bluuebunny/crossref_metadata_2025 huggingface dataset:
Cognitive Impairment in MRI-Negative Epilepsy: Relationship between Neurophysiological and Neuropsychological Measures
Authors: Papaliagkas Vasileios , Lokantidou-Argyraki Chrysanthi , Patrikelis Panayiotis , Zafeiridou Georgia , Spilioti Martha , Afrantou Theodora , Kosmidis Mary H. , Arnaoutoglou Marianthi , Kimiskidis Vasileios K.
Published: September 2023

Background: Epileptic patients frequently encounter cognitive impairment. Functions that are mostly affected involve memory, attention, and executive function; however, this is mainly dependent on the location of the epileptic activity. The aim of the present study is to assess cognitive functions in MRI-negative epilepsy patients by means of neurophysiological and neuropsychological measures, as well as study the concept of transient cognitive impairment in patients with epileptiform discharges during EEG acquisition. Methods: The patients were enrolled from an outpatient Epilepsy/Clinical Neurophysiology clinic over a time period of 6 months. The study sample comprised 20 MRI-negative epilepsy patients (mean age ± standard deviation (SD), 30.3 ± 12.56 years; age range, 16–60 years; average disease duration, 13.95 years) and 10 age-matched controls (mean age ± SD, 24.22 ± 15.39 years), who were also education-matched (p > 0.05). Patients with epileptogenic lesions were excluded from the study. Informed consent was obtained from all subjects involved in the study. Auditory ERPs and the cognitive screening tool EpiTrack were administered to all subjects. Results: Latencies of P300 and slow waves were prolonged in patients compared to controls (p < 0.05). The ASM load and patients’ performance in the EpiTrack maze subtest were the most significant predictors of P300 latency. A decline in the memory, attention, and speed of information processing was observed in patients with cryptogenic epilepsy compared to age-matched controls, as reflected by P300 latency and EpiTrack scores.
A Systematic Review on Password Guessing Tasks
Authors: Yu Wei , Yin Qingsong , Yin Hao , Xiao Wei , Chang Tao , He Liangliang , Ni Lulin , Ji Qingbing
Published: September 2023

Recently, many password guessing algorithms have been proposed, seriously threatening cyber security. In this paper, we systematically review over thirty methods for password guessing published between 2016 and 2023. First, we introduce a taxonomy for classifying the existing methods into trawling guessing and targeted guessing. Second, we present an extensive benchmark dataset that can assist researchers and practitioners in successive works. Third, we conduct a bibliometric analysis to present trends in this field and cross-citation between reviewed papers. Further, we discuss the open challenges of password guessing in terms of diverse application scenarios, guessing efficiency, and the combination of traditional and deep learning methods. Finally, this review presents future research directions to guide successive research and development of password guessing.
Adversarial Defense Method Based on Latent Representation Guidance for Remote Sensing Image Scene Classification
Authors: Da Qingan , Zhang Guoyin , Wang Wenshan , Zhao Yingnan , Lu Dan , Li Sizhao , Lang Dapeng
Published: September 2023

Deep neural networks have made great achievements in remote sensing image analyses; however, previous studies have shown that deep neural networks exhibit incredible vulnerability to adversarial examples, which raises concerns about regional safety and production safety. In this paper, we propose an adversarial denoising method based on latent representation guidance for remote sensing image scene classification. In the training phase, we train a variational autoencoder to reconstruct the data using only the clean dataset. At test time, we first calculate the normalized mutual information between the reconstructed image using the variational autoencoder and the reference image as denoised by a discrete cosine transform. The reconstructed image is selectively utilized according to the result of the image quality assessment. Then, the latent representation of the current image is iteratively updated according to the reconstruction loss so as to gradually eliminate the influence of adversarial noise. Because the training of the denoiser only involves clean data, the proposed method is more robust against unknown adversarial noise. Experimental results on the scene classification dataset show the effectiveness of the proposed method. Furthermore, the method achieves better robust accuracy compared with state-of-the-art adversarial defense methods in image classification tasks.
Modified Local Linear Estimators in Partially Linear Additive Models with Right-Censored Data Based on Different Censorship Solution Techniques
Authors: Yılmaz Ersin , Aydın Dursun , Ahmed S. Ejaz
Published: September 2023

This paper introduces a modified local linear estimator (LLR) for partially linear additive models (PLAM) when the response variable is subject to random right-censoring. In the case of modeling right-censored data, PLAM offers a more flexible and realistic approach to the estimation procedure by involving multiple parametric and nonparametric components. This differs from the widely used partially linear models that feature a univariate nonparametric function. The LLR method is employed to estimate unknown smooth functions using a modified backfitting algorithm, delivering a non-iterative solution for the right-censored PLAM. To address the censorship issue, three approaches are employed: synthetic data transformation (ST), Kaplan–Meier weights (KMW), and the kNN imputation technique (kNNI). Asymptotic properties of the modified backfitting estimators are detailed for both ST and KMW solutions. The advantages and disadvantages of these methods are discussed both theoretically and practically. Comprehensive simulation studies and real-world data examples are conducted to assess the performance of the introduced estimators. The results indicate that LLR performs well with both KMW and kNNI in the majority of scenarios, along with a real data example.
A Wavelet Investigation of Periodic Long Swings in the Economy: The Original Data of Kondratieff and Some Important Series of GDP per Capita
Authors: Focacci Antonio
Published: September 2023

The existence of fluctuations is part of the narrative, especially when there is a slowdown (or worse, a contraction) in economic activity. The presence of long waves with a period of about 50 years as proposed by Kondratieff is one of the most controversial and fascinating theories about economic cycles. This paper analyses both the original Kondratieff data (from which the hypothesis started) and a dataset that includes GDP per capita for several significant countries. By applying the wavelet analysis (WA), the main objective of the paper is to understand whether it is plausible to support the existence of periodic fluctuations consistent with long cycles theory. The outcomes for Kondratieff’s original dataset do not show the presence of a coherent periodicity for most cases. The same conclusion can be drawn for all the GDP per capita series.
Enhancing Software Project Monitoring with Multidimensional Data Repository Mining
Authors: Reszka Łukasz , Sosnowski Janusz , Dobrzyński Bartosz
Published: September 2023

Software project development and maintenance activities have been reported in various repositories. The data contained in these repositories have been widely used in various studies on specific problems, e.g., predicting bug appearance, allocating issues to developers, and identifying duplicated issues. Developed analysis schemes are usually based on simplified data models while issue report details are neglected. Confronting this problem requires a deep and wide-ranging exploration of software repository contents adapted to their specificities, which differs significantly from classical data mining. This paper is targeted at three aspects: the structural and semantic exploration of repositories, deriving characteristic features in value and time perspectives, and defining the space of project monitoring goals. The considerations presented demonstrate a holistic image of the project development process, which is useful in the assessment of its efficiency and identification of imperfections. The original analysis introduced in this work was verified using open source and some commercial software project repositories.
Hyperspectral Image Classification Using Geodesic Spatial–Spectral Collaborative Representation
Authors: Zheng Guifeng , Xiong Xuanrui , Li Ying , Xi Juan , Li Tengfei , Tolba Amr
Published: September 2023

With the continuous advancement of remote sensing technology, the information encapsulated within hyperspectral images has become increasingly enriched. The effective and comprehensive utilization of spatial and spectral information to achieve the accurate classification of hyperspectral images presents a significant challenge in the domain of hyperspectral image processing. To address this, this paper introduces a novel approach to hyperspectral image classification based on geodesic spatial–spectral collaborative representation. It introduces geodesic distance to extract spectral neighboring information from hyperspectral images and concurrently employs Euclidean distance to extract spatial neighboring information. By integrating collaborative representation with spatial–spectral information, the model is constructed. The collaborative representation coefficients are obtained by solving the model to reconstruct the testing samples, leading to the classification results derived from the minimum reconstruction residuals. Finally, with comparative experiments conducted on three classical hyperspectral image datasets, the effectiveness of the proposed method is substantiated. On the Indian Pines dataset, the proposed algorithm achieved overall accuracy (OA) of 91.33%, average accuracy (AA) of 93.81%, and kappa coefficient (Kappa) of 90.13%. In the case of the Salinas dataset, OA was 95.62%; AA was 97.30%; and Kappa was 93.84%. Lastly, on the PaviaU dataset, OA stood at 95.77%; AA was 94.13%; and Kappa was 94.38%.
Advances in Artificial Intelligence, Machine Learning and Deep Learning Applications
Authors: Haleem Muhammad Salman
Published: September 2023

Recent advances in the field of artificial intelligence (AI) have been pivotal in enhancing the effectiveness and efficiency of many systems and in all fields of knowledge, including medical diagnosis [...]
Underwater AUV Navigation Dataset in Natural Scenarios
Authors: Wang Can , Cheng Chensheng , Yang Dianyu , Pan Guang , Zhang Feihu
Published: September 2023

Autonomous underwater vehicles (AUVs) are extensively utilized in various autonomous underwater missions, encompassing ocean environment monitoring, underwater searching, and geological exploration. Owing to their profound underwater capabilities and robust autonomy, AUVs have emerged as indispensable instruments. Nevertheless, AUVs encounter several constraints in the domain of underwater navigation, primarily stemming from the cost-intensive nature of inertial navigation devices and Doppler velocity logs, which impede the acquisition of navigation data. Underwater simultaneous localization and mapping (SLAM) techniques, along with other navigation approaches reliant on perceptual sensors like vision and sonar, are employed to augment the precision of self-positioning. Particularly within the realm of machine learning, the utilization of extensive datasets for training purposes plays a pivotal role in enhancing algorithmic performance. However, it is common for data obtained exclusively from inertial sensors, a Doppler Velocity Log (DVL), and depth sensors in underwater environments to not be publicly accessible. This research paper introduces an underwater navigation dataset derived from a controllable AUV that is equipped with high-precision fiber-optic inertial sensors, a DVL, and depth sensors. The dataset underwent rigorous testing through numerical calculations and optimization-based algorithms, with the evaluation of various algorithms being based on both the actual surfacing position and the calculated position.
Measurement Interval Effect on Photovoltaic Parameters Estimation
Authors: Mesbahi Oumaima , Afonso Daruez , Tlemçani Mouhaydine , Bouich Amal , Janeiro Fernando M.
Published: September 2023

Recently, the estimation of photovoltaic parameters has drawn the attention of researchers, and most of them propose new optimization methods to solve this problem. However, the process of photovoltaic parameters estimation can be affected by other aspects. In a real experimental setup, the I–V characteristic is obtained with IV tracers. Depending on their technical specifications, these instruments can influence the quality of the I–V characteristic, which in turn is inevitably linked to the estimation of photovoltaic parameters. Besides the uncertainties that accompany the measurement process, a major effect on parameters estimation is the size of the measurement interval of current and voltage, where some instruments are limited to measure a small portion of the characteristic or cannot reach their extremum regions. In this paper, three case studies are presented to analyse this phenomenon: different characteristic measurement starting points and different measurement intervals. In the simulation study the parameters are extracted from 1000 trial runs of the simulated I-V curve. The results are then validated using an experimental study where an IV tracer was built to measure the I–V characteristic. Both simulation and experimental studies concluded that starting the measurements at the open circuit voltage and having an interval spanning a minimum of half of the I–V curve results in an optimal estimation of photovoltaic parameters.
A Long-Distance First Matching Algorithm for Charging Scheduling in Wireless Rechargeable Sensor Networks
Authors: Chen Jing-Jing , Yu Chang Wu , Liu Wen
Published: September 2023

In large wireless rechargeable sensor networks (WRSNs), the limited battery capacity of sensor nodes and finite network lifetime are commonly considered as performance bottlenecks. Previous works have employed wireless mobile vehicles (vehicles) to charge sensor nodes (nodes), but they face limitations in terms of low speed and offroad terrain. The rapid development of wireless charging drones (drones) brings a new perspective on charging nodes; nevertheless, their use is limited by small capacity batteries and cannot cover large regions alone. Most existing works consider the charging of nodes only with vehicles or drones. However, these solutions may not be robust enough, as some nodes’ energy will have run out before vehicles’ or drones’ arrival. Considering the merits and demerits of vehicles and drones comprehensively, we propose a novel WRSN model whose charging system integrates one vehicle, multiple drones and one base station together. Moreover, a charging strategy named long-distance first matching (LDFM) algorithm to schedule the vehicle and multiple drones collaboratively is proposed. In the proposed scheme, drones that are carried by the vehicle start from the base station. According to distance and deadline of nodes with charging requests, LDFM prioritizes nodes with the longest matching distance for allocation to drones. As a result, the proposed scheme aims to minimize the moving distance of charging scheduling of the WCV on premise of satisfying charging requests with the cooperation of WCVs and drones. Our proposed scheme is thus designed to maximize the efficiency of drone usage and shares the charging burden of the vehicle, which makes WRSNs work well in large and complex terrain regions, such as a hill, natural disaster areas or war zones. Simulation results confirm that our proposed scheme outperforms hybrid scheme in previous work with respect to total number of charging nodes and network energy consumption. Especially with heavy traffic load, the proposed scheme can charge more than 10% additional nodes compared to the hybrid. Moreover, the proposed scheme achieves a reduction of over 50% in the moving distance compared to the hybrid.
Differences in Direct Geothermal Energy Utilization for Heating and Cooling in Central and Northern European Countries
Authors: Nordgård-Hansen Ellen , Fjellså Ingvild Firman , Medgyes Tamás , Guðmundsdóttir María , Pétursson Baldur , Miecznik Maciej , Pająk Leszek , Halás Oto , Leknes Einar , Midttømme Kirsti
Published: September 2023

Geothermal energy has emerged as an alternative heating source that can replace fossil energy. This mature technology is already in use all over Europe, but there are significant differences in its use between European countries. One possible explanation for this phenomenon concerns societal differences directly related to geothermal energy, the topic that is investigated in this study. The present work proposes using the societal embeddedness level (SEL) method to analyze and compare the status of non-technical factors affecting geothermal energy use in Hungary, Iceland, Norway, Poland, and Slovakia. The method considers four dimensions: environment, stakeholder involvement, policy and regulations, and markets and financial resources. Only Iceland fully covers the four dimensions by reaching all the milestones in the SEL framework. Iceland has the advantage of a long history of active use of geothermal energy for domestic use. The other countries face challenges within several of the dimensions, while the form and cause of these challenges are specific to each country. The findings illustrate that to mitigate climate change and drive the energy transition forward, both technical and societal factors related to various renewable energy sources must be assessed.
Under-Expanded Jets in Advanced Propulsion Systems—A Review of Latest Theoretical and Experimental Research Activities
Authors: Duronio Francesco , Villante Carlo , De Vita Angelo
Published: September 2023

The current ongoing rise in environmental pollution is leading research efforts toward the adoption of propulsion systems powered by gaseous fuels like hydrogen, methane, e-fuels, etc. Although gaseous fuels have been used in several types of propulsion systems, there are still many aspects that can be improved and require further study. For this reason, we considered it important to provide a review of the latest research topics, with a particular focus on the injection process. In advanced engine systems, fuel supply is achieved via enhanced direct injection into the combustion chamber. The latter involves the presence of under-expanded jets. Under-expanded jets are a particular kind of compressible flow. For this reason, the review initially provides a brief physical explanation of them. Next, experimental and numerical CFD investigation techniques are discussed. The last section of this manuscript presents an analysis of the jet’s structure. The injection parameters commonly used are examined; next, the characteristics of the near-nozzle field are reviewed and finally, the far-field turbulent mixing, which strongly affects the air–fuel mixture formation process, is discussed.
Review of Hydro-Pneumatic Accumulator Models for the Study of the Energy Efficiency of Hydraulic Systems
Authors: Dindorf Ryszard , Takosoglu Jakub , Wos Piotr
Published: September 2023

This review article deals with hydro-pneumatic accumulators (HPAs) charged with nitrogen. The focus is on HPA models used in the study of the energy efficiency of hydraulic systems. Hydraulic circuits with HPA are presented along with their various applications for delivering the required volume of fluid, maintaining the required pressure, ensuring safe operation, safety stop, leak compensation, fluid volume change compensation, pulsation damping, and pressure shock absorption. A general regenerative hydraulic system and a general hybrid hydraulic system are also presented. The review focuses primarily on HPA computational, dynamic, and simulation models. Basic HPA calculation parameters and computational models of energy storage and thermodynamic cycle are presented. Various computational and dynamic models of HPA have been defined, such as the thermodynamic model, simulation model, dynamic model, pulsation damper model, and shock pulse damper model. Research projects that have used HPA in industrial hydraulic systems are reviewed, such as those maintaining operating pressure in an industrial 80 MN open-die hydraulic forging press and acting as a shock pulse absorber in the lifting and levelling module of a tracked mobile robotic bricklaying system. The development of energy storage technology in HPA from various sources is now a global challenge.
Optimization of Thermal Bridges Effect of Composite Lightweight Panels with Integrated Steel Load-Bearing Structure
Authors: Tkalčić Domagoj , Milovanović Bojan , Gaši Mergim , Jelčić Rukavina Marija , Banjad Pečur Ivana
Published: September 2023

In order to maintain the quality of construction for nearly zero energy buildings and to reduce the pressure on construction workers with the addition of the need for faster and simpler structures, the use of cavity-insulated LSF (lightweight steel frame) panels is increasing. Requirements for performance quality, quality of life, and low energy consumption have led to the need for closer examination of heat transfer through building elements. Due to the impact on increased heat losses, thermal bridges can cause structural damage due to the increased risk of water vapor condensation on the interior surface. In this paper, numerical heat transfer analysis with the optimization of thermal bridges for LSF cavity insulated walls was made in order to reduce the overall transmission heat losses. The effects of different cavity insulation materials (mineral wool and polyurethane foam) on overall heat transferred through the building elements were analyzed. Additionally, in order to reduce the effect of thermal bridges caused by the steel frame structure, the PVC spacers between the steel and sheathing panels are introduced into calculation models. Lastly, additional layers of insulation were added on the internal and external sides of the LFS panels in order to minimize the effect of thermal bridges and maximize air tightness. Combinations of all three setups were made for wall–window, ceiling–wall, wall–floor joints for the numerical calculation. For each setup, the temperature distribution and overall heat transferred through the building elements were calculated. Different thermal bridge designs have a significant influence on the overall heat transfer, and by choosing the optimal design, the transmission heat losses can be reduced by up to 67%.
Exploring Social Capital in Situation-Aware and Energy Hub-Based Smart Cities: Towards a Pandemic-Resilient City
Authors: Nozarian Mahdi , Fereidunian Alireza , Hajizadeh Amin , Shahinzadeh Hossein
Published: September 2023

Although the severity of the COVID-19 pandemic has appears to have subsided in most parts of the world, nevertheless, in addition to six million deaths, it has yielded unprecedented challenges in the economy, energy, education, urban services, and healthcare sectors. Meanwhile, based on some reports, smart solutions and technologies have had significant success in achieving pandemic-resilient cities. This paper reviews smart city initiatives and contributions to the prevention and treatment of coronavirus disease, as well as reducing its destructive impact, leading towards pandemic-resilient economic and health systems. Furthermore, the situational awareness contributions are reviewed in pandemic-resilient governance. The main contribution of this study is to describe the construction of social capital in smart cities as a facilitator in creating a pandemic-resilient society in crisis through two analyses. Moreover, this research describes smart cities’ energy as interconnection of energy hubs (EHs) that leads to a high level of resiliency in dealing with the main challenges of the electricity industry during the pandemic. Energy-hub-based smart cities can contribute to designing pandemic-resilient energy infrastructure, which can significantly affect resilience in economic and health infrastructure. In brief, this paper describes a smart city as a pandemic-resilient city in the economic, energy, and health infrastructural, social, and governmental areas.
Leakage Current Measurements of Surge Arresters
Authors: Olesz Marek , Litzbarski Leszek S. , Redlarski Grzegorz
Published: September 2023

The paper presents the methods of assessing the technical condition of varistor surge arresters used in laboratory tests and in operation—performed without disconnecting the arresters from the network. The analysis of the diagnostic methods was supplemented with the results of the measurements of the leakage current of arresters coming directly from their production and used in the power industry. Among the available methods of evaluating the technical condition of arresters, mainly indicator solutions (temperature and operation counter) and the measurement of the selected parameters of the leakage current are used. In the latter, the method of determining the resistive component of the leakage current, determined on the basis of the analysis of the voltage and current waveforms, or only the arrester current, has become widespread. In this type of measurement, current clamps are used in the operation, and additionally, in voltage measurements, voltage transformers are used, where you have to take into account the fundamental, additional sources of errors discussed in the article. These errors and the dispersion resulting from the production technology may fundamentally hinder the proper assessment of the technical condition; hence, it is so important to properly recognize the listed basic sources of measurement uncertainty. In addition, the analysis should take into account three factors related to external conditions: temperature, the voltage applied to the arrester, and the content of higher harmonics in the supply voltage, for which appropriate methods have been provided to determine the active component of the leakage current for reference conditions. This article presents the results of the measurements of the leakage currents of surge arresters measured with various methods.
The Carbon Sequestration Potential of Silky Oak (Grevillea robusta A.Cunn. ex R.Br.), a High-Value Economic Wood in Thailand
Authors: Laosuwan Teerawong , Uttaruk Yannawut , Sangpradid Satith , Butthep Chetphong , Leammanee Smith
Published: September 2023

Silky Oak or Silver Oak (Grevillea robusta A.Cunn. ex R.Br.) is classified as a high-value economic wood in Thailand, it is also considered to be a plant that can grow rapidly, and it has the potential to efficiently reduce greenhouse gases emitted into the atmosphere. This research aimed to study and develop an allometric equation to evaluate the biomass of F1 Silky Oak, which was imported to Thailand from Australia, and grown in Thailand’s economic woods in Silky Oak sites in Pak Chong District, Nakhon Ratchasima Province. The sample group consisted of trees of different ages (i.e., of 2 years, 3–4 years, and 7 years). An allometric equation was used to determine the tree biomass, based on mathematical models that describe the relationship between tree biomass and diameter at breast height (DBH). It was developed in the form of a quadratic equation by multiplying the square DBH by the total height (DBH2 × Ht). Subsequently, the equation was separated into different components, which corresponded with different parts of the tree (i.e., stem, branches, leaves, and roots). The following equations were obtained for the stem: Ws = 0.0721 (D2H) 0.8297 R2 = 0.998. The following equations were obtained for the branches: Wb = 0.0772 (D2H) 0.7027 R2 = 0.977. The following equations were obtained for the leaves, Wl = 0.2085 (D2H) 0.4313 R2 = 0.990. The following equations were obtained for the roots: Wr = 0.3337 (D2H) 0.4886 R2 = 0.957. The results of a laboratory elemental analysis of the carbon sequestration in the biomass, using a CHN elemental analyzer, showed that the mean percentage of carbon content in the stems, branches, leaves, and roots was 45.805. Applying the developed allometric equation for evaluating carbon sequestration, using the survey data from the sample sites of Silky Oak, it was found that the amount of carbon sequestration for the aboveground biomass in three sites was 130.63 tCO2eq. When the amount was converted into carbon dioxide, which was absorbed in the three sites, we obtained a value of 478.99 tCO2eq. The results of the application of the allometric equation showed that there was substantial carbon sequestration potential in the surveyed sites, emphasizing the role of Silky Oak plantations for climate change mitigation and sustainable land management. This study advances our understanding of Silky Oak growth and carbon storage dynamics, offering valuable tools for biomass estimation and promoting environmentally beneficial land use practices.
The Relationships between Root Traits and the Soil Erodibility of Farmland Shelterbelts in the Bashang Region of China
Authors: Liu Qianyuan , Li Jifeng , Guo Zhongling , Chang Chunping , Wang Huimin
Published: September 2023

Soil erodibility by wind is not only affected by the basic physical and chemical properties of the soil but also the functional traits of plant roots. However, the roles played by the morphological and architectural traits of plant roots on wind-based soil erodibility in the Bashang region of China are still unclear. Therefore, two typical tree shelterbelts and two shrub shelterbelts in the Bashang region were selected to assess and determine how the root traits affected soil erodibility, especially characteristics such as dry aggregate, soil organic matter, and shearing resistance. The results showed that the soil dry aggregates of the two shrubs (Lycium barbarum and Caragana korshinskii) had higher geometric mean diameters (0.40 ± 0.03 mm) and mean weight diameters (0.82 ± 0.08 mm) but a lower erodible fraction (81.81% ± 1.62%) compared to the two trees (Populus simonii and Ulmus pumila). The mean weight diameter (MWDd) and geometric mean diameter (GMDd) of dry soil aggregates were negatively correlated with the soil erodible fraction (EFd), but these parameters were positively correlated with shearing resistances. The specific root length (SRL) and surface area (SSA) of plant roots were positively correlated with the GMDd of the soils, though these two parameters negatively correlated with the soil erodible fraction. The root branching intensity (BI) was negatively correlated with the MWDd and GMDd of dry soil aggregates. The total carbon or nitrogen of the soil displayed significantly positive and negative correlations to the geometric mean diameters and erodible fractions of the soils, respectively. The findings showed that plant roots with higher SRLs, as well as lower root diameters and BIs, played positive key roles in soil stability. The same applied to soils with higher nitrogen, carbon, and water content. The results from this study suggest that L. barbarum is superior to the other three species based on root traits and wind erosion resistance. These findings provide critical information for selecting plants for the sustainable management of windbreak and sand fixation.
Zero-Valent Iron and Activated Carbon Coupled to Enhance Anaerobic Digestion of Food Waste: Alleviating Acid Inhibition at High Loads
Authors: Zhang Shuang , Zhao Pan , Gao Ming , Wu Chuanfu , Wang Qunhui , Sun Xiaohong
Published: September 2023

Anaerobic digestion (AD) has the advantages of utilizing complex substrates and producing renewable energy and is currently one of the mainstream technologies for food waste (FW) resourcing. However, at high organic loads and low inoculum-to-substrate ratios (ISRs), AD with FW as substrate is prone to acid accumulation, resulting in a drastic decrease in gas production and system collapse. This study investigated the effect of the coupled addition of zero-valent iron (ZVI) and activated carbon (AC) on the AD of FW at three low ISRs of 0.715, 0.625, and 0.5. The results showed that the control group acidified and stopped producing biogas when the ISR decreased to 0.625 and 0.5, but ZVI coupled with AC alleviated the acidification and increased the cumulative biogas yield. Especially at ISR = 0.5, the cumulative biogas yield for the ZVI + AC group was 31.5%, 99.5%, and 11.43 times higher than that of the ZVI, AC, and control groups, respectively. ZVI coupled with AC also increased the degradation of volatile fatty acids (70.5–84.4%) and soluble chemical oxygen demand (50.0–72.9%) while decreasing propionate concentration and improving the stability of the AD system. COD mass balance analyses indicated that the coupled addition of ZVI and AC promoted the conversion of particulate organic matter to soluble organic matter and increased the conversion of carbon sources to methane.
Temperature and pH Optimization for Protease Production Fermented by Yarrowia lipolytica from Agro-Industrial Waste
Authors: López-Trujillo Juan , Mellado-Bosque Miguel , Ascacio-Valdés Juan Alberto , Prado-Barragán Lilia Arely , Hernández-Herrera José Antonio , Aguilera-Carbó Antonio Francisco
Published: September 2023

The use of yeasts for the production of proteases has increased in demand in recent years. Y. lipolytica has been reported as a strain with high yields of protease production. This work aimed to evaluate the impact of pH and temperature on the production of proteases using Y. lipolytica in solid-state fermentation (SSF). Soybean, canola meal, cottonseed meal, and sesame meal wastes were used as nutrient sources at seven pH levels (4, 5, 6, 7, 8, 9, 10) and five temperatures (25, 30, 35, 40, 45 °C). The waste source and optimal conditions for maximum enzyme production (EP) were obtained by Box–Benhken design. The results revealed that at pH of 7, temperature of 30 °C, and for 48 h cultivation period, canola meal showed the best EP with 188.75 U/L, followed by soybean with 117.07 U/L, cottonseed meal with 66.71 U/L, and sesame with the lowest production, reaching 88.5 U/L up to 35 °C. The temperature factor exhibited the greatest effect on protease production. The biotechnological and economic potential of canola meal in the production of enzymes is highlighted.
Microbiological Quality of Polish Artisanal Varietal Honeys
Authors: Kędzierska-Matysek Monika , Teter Anna , Daszkiewicz Tomasz , Florek Mariusz
Published: September 2023

On the basis of routine microbiological tests and selected physicochemical parameters, the quality and food safety of Polish varietal honeys were evaluated. The study included 21 honey samples from 5 varieties (multifloral, honeydew, rapeseed, buckwheat and linden), in which the moisture and extract content, water activity, pH and free acids were determined, and the colony count, the presumptive Bacillus spp., the total fungal count and the presence of anaerobic spore-forming bacilli were examined. More than half (52%, 11/21) of the analyzed honeys contained fewer microorganisms than 10 cfu/g, and in the remaining samples, their numbers ranged from 5 × 101 cfu/g to 4.5 × 102 cfu/g. In all the honeys, the number of presumptive Bacillus spp. in 1 g was less than 10 cfu. In 81% (17/21) of the samples, the total count of fungi in 1 g of honey was less than 10 cfu, and the most contaminated was buckwheat honey (3 samples). The anaerobic spore-forming bacteria was detected in 0.1 g only in one sample of buckwheat honey. The values of the physicochemical parameters did not exceed the accepted limits, which indicated that the honey environment was unfavourable for the development of the tested microbial profile.
Effects of Extraction Strategies on Yield, Physicochemical and Antioxidant Properties of Pumpkin Seed Oil
Authors: Hu Tianyuan , Zhou Li , Kong Fan , Wang Shu , Hong Kunqiang , Lei Fenfen , He Dongping
Published: September 2023

This study investigated the effects of three extraction methods, including cold pressing (CP), microwave pretreatment pressing (MP), and supercritical fluid extraction (SFE), on the yield, physicochemical properties, bioactive compounds content, and antioxidant properties of pumpkin seed oil (PSO). Furthermore, the correlation between bioactive compounds and the antioxidant properties of PSO was determined. The results revealed that the yield of PSO extracted using the three methods was in the order of SFE > MP > CP. Additionally, the PSO generated by SFE showed the highest unsaturated fatty acid content, followed by MP and CP. Additionally, MP-PSO exhibited the highest acid value and saponification value, while SFE-PSO displayed the highest moisture content, peroxide value, and iodine value. Moreover, the PSO generated by MP demonstrated superior antioxidant properties compared to that of PSOs from CP and SFE in the oxidation induction, DPPH, FRAP, and ABTS tests. Finally, the correlation analysis revealed that specific types of bioactive compounds, such as β-sitosterol and γ-tocopherol, were highly correlated with the antioxidant properties of PSOs. Consequently, this study provides comprehensive knowledge regarding PSO extraction, physicochemical properties, bioactive compound extraction, and the correlated antioxidant properties.
Exploitation of Selected Sourdough Saccharomyces cerevisiae Strains for the Production of a Craft Raspberry Fruit Beer
Authors: Galli Viola , Venturi Manuel , Guerrini Simona , Mangani Silvia , Barbato Damiano , Vallesi Gianni , Granchi Lisa
Published: September 2023

Recent interest in the special beer category has encouraged the search for novel brewing materials, including new ingredients and novel yeast strains, in order to differentiate the finished products. The aim of this work was to select non-brewing S. cerevisiae strains for the production of a fruit beer with raspberry. The in vitro tests and the wort fermentations allowed the selection of two sourdough S. cerevisiae strains, showing high maltose and maltotriose consumption, high ethanol production, and high viability. Fruit beers (FB) and control beers (CB) without raspberries were prepared. Fruit addition accelerated sugar consumption (7 days compared to 13 days) and increased ethanol and glycerol production by yeasts. Raspberry addition and the inoculated yeast strongly affected the aroma profile of beers. FB samples showed a higher amount of volatile organic compounds (VOCs); the most represented classes were alcohols, followed by esters and acids. FB inoculated by the selected S. cerevisiae SD12 showed the highest VOCs concentration (507.33 mg/L). Results highlighted the possible application of sourdough yeast strains for the brewing process, which, combined with raspberry addition, can be exploited for the production of beers with enhanced aromatic features and suitable chemical properties.
Differences in Bacterial Communities of Retail Raw Pork in Different Market Types in Hangzhou, China
Authors: Wang Wen , Yi Zhengkai , Cai Wei , Ma Jiele , Yang Hua , Zhou Min , Xiao Xingning
Published: September 2023

Pork is widely consumed globally, and pigs’ microbiota can potentially harbor foodborne pathogens. Contaminated pork in retail markets poses significant implications for food quality and safety. However, limited studies have compared pork microbiomes in various marketing environments. In this study, we utilized traditional microbial culture methods and high-throughput 16S rRNA sequencing to assess pathogen contamination and bacterial diversity in raw pork samples purchased from farmers’ markets and two types of supermarkets (upscale and ordinary) in Hangzhou, China. Traditional microbial plate cultures identified E. coli and Salmonella spp. in 32.1% (27/84) and 15.5% (13/84) of the collected pork samples, respectively. Moreover, 12 out of 13 Salmonella strains were found in farmers’ markets. The MIC results indicated a high prevalence of MDR strains, accounting for 51.9% in E. coli and 53.8% in Salmonella. The prevalence of NaClO tolerant strains was 33.3% and 92.3% for E. coli and Salmonella, respectively. Sequencing results indicated significantly higher microbial diversity in farmers’ market samples compared to supermarket samples. Farmers’ market pork samples exhibited a greater abundance of Acinetobacter, while Pseudomonas and Brochothrix were predominant in supermarket samples. The total abundance of pathogenic and spoilage bacteria was also higher for the farmers’ market samples. Cross-contamination during market trading was evident through a high correlation between bacterial abundance in pork from different stalls within the same farmers’ market. PICRUSt2 analysis identified significant differences in the average proportions of genes for carbohydrate, energy, and lipid metabolism from the farmers’ markets, suggesting an exacerbation of microbial metabolic activity and increased perishability of pork in this environment. In conclusion, this study revealed variations in the characteristics of raw pork bacterial contamination across different types of retail stores, as well as differences in the composition and diversity of their respective bacterial communities.
The Effect of High Pressure Homogenization on the Structure of Dual-Protein and Its Emulsion Functional Properties
Authors: Wu Meishan , He Xiaoye , Feng Duo , Li Hu , Han Di , Li Qingye , Zhao Boya , Li Na , Liu Tianxin , Wang Jing
Published: September 2023

It has been proven that high-pressure homogenization (HPH) could improve the functional properties of proteins by modifying their structure. This study researched the effect of HPH on the structural and functional properties of whey-soy dual-protein (Soy Protein Isolation-Whey Protein Isolation, SPI-WPI). Different protein solution samples were treated with HPH at 30, 60, 90, 120 and 150 MPa, and the structure changed under different pressures was analyzed by measuring particle size, zeta potential, Fourier infrared spectrum (FTIR), fluorescence spectrum and scanning electron microscope (SEM). The results showed that HPH significantly reduced the particle size of SPI-WPI, changed the secondary and tertiary structures and improved the hydrophobic interaction between molecules. In addition, HPH significantly improved the solubility and emulsification of all proteins, and the improvement effect on SPI-WPI was significantly better than SPI and WPI. It was found that SPI-WPI treated with 60 MPa had the best physicochemical properties. Secondly, we researched the effect of HPH by 60 MPa on the emulsion properties of SPI-WPI. In this study, the SPI-WPI had the lowest surface tension compared to a single protein after HPH treatment. The emulsion droplet size was obviously decreased, and the elastic properties and physical stability of SPI-WPI emulsion were significantly enhanced. In conclusion, this study will provide a theoretical basis for the application of HPH in modifying the structure of dual-protein to improve its development and utilization in liquid specialty food.
Rapid Classification of Petroleum Waxes: A Vis-NIR Spectroscopy and Machine Learning Approach
Authors: Barea-Sepúlveda Marta , Calle José Luis P. , Ferreiro-González Marta , Palma Miguel
Published: September 2023

Petroleum-derived waxes are used in the food industry as additives to provide texture and as coatings for foodstuffs such as fruits and cheeses. Therefore, food waxes are subject to strict quality controls to comply with regulations. In this research, a combination of visible and near-infrared (Vis-NIR) spectroscopy with machine learning was employed to effectively characterize two commonly marketed petroleum waxes of food interest: macrocrystalline and microcrystalline. The present study employed unsupervised machine learning algorithms like hierarchical cluster analysis (HCA) and principal component analysis (PCA) to differentiate the wax samples based on their chemical composition. Furthermore, nonparametric supervised machine learning algorithms, such as support vector machines (SVMs) and random forest (RF), were applied to the spectroscopic data for precise classification. Results from the HCA and PCA demonstrated a clear trend of grouping the wax samples according to their chemical composition. In combination with five-fold cross-validation (CV), the SVM models accurately classified all samples as either macrocrystalline or microcrystalline wax during the test phase. Similar high-performance outcomes were observed with RF models along with five-fold CV, enabling the identification of specific wavelengths that facilitate discrimination between the wax types, which also made it possible to select the wavelengths that allow discrimination of the samples to build the characteristic spectralprint of each type of petroleum wax. This research underscores the effectiveness of the proposed analytical method in providing fast, environmentally friendly, and cost-effective quality control for waxes. The approach offers a promising alternative to existing techniques, making it a viable option for automated quality assessment of waxes in food industrial applications.
Acute Pancreatitis—Drivers of Hospitalisation Cost—A Seven-Year Retrospective Study from a Large Tertiary Center
Authors: Pahomeanu Mihai Radu , Constantinescu Dalia Ioana , Diaconu Irina Ștefania , Corbu Dana Gabriela , Negreanu Lucian
Published: September 2023

(1) Introduction: Acute pancreatitis (AP) remains a global burden of cost for healthcare services. We found a high degree of heterogeneity in cost-related reports and a scarcity of data regarding the cost of AP episodes in European and Asian populations. We aimed to estimate the median daily cost of hospitalisation (DCH) of AP in our population. Our secondary aims included estimating the total cost of hospitalisation (TCH) and the total cost of AP in Romania, as well as assessing the correlation between median DCH and ward, age, sex, length of stay (LoS), intensive care unit (ICU), outcome, severity, morphology, and aetiology of AP. (2) Material and methods: This retrospective cohort study included 1473 cases recruited from the electronic health records of the University Emergency Hospital of Bucharest. Statistical tests used included Kolmogorov–Smirnov, Kruskal–Wallis with post-hoc Dunn–Bonferroni, and Pearson correlation two-tailed. (3) Results: We found a median DCH of AP of USD 203.8 and a median TCH of USD 1360.5. The total yearly cost of AP in Romania was estimated at around USD 19 million. The majority of males with AP (61.8%) were mostly discharged as healed/ameliorated (83.8%); a majority had local complications (55.4%), which were mostly alcohol-related (35.1%). Regarding the aetiology, biliary-related AP was a cost driver, with significant statistical differences observed in all studied groups (p < 0.01). Morphology assessment revealed that acute necrotic collections were associated with high cost and meaningful disparities among the groups (p < 0.01). Cost was also associated with severity, with significant deviations among all groups (p < 0.01). Outcome-at-discharge as deceased correlated with higher costs, with substantial differences within groups (p < 0.01). The need for an intensive care unit was also a large driver of cost (p < 0.01). Females were prone to more expensive costs (p < 0.01). Surgical cases necessitated more financial resources (p < 0.01). (4) Conclusions: To the best of our knowledge, this is the first study on the cost of AP in Romania. Our findings showed that the drivers of increased AP costs might be older age, ICU, intra-hospital mortality, severe AP, local complications such as acute necrotic collections, biliary aetiology, and female sex. We found large heterogeneity and scarcity regarding cost-related data in the literature.