bluuebunny/crossref_metadata_2025 huggingface dataset:
Ophthalmomyiasis Externa and Importance of Risk Factors, Clinical Manifestations, and Diagnosis: Review of the Medical Literature
Authors: Martinez-Rojano Hugo , Huerta Herón , Sámano Reyna , Chico-Barba Gabriela , Mier-Cabrera Jennifer , Plascencia-Nieto Estibeyesbo Said
Published: December 2023

Objective: This review aims to describe and compare the risk factors, clinical course, diagnosis, and parasitologic features of external ophthalmomyiasis. We also discuss the different preventive measures and the effect of global warming in a large case series reported from January 2000 to December 2022. Methods: We perform a literature review of reported cases of external ophthalmomyiasis to determine the clinical characteristics, therapeutic management, and information on the organisms that most commonly cause external ophthalmomyiasis. Results: A total of 312 cases of external ophthalmomyiasis were recorded. The most common causative organism was Oestrus ovis (Linnaeus) (Diptera: Oestridae), accounting for 72.1% of cases, followed by Dermatobia hominis (Linnaeus Jr. in Pallas, 1781) (Diptera: Oestridae) at 5.4%, Lucilia sericata (Meigen) (Diptera: Calliphoridae) at 0.96%, and Chrysomyia bezziana (Villeneuve) (Diptera: Calliphoridae) at 0.96%. Before experiencing symptoms, 43.6% of cases reported having direct contact with flies or being hit in the eye, 33% reported no associated risk factors, 9.3% reported living with sheep and goats, and 7.7% reported a history of foreign objects entering the eye. The most common symptoms for those affected by O. ovis were sudden onset, including a foreign body sensation and movement, redness, tearing, itching, swelling, irritation, photophobia, burning, and ocular secretion. In cases caused by D. hominis, symptoms included eyelid edema, pain, redness, itching, movement within the lesion, ocular irritation, and discharge. Regarding occupational or recreational activity, agriculture, and livestock had the highest incidence, followed by trades and technical activities, being a student, and having traveled to an endemic region for work or recreation. Conclusion: Patients with red eyes may suffer from external ophthalmomyiasis, which requires a thorough examination to diagnose and treat it early. Moreover, as the temperature increases due to climate change, it is essential to consider how this will affect the spread of different pathogens.
Three-Year Analysis of the Rectal Cancer Care Trajectory after the COVID-19 Pandemic
Authors: Braicu Vlad , Fulger Lazar , Nelluri Aditya , Maganti Ram Kiran , Shetty Uday Shree Akkala , Verdes Gabriel , Brebu Dan , Dumitru Catalin , Toma Ana-Olivia , Rosca Ovidiu , Duta Ciprian
Published: December 2023

The global pandemic period from 2020 to 2022 caused important alterations in oncology care. This study aimed to describe the trends and variations in patient characteristics, comorbidities, and treatment approaches during this time in Romania. We conducted a retrospective database search to identify patients with rectal cancer who underwent surgical intervention between 2020 and 2022 and the year 2019, which served as a pre-pandemic period control. This study included 164 patients, with a yearly increase of approximately 10% in surgical interventions noted from 2020 (1709 interventions) to 2022 (2118 interventions), but an overall 34.4% decrease compared with the pre-pandemic period. Notable shifts were observed in the type of surgeries performed, with laparoscopic procedures doubling from 2020 (25%) to 2022 (47.5%), confirming the decrease in emergency presentations during the last year of the COVID-19 pandemic and a recovery to normality with planned, elective interventions. Elective interventions increased significantly in 2022 (79.7%) compared with the previous years (p = 0.043), with a concurrent rise in neoadjuvant therapy uptake in 2022 (35.6%). However, significant alterations in the TNM staging, from 12.5% stage IV cases in 2020 to 25.4% in 2022 (p = 0.039), indicated an increased diagnosis of advanced stages of rectal cancer as the years progressed. There was a significant difference in albumin levels over the years (p = 0.019) and in the American Society of Anesthesiology (ASA) scores (from 6.2% ASA stage IV in 2020 to 16.9% in 2022), denoting an increase in case complexity (p = 0.043). This study reveals a trend of increasing surgical interventions and the prevalence of more advanced stages of rectal cancer during the pandemic years. Despite the subtle fluctuations in various patient characteristics and treatment approaches, notable shifts were documented in the severity at diagnosis and surgery types, pointing toward more advanced disease presentations and changes in surgical strategies over the period studied. Nevertheless, the trends in ICU admission rates and mortality did not alter significantly during the pandemic period.
‘I Should Do Her Finger Here, on Her Cheek—Hmm, to Play or to Draw? That’s How One Thinks’: What Preschoolers Tell Us about Thinking through Drawing
Authors: Papandreou Maria , Kalouda Anna
Published: December 2023

As the importance of developing good thinkers from an early age is widely recognised as an overarching goal of contemporary early childhood curricula, research on nurturing children’s thinking continues to grow. This study sought to gain insight into kindergarten children’s understanding of the concept and process of thinking. A socio-cultural perspective that conceptualises thinking and its development as a dynamic, socially mediated process, in which signs, including drawing, guided the research. 35 children from Greece, aged 4–6 years, participated in the study and were involved in a drawing-telling activity. Thematic analysis, which included both the drawings and verbal responses of the participants, resulted in five main themes that reveal new aspects of children’s perceptions and reflect the inherent complexity of the concept of thinking. Although the findings have direct implications for teaching and learning in early childhood classrooms, the discussion calls for further research in this area.
WRA-MF: A Bit-Level Convolutional-Weight-Decomposition Approach to Improve Parallel Computing Efficiency for Winograd-Based CNN Acceleration
Authors: Xiang Siwei , Lv Xianxian , Meng Yishuo , Wang Jianfei , Lu Cimang , Yang Chen
Published: December 2023

FPGA-based convolutional neural network (CNN) accelerators have been extensively studied recently. To exploit the parallelism of multiplier–accumulator computation in convolution, most FPGA-based CNN accelerators heavily depend on the number of on-chip DSP blocks in the FPGA. Consequently, the performance of the accelerators is restricted by the limitation of the DSPs, leading to an imbalance in the utilization of other FPGA resources. This work proposes a multiplication-free convolutional acceleration scheme (named WRA-MF) to relax the pressure on the required DSP resources. Firstly, the proposed WRA-MF employs the Winograd algorithm to reduce the computational density, and it then performs bit-level convolutional weight decomposition to eliminate the multiplication operations. Furthermore, by extracting common factors, the complexity of the addition operations is reduced. Experimental results on the Xilinx XCVU9P platform show that the WRA-MF can achieve 7559 GOP/s throughput at a 509 MHz clock frequency for VGG16. Compared with state-of-the-art works, the WRA-MF achieves up to a 3.47×–27.55× area efficiency improvement. The results indicate that the proposed architecture achieves a high area efficiency while ameliorating the imbalance in the resource utilization.
Two-Stage Progressive Learning for Vehicle Re-Identification in Variable Illumination Conditions
Authors: Wu Zhihe , Jin Zhi , Li Xiying
Published: December 2023

Vehicle matching in variable illumination environments can be challenging due to the heavy dependence of vehicle appearance on lighting conditions. To address this issue, we propose a two-stage progressive learning (TSPL) framework. In the first stage, illumination-aware metric learning is enforced using a two-branch network via two illumination-specific feature spaces, used to explicitly model differences in lighting. In the second stage, discriminative feature learning is introduced to extract distinguishing features from a given vehicle. This process consists of a local feature extraction attention module, a local constraint, and a balanced sampling strategy. During the metric learning phase, the model expresses the union of local features, extracted from the attention module, with illumination-specific global features to form joint vehicle features. As part of the study, we construct a large-scale dataset, termed VERI-DAN (vehicle re-identification across day and night), to address the current lack of vehicle datasets exhibiting variable lighting conditions. This set is composed of 200,004 images from 16,654 vehicles, collected in various natural illumination environments. Validation experiments conducted with the VERI-DAN and Vehicle-1M datasets demonstrated that our proposed methodology effectively improved vehicle re-identification Rank-1 accuracy.
Temporal Attention Mechanism Based Indirect Battery Capacity Prediction Combined with Health Feature Extraction
Authors: Chu Fanyuan , Shan Ce , Guo Lulu
Published: December 2023

The burgeoning utilization of lithium-ion batteries within electric vehicles and renewable energy storage systems has catapulted the capacity prediction of such batteries to a pivotal research frontier in the energy storage domain. Precise capacity prognostication is instrumental not merely in safeguarding battery operation but also in prolonging its operational lifespan. The indirect battery capacity prediction model presented in this study is based on a time-attention mechanism and aims to reveal hidden patterns in battery data and improve the accuracy of battery capacity prediction, thereby facilitating the development of a robust time series prediction model. Initially, pivotal health indicators are distilled from an extensive corpus of battery data. Subsequently, this study proposes an indirect battery capacity prediction model intertwined with health feature extraction, hinged on the time-attention mechanism. The efficacy of the proposed model is assayed through a spectrum of assessment metrics and juxtaposed against other well-entrenched deep learning models. The model’s efficacy is validated across various battery datasets, with the Test Mean Absolute Error (MAE) and Test Root Mean Squared Error (RMSE) values consistently falling below 0.74% and 1.63%, respectively, showcasing the model’s commendable predictive prowess and reliability in the lithium-ion battery capacity prediction arena.
Reliability Analysis of a Multilevel Inverter Applied to Stratospheric Drones Using Fault Tree Analysis
Authors: Lee Jung-Hwan , Jun Hyang-Sig , Park Hee-Mun , Park Jin-Hyun , Lim Sang-Kil
Published: December 2023

Stratospheric drones operating in extreme environments are very important for predicting reliability and are high-efficiency, high-performance, and lightweight power units. Multilevel inverters are suitable for application as power conversion units for stratospheric drones. A guideline is needed to evaluate whether it is suitable for practical application from a reliability perspective among various multilevel topologies. Existing reliability prediction models cannot reflect the operating characteristics of multilevel inverters. In this paper, we analyze the driving characteristics of each topology from the perspective of half-bride, which is the basic configuration of multilevel inverters, and we propose a fault tree analysis (FTA) design with three operating modes. The proposed method has the advantage of being able to easily analyze the failure rate by expanding to single-phase and three-phase and to analyze the failure rate according to changes in modulation index (MI) and power factor (PF). The failure rates of the proposed method and the part count method are analyzed using MIL-HDBK-217F. We also analyze the impact of different various operating characteristics on the failure rate. From a reliability perspective, we provide a variety of guidelines for selecting a multilevel topology that fits the operation conditions.
Feature Importance-Based Backdoor Attack in NSL-KDD
Authors: Jang Jinhyeok , An Yoonsoo , Kim Dowan , Choi Daeseon
Published: December 2023

In this study, we explore the implications of advancing AI technology on the safety of machine learning models, specifically in decision-making across diverse applications. Our research delves into the domain of network intrusion detection, covering rule-based and anomaly-based detection methods. There is a growing interest in anomaly detection within network intrusion detection systems, accompanied by an increase in adversarial attacks using maliciously crafted examples. However, the vulnerability of intrusion detection systems to backdoor attacks, a form of adversarial attack, is frequently overlooked in untrustworthy environments. This paper proposes a backdoor attack scenario, centering on the “AlertNet” intrusion detection model and utilizing the NSL-KDD dataset, a benchmark widely employed in NIDS research. The attack involves modifying features at the packet level, as network datasets are typically constructed from packets using statistical methods. Evaluation metrics include accuracy, attack success rate, baseline comparisons with clean and random data, and comparisons involving the proposed backdoor. Additionally, the study employs KL-divergence and OneClassSVM for distribution comparisons to demonstrate resilience against manual inspection by a human expert from outliers. In conclusion, the paper outlines applications and limitations and emphasizes the direction and importance of research on backdoor attacks in network intrusion detection systems.
Design and Analysis of a Long-Stroke and High-Precision Positioning System for Scanning Beam Interference Lithography
Authors: Chen Hao , Li Longxiang , Li Ruigang , Yu Guangdong , Chen Qi
Published: December 2023

A macro–micro dual-drive positioning system was developed for Scanning Beam Interference Lithography (SBIL) which uses a dual-frequency laser interferometer as the position reference and exhibits the characteristics of long travel, heavy load, and high accuracy. The macro-motion system adopts a friction-driven structure and a feedforward PID control algorithm, and the stroke can reach 1800 mm. The micro-motion system adopts a flexible hinge–plus-PZT driving method and a PID control algorithm based on neural networks, which achieves sufficient positioning accuracy of this system at the nanometer level. An optical-path-sealing system was used to reduce the measurement noise of the dual-frequency laser interferometer. The static stability of the positioning system, the stepping capacity of the macro-motion system, the stepping capacity of the micro-motion system, and the positioning accuracy of the system were tested and analyzed. Additionally, the sources and effects of errors during the motion process were assessed in detail. Finally, the experimental results show that the workbench can locate at the nanoscale within the full range of travel, which can satisfy the SBIL exposure requirement.
Changes in Commercial Dendromass Properties Depending on Type and Acquisition Time
Authors: Stolarski Mariusz Jerzy , Krzyżaniak Michał , Olba-Zięty Ewelina , Stolarski Jakub
Published: December 2023

Forest dendromass is still the major raw material in the production of solid biofuels, which are still the most important feedstock in the structure of primary energy production from renewable energy sources. Because of the high species and type diversity of production residues generated at wood processing sites, as well as at logging sites, the quality of commercial solid biomass produced there has to be evaluated. The aim of this study was to assess the thermophysical characteristics and the elemental composition of ten types of commercial solid biofuels (pinewood sawdust; energy chips I, II, and III; veneer sheets; shavings; birch bark; pine bark; pulp chips; and veneer chips), depending on their acquisition time (August, October, December, February, April, and June). Pulp chips had the significantly lowest moisture content (mean 26.92%), ash content (mean 0.39% DM—dry matter), nitrogen (N) content (mean 0.11% DM), and sulfur (S) content (mean 0.011% DM) and the highest carbon (C) content (mean 56.09% DM), hydrogen (H) content (6.40% DM), and lower heating value (LHV) (mean 13.61 GJ Mg−1). The three types of energy chips (I, II, and III) had good energy parameters, especially regarding their satisfactory LHV and ash, S, and N content. On the other hand, pine and birch bark had the worst ash, S, and N contents, although they had beneficial higher heating values (HHVs) and C contents. Solid biofuels acquired in summer (June) had the lowest levels of moisture and ash and the highest LHV. The highest moisture content and the lowest LHV were found in winter (December).
Experimental and Numerical Investigations into the Effects of Rim Seal Structure on Endwall Film Cooling and Flow Field Characteristics
Authors: Lu Yixuan , Liu Zhao , Zhang Weixin , Ding Yuqiang , Feng Zhenping
Published: December 2023

During the practical operation of gas turbines, relatively cooled air from the compressor and the rim seal is applied in order to prevent mainstream ingestion into the space between the rotor and stator disc cavities, which can prolong the service life of hot components. On the one hand, the purge flow from the rim seal will inevitably interact with the mainstream and result in secondary flow on the endwall. On the other hand, it can also provide an additional cooling effect. In this paper, four rim seal structures, including an original single-tooth seal (ORI), a double-tooth seal (DS), a single-tooth seal with an adverse direction of the coolant purge flow and mainstream (AS) and a double-tooth seal with an adverse direction of the coolant purge flow and mainstream (ASDS), are experimentally and numerically investigated with mass flow ratios of 0.5%, 1.0% and 1.5%. The flow orientation of the coolant from the rim seal is considered as one of the main factors. The pressure-sensitive paint technique is used to experimentally measure the film cooling effectiveness on the endwall, and flow field analysis is conducted via numerical simulations. The results show that the cooling effect decreases in the cases of DS and ASDS. AS and ASDS can achieve a better film cooling performance, especially under a higher mass flow ratio. Furthermore, the structural changes in the rim seal have little impact on the aerodynamic performance. AS and ASDS can both achieve a better aerodynamic and film cooling performance.
Numerical Study of the Influence of Different Bending Shapes on the Heat Transfer Characteristics of Annular Cross Wavy Primary Surface Recuperator (CW-PSR)
Authors: Jiang Huadong , Chen Fu , Huang Chonghai , Yu Jianyang , Song Yanping , Zhang Juanshu
Published: December 2023

The cross-wave primary surface recuperator (CW-PSR) is a dependable option as a recuperator for micro gas turbines (MGT). The micro CW-PSR studied in this paper is composed of 171 stacked curved plates, with each plate containing 33 micro heat transfer channels with equivalent diameters of less than 1 mm. In this study, the influence of bending curvature on the thermal performance of CW-PSR plates is investigated through three-dimensional numerical simulation with fluid–solid–thermal coupling. The results indicate that the variation in bending curvature studied can result in a noteworthy 8% difference in the total heat transfer coefficient of CW-PSR plates. A direct correlation between heat transfer capacity and secondary flow strength is derived mathematically, explaining the mechanism by which secondary flow enhances heat transfer. By employing this relationship, a comprehensive analysis of CW-PSR plates with diverse bending curvatures is conducted, effectively showcasing how curvature influences the secondary flow pattern and enhances the channel’s heat transfer capacity. In addition, this paper considers the comprehensive influence of the size parameters of the heat transfer unit and the bending curvature of the heat transfer plate on the heat transfer and flow characteristics of the CW-PSR, and a dominant mathematical expression is obtained, which can be used for the design of similar heat exchangers of the same type.
Density Functional Theory Optimization of Cobalt- and Nitrogen-Doped Graphene Catalysts for Enhanced Oxygen Evolution Reaction
Authors: Wang Jiatang , He Huawei , Cai Weiwei , Yang Chao , Wu Yu , Zhang Houcheng , Liu Rui , Cheng Hansong
Published: December 2023

The optimization and advancement of effective catalysts in the oxygen evolution reaction (OER) are integral to the evolution of diverse green power technologies. In this study, cobalt–nitrogen–graphene (Co-N-g) catalysts are analyzed for their OER contribution via density functional theory (DFT). The influence of vacancies and nitrogen doping on catalyst performance was probed via electronic features and related Frontier Molecular Orbitals. The research reveals that the double-vacancy nitrogen-doped catalyst (DV-N4) exhibits remarkable OER effectiveness, characterized by a notably low overpotential of 0.61 V. This is primarily attributed to enhanced metal–ligand bonding interactions, a diminished energy gap indicating augmented reactivity, and advantageous charge redistribution upon water adsorption. Additionally, nitrogen doping is found to facilitate electron loss from Co, thus promoting water oxidation and improving OER performance. This research provides crucial insights into high-performance OER catalyst design, informing future developments in efficient renewable energy devices.
Research on Hydrogen Production System Technology Based on Photovoltaic-Photothermal Coupling Electrolyzer
Authors: Li Yuanyuan , Xu Xiaoyu , Bao Daorina , Rasakhodzhaev Bakhramzhan , Jobir Akhadov , Chang Chun , Zhao Mingzhi
Published: December 2023

Solar hydrogen production technology is a key technology for building a clean, low-carbon, safe, and efficient energy system. At present, the intermittency and volatility of renewable energy have caused a lot of “wind and light”. By combining renewable energy with electrolytic water technology to produce high-purity hydrogen and oxygen, which can be converted into electricity, the utilization rate of renewable energy can be effectively improved, while helping to improve the solar hydrogen production system. This paper summarizes and analyzes the research status and development direction of solar hydrogen production technology from three aspects. Energy supply mode: the role of solar PV systems and PT systems in this technology is analyzed. System control: the key technology and system structure of different types of electrolytic cells are introduced in detail. System economy: the economy and improvement measures of electrolytic cells are analyzed from the perspectives of cost, consumption, efficiency, and durability. Finally, the development prospects of solar hydrogen production systems in China are summarized and anticipated. This article reviews the current research status of photovoltaic-photothermal coupled electrolysis cell systems, fills the current research gap, and provides theoretical reference for the further development of solar hydrogen production systems.
The Use of Biomethane in Internal Combustion Engines for Public Transport Decarbonization: A Case Study
Authors: Noussan Michel
Published: December 2023

Public transport can play a central role in representing a viable and sustainable mobility solution, especially in urban areas. Average energy consumption and emissions per passenger are much lower than for private cars. At the same time, current buses often mostly rely on diesel, and there are different solutions that can contribute to public transport decarbonization. Biomethane is among the options to exploit local low-carbon resources to decrease the emissions of public transport in urban environments. This paper presents the analysis of a real case study considering real data on the fuel consumption and mileage of the existing bus fleet in the city of Turin, Italy, composed by diesel and natural gas buses. The aim of this study is to estimate the effect of different penetration levels of biomethane in substitution of the current fuels. The results show that the use of biomethane in urban buses could save to up to 71% of emissions compared to the current situation, and savings would increase to 75% when deploying biomethane and electric buses together. Average emissions per pkm could decrease from a current level of 85.5 gCO2/pkm to 21.3–63.4 gCO2/pkm depending on the penetration of biomethane and electric buses. The sensitivity analysis shows even higher savings when accounting for the future decrease of the electricity carbon intensity in Italy and for the additional benefits related to avoided emissions from manure disposal. The results of the analysis demonstrate the potential contribution of biomethane in decarbonizing urban buses, and the findings presented for this case study can be of use for policy makers and researchers that deal with a similar situation in other cities and countries.
Low-Carbon Economic Dispatch of an Integrated Electricity–Gas–Heat Energy System with Carbon Capture System and Organic Rankine Cycle
Authors: Xiong Junhua , Li Huihang , Wang Tingling
Published: December 2023

A low-carbon economic optimization dispatch model of integrated energy system is proposed to improve the low-carbon and economic efficiency of the integrated energy systems. Firstly, the waste heat generator with the organic Rankine cycle is introduced into the combined heat and power to decouple the combined heat and power operation, and a coupled model with an organic Rankine cycle, power to gas, combined heat and power and carbon capture system is established. Then, the ladder-type carbon trading mechanism is introduced to improve the low-carbon model. Finally, the function is established to minimize the sum of energy purchase costs, operation and maintenance costs, and environmental costs. The proposed integrated energy systems’ low-carbon economic dispatch model reduces the total operating cost by 18.9% and the carbon emissions by 83.7% by setting up different models for comparative analysis.
New Graph-Based and Transformer Deep Learning Models for River Dissolved Oxygen Forecasting
Authors: Costa Rocha Paulo Alexandre , Oliveira Santos Victor , Van Griensven Thé Jesse , Gharabaghi Bahram
Published: December 2023

Dissolved oxygen (DO) is a key indicator of water quality and the health of an aquatic ecosystem. Aspiring to reach a more accurate forecasting approach for DO levels of natural streams, the present work proposes new graph-based and transformer-based deep learning models. The models were trained and validated using a network of real-time hydrometric and water quality monitoring stations for the Credit River Watershed, Ontario, Canada, and the results were compared with both benchmarking and state-of-the-art approaches. The proposed new Graph Neural Network Sample and Aggregate (GNN-SAGE) model was the best-performing approach, reaching coefficient of determination (R2) and root mean squared error (RMSE) values of 97% and 0.34 mg/L, respectively, when compared with benchmarking models. The findings from the Shapley additive explanations (SHAP) indicated that the GNN-SAGE benefited from spatiotemporal information from the surrounding stations, improving the model’s results. Furthermore, temperature has been found to be a major input attribute for determining future DO levels. The results established that the proposed GNN-SAGE model outperforms the accuracy of existing models for DO forecasting, with great potential for real-time water quality management in urban watersheds.
The Black Soldier Fly (Hermetia illucens) Larvae Meal Can Cost-Effectively Replace Fish Meal in Practical Nursery Diets for Post-Larval Penaeus vannamei under High-Density Culture
Authors: Nunes Alberto J. P. , Yamamoto Hiroshi , Simões João Paulo , Pisa João Luiz , Miyamoto Nelson , Leite Jordana Sampaio
Published: December 2023

The black soldier larvae meal (BSFLM) has been the most extensively studied insect protein source in shrimp nutrition. However, both the availability and prices of BSFLM are still a constraint for its widespread use as an ingredient in animal feeds. The present study investigated the growth and economic performance of post-larval (PL) P. vannamei fed nursery diets with a progressive replacement of fish meal (FML) for BSFLM at 0, 25, 50, 75, and 100%. These replacements corresponded to a dietary inclusion (% of the diet, as-is) of FML and BSFLM of 16.50 and 6.33%, 11.00 and 13.04%, 5.50 and 19.74%, and 0 and 26.46%, respectively. A total of 102,647 shrimp at the age of PL15 with 2.7 ± 0.2 mg body weight (BW) were stocked in fifty 1.5 m3 tanks under 1369 PLs/m3 (2053 ± 33 PLs/tank) and reared for 42 days. Final shrimp survival (90.5 ± 7.6%), daily weight gain (14.7 ± 1.1 mg/day), and apparent feed intake (0.67 ± 0.03 g of feed per stocked shrimp) were unaffected by dietary treatment. The highest gained yield (791 ± 52 and 776 ± 38 g/m3) and final BW (621 ± 7.2 and 632 ± 7.2 mg) were attained when FML was replaced for BSFLM at 50 and 75% with the lowest at 0% (726 ± 34 g/m3 and 598 ± 8.1 mg, respectively). Shrimp fed diets with 0 and 100% replacement of FML exhibited the highest feed conversion ratio (1.25 ± 0.04 and 1.24 ± 0.08) compared to those fed a diet with 50% (1.16 ± 0.06). At a price of USD 2.00/kg, BSFLM demonstrated a favorable ROI (return of investment) when compared to FML, irrespective of the replacement level. With 25 and 50% replacement, BSFLM remained cost-competitive up to 3.50 USD/kg. At 75% FML replacement, there were no significant differences in ROI with a price range of 2.00 up to 3.04 USD/kg. At full replacement, ROI dropped significantly at a BSFLM price of 2.50 USD/kg and beyond. It can be concluded that FML can be fully replaced for BSFLM in well-balanced nursery diets for P. vannamei. Although the full replacement of FML for BSFLM was successfully accomplished, the competitive ROI was sustained only when the price of BSFLM did not exceed 3.04 USD/kg at its dietary highest inclusion of 19.74%. Further research may be necessary to fine-tune cost-effective inclusion levels of BSFLM to optimize the economic outcomes while considering the fluctuating prices of FML.
Study of the Oxidative and Microbiological Stability of Nitrite-Reduced, Vacuum-Packed, Refrigerated Lamb Sausages Supplemented with Red Propolis Extract
Authors: Gotardo Luciana Ruggeri Menezes , Carvalho Francisco Allan Leandro de , Gomes Quirino Dannaya Julliethy , Favaro-Trindade Carmen Sílvia , Alencar Severino Mathias de , Oliveira Alessandra Lopes de , Trindade Marco Antonio
Published: December 2023

Vacuum-packed lamb sausages with or without red propolis extract and a reduced sodium nitrite content were evaluated for oxidative and microbiological stability during storage for 21 days at 2 °C. The following treatments were evaluated: EN150 (control, base formulation (BF) + 500 mg/kg sodium erythorbate and 150 mg/kg sodium nitrite); EN75 (BF + 500 mg/kg sodium erythorbate and 75 mg/kg sodium nitrite); P1N75 (without the addition of erythorbate, BF + 1800 mg/kg propolis extract and 75 mg/kg sodium nitrite); and P2N75 (without the addition of erythorbate, BF + 3600 mg/kg propolis extract and 75 mg/kg sodium nitrite). Analyses were conducted to characterize the samples on day 0 with respect to the proximate composition (moisture, protein, fat, and ash) and sensory acceptance. Stability during refrigerated storage was evaluated on days 0, 7, 14 and 21 for the parameters pH, color profile (L*, a*, and b*), TBARs index (oxidative stability) and microbiological count of aerobic psychrotrophic microorganisms. Texture profile, cooking weight loss (WLC), peroxide index and free fatty acids were evaluated on days 0 and 21. The treatments with propolis and reduced nitrite (EN150 and P1N75) showed a red color intensity (a*) similar to the treatment with erythorbate and the same nitrite content (EN75) at the end of storage, maintaining the characteristic reddish color of the sausages. The extract slowed down lipid oxidation during storage, especially P2N75, which showed the lowest level of TBARS (0.39 mg MDA/kg) and the peroxide index (2.13 mEq g O2) on day 21. The residual nitrite value in EN75 was the lowest (p < 0.05) on day 21, showing that synthetic antioxidants are more efficient than the extract in nitrite reduction reactions. The results for the counts of psychrotrophic microorganisms showed that the extract did not have the expected antimicrobial effect on the growth of this microorganisms, and leveling the results revealed no differences (p < 0.05) between the treatments. Despite the red propolis extract not showing a significant antimicrobial improvement in lamb sausages, it can be considered a healthy option with good prospects for replacing synthetic antioxidants with a natural product.
A General Review of Methodologies Used in the Determination of Cholesterol (C27H46O) Levels in Foods
Authors: Ndhlala Ashwell , Kavaz Yüksel Arzu , Çelebi Neslihan , Doğan Hülya
Published: December 2023

Cholesterol (C27H46O) is a lipid-derived substance found in lipoproteins and cell membranes. It is also one of the main sources for the production of bile acids, vitamin D, and steroid hormones. Today, foods are evaluated by consumers not only according to their taste and nutritional content but also according to their effects on consumer health. For example, many consumers choose foods according to their cholesterol level. The cholesterol in the food can directly affect the blood cholesterol level when consumed, which can lead to cardiovascular diseases. High levels of cholesterol can lead to diet-related human diseases such as cardiac arrest, paralysis, type II diabetes, and cerebral hemorrhage. In societies with high living standards, interest in and consumption of foods that lower or have low cholesterol levels have increased recently. Accordingly, efforts to increase the variety of foods with reduced cholesterol levels are on the rise. This has indirectly led to the accurate measurement of cholesterol levels in blood and food being of great importance. Classical chemical, enzymatic, colorimetric, polarographic, chromatographic, and spectrophotometric methods; enzymatic, nonenzymatic, and electrochemical sensors; and biosensors are used for the determination of cholesterol in foods. The purpose of this review is to reveal and explore current and future trends in cholesterol detection methods in foods. This review will summarize the most appropriate and standard methods for measuring cholesterol in biological components and foods.
Perception of Food Safety Associated with Entomophagy among Higher-Education Students: Exploring Insects as a Novel Food Source
Authors: Cantalapiedra Fernando , Juan-García Ana , Juan Cristina
Published: December 2023

Edible insects can diversify diets, improve livelihoods, contribute to food and nutrition security, and have a smaller ecological impact. The European Union has categorized insects as novel food, and recently, in 2021 and 2022, two species, Tenebrio molitor and Acheta domesticus, were authorized for commercialization. The acceptance and perception of food risk derived from insect consumption vary depending on factors impacting insect consumption acceptability, including neophobic tendencies, gender differences, familiarity, and gastronomic perceptions. The aim of this work was to evaluate the perception and acceptance of edible insects by exploring these factors. This study was carried out on higher-education students from universities in Valencia (Spain). The students recognized insects’ high nutritional value, particularly protein content, and had varying levels of knowledge about specific nutritional components. In terms of labeling and marketing, removing health and sustainability benefits from packaging can improve consumer responses. Most respondents prefer clear labeling of insect derivatives, quality certification seals, and complete information about insect content. Students consider marketing and knowledge to be significant influencers of insect consumption. In summary, this text highlights the multifaceted nature of insect consumption acceptability. These insights offer valuable perspectives on insect consumption dynamics.
A Novel Variable Selection Method Based on Ordered Predictors Selection and Successive Projections Algorithm for Predicting Gastrodin Content in Fresh Gastrodia elata Using Fourier Transform Near-Infrared Spectroscopy and Chemometrics
Authors: Wang Zhenjie , Zuo Changzhou , Chen Min , Song Jin , Tu Kang , Lan Weijie , Li Chunyang , Pan Leiqing
Published: December 2023

Gastrodin is one of the most important biologically active components of Gastrodia elata, which has many health benefits as a dietary and health food supplement. However, gastrodin measurement traditionally relies on laboratory and sophisticated instruments. This research was aimed at developing a rapid and non-destructive method based on Fourier transform near infrared (FT-NIR) to predict gastrodin content in fresh Gastrodia elata. Auto-ordered predictors selection (autoOPS) and successive projections algorithm (SPA) were applied to select the most informative variables related to gastrodin content. Based on that, partial least squares regression (PLSR) and multiple linear regression (MLR) models were compared. The autoOPS-SPA-MLR model showed the best prediction performances, with the determination coefficient of prediction (Rp2), ratio performance deviation (RPD) and range error ratio (RER) values of 0.9712, 5.83 and 27.65, respectively. Consequently, these results indicated that FT-NIRS technique combined with chemometrics could be an efficient tool to rapidly quantify gastrodin in Gastrodia elata and thus facilitate quality control of Gastrodia elata.
A Critical Review of Risk Assessment Models for Listeria monocytogenes in Dairy Products
Authors: Gonzales-Barron Ursula , Cadavez Vasco , Guillier Laurent , Sanaa Moez
Published: December 2023

A review of the published quantitative risk assessment (QRA) models of L. monocytogenes in dairy products was undertaken in order to identify and appraise the relative effectiveness of control measures and intervention strategies implemented at primary production, processing, retail, and consumer practices. A systematic literature search retrieved 18 QRA models, most of them (9) investigated raw and pasteurized milk cheeses, with the majority covering long supply chains (4 farm-to-table and 3 processing-to-table scopes). On-farm contamination sources, either from shedding animals or from the broad environment, have been demonstrated by different QRA models to impact the risk of listeriosis, in particular for raw milk cheeses. Through scenarios and sensitivity analysis, QRA models demonstrated the importance of the modeled growth rate and lag phase duration and showed that the risk contribution of consumers’ practices is greater than in retail conditions. Storage temperature was proven to be more determinant of the final risk than storage time. Despite the pathogen’s known ability to reside in damp spots or niches, re-contamination and/or cross-contamination were modeled in only two QRA studies. Future QRA models in dairy products should entail the full farm-to-table scope, should represent cross-contamination and the use of novel technologies, and should estimate L. monocytogenes growth more accurately by means of better-informed kinetic parameters and realistic time–temperature trajectories.
A Multi-Task Learning Framework of Stable Q-Compensated Reverse Time Migration Based on Fractional Viscoacoustic Wave Equation
Authors: Xue Zongan , Ma Yanyan , Wang Shengjian , Hu Huayu , Li Qingqing
Published: December 2023

Q-compensated reverse time migration (Q-RTM) is a crucial technique in seismic imaging. However, stability is a prominent concern due to the exponential increase in high-frequency ambient noise during seismic wavefield propagation. The two primary strategies for mitigating instability in Q-RTM are regularization and low-pass filtering. Q-RTM instability can be addressed through regularization. However, determining the appropriate regularization parameters is often an experimental process, leading to challenges in accurately recovering the wavefield. Another approach to control instability is low-pass filtering. Nevertheless, selecting the cutoff frequency for different Q values is a complex task. In situations with low signal-to-noise ratios (SNRs) in seismic data, using low-pass filtering can make Q-RTM highly unstable. The need for a small cutoff frequency for stability can result in a significant loss of high-frequency signals. In this study, we propose a multi-task learning (MTL) framework that leverages data-driven concepts to address the issue of amplitude attenuation in seismic records, particularly when dealing with instability during the Q-RTM (reverse time migration with Q-attenuation) process. Our innovative framework is executed using a convolutional neural network. This network has the capability to both predict and compensate for the missing high-frequency components caused by Q-effects while simultaneously reconstructing the low-frequency information present in seismograms. This approach helps mitigate overwhelming instability phenomena and enhances the overall generalization capacity of the model. Numerical examples demonstrate that our Q-RTM results closely align with the reference images, indicating the effectiveness of our proposed MTL frequency-extension method. This method effectively compensates for the attenuation of high-frequency signals and mitigates the instability issues associated with the traditional Q-RTM process.
3D Geophysical Modeling Based on Multi-Scale Edge Detection, Magnetic Susceptibility Inversion, and Magnetization Vector Inversion in Panjshir, Afghanistan to Detect Probabilistic Fe-Polymetallic Bearing Zone
Authors: Rezayee Mohammad Hakim , Akbar Ahamd Qasim , Poyesh Torabaz , Rawnaq Ezatullah , Samim Khair Mohammad , Mizunaga Hideki
Published: December 2023

The Panjshir Fe-Polymetallic ore deposit is a valuable geological resource in Afghanistan, rich in iron and multiple essential metallic minerals, with substantial potential for industrial development. The exploration phase faces challenges related to the complex geological settings, high variability of mineral compositions, and the need for advanced geophysical techniques to accurately locate and assess valuable metallic resources. Considering the strong magnetic characteristics exhibited by Fe-Polymetallic elements, geomagnetic data were employed to analyze and map the likely prospectivity of Fe-Polymetallic deposits within the study area. Multi-scale edge detection techniques were employed to accurately map the boundaries of magnetic bodies by utilizing the upward continued analytical signal amplitude. The presence of a fault system on the geological map confirmed the structural information derived from our edge detection techniques. Advanced magnetic data inversion techniques were employed to create a three-dimensional representation of the distribution of magnetic bodies linked to Fe-Polymetallic deposits. In our efforts to reduce the impact of remnant magnetization in the study area, we adopted a comprehensive strategy by employing both magnetic susceptibility and magnetization vector inversion techniques. The use of a sparse and blocky norm regularization [0,1,1,1] is well-suited for magnetic susceptibility inversion, while a blocky norm [0000,0000,0000] is the appropriate choice for magnetization vector inversion in our study. Ultimately, the zones characterized by a high magnetic susceptibility and a high magnetization amplitude are considered promising areas for potential Fe-polymetallic occurrences.
Melting Processes of Pelitic Rocks in Combustion Metamorphic Complexes of Mongolia: Mineral Chemistry, Raman Spectroscopy, Formation Conditions of Mullite, Silicate Spinel, Silica Polymorphs, and Cordierite-Group Minerals
Authors: Peretyazhko Igor S. , Savina Elena A.
Published: December 2023

Melted rocks (clinkers and paralavas) of the Mongolian combustion metamorphic (CM) complexes were formed during modern and ancient (since the Quaternary) wild-fires of brown coal layers in the sedimentary strata of the Early Cretaceous Dzunbain Formation. According to XRD, Raman spectroscopy, and SEM-EDS data, cordierite, sekaninaite, indialite, ferroindialite, silica polymorphs, mullite, Fe-mullite, anhydrous Al-Fe-Mg silicate spinel (presumably new mineral), and other phases were identified. It has been established that isomorphic impurity of potassium in the cordierite-group minerals does not correlate with their crystal structure (hexagonal or orthorhombic). Indialite and ferroindialite retained their hexagonal structure in some fragments of the CM rocks, possibly due to the very fast cooling of local zones of sedimentary strata and the quenching of high-temperature K-rich peraluminous melt. Clinkers, tridymite–sekaninaite, and cristobalite–fayalite ferroan paralavas were produced by partial melting of Fe-enriched pelitic rocks (mudstone, siltstone, and silty sandstone) in a wide temperature range. The formation of mullite, Fe-mullite, and Al-Fe-Mg silicate spinel in clinkers developed from dehydration–dehydroxylation and incongruent partial melting of Fe-enriched pelitic matter (Al-Mg-Fe-rich phyllosilicates, ‘meta-kaolinite’, and ‘meta-illite’). Large-scale crystallization of these minerals in the K-rich peraluminous melts occurred, presumably, in the range of T > 850–900 °C. The subsurface combustion of coal layers heated the overburden pelitic rocks from sedimentary strata to T > 1050 °C (judging by the formation of cordierite-group minerals) or locally till the melting point of detrital quartz grains at T > 1300 °C and, possibly, till the stability field of stable β-cristobalite at T > 1470 °C. Ferroan paralavas were formed during the rapid crystallization of Fe-rich silicate melts under various redox conditions. From the analysis of the liquidus surface in the Al2O3–FeO–Fe2O3–SiO2 major-oxide system, it follows that the least high-temperature (<1250 °C) and the most oxidizing conditions occurred during the crystallization of mineral assemblages in the most-enriched iron silicate melts parental for cristobalite–fayalite paralava.
Numerical Modelling and Sensitivity Analysis of the Pitztal Valley Debris Flow Event
Authors: Naqvi Mohammad Wasif , Kc Diwakar , Hu Liangbo
Published: December 2023

Debris flows characterized by their rapid velocity and composition of water, mud, soil, and boulders, have the potential to inflict significant harm and present hazards to human life, infrastructure, and the natural surroundings. Numerical simulations provide a cost-effective approach for investigating different scenarios, hence boosting comprehension of flow dynamics and interactions. However, accurate modelling of these flows typically face difficult challenges arising from inherent modeling constraints and insufficient historical event data. The primary objective of the present study is to conduct numerical modeling and sensitivity analysis of the debris flow event that occurred in the Pitztal Valley, Austria in August of 2009, based on a multi-phase model for debris flows. The validation of the simulation results involves the comparison with the observed deposition patterns in the field. Various validation factors are employed to evaluate the accuracy of the simulated deposit and demonstrate a satisfactory level of precision in predicting deposition patterns. A sensitivity analysis is also conducted to examine the influence of in situ conditions on the effects of debris flow. The results demonstrate that numerical modelling can play an important role in engineering hazard assessment by analyzing the existing model’s effectiveness in simulating both historical and projected debris flow events.