bluuebunny/crossref_metadata_2025 huggingface dataset:
An Interpretable Machine Learning Approach to Predict Sensory Processing Sensitivity Trait in Nursing Students
Authors: Ponce-Valencia Alicia , Jiménez-Rodríguez Diana , Hernández Morante Juan José , Martínez Cortés Carlos , Pérez-Sánchez Horacio , Echevarría Pérez Paloma
Published: April 2024

Sensory processing sensitivity (SPS) is a personality trait that makes certain individuals excessively sensitive to stimuli. People carrying this trait are defined as Highly Sensitive People (HSP). The SPS trait is notably prevalent among nursing students and nurse staff. Although there are HSP diagnostic tools, there is little information about early detection. Therefore, the aim of this work was to develop a prediction model to identify HSP and provide an individualized nursing assessment. A total of 672 nursing students completed all the evaluations. In addition to the HSP diagnosis, emotional intelligence, communication skills, and conflict styles were evaluated. An interpretable machine learning model was trained to predict the SPS trait. We observed a 33% prevalence of HSP, which was higher in women and people with previous health training. HSP were characterized by greater emotional repair (p = 0.033), empathy (p = 0.030), respect (p = 0.038), and global communication skills (p = 0.036). Overall, sex and emotional intelligence dimensions are important to detect this trait, although personal characteristics should be considered. The present individualized prediction model could help to predict the presence of the SPS trait in nursing students, which may be useful in conducting intervention strategies to avoid the negative consequences and reinforce the positive ones of this trait.
Predicting the Remaining Useful Life of Lithium-Ion Batteries Using 10 Random Data Points and a Flexible Parallel Neural Network
Authors: Jiang Lidang , Huang Qingsong , He Ge
Published: April 2024

Accurate Remaining Useful Life (RUL) prediction of lithium batteries is crucial for enhancing their performance and extending their lifespan. Existing studies focus on continuous or relatively sparse datasets; however, continuous and complete datasets are rarely available in practical applications due to missing or inaccessible data. This study attempts to achieve the prediction of lithium battery RUL using random sparse data from only 10 data points, aligning more closely with practical industrial scenarios. Furthermore, we introduce the application of a Flexible Parallel Neural Network (FPNN) for the first time in predicting the RUL of lithium batteries. By combining these two approaches, our tests on the MIT dataset show that by randomly downsampling 10 points per cycle from 10 cycles, we can reconstruct new meaningful features and achieve a Mean Absolute Percentage Error (MAPE) of 2.36% in predicting the RUL. When the input data are limited to the first 10 cycles using the dataset constructed from random downsampling and the FPNN, the predicted RUL MAPE is 0.75%. The method proposed in this study offers an accurate, adaptable, and comprehensible new solution for predicting the RUL of lithium batteries, paving a new research path in the field of battery health monitoring.
Hybrid Metaheuristic Algorithms for Optimization of Countrywide Primary Energy: Analysing Estimation and Year-Ahead Prediction
Authors: Jamil Basharat , Serrano-Luján Lucía
Published: April 2024

In the present work, India’s primary energy use is analysed in terms of four socio-economic variables, including Gross Domestic Product, population, and the amounts of exports and imports. Historical data were obtained from the World Bank database for 44 years as annual values (1971–2014). Energy use is analysed as an optimisation problem, where a unique ensemble of two metaheuristic algorithms, Grammatical Evolution (GE), and Differential Evolution (DE), is applied. The energy optimisation problem has been investigated in two ways: estimation and a year-ahead prediction. Models are compared using RMSE (objective function) and further ranked using the Global Performance Index (GPI). For the estimation problem, RMSE values are found to be as low as 0.0078 and 0.0103 on training and test datasets, respectively. The average estimated energy use is found in good agreement with the data (RMSE = 6.3749 kgoe/capita), and the best model (E10) has an RMSE of 5.8183 kgoe/capita, with a GPI of 1.7249. For the prediction problem, RMSE is found to be 0.0096 and 0.0122 on training and test datasets, respectively. The average predicted energy use has RMSE of 7.8857 (kgoe/capita), while Model P20 has the best value of RMSE (7.9201 kgoe/capita) and a GPI of 1.8836.
Simplified V/f Control Algorithm for Reduction of Current Fluctuations in Variable-Speed Operation of Induction Motors
Authors: Son Dong-Hyeok , Kim Sung-An
Published: April 2024

This paper introduces a straightforward control strategy aimed at the reduction of current fluctuations within the low-frequency domain of open-loop V/f control in induction motor drives. Traditional control techniques necessitate the addition of a current compensator based on motor parameters and the use of digital filters such as band-pass or high-pass filters. These methods, however, rely on precise motor parameters and involve complex filter design and implementation. The proposed control is capable of suppressing current fluctuations without controlling the slip of the induction motor. The proposed control strategy generates the forced rotation angle and command input voltage using the V/f block and outputs the d-axis voltage using a proportional integral controller to keep the d-axis current constant at zero. The difference between the command input voltage and the d-axis voltage is applied as the q-axis voltage and then applied through SVPWM. In order to verify the effectiveness of the proposed control, the proposed control is implemented and analyzed using power simulation based on the results of the analysis of the causes of current fluctuations in the induction motor. Finally, the effect of suppressing current fluctuations of the induction motor is verified through experimental results. In the 10~19 Hz range, where the conventional V/f control method resulted in current fluctuation rates exceeding 10% and peaking at 113.3% at 13 Hz, the proposed method suppressed the fluctuation rate to below 8.6% across all frequencies. This paper validates the effectiveness of the proposed control strategy through these results.
Mathematical Model of a Nonlinear Power Transformer for Needs of Relay Protection
Authors: Kolesnikov Evgeniy , Novozhilov Aleksandr , Rakhimberdinova Dilara , Kislov Aleksandr , Novozhilov Timofey
Published: April 2024

In this work, a mathematical model of a three-phase nonlinear transformer is suggested. The model enables simulating the transformer operation with allowance for its nonlinearity and covers needs of the relay protection. Our model has been developed on the basis of a mathematical model with phase coordinates, where differential equations are composed by the Kirchhoff’s phase-voltage law. Based on this model, we first compose a mathematical model for simulating steady-state operation modes of a transformer, taking into account the asymmetry and nonlinearity of its ferromagnetic core. In this model, the initial values of inductances and mutual inductances of loops are determined from the main phase inductance calculated by the experimentally found no-load current, and their current values are determined from the currents in windings and the magnetic fluxes in legs of the transformer core. The magnetic fluxes are calculated by the nodal-pair method. This improved mathematical model is verified through a comparison between the calculated harmonic components of the phase currents and the experimental results. The harmonic components are calculated with the use of Fourier expansion of the calculated phase currents. Their experimental values are determined with a spectrum analyzer. The calculated and experimental harmonic components of the currents of phase A during no-load and rated-load operation of the transformer are tabulated. The comparison of these results shows that the mathematical model of a three-phase transformer we suggest makes it possible to simulate currents in transformer windings under steady-state operation modes with accuracy acceptable for relay protection.
Design and Analysis of a Linear Electric Generator for Harvesting Vibration Energy
Authors: Then Joshua , Agalgaonkar Ashish P. , Safaei Farzad , Muttaqi Kashem M.
Published: April 2024

This paper provides a proof of concept for a linear electric generator that can be used to harvest energy from various sources of linear motion, such as vibrations, free-piston engines and wave energy. The generator can be used to power small electronic devices, such as sensors, or charge household batteries. The literature was reviewed to develop an understanding about the applications, control methods, excitation methods and mechanics of rotating and linear electric machines. A bidirectional, two-sided linear machine was designed with two stator cores and a single mover core. The stator windings and mover winding can be independently excited, allowing for three modes: no mover excitation, a DC excited mover, and an AC excited mover. Simulation results showed that the magnetic flux generated by DC excited stator cores were concentrated in the centre of the mover core. The use of two stator cores eliminates lateral flux in the mover core when it is not excited, minimising attraction and repulsion forces. Parametric analysis showed that flux cutting occurred in all operation modes, verifying that the generator will produce power when operating. Hardware tests produced an output current when the machine was electrically and mechanically excited, verifying the proposed design.
Power to the People: On the Role of Districts in Decentralized Energy Systems
Authors: Schnidrig Jonas , Chuat Arthur , Terrier Cédric , Maréchal François , Margni Manuele
Published: April 2024

The transition towards renewable and decentralized energy systems is propelled by the urgent need to address climate concerns and advance sustainable development globally. This transformation requires innovative methods to integrate stochastic renewable sources such as solar and wind power and challenging traditional energy paradigms rooted in centralized and continuous energy production. The present study focuses on the Swiss energy system to explore the optimization of energy planning strategies that incorporate decentralized energy production within a centralized framework. Here, we show that a strategic approach to decentralization can significantly reduce annual system costs by 10% to CHF 1230 per capita and increase self-consumption to 68% of the decentralized PV production, emphasizing the need for a hybrid energy-planning model that balances centralized and decentralized models for enhanced system resilience, efficiency, and cost-effectiveness. This research underscores the strategic importance of diversifying energy sources, enhancing energy storage, improving grid flexibility, and laying a foundational framework for policy making and strategic planning. It encourages further investigation into climate impacts, technology synergy, and the integration of district heating, aiming to establish a resilient, sustainable, and autonomous energy future.
Renewable Methanol as a Fuel for Heavy-Duty Engines: A Review of Technologies Enabling Single-Fuel Solutions
Authors: Pu Yi-Hao , Dejaegere Quinten , Svensson Magnus , Verhelst Sebastian
Published: April 2024

To meet climate targets, a global shift away from fossil fuels is essential. For sectors where electrification is impractical, it is crucial to find sustainable energy carriers. Renewable methanol is widely considered a promising fuel for powering heavy-duty applications like shipping, freight transport, agriculture, and industrial machines due to its various sustainable production methods. While current technological efforts focus mainly on dual-fuel engines in shipping, future progress hinges on single-fuel solutions using renewable methanol to achieve net-zero goals in the heavy-duty sector. This review examines the research status of technologies enabling methanol as the sole fuel for heavy-duty applications. Three main categories emerged from the literature: spark-ignition, compression-ignition, and pre-chamber systems. Each concept’s operational principles and characteristics regarding efficiency, stability, and emissions were analyzed. Spark-ignition concepts are a proven and cost-effective solution with high maturity. However, they face limitations due to knock issues, restricting power output with larger bore sizes. Compression-ignition concepts inherently do not suffer from end-gas autoignition, but encounter challenges related to ignitability due to the low cetane number of methanol. Nonetheless, various methods for achieving autoignition of methanol exist. To obtain stable combustion at all load points, a combination of techniques will be required. Pre-chamber technology, despite its lower maturity, holds promise for extending the knock limit and enhancing efficiency by acting as a distributed ignition source. Furthermore, mixing-controlled pre-chamber concepts show potential for eliminating knock and the associated size and power limitations. The review concludes by comparing each technology and identifying research gaps for future work.
Exploring the Environmental Benefits of an Open-Loop Circular Economy Strategy for Automotive Batteries in Industrial Applications
Authors: Silvestri Luca , Forcina Antonio , Silvestri Cecilia , Arcese Gabriella , Falcone Domenico
Published: April 2024

Battery energy storage systems (BESSs) can overwhelm some of the environmental challenges of a low-carbon power sector through self-consumption with standalone photovoltaic (PV) systems. This solution can be adapted for different applications such as residential, commercial, and industrial uses. Furthermore, the option to employ second-life batteries derived from electric vehicles represents a promising opportunity for preserving the environment and improving the circular economy (CE) development. Nowadays, the industrial sector is progressively applying CE principles in their business strategies, and focusing on the potential positive consequences of CE eco-innovations on climate change mitigation. With the aim to promote the transition to an open-loop circular economy for automotive batteries, this study assesses and quantifies the potential environmental benefits resulting from the integration of a second-life battery-based BESS (SL-BESS) connected to an industrial machine. For this purpose, various scenarios involving the use of BESS, SL-BESS, and a standalone PV system are compared with a base case, where the machine is entirely powered by electricity from the grid. The examination of life cycle stages follows the life cycle assessment (LCA) cradle-to-grave methodology as outlined in ISO 14040:2006 and ISO 14044:2006/Amd 1:2017. Simapro® 9 is utilized as the software platform. Results demonstrate that the combination of the SL-BESS with a standalone photovoltaic (PV) system represents the optimal solution in terms of global warming potential (GWP) reduction, with a saving of up to −74.8%. However, manufacturing and end-of-life stages of PV and batteries contribute to abiotic depletion and human toxicity, resulting from the use of chemicals and the extraction of resources essential for their manufacture. Indeed, when BESS is made of new batteries, it demonstrates the most significant impacts in terms of AD at 1.22 × 10−1 kg Sb eq and human toxicity (HT) at 3.87 × 103 kg 1,4-DB eq, primarily attributable to the manufacturing stages of both BESS and PV systems. The findings represent a significant breakthrough, highlighting the substantial capacity of incorporating SL-BESS alongside renewable energy sources to mitigate GWP resulting from industrial applications, and the criticality of repurposing decommissioned batteries from the automotive industry for secondary use.
Is Greece Ready for a Hydrogen Energy Transition?—Quantifying Relative Costs in Hard to Abate Industries
Authors: Nanaki Evanthia A. , Kiartzis Spyros , Xydis George
Published: April 2024

During the past few years, hydrogen use has come to be considered as an alternative energy carrier in a future decarbonized world. Many developed nations are undergoing a shift towards low-carbon energy sources, driven by the excessive reliance on fossil fuels and the detrimental effects of climate change. This study aims to investigate the potential for hydrogen deployment in the Greek energy market during the next few decades. In this context, green hydrogen’s potential application in the Greek market is being assessed, employing an integrated techno-economic model grounded in worldwide trends and localized expenses. The forthcoming years will see an analysis of both the challenges and opportunities surrounding the integration and implementation of hydrogen in new and existing processes within Greece. Many alternative ways to produce hydrogen in Greece are investigated, contemplating different production paths. We evaluate how fluctuations in hydrogen, oil, and carbon prices affect the economics of green hydrogen adoption in oil refining, as is detailed in the draft of the European Union delegated act published in May 2022. The Levelized Cost of Hydrogen (LCOH) for different scenarios is calculated for the time frame up until 2050. A sensitivity analysis reveals that investment costs, electricity prices, electrolyzer efficiency, and carbon taxes significantly influence the LCOH, ultimately impacting the economic competitiveness of hydrogen production. These findings underscore the importance of aligning public–private partnership agendas in hydrogen production to create optimal conditions for investment attraction and development.
Insights from a Comprehensive Capacity Expansion Planning Modeling on the Operation and Value of Hydropower Plants under High Renewable Penetrations
Authors: Chatzistylianos Evangelos S. , Psarros Georgios N. , Papathanassiou Stavros A.
Published: April 2024

This paper presents a quantitative assessment of the value of hydroelectric power plants (HPPs) in power systems with a significant penetration of variable renewable energy sources (VRESs). Through a capacity expansion planning (CEP) model that incorporates a detailed representation of HPP operating principles, the study investigates the construction and application of HPP rule curves essential for seasonal operation. A comparative analysis is also conducted between the proposed rule curve formulation and alternative modeling techniques from the literature. The CEP model optimizes installed capacities per technology to achieve predefined VRES penetration targets, considering hourly granularity and separate rule curves for each HPP. A case study involving twelve reservoir hydropower stations and two open-loop pumped hydro stations is examined, accounting for standalone plants and cascaded hydro systems across six river basins. The study evaluates the additional generation and storage required to replace the hydropower fleet under high VRES penetration levels, assessing the resulting increases in total system cost emanating from introducing such new investments. Furthermore, the study approximates the storage capabilities of HPPs and investigates the impact of simplified HPP modeling on system operation and investment decisions. Overall, the findings underscore the importance of reevaluating hydro rule curves for future high VRES penetration conditions and highlight the significance of HPPs in the energy transition towards carbon neutrality.
Three-Water Differential Parallel Conductivity Saturation Model of Low-Permeability Tight Oil and Gas Reservoirs
Authors: Hu Xiangyang , Cheng Renjie , Zhang Hengrong , Zhu Jitian , Chi Peng , Sun Jianmeng
Published: April 2024

Addressing the poor performance of existing logging saturation models in low-permeability tight sandstone reservoirs and the challenges in determining model parameters, this study investigates the pore structure and fluid occurrence state of such reservoirs through petrophysical experiments and digital rock visualization simulations. The aim is to uncover new insights into fluid occurrence state and electrical conduction properties and subsequently develop a low-permeability tight sandstone reservoir saturation model with easily determinable parameters. This model is suitable for practical oilfield exploration and development applications with high evaluation accuracy. The research findings reveal that such reservoirs comprise three types of formation water: strongly bound water, weakly bound water, and free water. These types are found in non-connected micropores, poorly connected mesopores where fluid flow occurs when the pressure differential exceeds the critical value, and well-connected macropores. Furthermore, the three types of formation water demonstrate variations in their electrical conduction contributions. By inversely solving rock electrical experiment data, it was determined that for a single sample, the overall cementation index is the highest, followed by the cementation index of pore throats containing strongly bound water, and the lowest for the pore throats with free water. Building on the aforementioned insights, this study develops a parallel electrical pore cementation index term, ϕm′, to account for the differences among the three types of water and introduces a parallel electrical saturation model suitable for logging evaluation of low-permeability tight oil and gas reservoirs. This model demonstrated positive application effects in the logging evaluation of low-permeability tight gas reservoirs in a specific basin in the Chinese offshore area, thereby confirming the advantages of its application.
School Electricity Consumption in a Small Island Country: The Case of Fiji
Authors: Prasad Ravita D.
Published: April 2024

Electricity consumption in buildings is one of the major causes of energy usage and knowledge of this can help building owners and users increase energy efficiency and conservation efforts. For Pacific Island countries, building electricity demand data is not readily accessible or available for constructing models to predict electricity demand. This paper starts to fill this gap by studying the case of schools in Fiji. The aim of the paper is to assess the factors affecting electricity demand for grid-connected Fijian schools and use this assessment to build mathematical models (multiple linear regression (MLR) and artificial neural network (ANN)) to predict electricity consumption. The average grid-connected electricity demand in kWh/year was 1411 for early childhood education schools, 5403 for primary schools, and 23,895 for secondary schools. For predicting electricity demand (ED) for all grid-connected schools, the stepwise MLR model shows that taking logarithm transformations on both the dependent variable and independent variables (number of students, lights, and air conditioning systems) yields statistically significant independent variables with an R2 value of 73.3% and RMSE of 0.2248. To improve the predicting performance, ANN models were constructed on both the natural form of variables and transformed variables. The optimum ANN model had an R2 value of 95.3% and an RMSE of 59.4 kWh/year. The findings of this study can assist schools in putting measures in place to reduce their electricity demand, associated costs, and carbon footprint, as well as help government ministries make better-informed policies.
Impacts of Freshwater Sources on Salinity Structure in a Large, Shallow Estuary
Authors: Moustafa Mohamed Z. , Ji Zhen-Gang , Hamrick John
Published: April 2024

Florida Bay, a large and shallow estuary, serves as a vital habitat for a diverse range of marine species and holds significant environmental, commercial, and recreational value. The salinity structure of the bay plays a key role in the bay’s ecosystem. Florida Bay receives 45% of its freshwater directly from rainfall, the largest source of freshwater, while the Taylor River is the second largest source. A hydrodynamic model was applied to determine if doubling the Taylor River flow, as currently planned, is adequate to meet salinity performance measures and protect the bay’s ecosystem health. Model-predicted salinity indicated that rainfall caused the largest reduction (10–15 ppt) followed by Taylor River discharges, and none of the predicted salinity scenario means exceeded 38 ppt. The salinity restoration target was achieved more than 70% of the time, by doubling the Taylor River freshwater discharges, only for the existing bay conditions. To protect Florida Bay’s ecosystem health and counterbalance saltwater intrusion in the Everglades wetlands, caused by future sea-level rise, additional freshwater sources needs to be identified. Yet, the question becomes, do we have enough available freshwater sources to achieve the restoration target and protect the bay’s ecosystem health now and for future sea-level rise?
The Browning Properties, Antioxidant Activity, and α-Glucosidase Inhibitory Improvement of Aged Oranges (Citrus sinensis)
Authors: Hsu Ting-Yu , Yang Kai-Min , Chiang Yi-Chan , Lin Li-Yun , Chiang Po-Yuan
Published: April 2024

Oranges contain many natural active chemicals, organic acids, and polysaccharides. Aging processing is commonly used to modify the color, quality, functional components, and stability of fruits. This study assesses the preparation of aging black oranges using various pre-treatments and solid fermentation. Oranges were aged for six weeks in fresh, non-blanching, blanching, and hot air-assisted aging cycle (AA) groups. The oranges’ shrinkage ratio, color difference values, and soluble solids content changed significantly (p < 0.05). Principal component analysis indicated that aging fermentation treatment accelerated glycolysis and increased the ratio of reducing sugars. The enhanced browning can be associated with the oxidation of ascorbic acid (0.66–0.47 mg/g) and the formation of 5-hydroxymethylfurfural (5-HMF) (0.09 mg/g). Furthermore, the presence of free polyphenols led to an increase in the total polyphenol and total flavonoid content. It also had a synergistic effect with 5-HMF in increasing the 2,2-diphenyl-1-picrylhydrazyl free radical-scavenging capacity and ferric ion-reducing antioxidant power (p < 0.05). AA had superior α-glucosidase inhibitory ability increasing from 67.31 to 80.48%. It also reduced the development time by 33%. Therefore, aging technology can enhance the bioactive compounds in oranges and provide a reference for future whole-fruit aging fermentation and health product creation.
Valorization of Peach Fruit and Wine Lees through the Production of a Functional Peach and Grape Juice
Authors: Prieto-Santiago Virginia , Aguiló-Aguayo Ingrid , Bravo Francisca Isabel , Mulero Miquel , Abadias Maribel
Published: April 2024

The valorization of agri-food products not only represents important economic and environmental benefits but can also be a source of potentially profitable, functional, and safe ingredients. This study aimed to valorize peach fruit and wine lees (WL) by producing functional juice. WL were incorporated at different concentrations (1.5 and 2%; w:w) in unpasteurized peach and grape juice and subsequently stored under refrigeration (5 °C). The antimicrobial activity of WL in peach and grape juices was assessed against Listeria monocytogenes and Saccharomyces cerevisiae as well as physicochemical, nutritional microbiological, and sensory acceptability. The maximum addition of WL to the juice (2%) showed a significant inhibitory effect against L. monocytogenes (4-log reduction) and increased the content of total soluble solids (TSS) (10%), total polyphenol content (TPC) (75%), and total antioxidant activity (AOX) (86%). During storage, AOX, TPC, TSS, pH, and titratable acidity (TA) remained stable. A significant correlation was observed between TPC and AOX. Total mesophilic aerobic bacteria and yeast counts increased during storage. Fifty-seven percent of tasters (n = 26) rated the functional juice positively. Thus, these agri-food products could be useful for producing functional juices with a longer shelf life, contributing to their valorization.
Impact of Air-Drying Temperatures on Drying Kinetics, Physicochemical Properties, and Bioactive Profile of Ginger
Authors: Shaukat Muhammad Nouman , Fallico Biagio , Nazir Akmal
Published: April 2024

Ginger (Zingiber officinale Roscoe) is a perishable commodity that requires proper processing to maintain its bioactivity. This study evaluated the effect of different air-drying temperatures (50 °C, 60 °C, and 70 °C) on ginger’s drying kinetics and quality attributes. For an enhanced understanding of the drying kinetics, we employed a detailed approach by combining an existing drying model (namely, Midilli) with the Arrhenius model. This combined model facilitates a thorough analysis of how temperature and time concurrently affect the moisture ratio, offering more profound insights into the drying mechanism. A higher drying rate was achieved at 70 °C, yet elevated drying temperatures could compromise the quality attributes of ginger slices. Ginger slices dried at 50 °C displayed improved physicochemical properties and less color browning. The evaluation of the bioactivity profile of resultant ginger extracts also revealed higher total phenolic contents (1875.87 ± 31.40 mg GAE/100 g) and DPPH radical scavenging activity (18.2 ± 0.9 mg TE/kg) in 50 °C treated ginger samples. Meanwhile, the hydroethanolic mixture (70% ethanol) was also reorganized with better extraction efficiency than water and MWF (a ternary blend of methanol, water, and formic acid) solution. The promising outcomes of this study endorse the influence of drying temperature on the quality characteristics and bioactive profile of ginger and the selection of suitable extraction solvents to acquire phenolic-rich extract.
Authentication of Laying Hen Housing Systems Based on Egg Yolk Using 1H NMR Spectroscopy and Machine Learning
Authors: Bischof Greta , Januschewski Edwin , Juadjur Andreas
Published: April 2024

(1) Background: The authenticity of eggs in relation to the housing system of laying hens is susceptible to food fraud due to the potential for egg mislabeling. (2) Methods: A total of 4188 egg yolks, obtained from four different breeds of laying hens housed in colony cage, barn, free-range, and organic systems, were analyzed using 1H NMR spectroscopy. The data of the resulting 1H NMR spectra were used for different machine learning methods to build classification models for the four housing systems. (3) Results: The comparison of the seven computed models showed that the support vector machine (SVM) model gave the best results with a cross-validation accuracy of 98.5%. The test of classification models with eggs from supermarkets showed that only a maximum of 62.8% of samples were classified according to the housing system labeled on the eggs. (4) Conclusion: The classification models developed in this study included the largest sample size compared to the literature. The SVM model is most suitable for evaluating 1H NMR data in terms of the hen housing system. The test with supermarket samples showed that more authentic samples to analyze influencing factors such as breed, feeding, and housing changes are required.
Revealing Consequences of the Husking Process on Nutritional Profiles of Two Sorghum Races on the Male Sterility Line
Authors: Khalfalla Maha , Zsombik László , Győri Zoltán
Published: April 2024

The male sterility line is a vital approach in the genetic breeding of sorghum. The husking process affects the grain’s nutritional composition, emphasizing the intricate relationship between genetic enhancement and dietary requirements. The current study assessed the influence of the Husking Fraction Time Unit (HFTU) process, which was set at 30 (S) and 80 (S) time units per second (S). The study assessed the impact of the (HFTU) process on fifty-one inbred line sorghum race varieties, which implied diverse nutritional profiles considering the pericarp color variations. The assessment of the nutritional profile involved dry matter, total protein, and minerals (P, K, S, Ca, Mg, Na, Fe, Zn, and Mn). The variety groups showed a significance value of p ≤ 0.05, indicating the study hypothesis’s truth. While results demonstrated substantial impacts implied by the Husking Fraction Time Unit (HFTU) technique, the occurrence was noted when the dry matter percentage was increased in the husked products, specifically the endosperm (grits) and bran. Conversely, the protein variation percentage between the bran and endosperm (grits) for the S. bicolor race was calculated at 33.7%. In comparison, the percentage was 11.8% for the Kafirin race. The 80 (S) time unit, on the other hand, had an observable effect on the mineral reconcentration when the Kafirin race had the highest averages of K mg/kg−1, Ca mg/kg−1, and Fe mg/kg−1, which were 5700.5 mg/kg−1, 551.5 mg/kg−1 and 66.5 mg/kg−1, respectively. The results of this study could benefit breeders and nutrition specialists in developing genotypes and processing sorghum grains, promoting research, and aiding several industrial sectors owing to the grain’s adaptability and nutritional properties.
Fractal Characteristics of Natural Fiber-Reinforced Soil in Arid Climate Due to Cracking
Authors: Yang Binbin , Jin Lichuang
Published: April 2024

Fractal geometry is a geometry that focuses on irregular geometric forms and can quantitatively describe rough and uneven surfaces and interfaces. As the main material for making natural fiber geotextile, rice straw fiber can reduce the direct impact of rainfall on soil and reduce the intensity of hydraulic erosion. This study investigates whether the use of rice straw fiber as an additive to reinforce arid soil can inhibit moisture evaporation and prevent cracking. Samples with different fiber contents added (0%, 1%, 2%, and 4%) are placed in an environmental chamber to simulate the effects of an arid climatic condition and control the temperature and humidity levels. The cracking process of the samples is recorded by using a digital camera, and the parameters of the evaporation and cracking processes are quantitatively examined through digital image processing. The results show that all of the samples with fiber have a higher residual water content and can retain 31.4%, 58.5%, and 101.9% more water than without the fibers, respectively. Furthermore, both the primary and secondary cracks as well as crack networks are inhibited in samples with a higher fiber content, that is, 2% or 4% fiber contents. The samples reinforced with fiber also have a smaller crack ratio. Compared with the samples without straw fiber, the final crack ratio of the samples with 1%, 2%, and 4% fiber is reduced by 8.05%, 24.09%, and 35.01% respectively. Finally, the final fractal dimensions of the cracks in samples with fiber contents are also reduced by 0.54%, 5.50%, and 6.40% for the samples with 1%, 2%, and 4% fiber, respectively. The addition of natural fiber as an additive to reduce evaporative cracking in soil can: (1) reduce the soil porosity; (2) enhance the binding force between the soil particles; and (3) block the hydrophobic channels. Therefore, the addition of rice straw fiber to soil can effectively reduce soil evaporation and inhibit soil cracking.
Extracting the Ultimate New Soliton Solutions of Some Nonlinear Time Fractional PDEs via the Conformable Fractional Derivative
Authors: Iqbal Md Ashik , Ganie Abdul Hamid , Miah Md Mamun , Osman Mohamed S.
Published: April 2024

Nonlinear fractional-order differential equations have an important role in various branches of applied science and fractional engineering. This research paper shows the practical application of three such fractional mathematical models, which are the time-fractional Klein–Gordon equation (KGE), the time-fractional Sharma–Tasso–Olever equation (STOE), and the time-fractional Clannish Random Walker’s Parabolic equation (CRWPE). These models were investigated by using an expansion method for extracting new soliton solutions. Two types of results were found: one was trigonometric and the other one was an exponential form. For a profound explanation of the physical phenomena of the studied fractional models, some results were graphed in 2D, 3D, and contour plots by imposing the distinctive results for some parameters under the oblige conditions. From the numerical investigation, it was noticed that the obtained results referred smooth kink-shaped soliton, ant-kink-shaped soliton, bright kink-shaped soliton, singular periodic solution, and multiple singular periodic solutions. The results also showed that the amplitude of the wave augmented with the pulsation in time, which derived the order of time fractional coefficient, remarkably enhanced the wave propagation, and influenced the nonlinearity impacts.
Volatility, Uncertainty, Complexity, and Ambiguity (VUCA) in Healthcare
Authors: Cernega Ana , Nicolescu Dragoș Nicolae , Meleșcanu Imre Marina , Ripszky Totan Alexandra , Arsene Andreea Letiția , Șerban Robert Sabiniu , Perpelea Anca-Cristina , Nedea Marina-Ionela (Ilie) , Pițuru Silviu-Mirel
Published: April 2024

Our professional activity is constantly under pressure from a multitude of elements and factors that can be classified into the four components of the VUCA phenomenon—volatility, uncertainty, complexity, and ambiguity—components that define the turbulence and challenges of the external environment. Considering the general elements of this phenomenon, we designed a new VUCA dimension specific to the healthcare field within which we have identified and analyzed all the factors that can influence the main actors of the doctor–patient relationship and the effects that can occur within the healthcare system in which this relationship is born. In this context, we generated the VUCA treatment in healthcare capable of mitigating the impact of this phenomenon; this treatment involves essential elements in overcoming possible crises and vulnerabilities of the medical profession. The VUCA treatment in healthcare requires combating volatility, uncertainty, complexity, and ambiguity through vision, understanding, clarity, and agility, which are grounded in the doctor’s need to acquire cross-functional competencies (soft skills). These competencies are applicable by using functional mechanisms and techniques that support the doctor in developing adaptability and anticipation skills, understanding the patient’s needs and addressing them, and ensuring the functionality and efficiency of the healthcare system by transferring these elements from micro-management to macro-management levels.
Co-Designed Cardiac Rehabilitation for the Secondary Prevention of Stroke (CARESS): A Pilot Program Evaluation
Authors: Rehman Sabah , Barker Seamus , Jose Kim , Callisaya Michele , Castley Helen , Schultz Martin G. , Moore Myles N. , Simpson Dawn B. , Peterson Gregory M. , Gall Seana
Published: April 2024

Structured health system-based programs, such as cardiac rehabilitation, may reduce the risk of recurrent stroke. This study aimed to co-design and evaluate a structured program of rehabilitation, developed based on insights from focus groups involving stroke survivors and health professionals. Conducted in Tasmania, Australia in 2019, the 7-week program comprised one hour of group exercise and one hour of education each week. Functional capacity (6 min walk test), fatigue, symptoms of depression (Patient Health Questionnaire), and lifestyle were assessed pre- and post-program, with a historical control group for comparison. Propensity score matching determined the average treatment effect (ATE) of the program. Key themes from the co-design focus groups included the need for coordinated care, improved psychosocial management, and including carers and peers in programs. Of the 23 people approached, 10 participants (70% men, mean age 67.4 ± 8.6 years) completed the program without adverse events. ATE analysis revealed improvements in functional capacity (139 m, 95% CI 44, 234) and fatigue (−5 units, 95% CI −9, −1), with a small improvement in symptoms of depression (−0.8 units, 95% CI −1.8, 0.2) compared to controls. The co-designed program demonstrated feasibility, acceptability, and positive outcomes, suggesting its potential to support stroke survivors.
Effects of Neuromuscular Electrical Stimulation with Gastrocnemius Strengthening on Foot Morphology in Stroke Patients: A Randomized Controlled Trial
Authors: Choi Yusik , Lee Sooyong , Kim Minhyuk , Chang Woonam
Published: April 2024

This study aimed to investigate the effects of neuromuscular electrical stimulation (NMES) with gastrocnemius (GCM) strength exercise on foot morphology in patients with stroke. Herein, 31 patients with chronic stroke meeting the study criteria were enrolled and divided into two groups; 16 patients were randomized to the gastrocnemius neuromuscular electrical stimulation (GCMNMES) group, and 15 patients to the conventional neuromuscular electrical stimulation (CNMES) group. The GCMNMES group conducted GCM-strengthening exercise with NMES. CNMES group conducted NMES at paretic tibialis anterior muscle with ankle dorsiflexion movement. These patients underwent therapeutic interventions lasting 30 min/session, five times a week for 4 weeks. To analyze changes in foot morphology, 3D foot scanning was used, while a foot-pressure measurement device was used to evaluate foot pressure and weight-bearing area. In an intra-group comparison of 3D-foot-scanning results, the experimental group showed significant changes in longitudinal arch angle (p < 0.05), medial longitudinal arch angle (MLAA) (p < 0.01), transverse arch angle (TAA) (p < 0.01), rearfoot angle (RA) (p < 0.05), foot length (FL) (p < 0.05), foot width (FW) (p < 0.05), and arch height index (AHI) (p < 0.01) of the paretic side and in TAA (p < 0.05) and AHI (p < 0.05) of the non-paretic side. The CNMES group showed significant changes in TAA (p < 0.05) and FW (p < 0.05) of the paretic side and TAA (p < 0.05) and AHI (p < 0.05) of the non-paretic side. An inter-group comparison showed significant differences in MLAA (p < 0.05) and RA (p < 0.05) of the paretic side. In an intra-group comparison of foot pressure assessment, the experimental group showed significant differences in footprint area (FPA) (p < 0.05) of the paretic side and FPA symmetry (p < 0.05). The CNMES group showed a significant difference in only FPA symmetry (p < 0.05). An inter-group comparison showed no significant difference between the two groups (p < 0.05). Thus, NMES with GCM-strengthening exercises yielded positive effects on foot morphology in patients with stroke.
The Effectiveness of the Chronic Disease Self-Management Program in Improving Patients’ Self-Efficacy and Health-Related Behaviors: A Quasi-Experimental Study
Authors: Kerari Ali , Bahari Ghareeb , Alharbi Kholoud , Alenazi Latifah
Published: April 2024

The Stanford Chronic Disease Self-Management Program (CDSMP) is a valuable educational resource for supporting patients’ self-management behaviors. However, no evidence supporting its effectiveness in the Saudi Arabian population exists. Therefore, this study aimed to evaluate the effectiveness of the 6-month CDSMP in individuals with chronic conditions in Saudi Arabia within a primary care context. A quasi-experimental design was conducted in 110 adults living with ≥1 chronic disease in Saudi Arabia. The patients in the experimental group (n = 45) participated in a six-session CDSMP, whereas those in the control group (n = 65) continued their usual care. Baseline and 6-month assessments were conducted using relevant questionnaires to assess outcome measures. Analysis of covariance revealed that the participants who underwent the CDSMP had significantly higher self-efficacy levels in managing their conditions (F = 9.80, p < 0.01) and a greater tendency to adopt healthy behaviors to successfully manage their chronic illnesses (F = 11.17, p < 0.01). The participants who underwent the CDSMP also showed significant improvements in all health-related outcomes compared with those in the control group (p < 0.01). These findings indicated that the program had a positive effectiveness in self-efficacy, self-management behaviors, and health-related outcomes among adults with chronic diseases in Saudi Arabia. The CDSMP may be integrated into primary care settings to help patients successfully manage their chronic conditions.
Predicting Male Infertility Using Artificial Neural Networks: A Review of the Literature
Authors: Schmeis Arroyo Vivian , Iosa Marco , Antonucci Gabriella , De Bartolo Daniela
Published: April 2024

Male infertility is a relevant public health problem, but there is no systematic review of the different machine learning (ML) models and their accuracy so far. The present review aims to comprehensively investigate the use of ML algorithms in predicting male infertility, thus reporting the accuracy of the used models in the prediction of male infertility as a primary outcome. Particular attention will be paid to the use of artificial neural networks (ANNs). A comprehensive literature search was conducted in PubMed, Scopus, and Science Direct between 15 July and 23 October 2023, conducted under the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. We performed a quality assessment of the included studies using the recommended tools suggested for the type of study design adopted. We also made a screening of the Risk of Bias (RoB) associated with the included studies. Thus, 43 relevant publications were included in this review, for a total of 40 different ML models detected. The studies included reported a good quality, even if RoB was not always good for all the types of studies. The included studies reported a median accuracy of 88% in predicting male infertility using ML models. We found only seven studies using ANN models for male infertility prediction, reporting a median accuracy of 84%.
The Reliability of Carotid Artery Doppler Ultrasonography Indices in Predicting Fluid Responsiveness during Surgery for Geriatric Patients: A Prospective, Observational Study
Authors: Bilgili Beliz , Saracoglu Ayten , Saracoglu Kemal T. , Ratajczyk Pawel , Kararmaz Alper
Published: April 2024

Background: The reliability of determining fluid responsiveness during surgery in geriatric patients is challenging. Our primary outcome was to determine the reliability of Corrected Flow Time (FTc) in predicting fluid responsiveness. Methods: Elderly patients undergoing major surgery under general anesthesia were included. Measurements of common carotid artery diameter, velocity time integral, and systolic flow time (FT) were performed before and after a fluid challenge. FTc and carotid blood flow (CBF) were subsequently calculated. Results: The median change in carotid diameter was significantly higher in the fluid-responder (R) compared to the non-responder (NR) (6.51% vs. 0.65%, p = 0.049). The median change in CBF was notably higher in R compared to NR (30.04% vs. 9.72%, p = 0.024). Prior to the fluid challenge, systolic FT was significantly shorter in R than NR (285 ms vs. 315 ms, p = 0.027), but after the fluid challenge, these measurements became comparable among the groups. The change in systolic FT was higher in R (15.38% vs. 7.49%, p = 0.027). FTc and the change in FTc exhibited similarities among the groups at all study time points. Receiver operating characteristic analysis demonstrated an area under the curve of 0.682 (95% CI: 0.509–0.855, p = 0.039) for carotid diameter, 0.710 (95% CI: 0.547–0.872, p = 0.011) for CBF, 0.706 (95% CI: 0.540–0.872, p = 0.015) for systolic FT, and 0.580 (95% CI = 0.389–0.770, p = 0.413) for FTc. Conclusions: In geriatric patients, potential endothelial changes in the carotid artery may influence the dynamic markers of fluid responsiveness. Despite the demonstrated effectiveness of FTc in predicting fluid responsiveness in the general population, this study underscores the limited reliability of carotid Doppler ultrasonography indices for prediction in a geriatric patient population.