a deep traumatic laceration violating the joint capsule. This study aimed to assess outcomes and associated factors of open knee joint injuries in trauma patients at Tibebe-Ghion Specialized Hospital, Bahirdar, Ethiopia, 2021. Methods: An institution-based retrospective cross-sectional study was conducted by reviewing the medical records of 43 patients treated for open knee joint injuries from January 2019 to July 2021, with an 86% response rate. Data were entered and exported using Epidata Manager Version 4.4 and analyzed using SPSS version 25. Results: Fourteen (32.5%) of the study subjects developed either infections or limping during follow-up due to open knee injuries, with the majority of injuries (58.1%) being caused by car accidents. The presence of associated injuries, wound size, time to wound closure, debridement as a management option, sex, and diagnostic methods showed a significant association with complication outcomes. Among the diagnosed infections, 90% were cases of septic arthritis. During the study period, eight patients (18.6%) developed limping during follow-up. The complication rate among open knee injury patients in the study area was significantly high, with one-third of patients experiencing complications. Targeted public awareness campaigns like car accidents should be conducted to educate the population about orthopedic emergencies and strategies to reduce complication risks, aiming for more favorable outcomes.
Hydropower Plant Available Energy Forecasting Using Artificial Neural Network and Particle Swarm Optimization
Authors: Kaewarsa Suriya , Kongpaseuth Vanhkham
Published: October 2024

Accurate forecasting of the available energy portion that corresponds to the reservoir inflow of the month(s) ahead provides important decision support for hydropower plants in energy production planning for revenue maximization, as well as for environmental impact prevention and flood control upstream and downstream of a basin. Therefore, a reliable forecasting tool or model is deemed necessary and crucial. Considering the fluctuation and nonlinearity of data which significantly influence the forecasting results, this study develops an effective hybrid model by integrating an Artificial Neural Network (ANN) and Particle Swarm Optimization (PSO) called “PSO-ANN” model based on the hydrological and meteorological data pre-processed by cross-correlation function (CCF), autocorrelation function (AFC), and normalization techniques for predicting the available energy portion corresponding to the reservoir inflow mentioned above for a case study hydropower plant in Laos, namely, the Theun-Hinboun hydropower plant (THHP). The model was evaluated by using correlation coefficient (r), relative error (RE), root mean square error (RMSE), and Taylor diagram plots in comparison with popular single-algorithm approaches such as ANN, and NARX models. The results demonstrated the superiority of the proposed PSO-ANN approach over the other two models, in addition to being comparable to those proposed by previous studies.
Design and Optimization of High Performance Multi-Step Separated Trench 4H-SiC JBS Diode
Authors: Li Jinlan , Wu Ziheng , Sheng Huaren , Xu Yan , Zhou Liming
Published: October 2024

In this paper, a novel 3300 V/40 A 4H-SiC junction barrier Schottky diode (JBS) with a multi-step separated trench (MST) structure is proposed and thoroughly investigated using TCAD simulations. The results show that the introduction of MST expands the Schottky contact area, resulting in a decrease in the forward voltage drop. Furthermore, the combination of the deep P+ shielded region and the central P+ region effectively reduces the leakage current, leading to a 43.7% increase in the blocking voltage compared to conventional 4H-SiC JBS. The effects of the step depth (ds) and the width of the central P+ region (wm) on the device performance are analyzed in depth. In addition, a multi-step trenched linearly graded field-limiting rings (MTLG-FLR) termination ensures a more uniform electric field distribution, and the terminal protection efficiency reaches up to 90%, which further enhances the reliability of the terminal structure.
Ultrasonic A-Scan Signals Data Augmentation Using Electromechanical System Modelling to Enhance Cataract Classification Methods
Authors: Santos Mário J. , Petrella Lorena I. , Perdigão Fernando , Santos Jaime
Published: October 2024

The use of artificial intelligence in diverse diagnosis areas has significantly increased in the past few years because of the advantages it represents in clinical routine. Among the diverse diagnostic techniques, the use of ultrasounds is often preferred because of their simplicity, low cost, non-invasiveness, and non-ionizing characteristic. However, obtaining an adequate number of patients and data for training and testing machine learning models is challenging. To overcome this limitation, a novel approach is proposed for simulating data produced by ultrasonic diagnostic devices. The implemented method was based on a clinical prototype for eye cataract diagnosis, although the method can be extended to other applications as well. The proposed model encompasses the electric-to-acoustic signal conversion in the ultrasonic transducer, the wave propagation through the biological medium, and the subsequent acoustic-to-electric signal conversion in the transducer. Electrical modelling of the transducer was performed using a two-port network approach, while the acoustic wave propagation was modelled by using the k-Wave MATLAB toolbox. It was verified that the holistic modelling approach enabled the generation of synthetic data augmentation, presenting high similarity with real data.
Composition and Injection Rate Co-Optimization Method of Supercritical Multicomponent Thermal Fluid Used for Offshore Heavy Oil Thermal Recovery
Authors: Yang Shenyao , Qi Zhilin , Tian Jie , Dong Mingda , Zhang Wei , Yan Wende
Published: October 2024

Supercritical multicomponent thermal fluid injection is a new technology with great potential for offshore heavy oil thermal recovery. In the process of thermal fluid generation, the reaction conditions including temperature, pressure, and the organic mass concentration in the reaction material will significantly affect its composition and injection rate and will further affect the thermal recovery and development quality of heavy oil. However, there is a lack of relevant research on the variation rules and control methods of the composition and injection rate of supercritical multicomponent thermal fluids, resulting in a lack of technical mechanisms for effective optimization. To fill this gap, a reaction molecular dynamics simulation method was used to simulate thermal fluid generation under different temperatures, pressures, and organic mass concentrations. The changes in thermal fluid composition and yield with reaction conditions were studied, and a control model of thermal fluid composition and yield was established. The proportional relationship between the thermal fluid generation scale of an offshore heavy oil platform and the simulated thermal fluid generation scale is analyzed, and a collaborative optimization method of thermal fluid composition and injection rate in field applications is proposed. The results show the following: (1) The higher the mass concentration of organic matter, the higher the content of supercritical carbon dioxide and supercritical nitrogen in thermal fluids, and the lower the content of supercritical water. (2) The higher the temperature and pressure, the higher the thermal fluid yield, and the higher the organic mass concentration, the lower the thermal fluid yield. (3) The component fitting model conforms to the power function relationship, and the coefficient of determination R2 is greater than 0.9; the yield fitting model conforms to the modified inverse linear logarithmic function relationship, the determination coefficient R2 is greater than 0.8, and the fitting degree is high. (4) The ratio between the actual injection rate of thermal fluids in the mine field and the molecular simulated thermal fluid yield is the ratio of organic matter mass in the platform thermal fluid generator and organic matter mass in the simulated box. (5) Based on the composition and yield control model, combined with the simulation of the ratio relationship between yield and injection rate in the field, a collaborative optimization method of thermal fluid composition and injection rate was established. The research results can provide an effective technical method for predicting, controlling, and optimizing the composition and injection rate of supercritical multicomponent thermal fluids.
Testing the Stability of NASICON Solid Electrolyte in Seawater Batteries
Authors: Iordache Mihaela , Oubraham Anisoara , Borta Simona , Ungureanu George , Marinoiu Adriana
Published: October 2024

Rechargeable batteries play a crucial role in the utilization of renewable energy sources. Energy storage systems (ESSs) are designed to store renewable energy efficiently for immediate use. The market for energy storage systems heavily relies on lithium-ion batteries due to their high energy density, capacity, and competitiveness. However, the increasing cost and limited availability of lithium make long-term use challenging. As an alternative to Li-ion batteries, rechargeable seawater batteries are gaining attention due to their abundant and complementary sodium ion active materials. This study focuses on the preparation and characterization of Na3.0Zr2Si2PO12- and Na3.15Zr2Si2PO12-type ceramic membranes and testing their stability in seawater batteries used as solid electrolyte. From the surface analysis, it was observed that the Na3.15Zr2Si2PO12 powder showed a specific surface area of 2.94 m2/g compared to 2.69 m2/g for the Na3.0Zr2Si2PO12 powder. The measured NASICON samples achieved ionic conductivities between 7.42 × 10−5 and 4.4 × 10−4 S/cm compared to the NASICON commercial membrane with an ionic conductivity of 3.9 × 10−4 S/cm. Battery testing involved charging/discharging at various constant current values (0.6–2.0 mA), using Pt/C as the catalyst and seawater as the catholyte.
Numerical Investigation of Solar Collector Performance with Encapsulated PCM: A Transient, 3D Approach
Authors: Faisal Malik Adnan , Rahmani Amin , Akrami Mohammad
Published: October 2024

This study presents a comprehensive numerical investigation into the thermal performance of solar collectors integrated with encapsulated phase change materials (PCMs) using a transient three-dimensional (3D) approach. The performance of two distinct PCMs—paraffin wax and RT60—was evaluated under varying operational conditions, including seasonal variations, inlet pipe velocities, and inlet temperatures. The results indicate that paraffin wax exhibits a higher peak temperature, reaching approximately 360 K, compared to RT60’s peak of 345 K, making paraffin wax more effective for consistent thermal energy storage. Paraffin wax also maintained higher fluid fractions, with a maximum of 0.9 in summer, indicating superior heat absorption and retention capabilities. In contrast, RT60 demonstrated a quicker phase transition, fully liquefying at a lower fluid fraction, which is advantageous for rapid heat release. Seasonal variations significantly impacted system efficiency, with the highest efficiency observed in June at 365 K and the lowest in December at 340 K. The study also found that lower inlet velocities (e.g., 0.25 L/s) significantly improved heat retention, resulting in higher outlet temperatures, while increasing the inlet temperature from 290 K to 310 K led to a marked increase in outlet temperatures throughout the day. These findings underscore the importance of optimizing PCM selection, inlet velocity, and temperature in enhancing the performance of solar thermal systems, offering quantitative insights that contribute to the development of more efficient and reliable renewable energy solutions.
Application of Two-Dimensional NMR for Quantitative Analysis of Viscosity in Medium–High-Porosity-and-Permeability Sandstones in North China Oilfields
Authors: Zhang Wei , Li Si , Wang Shaoqing , Sun Jianmeng , Cai Wenyuan , Yu Weigao , Dai Hongxia , Yang Wenkai
Published: October 2024

The viscosity of crude oil plays a pivotal role in the exploration and development of oil fields. The predominant reliance on laboratory measurements, which are constrained by manual expertise, represents a significant limitation in terms of efficiency. Two-dimensional nuclear magnetic resonance (NMR) logging offers a number of advantages over traditional methods. It is capable of providing faster measurement rates, as well as insights into fluid properties, which can facilitate timely adjustments in oil and gas development strategies. This study focuses on the loose sandstone reservoirs with high porosity and permeability containing heavy oil in the Huabei oilfield. Two-dimensional nuclear magnetic resonance (NMR) measurements and analyses were conducted on saturated rocks with different-viscosity crude oils and varying oil saturation levels, in both natural and artificial rock samples. This study elucidates the distribution patterns of different-viscosity crude oils within the two-dimensional NMR spectra. Furthermore, the T1 and T2 peak values of the extracted oil signals were employed to establish a model correlating oil viscosity with NMR parameters. Consequently, a criterion for determining oil viscosity based on two-dimensional NMR was formulated, providing a novel approach for estimating oil viscosity. The application of this technique in the BQ well group of the Huabei oilfield region yielded an average relative error of 15% between the actual oil viscosity and the computed results. Furthermore, the consistency between the oil types and the oil discrimination chart confirms the reliability of the method. The final outcomes meet the precision requirements for practical log interpretation and demonstrate the excellent performance of two-dimensional nuclear magnetic resonance (NMR) logging in calculating oil viscosity. The findings of this study have significant implications for subsequent exploration and development endeavors in the research area’s oilfields.
Geomasking to Safeguard Geoprivacy in Geospatial Health Data
Authors: Wang Jue
Published: October 2024

Geomasking is a set of techniques that introduces noise or intentional errors into geospatial data to minimize the risk of identifying exact location information related to individuals while preserving the utility of the data to a controlled extent. It protects the geoprivacy of the data contributor and mitigates potential harm from data breaches while promoting safer data sharing. The development of digital health technologies and the extensive use of individual geospatial data in health studies have raised concerns about geoprivacy. The individual tracking data and health information, if accessed by unauthorized parties, may lead to privacy invasions, criminal activities, and discrimination. These risks underscore the importance of robust protective measures in the collection, management, and sharing of sensitive data. Geomasking techniques have been developed to safeguard geoprivacy in geospatial health data, addressing the risks and challenges associated with data sharing. This entry paper discusses the importance of geoprivacy in geospatial health data and introduces various kinds of geomasking methods and their applications in balancing the protection of individual privacy with the need for data sharing to ensure scientific reproducibility, highlighting the urgent need for more effective geomasking techniques and their applications.
Social Media Sentiment Analysis
Authors: Nip Joyce Y. M. , Berthelier Benoit
Published: October 2024

Social media sentiment analysis is the computational detection and extraction of human subjective evaluation of objects embedded on social media. Previous sentiment analysis was conducted on isolated written texts, and typically classified sentiment into positive, negative, and neutral states. Social media sentiment analysis has included multi-modal texts, temporal dynamics, interactions, network relationships, and sentiment propagation. Specific emotions and sentiment intensity are also detected.
Elevation, Soil and Environmental Factors Determine the Spatial and Quantitative Distribution of Qinghai Spruce Recruitment Biomass in Mountainous (Alpine) Watersheds
Authors: Liu Jimeng , Kastridis Aristeidis , Xu Erwen , Jing Wenmao , Ren Xiaofeng , Ma Rui , Wang Rongxin , Zheng Wenli , Yang Xiaohu
Published: October 2024

Soil heterogeneity observed in the alpine environment plays a very important role in the growth of forest recruitment. However, the mechanisms by which the biomass accumulation and allocation patterns of forest recruitment respond to such environmental differences are unclear, which hinders a thorough understanding of climate change’s impact on forest biomass. We hypothesized that soil heterogeneity influences the distribution of Qinghai spruce recruitment biomass along with elevation. In the frame of this study, carried out in the northern Tibetan Plateau, forest Qinghai spruce recruitment data were combined with soil data derived from 24 sample plots, while permutation multifactor ANOVA and multiple linear regression were utilized to reveal the characteristics of forest recruits’ above- and below-ground biomass and their allocation patterns in response to soil heterogeneity. According to the results, the soil heterogeneity mainly affected the distribution characteristics of recruits’ above- and below-ground biomass at different elevations, while the recruits’ root–shoot ratio variability was influenced by a combination of soil and other environmental factors. Soil organic carbon (SOC) had the greatest effect on the variability of the above- and below-ground biomass of spruce recruits, with R2 of 0.280 and 0.257, respectively. Soil organic carbon and soil moisture content (SMC) had a significant effect on the variability of the root–shoot ratio, with R2 of 0.168 and 0.165, respectively. Soil total nitrogen (TN) and soil organic carbon were the main influencing factors of the above-ground biomass of forest recruits, with contribution rates of 43.15% and 35.28%, respectively. Soil total nitrogen and soil organic carbon were also the main factors influencing the below-ground biomass of forest recruits, with contribution rates of 42.52% and 37.24%, respectively, and both of them had a positive effect on biomass accumulation, and the magnitude of the influence varied with the elevation gradient. Soil moisture content was the main influence factor of spruce recruits’ root–shoot ratio, with a contribution rate of 54.12%. Decreasing soil moisture content would significantly increase the root–shoot ratio of spruce recruits and promote plants to allocate more biomass to root growth. Changes in elevation not only affected the intensity of the effect of soil factors on spruce recruitment biomass and its allocation pattern but even led to a change in the positive and negative effects.
Nature-Based Solution for Climate Change Adaptation: Coastal Habitats Restoration in Xiamen Bay, China
Authors: Yang Suzhen , Fang Qinhua , Zhang Dian , Meilana Lusita , Ikhumhen Harrison Odion , Zhang Xue , Jiang Xiaoyan , Lin Boding
Published: October 2024

Nature-based solutions (NbSs) of biodiversity conservation and ecosystem restoration have been paid increasing attention as an essential approach to slow down climate change. However, to what degree an NbS approach will contribute to the combined effects of human intervention and climate change has not been well studied. From a habitat quality perspective, we set four NbS scenarios to analyze whether the NbS—mangrove restoration in particular—will be enough for climate change in Xiamen Bay of Fujian Province, China. The habitat quality module of the InVEST model (InVEST-HQ) and the Sea Level Affecting Marshes Model (SLAMM) were used to simulate the spatial-temporal changes in habitat types and habitat quality. Results show that (1) rising sea levels will cause coastal squeeze effects, impacting habitat quality due to erosion and inundation in the study area; (2) mangrove restoration is an effective way to mitigate climate change effects and to increase habitat quality; and (3) further analysis of the effectiveness of mangrove restoration shows that the consideration of mangrove fragmentation effects and sea-use impacts are necessary. The findings in this study will enrich the international discussion of NbSs to climate change in coastal areas.
Accelerating and Compressing Transformer-Based PLMs for Enhanced Comprehension of Computer Terminology
Authors: Peng Jian , Zhong Kai
Published: October 2024

Pretrained language models (PLMs) have significantly advanced natural language processing (NLP), establishing the "pretraining + fine-tuning" paradigm as a cornerstone approach in the field. However, the vast size and computational demands of transformer-based PLMs present challenges, particularly regarding storage efficiency and processing speed. This paper addresses these limitations by proposing a novel lightweight PLM optimized for accurately understanding domain-specific computer terminology. Our method involves a pipeline parallelism algorithm designed to accelerate training. It is paired with an innovative mixed compression strategy that combines pruning and knowledge distillation to effectively reduce the model size while preserving its performance. The model is further fine-tuned using a dataset that mixes source and target languages to enhance its versatility. Comprehensive experimental evaluations demonstrate that the proposed approach successfully achieves a balance between model efficiency and performance, offering a scalable solution for NLP tasks involving specialized terminology.
Species Composition of Fish Larvae and Juveniles in the Nanji Islands, China
Authors: Wang Xiaodong , Zhao Dubin , Liu Qiang , Lu Tianyu , Zhong Junsheng , Chen Wandong , Xie Shangwei , Chen Shun
Published: October 2024

To describe the species composition of fish larvae and juveniles in the Nanji Islands, monthly collection was conducted at 12 stations around the Nanji Islands from March 2023 to February 2024 by using horizontal tow at the surface layer. The fish larvae and juveniles were collected by a larva net (1.3 m mouth diameter, 0.5 mm mesh aperture). A total of 6446 fish larvae and juveniles were collected, belonging to 59 species and 54 genera in 11 families. Most of them were preflexion larvae (93.31%). Gobiidae, with eight species, was the most diverse family. Based on the index of relative importance (IRI) result, the dominant species was Sebastiscus marmoratus (Cuvier, 1829), accounting for 84.52% of the total number of samples collected. The common species were Stolephorus commersonii Lacepède, 1803; Omobranchus elegans (Steindachner, 1876); Nibea albiflora (Richardson, 1846); Parablennius yatabei (Jordan & Snyder, 1900); Lateolabrax maculatus (McClelland, 1844); and Odontamblyopus lacepediiand (Temminck & Schlegel, 1845). Other species were all rare species. The highest species number was in September (18 species), and the highest density was in December. Only one fish larva was collected in April. Pielou’s evenness index was highest in November, while Margalef’s richness index and Shannon–Wiener diversity were highest in September. The result of cluster analysis showed that the 11 months (except April) could be divided into four groups. The species number, density, and three diversity indexes showed no significant correlation with temperature and salinity.
Evaluating Chlorine Sanitization at Practical Concentrations for Controlling Listeria monocytogenes and Salmonella on Fresh Peaches
Authors: Shen Xiaoye , Hang Mengqian , Su Yuan , de Avila Jeanene Marie , Zhu Mei-Jun
Published: October 2024

Recent foodborne outbreaks and recalls involving Listeria monocytogenes and Salmonella-contaminated peaches have caused significant economic losses to the peach industry. This study evaluated the effectiveness of chlorine, a commonly used sanitizer in the fresh produce industry, against L. monocytogenes and Salmonella and its ability to control cross-contamination in fresh peaches. Peaches inoculated with L. monocytogenes or Salmonella (~6 log10 CFU/peach) were treated with 50–150 mg/L of free chlorine (FC, pH6.8) 24 h post-inoculation. The results revealed that chlorine had similar efficacy against L. monocytogenes and Salmonella on peaches (p > 0.05). A 30 s treatment at 50, 100, and 150 mg/L FC resulted in dose-dependent reductions (p < 0.05), achieving reductions of 0.88–0.92, 1.54–1.61, and 1.73–1.79 log10 CFU/peach, respectively. Extending the contact time to 2 min slightly but significantly enhanced the chlorine efficacy (p < 0.05). Additionally, a 30 s to 2 min exposure to chlorine with 50–150 mg/L FC resulted in a 1.05–1.43 log10 CFU/peach reduction in yeasts and molds. Tap water exposure led to substantial cross-contamination between inoculated and uninoculated fruits and processed water, with Salmonella exhibiting higher transfer rates than L. monocytogenes. The application of chlorine mitigated the cross-contamination of both pathogens but did not entirely prevent it. These findings offer valuable insights for the peach and other stone fruit industries to verify process controls.
Does Google Analytics Improve the Prediction of Tourism Demand Recovery?
Authors: Botha Ilsé , Saayman Andrea
Published: October 2024

Research shows that Google Trend indices can improve tourism-demand forecasts. Given the impact of the recent pandemic, this may prove to be an important predictor of tourism recovery in countries that are still struggling to recover, including South Africa. The purpose of this paper is firstly, to build on previous research that indicates that Google Trends improves tourism-demand forecasting by testing this within the context of tourism recovery. Secondly, this paper extends previous research by not only including Google Trends in time-series forecasting models but also typical tourism-demand covariates in an econometric specification. Finally, we test the performance of Google Trends in forecasting over a longer time period, because the destination country is a long-haul destination where more lead time may be required in decision-making. Additionally, this research contributes to the body of knowledge by including lower frequency data (quarterly) instead of the higher frequency data commonly used in current research, while also focusing on an important destination country in Africa. Due to the differing data frequencies, the MIDAS modelling approach is used. The MIDAS models are compared to typical time-series and naïve benchmarks. The findings show that monthly Google Trends improve forecasts on lower frequency data. Furthermore, forecasts that include Google Trends are more effective in forecasting one to two quarters ahead, pre-COVID. This trend changed after COVID, when Google Trends led to improved recovery forecasts even over a longer term.
Transforming Agricultural Productivity with AI-Driven Forecasting: Innovations in Food Security and Supply Chain Optimization
Authors: Dhal Sambandh Bhusan , Kar Debashish
Published: October 2024

Global food security is under significant threat from climate change, population growth, and resource scarcity. This review examines how advanced AI-driven forecasting models, including machine learning (ML), deep learning (DL), and time-series forecasting models like SARIMA/ARIMA, are transforming regional agricultural practices and food supply chains. Through the integration of Internet of Things (IoT), remote sensing, and blockchain technologies, these models facilitate the real-time monitoring of crop growth, resource allocation, and market dynamics, enhancing decision making and sustainability. The study adopts a mixed-methods approach, including systematic literature analysis and regional case studies. Highlights include AI-driven yield forecasting in European hydroponic systems and resource optimization in southeast Asian aquaponics, showcasing localized efficiency gains. Furthermore, AI applications in food processing, such as plasma, ozone and Pulsed Electric Field (PEF) treatments, are shown to improve food preservation and reduce spoilage. Key challenges—such as data quality, model scalability, and prediction accuracy—are discussed, particularly in the context of data-poor environments, limiting broader model applicability. The paper concludes by outlining future directions, emphasizing context-specific AI implementations, the need for public–private collaboration, and policy interventions to enhance scalability and adoption in food security contexts.
Mechanical Foundations of the Generalized Second Law and the Irreversibility Principle
Authors: Gujrati Purushottam Das
Published: October 2024

We follow the Boltzmann-Clausius-Maxwell (BCM) proposal to establish the generalized second law (GSL) that is applicable to a system of any size, including a single particle system as our example establishes, and that supercedes the celebrated second law (SL) of increase of entropy of an isolated system. It is merely a consequence of the mechanical equilibrium (stable or unstable) principle (Mec-EQ-P) of analytical mechanics and the first law. We justify an irreversibility priciple that covers all processes, spontaneous or not, and having both positive and negative nonequilibrium temperatures temperatures T defined by (dQ/dS)E. Our novel approach to establish GSL/SL is the inverse of the one used in classical thermodynamics and clarifies the concept of spontaneous processes so that dS≥0 for T>0 and dS<0 for T<0. Nonspontaneous processes such as creation of internal constraints are not covered by GSL/SL. Our demonstration establishes that Mec-EQ-P controls spontaneous processes, and that temperature (positive and negative) must be considered an integral part of dissipation.
Fractal Analysis of Particle Size and Morphology in Single-Particle Breakage Based on 3D Images
Authors: Li Ruidong , Gao Xiang , He Shao-Heng , Ru Dongheng , Ding Zhi
Published: October 2024

The accurate modeling of single-particle breakage based on three-dimensional (3D) images is crucial for understanding the particle-level mechanics of granular materials. This study aims to propose a systematic framework incorporating single-particle breakage experiments and numerical simulations based on a novel 3D particle reconstruction technique for fractal analysis of particle size and morphology in single-particle breakage. First, the vision foundation model is used to generate accurate particles from 3D images. The numerical approach is validated by simulating the single-particle breakage test with multiple Fujian sand particles. Then, the breakage processes of reconstructed sand particles under axial compression are numerically modeled. The relationship between 3D fractal dimensions and particle size, particle crushing strength, and morphology is meticulously investigated. Furthermore, the implications of these relationships on the particle breakage processes are thoroughly discussed, shedding light on the underlying mechanisms that govern particle breakage. The framework offers an effective way to investigate the breakage behavior of single sand particles, which will enhance understanding of the mechanism of the whole particle breakage process.
Quantitative Macromolecular Modeling Assay of Biopolymer-Based Hydrogels
Authors: Abroug Nada , Schöbel Lisa , Boccaccini Aldo R. , Seitz Hermann
Published: October 2024

The rubber elasticity theory has been lengthily applied to several polymeric hydrogel substances and upgraded from idealistic models to consider imperfections in the polymer network. The theory relies solely on hyperelastic material models in order to provide a description of the elastic polymer network. While this is also applicable to polymer gels, such hydrogels are rather characterized by their water content and visco-elastic mechanical properties. In this work, we applied rubber elasticity constitutive models through hyperelastic parameter identification of hydrogels based on their stress–strain response to compression. We further performed swelling experiments and determined the intrinsic properties, i.e., density, of the specimens and their components. Additionally, we estimated their equilibrium swelling and employed it in the swelling-equilibrium theory in order to determine the polymer–solvent interaction parameter of each hydrogel with regard to cross-linking. Our results show that the average mesh size obtained from the rubber elasticity theory can be regarded as a concentration-dependent characteristic length of the hydrogel’s network and couples the non-linear elastic response to the specimens’ inherent visco-elasticity through hysteresis as a quantifier of energy dissipation under large deformation.
A Novel Polarized Light Microscope for the Examination of Birefringent Crystals in Synovial Fluid
Authors: FitzGerald John D. , Barrios Chesca , Liu Tairan , Rosenthal Ann , McCarthy Geraldine M. , Chen Lillian , Bai Bijie , Ma Guangdong , Ozcan Aydogan
Published: October 2024

Background: The gold standard for crystal arthritis diagnosis relies on the identification of either monosodium urate (MSU) or calcium pyrophosphate (CPP) crystals in synovial fluid. With the goal of enhanced crystal detection, we adapted a standard compensated polarized light microscope (CPLM) with a polarized digital camera and multi-focal depth imaging capabilities to create digital images from synovial fluid mounted on microscope slides. Using this single-shot computational polarized light microscopy (SCPLM) method, we compared rates of crystal detection and raters’ preference for image. Methods: Microscope slides from patients with either CPP, MSU, or no crystals in synovial fluid were acquired using CPLM and SCPLM methodologies. Detection rate, sensitivity, and specificity were evaluated by presenting expert crystal raters with (randomly sorted) CPLM and SCPLM digital images, from FOV above clinical samples. For each FOV and each method, each rater was asked to identify crystal suspects and their level of certainty for each crystal suspect and crystal type (MSU vs. CPP). Results: For the 283 crystal suspects evaluated, SCPLM resulted in higher crystal detection rates than did CPLM, for both CPP (51%. vs. 28%) and MSU (78% vs. 46%) crystals. Similarly, sensitivity was greater for SCPLM for CPP (0.63 vs. 0.35) and MSU (0.88 vs. 0.52) without giving up much specificity resulting in higher AUC. Conclusions: Subjective and objective measures of greater detection and higher certainty were observed for SCPLM over CPLM, particularly for CPP crystals. The digital data associated with these images can ultimately be incorporated into an automated crystal detection system that provides a quantitative report on crystal count, size, and morphology.
Study Protocol for the Development and Preliminary Efficacy Assessment of AdoptMindful2Care@Web: A Web-Based Mindful Parenting Postadoption Intervention
Authors: Chorão Ana Luz , Canavarro Maria Cristina , Pires Raquel
Published: October 2024

Developing postadoption interventions to prevent parenting stress and promote parents’ well-being is urgent. Mindful parenting-based interventions show promise in achieving these goals and are well received by adoptive parents (APs). However, face-to-face interventions face significant barriers. E-health tools offer a solution by improving accessibility and flexibility and reducing long-term costs. Our main aim is to develop and assess the preliminary efficacy of a web-based mindful parenting postadoption intervention, AdoptMindful2Care@Web, by using a user-centered approach to ensure its effectiveness and cost efficiency. First, two quantitative surveys will be conducted to assess the needs and preferences of APs and gather input from adoption professionals. Then, a prototype version of the intervention will be created and tested for usability with the APs via the Think-Aloud method. Finally, a pilot version will undergo a two-arm pilot randomized controlled trial to assess its feasibility, acceptability, and preliminary efficacy via self-report questionnaires. By developing AdoptMindful2Care@Web employing a user-centered approach, we hope to provide an effective intervention that is adjusted to the reality of its users and the surrounding context. In doing so, we will be able to promote AP access to specialized mental health care efficiently.
Gene Therapy: A Revolutionary Step in Treating Thalassemia
Authors: Malay Jhancy , Salama Rasha Aziz Attia , Alam Qureshi Ghania Shehzad , Ammar Ali Raafat Ali Ahmed , Janardhan Gayatri , Safdar Maryam , Elshamy Hesham Amin Hamdy
Published: October 2024

Beta thalassemia is an inherited blood disorder that results in inefficient erythropoiesis due to genetic mutation that leads to the reduction or absence of the hemoglobin beta-globulin protein. Approximately 8.5% of UAE residents suffer from β-thalassemia, a significant health and financial problem. The treatment options available for β-Thalassemia major are limited and associated with a wide range of complications. β-thalassemia gene therapy is emerging as a potential novel treatment option that eliminates the complications caused by the current long-term treatment modalities and the associated economic burden. This paper reviews the scientific literature related to emerging gene therapy for β-Thalassemia by analyzing all the articles published from January 2010 to December 2023 in the English language on Databases like PubMed, Scopus, ProQuest, and CINAHL. The use of gene therapy has demonstrated promising outcomes for a permanent cure of β-Thalassemia. To conclude, gene therapy is an innovative solution. It demonstrates a promising future, but does come with its own setbacks and is something that must be tackled in order to revolutionize it in the medical world. FDA-approved ZYNTEGLO is a potentially one-time curative treatment for β-Thalassemia. Although cutting-edge, its use is limited because of the high cost—a price of USD 2.8 million per patient.
Morpho–Molecular Characterization of Brazilian Common Bean Landraces Varieties and Commercial Cultivars
Authors: Schllemer dos Santos Ana Claudia , Bischoff Nunes Isadora , Teixeira da Silva Lucas , de Sousa Alcântara Lucas Vinícius , da Silva Domingues Lucas , Donazzolo Joel , Morini Kupper Cardoso Perseguini Juliana , Possenti Jean Carlo
Published: October 2024

This study aimed to characterize forty genotypes (31 landrace varieties and nine commercial cultivars) of common bean (Phaseolus vulgaris L.) morphologically and molecularly. Morphological descriptors were evaluated during seedling, flowering, physiological maturation and post-harvest stages. Ten microsatellite markers were used for molecular screening. The markers were analyzed according to the number of alleles per locus, the allele frequency per locus and the polymorphism in content (PIC). Genetic distances and cluster analysis were performed using Bayesian inference and the UPGMA method. All black beans evaluated have anthocyanin in the cotyledons, hypocotyls and stems, and their flowers are purple. For the growth habit, 50% of the evaluated genotypes belong to type III, and at the physiological maturation stage, yellow color is predominant in the pods (85%). Through the genetic dissimilarity, three groups were observed for the likelihood reason, and five groups were observed through the UPGMA method, a strong indication of the wide genetic diversity among the evaluated genotypes. All genotypes from the Andean center of origin were grouped into the same cluster.
Classification of Citrus Leaf Diseases Using Hyperspectral Reflectance and Fluorescence Imaging and Machine Learning Techniques
Authors: Min Hyun Jung , Qin Jianwei , Yadav Pappu Kumar , Frederick Quentin , Burks Thomas , Dewdney Megan , Baek Insuck , Kim Moon
Published: October 2024

Citrus diseases are significant threats to citrus groves, causing financial losses through reduced fruit size, blemishes, premature fruit drop, and tree death. The detection of citrus diseases via leaf inspection can improve grove management and mitigation efforts. This study explores the potential of a portable reflectance and fluorescence hyperspectral imaging (HSI) system for detecting and classifying a control group and citrus leaf diseases, including canker, Huanglongbing (HLB), greasy spot, melanose, scab, and zinc deficiency. The HSI system was used to simultaneously collect reflectance and fluorescence images from the front and back sides of the leaves. Nine machine learning classifiers were trained using full spectra and spectral bands selected through principal component analysis (PCA) from the HSI with pixel-based and leaf-based spectra. A support vector machine (SVM) classifier achieved the highest overall classification accuracy of 90.7% when employing the full spectra of combined reflectance and fluorescence data and pixel-based analysis from the back side of the leaves, whereas a discriminant analysis classifier yielded the best accuracy of 94.5% with the full spectra of combined reflectance and fluorescence data and leaf-based analysis. Among the diseases, control, scab, and melanose were classified most accurately, each with over 90% accuracy. Therefore, the integration of the reflectance and fluorescence HSI with advanced machine learning techniques demonstrated the capability to accurately detect and classify these citrus leaf diseases with high precision.
Glutathione and Selenium Supplementation Attenuates Liver Injury in Diethylnitrosamine-Induced Hepatocarcinogenic Mice by Enhancing Glutathione-Related Antioxidant Capacities
Authors: Hsiao Yung-Fang , Huang Shih-Chien , Cheng Shao-Bin , Hsu Cheng-Chin , Huang Yi-Chia
Published: October 2024

Excess oxidative stress and inadequate antioxidant capacities are critical features in the development of hepatocellular carcinoma. This study aimed to determine whether supplementation with glutathione (GSH) and/or selenium (Se), as antioxidants, attenuates diethylnitrosamine (DEN)-induced hepatocarcinogenesis in mice. C57BL/6J male mice were randomly assigned to control, DEN, DEN + GSH, DEN + Se, and DEN + GSH + Se groups for 20 weeks. Daily supplementation with GSH and/or Se commenced in the first experimental week and continued throughout the study. DEN was administered in weeks 2–9 and 16–19 of the experimental period. DEN administration induced significant pathological alterations of hepatic foci, evidenced by elevated levels of liver function, accompanied by high malondialdehyde (MDA) levels; low GSH levels; and glutathione peroxidase (GPx), glutathione reductase (GR), and glutathione S-transferase (GST) activities. Supplementation with GSH and Se significantly ameliorated liver pathological changes, reducing liver function and MDA levels while increasing GSH levels and GPx, GR, and GST activities. Notably, combined supplementation with GSH and Se more effectively increased the GSH/glutathione disulfide ratio and GPx activity than individual supplementation. Supplementation with GSH and Se attenuated liver injury in DEN-induced hepatocarcinogenic mice by enhancing GSH and its related antioxidant capacities, thereby mitigating oxidative damage.
Molecular and Genetics Perspectives on Primary Adrenocortical Hyperfunction Disorders
Authors: Kim Sanggu , Chaudhary Preeti Kumari , Kim Soochong
Published: October 2024

Adrenocortical disorders encompass a broad spectrum of conditions ranging from benign hyperplasia to malignant tumors, significantly disrupting hormone balance and causing a variety of clinical manifestations. By leveraging next-generation sequencing and in silico analyses, recent studies have uncovered the genetic and molecular pathways implicated in these transitions. In this review, we explored the molecular and genetic alterations in adrenocortical disorders, with a particular focus on the transitions from normal adrenal function to hyperfunction. The insights gained are intended to enhance diagnostic and therapeutic strategies, offering up-to-date knowledge for managing these complex conditions effectively.