bluuebunny/crossref_metadata_2025 huggingface dataset:
Potential Clinical Impact of LAFOV PET/CT: A Systematic Evaluation of Image Quality and Lesion Detection
Authors: Honoré d’Este Sabrina , Andersen Flemming Littrup , Andersen Julie Bjerglund , Jakobsen Annika Loft , Sanchez Saxtoft Eunice , Schulze Christina , Hansen Naja Liv , Andersen Kim Francis , Reichkendler Michala Holm , Højgaard Liselotte , Fischer Barbara Malene
Published: October 2023

We performed a systematic evaluation of the diagnostic performance of LAFOV PET/CT with increasing acquisition time. The first 100 oncologic adult patients referred for 3 MBq/kg 2-[18F]fluoro-2-deoxy-D-glucose PET/CT on the Siemens Biograph Vision Quadra were included. A standard imaging protocol of 10 min was used and scans were reconstructed at 30 s, 60 s, 90 s, 180 s, 300 s, and 600 s. Paired comparisons of quantitative image noise, qualitative image quality, lesion detection, and lesion classification were performed. Image noise (n = 50, 34 women) was acceptable according to the current standard of care (coefficient-of-varianceref < 0.15) after 90 s and improved significantly with increasing acquisition time (PB < 0.001). The same was seen in observer rankings (PB < 0.001). Lesion detection (n = 100, 74 women) improved significantly from 30 s to 90 s (PB < 0.001), 90 s to 180 s (PB = 0.001), and 90 s to 300 s (PB = 0.002), while lesion classification improved from 90 s to 180 s (PB < 0.001), 180 s to 300 s (PB = 0.021), and 90 s to 300 s (PB < 0.001). We observed improved image quality, lesion detection, and lesion classification with increasing acquisition time while maintaining a total scan time of less than 5 min, which demonstrates a potential clinical benefit. Based on these results we recommend a standard imaging acquisition protocol for LAFOV PET/CT of minimum 180 s to maximum 300 s after injection of 3 MBq/kg 2-[18F]fluoro-2-deoxy-D-glucose.
Pain Assessment in Oral Medicine through Its Different Dimensions: A Comprehensive Review
Authors: Scribante Andrea , Pellegrini Matteo , Pulicari Federica , Ghizzoni Martina , Modugno Francesco Paolo , Spadari Francesco
Published: October 2023

Orofacial pain is a complex experience made up of different features and involving various aspects of life. It has demonstrated a connection, especially when chronic, with conditions such as anxiety, depression, and sleep disorders, through paths that still have not been completely clarified. A deep understanding of orofacial pain and its impact on an individual’s life is critical for planning accurate diagnostic and therapeutic approaches. This review seeks to provide a comprehensive overview of the components constituting the pain experience, its implications in an individual’s life, the different tools for multidimensional pain assessment, and the specific applications for each tool. A comprehensive review was performed using the PubMed, Scopus, and Web of Science electronic databases. Ninety-five studies, including observational studies, clinical trials, case–control studies, and case reports, were included and analyzed in this review. Orofacial pain assessment exploits several methods, ranging from clinical evaluation to rating scales, questionnaires, and daily diaries. The choice of the correct instrument requires an evaluation of the type of pain experienced, of the patient’s characteristics and abilities to complete particular tasks, and finally, of the assessment tool features.
A Multicenter Prospective Study on the Use of a Mandibular Advancement Device in the Treatment of Obstructive Sleep Apnea
Authors: Anitua Eduardo , Mayoral Pedro , Almeida Gabriela Zamora , Durán-Cantolla Joaquín , Alkhraisat Mohammad Hamdan
Published: October 2023

The main objective of this prospective study was the evaluation of 1 mm step titration of mandible advancement in the success of treating obstructive sleep apnea (OSA). For that, a multicenter prospective study was designed to recruit patients with OSA who were eligible to receive a mandibular advancement device. Gradual titration of mandibular advancement (steps of 1 mm) from maximum intercuspidation was performed to determine the optimal mandibular advancement (highest reduction in the apnea-hypopnea index (AHI)). The principal variable was the percentage of patients where a reduction ≥50% of the AHI was achieved at the end of the titration phase. A total of 102 patients participated in this study. Fifty-six percent of the participants were males and 67% had a BMI ≥ 25 kg/m2. Most of the patients (79%) had an age ≥ 50 years and the majority (74%) were either non-smokers or ex-smokers. Excessive daytime sleepiness was reported by 40% of the patients. The mean AHI at baseline was 20.6 ± 12.7 events/h. The mean advancement of the mandible was 3.1 ± 1.6 mm. The device achieved a reduction in the AHI in 93% of the patients and success (≥50% reduction in the AHI) in 69% of the patients. Success was achieved in 50%, 81.6%, and 73.3% of the patients with mild, moderate, and severe OSA, respectively. Decreasing the magnitude of mandibular advancement could be possible by controlling the vertical mouth opening and step-by-step titration.
Understanding the Feature Space and Decision Boundaries of Commercial WAFs Using Maximum Entropy in the Mean
Authors: Gzyl Henryk , ter Horst Enrique , Peña-Garcia Nathalie , Torres Andres
Published: October 2023

The security of a network requires the correct identification and characterization of the attacks through its ports. This involves the follow-up of all the requests for access to the networks by all kinds of users. We consider the frequency of connections and the type of connections to a network, and determine their joint probability. This leads to the problem of determining a joint probability distribution from the knowledge of its marginals in the presence of errors of measurement. Mathematically, this consists of an ill-posed linear problem with convex constraints, which we solved by the method of maximum entropy in the mean. This procedure is flexible enough to accommodate errors in the data in a natural way. Also, the procedure is model-free and, hence, it does not require fitting unknown parameters.
Investigating the Inflation–Output Nexus for the Euro Area: Old Questions and New Results
Authors: Reimers Hans-Eggert , Gerdesmeier Dieter , Roffia Barbara
Published: October 2023

The relationship between inflation and real GDP growth is one of the most widely researched topics in macroeconomics. At the same time, it is certainly not an exaggeration to claim that this nexus also stands at the heart of monetary policy, given the fact that low inflation in combination with high and sustained output growth should be the central objective of any sound economic policy. The latter notion becomes even more obvious when taking account of the fact that many central banks all over the world have selected target levels for inflation and communicated them to the public. Against this background, it is of utmost importance for central banks to understand more about the nature and formation of the relationship between inflation and real GDP. This study attempts to shed more light on the specific shape of this relationship for the euro area and, more specifically, on the issue of possible regime shifts therein. The analysis provides strong evidence for non-linear effects in the euro area. As a by-product, and seemingly the novel contribution, of this paper, the methods used allow for the quantification of a switching point across the different regimes. It is found that this breakpoint closely matches the ECB’s previous definitions of price stability and its new inflation target of 2%. While these results look encouraging, further research in this area seems to be warranted.
A Proposed Single-Input Multi-Output Battery-Connected DC–DC Buck–Boost Converter for Automotive Applications
Authors: Tekin Hakan , Setrekli Göknur , Murtulu Eren , Karşıyaka Hikmet , Ertekin Davut
Published: October 2023

In the realm of electric vehicles (EVs), achieving diverse direct current (DC) voltage levels is essential to meet varying electrical load demands. This requires meticulous control of the battery voltage, which must be adjusted in line with specific load characteristics. Therefore, the integration of a well-designed power converter circuit is crucial, as it plays a pivotal role in generating different DC voltage outputs. In this study, we also consider the incorporation of two additional doubler/divider circuits at the end of the proposed converter, further enhancing its capacity to produce distinct DC voltage levels, thus increasing its versatility. The standout feature of the proposed converter lies in its remarkable ability to amplify DC voltages significantly. For instance, when the input battery voltage is set at 48 VDC with a duty cycle (D) of 0.8, the resulting output demonstrates a remarkable augmentation, producing voltages 18, 36, and 72 times higher than the input voltage. Conversely, with a reduced D of 0.2 while maintaining the input voltage at 48 VDC, the converter yields diminished voltages of 0.1875, 0.375, and 0.75 times the initial voltage. This adaptability, based on the parameterization of D, underscores the converter’s ability to cater to a wide range of voltage requirements. To oversee the intricate operations of this versatile converter, a high-speed DSP-based controller system is employed. It utilizes the renowned PID approach, known for its proficiency in navigating complex, nonlinear systems. Experimental results validate the theoretical and simulation findings, reaffirming the converter’s practical utility in EV applications. The study introduces a simple control mechanism with a single power switch, high efficiency for high-power applications, wide voltage range, especially with VDC and VMC cells, and continuous current operation for the load in CCM mode. This study underscores the significance of advanced power conversion systems in shaping the future of electric transportation.
Design of a Collaborative Vehicle Formation Control Simulation Test System
Authors: Xu Zhijing , Zhang Yuqiong , Ding Pengren , Tu Fangze
Published: October 2023

The purpose of this research is to tackle one of the most difficult issues in the realm of self-driving cars, which is the testing of advanced self-driving application scenarios. Thus, this study proposes a simulation testing system based on hardware-in-the-loop simulation technology. This system can enable data exchange between hardware systems as well as replicate and evaluate the algorithmic operations of the equipment under laboratory conditions. The system can integrate scenario simulation software with MATLAB to evaluate algorithm performance. The vehicle formation control system is tailored for collaborative vehicle formation management scenarios and tested in the simulation test system. The findings display the functional integrity of the vehicle formation control system, the reliability of lane changing and the stability and safety of cruising. It additionally demonstrates that the simulation testing system has the ability to recreate cooperative vehicle arrangement management situations and assess their functionality and performance. In forthcoming research, comprehensive functional and performance assessments will be executed on various typical scenarios for advanced autonomous driving applications in order to authenticate the simulation test system’s applicability.
Real-Time Defect Detection Model in Industrial Environment Based on Lightweight Deep Learning Network
Authors: Lu Jiaqi , Lee Soo-Hong
Published: October 2023

Surface defect detection in industrial environments is crucial for quality management and has significant research value. General detection networks, such as the YOLO series, have proven effective in various dataset detections. However, due to the complex and varied surface defects of industrial products, many defects occupy a small proportion of the surface and fall into the category of typical small target detection problems. Moreover, the complexity of general detection network architectures relies on high-tech hardware, making it difficult to deploy on devices without GPUs or on edge computing and mobile devices. To meet the practical needs of industrial product defect inspection applications, this paper proposes a lightweight network specifically designed for defect detection in industrial fields. This network is composed of four parts: a backbone network, a multiscale feature aggregation network, a residual enhancement network, and an attention enhancement network. The network includes a backbone network that integrates attention layers for feature extraction, a multiscale feature aggregation network for semantic information, a residual enhancement network for spatial focus, and an attention enhancement network for global–local feature interaction. These components enhance detection performance for diverse defects while maintaining low hardware requirements. Experimental results show that this network outperforms the latest and most popular YOLOv5n and YOLOv8n models in the five indicators P, R, F1, mAP@.5, and GFLOPS when used on four public datasets. It even approaches or surpasses the YOLOv8s and YOLOv5s models with several times the GFLOPS computation. It balances the requirements of lightweight real-time and accuracy in the scenario of industrial product surface defect detection.
Disentangled Prototypical Graph Convolutional Network for Phishing Scam Detection in Cryptocurrency Transactions
Authors: Buu Seok-Jun , Kim Hae-Jung
Published: October 2023

Blockchain technology has generated an influx of transaction data and complex interactions, posing significant challenges for traditional machine learning methods, which struggle to capture high-dimensional patterns in transaction networks. In this paper, we present the disentangled prototypical graph convolutional network (DP-GCN), an innovative approach to account classification in Ethereum transaction records. Our method employs a unique disentanglement mechanism that isolates relevant features, enhancing pattern recognition within the network. Additionally, we apply prototyping to disentangled representations, to classify scam nodes robustly, despite extreme class imbalances. We further employ a joint learning strategy, combining triplet loss and prototypical loss with a gamma coefficient, achieving an effective balance between the two. Experiments on real Ethereum data showcase the success of our approach, as the DP-GCN attained an F1 score improvement of 32.54%p over the previous best-performing GCN model and an area under the ROC curve (AUC) improvement of 4.28%p by incorporating our novel disentangled prototyping concept. Our research highlights the importance of advanced techniques in detecting malicious activities within large-scale real-world cryptocurrency transactions.
Advancements in Complex Knowledge Graph Question Answering: A Survey
Authors: Song Yiqing , Li Wenfa , Dai Guiren , Shang Xinna
Published: October 2023

Complex Question Answering over Knowledge Graph (C-KGQA) seeks to solve complex questions using knowledge graphs. Currently, KGQA systems achieve great success in answering simple questions, while complex questions still present challenging issues. As a result, an increasing number of novel methods have been proposed to remedy this challenge. In this survey, we proposed two mainstream categories of methods for C-KGQA, which are divided according to their use for knowledge graph representation and construction, namely, graph metric (GM)-Based Methods and graph neural network (GNN)-based methods. Additionally, we also acknowledge the influence of ChatGPT, which has prompted further research into utilizing knowledge graphs as a knowledge source to assist in answering complex questions. We also introduced methods based on pre-trained models and knowledge graph joint reasoning. Furthermore, we have compiled research achievements from the past three years to make it easier for researchers with similar interests to obtain state-of-the-art research. Finally, we discussed the resources and evaluation methods for tackling C-KGQA tasks and summarized several research prospects in this field.
Fault Detection in Solar Energy Systems: A Deep Learning Approach
Authors: Duranay Zeynep Bala
Published: October 2023

While solar energy holds great significance as a clean and sustainable energy source, photovoltaic panels serve as the linchpin of this energy conversion process. However, defects in these panels can adversely impact energy production, necessitating the rapid and effective detection of such faults. This study explores the potential of using infrared solar module images for the detection of photovoltaic panel defects through deep learning, which represents a crucial step toward enhancing the efficiency and sustainability of solar energy systems. A dataset comprising 20,000 images, derived from infrared solar modules, was utilized in this study, consisting of 12 classes: cell, cell-multi, cracking, diode, diode-multi, hot spot, hot spot-multi, no-anomaly, offline-module, shadowing, soiling, and vegetation. The methodology employed the exemplar Efficientb0 model. From the exemplar model, 17,000 features were selected using the NCA feature selector. Subsequently, classification was performed using an SVM classifier. The proposed method applied to a dataset consisting of 12 classes has yielded successful results in terms of accuracy, F1-score, precision, and sensitivity metrics. These results indicate average values of 93.93% accuracy, 89.82% F1-score, 91.50% precision, and 88.28% sensitivity, respectively. The proposed method in this study accurately classifies photovoltaic panel defects based on images of infrared solar modules.
High Accuracy and Wide Range Recognition of Micro AR Markers with Dynamic Camera Parameter Control
Authors: Haraguchi Daisuke , Miyahara Ryu
Published: October 2023

This paper presents a novel dynamic camera parameter control method for the position and posture estimation of highly miniaturized AR markers (micro AR markers) using a low-cost general camera. The proposed method captures images from the camera at each cycle and detects markers from these images. Subsequently, it performs iterative calculations of the marker’s position and posture to converge them to a specified accuracy while dynamically updating the camera’s zoom, focus, and other parameter values based on the detected marker’s depth distances. For a 10 mm square micro AR marker, the proposed system demonstrated recognition accuracy better than ±1.0% for depth distance and 2.5∘ for posture angle, with a maximum recognition range of 1.0 m. In addition, the iterative calculation time was 0.7 s for the initial detection of the marker. These experimental results indicate that the proposed method and system can be applied to the precise robotic handling of small objects at a low cost.
Research on the Enhancement Method of Specific Emitter Open Set Recognition
Authors: Sun Chengyuan , Du Yihang , Qiao Xiaoqiang , Wu Hao , Zhang Tao
Published: October 2023

Open set recognition (OSR) aims at dealing with unknown classes that are not included in the train set. However, existing OSR methods rely on deep learning networks that perform supervised learning on known classes in the train set, resulting in poor performance when the unknown class is very similar to the known class. Considering the subtle individual differences under the same type in specific emitter identification (SEI) applications, it is difficult to distinguish between known classes and unknown classes in open set scenarios. This paper proposes a pseudo signal generation and recognition neural network (PSGRNN) to address relevant problems in this situation. PSGRNN applies complex-value convolution operations to accommodate IQ signal inputs. Its key idea is to utilize samples of known classes to generate pseudo samples of unknown classes. Then, the samples of known classes and the generated pseudo samples of unknown classes are jointly input into the neural network to construct a new classification task for training. Moreover, the center loss is improved by adding inter-class penalties to maximize the inter-class difference. This helps to learn useful information for separating known and unknown classes, resulting in clearer decision boundaries between the known and the unknown. Extensive experiments on various benchmark signal datasets indicate that the proposed method achieves more accurate and robust open set classification results, with an average accuracy improvement of 4.62%.
Enhancement Effect of a Diamond Network on the Flow Boiling Heat Transfer Characteristics of a Diamond/Cu Heat Sink
Authors: Wu Nan , Sun Mingmei , Guo Hong , Xie Zhongnan , Du Shijie
Published: October 2023

The use of a micro heat sink is an effective means of solving the problem of high-power chip heat dissipation. Diamond/Cu composites exhibit high thermal conductivity and a linear thermal expansion coefficient that is compatible with semiconductor materials, rendering them ideal micro heat sink materials. The aim of this study was to fabricate diamond/Cu and Cu separately as heat sinks and subject them to flow boiling heat transfer experiments. The results indicate that the diamond/Cu heat sink displayed a decrease in wall superheat of 10.2–14.5 °C and an improvement in heat transfer coefficient of 38–51% compared with the Cu heat sink under identical heat fluxes. The heat sink also exhibits enhanced thermal uniformity. Secondary diamond particles are incorporated into the gaps of the main diamonds, thereby constructing a three-dimensional heat conduction network within the composite material. The diamond network enhances the internal heat flux of the material while also creating more nucleation sites on the surface. These increase the boiling intensity of the diamond/Cu heat sink, leading to better heat transfer performance. By combining the transient thermal model with computational fluid dynamics, a heat transfer model based on the diamond/Cu heat sink is proposed. The efficient heat dissipation capability of diamond/Cu heat sinks can lower the working temperature of microelectronic devices, thereby improving device performance and reliability during operation.
Assessing the Potential of Urban Trees to Accumulate Potentially Toxic Elements: A Network Approach
Authors: Matić Marija , Pavlović Dragana , Perović Veljko , Čakmak Dragan , Kostić Olga , Mitrović Miroslava , Pavlović Pavle
Published: October 2023

In urbanized areas, mitigating the negative effects of pollutants from various anthropogenic sources is one of the most important issues in planning urban functioning and development. In this sense, urban vegetation plays one of the most important roles. The aim of this study was to investigate the performance of network analysis (NA) as a novel and potential method for determining different associations between potentially toxic elements (PTEs) in leaves of urban trees, their accumulation capacity and ecophysiological response to different types of pollution in urban environments. The results of NA showed that there is no association between elements in species that have lower or higher efficiency in uptake of PTEs, leading to the conclusion that the elements do not depend on mutual association but on accumulation itself. It was also found that there are differences in the content of photosynthetic pigments and carotenoids among the studied species, but these differences are not reflected in the values of the photosynthetic efficiency parameters. Overall, the studied species have good ecophysiological potential for growth and existence in the urban environment, despite the varying ability to accumulate elements and the different associations between them. This is the first study to investigate the interactions between PTEs in leaves of urban tree species using NA and provides a good basis for future research under different environmental conditions.
Plant Species Richness and Invasional Meltdown in Different Parts of Acer negundo L. Secondary Range
Authors: Dubrovin Denis I. , Veselkin Denis V. , Gusev Andrei P.
Published: October 2023

To understand the alien plant invasion patterns, it is important to know if their consequences are equal in different regions, particularly in different parts of the secondary range. In this article, we estimated plant species richness in communities invaded by the North American tree Acer negundo L. in two remote regions: the Belarusian Polesia and the Middle Urals. We tested three hypotheses about: (1) decreased plant species richness in communities invaded by A. negundo; (2) presence of alien species in invaded communities—invasional meltdown hypothesis; and (3) different alien plant species richness in communities of different regions. In each region, 24 sample plots of 400 m2 were described: 12 invaded and 12 non-invaded by A. negundo. The species richness of invaded plots decreased: total richness decreased by 21%–43%; the richness of herbaceous plants decreased by 24%–43%; and woody richness decreased by 8%–44%. The proportion of alien herbs in plots invaded by A. negundo increased by 35%. This is the first, although not exhaustive, confirmation of the invasional meltdown hypothesis for communities invaded by A. negundo. Alien herbs increasingly invaded communities of the Belarusian Polesia, alien trees—communities of the Middle Urals. Thus, regional geographical and floristic conditions should be considered when assessing the invasion consequences.
The Effect of Terminal Freezing and Thawing on the Quality of Frozen Dough: From the View of Water, Starch, and Protein Properties
Authors: Liu Xiaorong , Chen Luncai , Chen Lei , Liu Dezheng , Liu Hongyan , Jiang Dengyue , Fu Yang , Wang Xuedong
Published: October 2023

Frozen dough is suitable for industrial cold chain transportation, but usually experiences temperature fluctuations through the cold chain to the store after being refrigerated in a factory, seriously damaging the product yield. In order to analyze the influence mechanism of temperature fluctuation during the terminal cold chain on frozen dough, the effects of terminal freezing and thawing (TFT) on the quality (texture and rheology) and component (water, starch, protein) behaviors of dough were investigated. Results showed that the TFT treatment significantly increased the hardness and decreased the springiness of dough and that the storage modules were also reduced. Furthermore, TFT increased the content of freezable water and reduced the bound water with increased migration. Additionally, the peak viscosity and breakdown value after TFT with the increased number of cycles were also increased. Moreover, the protein characteristics showed that the low-molecular-weight region and the β-sheet in the gluten secondary structure after the TFT treatment were increased, which was confirmed by the increased number of free sulfhydryl groups. Microstructure results showed that pores and loose connection were observed during the TFT treatment. In conclusion, the theoretical support was provided for understanding and eliminating the influence of the terminal nodes in a cold chain.
The Effect of Multistage Refinement on the Bio-Physico-Chemical Properties and Gel-Forming Ability of Fish Protein Isolates from Mackerel (Rastrelliger kanagurta)
Authors: Somjid Panumas , Chaijan Manat , Rawdkuen Saroat , Grossmann Lutz , Panpipat Worawan
Published: October 2023

The objective of this research was to improve the protein extraction processes of Rastrelliger kanagurta (Indian mackerel) to generate protein isolate with enhanced bio-physico-chemical properties and gel-forming ability. To achieve this, two novel approaches were designed that utilized an additional alkaline separation step and were compared to a conventional process: acid solubilization → alkaline solubilization → pI and acid solubilization → pI → alkaline solubilization. The novel extraction designs resulted in a lower lipid content, lipid oxidation, and TCA-soluble peptides, as well as improving the color and sensory features of the refined proteins, which corresponded to the lowest total heme pigments (p < 0.05). Furthermore, the protein isolate recovered with the modified processes showed significant changes in biochemical properties (decreases in Ca2+-ATPase activity/reactive sulfhydryl content and an increase in surface hydrophobicity) and dynamic rheological behavior. As a result, by altering the extraction procedure it was possible to obtain improved gel characteristics such as gel strength, color, expelled moisture, and improved gel microstructure. Moreover, this study demonstrated that the gel network was partly stabilized by disulfide bonds, according to SDS-PAGE. Overall, this study demonstrates that by optimizing protein extraction procedures a considerable improvement in quality can be achieved and that an additional alkaline extraction after isoelectric point precipitation results in the optimized gel-forming ability of mackerel proteins.
The Rapid Detection of Salmonella enterica, Listeria monocytogenes, and Staphylococcus aureus via Polymerase Chain Reaction Combined with Magnetic Beads and Capillary Electrophoresis
Authors: Ndraha Nodali , Lin Hung-Yun , Tsai Shou-Kuan , Hsiao Hsin-I , Lin Han-Jia
Published: October 2023

Food safety concerns regarding foodborne pathogen contamination have gained global attention due to its significant implications. In this study, we developed a detection system utilizing a PCR array combined with an automated magnetic bead-based system and CE technology to enable the detection of three foodborne pathogens, namely Salmonella enterica, Listeria monocytogenes, and Staphylococcus aureus. The results showed that our developed method could detect these pathogens at concentrations as low as 7.3 × 101, 6.7 × 102, and 6.9 × 102 cfu/mL, respectively, in the broth samples. In chicken samples, the limit of detection for these pathogens was 3.1 × 104, 3.5 × 103, and 3.9 × 102 cfu/g, respectively. The detection of these pathogens was accomplished without the necessity for sample enrichment, and the entire protocols, from sample preparation to amplicon analysis, were completed in approximately 3.5 h. Regarding the impact of the extraction method on detection capability, our study observed that an automated DNA extraction system based on the magnetic bead method demonstrated a 10-fold improvement or, at the very least, yielded similar results compared to the column-based method. These findings demonstrated that our developed model is effective in detecting low levels of these pathogens in the samples analyzed in this study. The PCR-CE method developed in this study may help monitor food safety in the future. It may also be extended to identify other foodborne pathogens across a wide range of food samples.
Recent Trends in Cereal- and Legume-Based Protein-Mineral Complexes: Formulation Methods, Toxicity, and Food Applications
Authors: Jindal Aprajita , Patil Nikhil , Bains Aarti , Sridhar Kandi , Stephen Inbaraj Baskaran , Tripathi Manikant , Chawla Prince , Sharma Minaxi
Published: October 2023

Minerals play an important role in maintaining human health as the deficiency of these minerals can lead to serious health issues. To address these deficiencies, current research efforts are actively investigating the utilization of protein-mineral complexes as eco-friendly, non-hazardous, suitable mineral fortifiers, characterized by minimal toxicity, for incorporation into food products. Thus, we reviewed the current challenges in incorporating the cereal-legume protein-inorganic minerals complexes’ structure, binding properties, and toxicity during fortification on human health. Moreover, we further reviewed the development of protein-mineral complexes, characterization, and their food applications. The use of inorganic minerals has been associated with several toxic effects, leading to tissue-level toxicity. Cereal- and legume-based protein-mineral complexes effectively reduced the toxicity, improved bone mineral density, and has antioxidant properties. The characterization techniques provided a better understanding of the binding efficiency of cereal- and legume-based protein-mineral complexes. Overall, understanding the mechanism and binding efficiency underlying protein-mineral complex formation provided a novel insight into the design of therapeutic strategies for mineral-related diseases with minimal toxicity.
Unlocking the Potential of Sprouted Cereals, Pseudocereals, and Pulses in Combating Malnutrition
Authors: Majzoobi Mahsa , Wang Ziyu , Teimouri Shahla , Pematilleke Nelum , Brennan Charles Stephen , Farahnaky Asgar
Published: October 2023

Due to the global rise in food insecurity, micronutrient deficiency, and diet-related health issues, the United Nations (UN) has called for action to eradicate hunger and malnutrition. Grains are the staple food worldwide; hence, improving their nutritional quality can certainly be an appropriate approach to mitigate malnutrition. This review article aims to collect recent information on developing nutrient-dense grains using a sustainable and natural process known as “sprouting or germination” and to discuss novel applications of sprouted grains to tackle malnutrition (specifically undernutrition). This article discusses applicable interventions and strategies to encourage biochemical changes in sprouting grains further to boost their nutritional value and health benefits. It also explains opportunities to use spouted grains at home and in industrial food applications, especially focusing on domestic grains in regions with prevalent malnutrition. The common challenges for producing sprouted grains, their future trends, and research opportunities have been covered. This review article will benefit scientists and researchers in food, nutrition, and agriculture, as well as agrifood businesses and policymakers who aim to develop nutrient-enriched foods to enhance public health.
Fractional Simpson-like Inequalities with Parameter for Differential s-tgs-Convex Functions
Authors: Merad Meriem , Meftah Badreddine , Boulares Hamid , Moumen Abdelkader , Bouye Mohamed
Published: October 2023

In this paper, we first prove a new parameterized identity. Based on this identity we establish some parametrized Simpson-like type symmetric inequalities, for functions whose first derivatives are s-tgs-convex via Reimann–Liouville frational operators. Some special cases are discussed. Applications to numerical quadrature are provided.
On a Special Two-Person Dynamic Game
Authors: Matsumoto Akio , Szidarovszky Ferenc , Hamidi Maryam
Published: October 2023

The asymptotical properties of a special dynamic two-person game are examined under best-response dynamics in both discrete and continuos time scales. The direction of strategy changes by the players depend on the best responses to the strategies of the competitors and on their own strategies. Conditions are given first for the local asymptotical stability of the equilibrium if instantaneous data are available to the players concerning all current strategies. Next, it is assumed that only delayed information is available about one or more strategies. In the discrete case, the presence of delays has an effect on only the order of the governing difference equations. Under continuous scales, several possibilities are considered: each player has a delay in the strategy of its competitor; player 1 has identical delays in both strategies; the players have identical delays in their own strategies; player 1 has different delays in both strategies; and the players have different delays in their own strategies. In all cases, it is assumed that the equilibrium is asymptotically stable without delays, and we examine how delays can make the equilibrium unstable. For small delays, the stability is preserved. In the cases of one-delay models, the critical value of the delay is determined when stability changes to instability. In the cases of two and three delays, the stability-switching curves are determined in the two-dimensional space of the delays, where stability becomes lost if the delay pair crosses this curve. The methodology is different for the one-, two-, and three-delay cases outlined in this paper.
Re-Search on the Hyphen: (Re)writing the Fragmented Self within Contexts of Displacement
Authors: Fadel Lina
Published: October 2023

In responding to the call for exploring and explicating aspects of the research process that remain unspoken about in most social science fields, this narrative asks deceptively simple questions: what does it mean to carry out research as an academic with a lived experience of displacement, loss and pain? What are the methodological choices available to me as a migrant scholar? What does it really mean to write (about) the displaced-turned-emplaced self from the margin—myself being a case in point—within contexts of loss and displacement? My aim is to present a personal narrative that is uniquely mine, a story that may work with or against what is thought to be the official story. I defend the use of fragments, theoretically and methodologically, to avoid the homogenisation of narratives and assumptions about how research is carried out, how knowledge(s) are produced and reproduced, and who has the power to produce them. Thus, building on established scholarship cutting across various fields and guided by postcolonial and postmodernist theories, I hope to unpack the tensions and possibilities inherent in thinking about borders and positionality in academia (when the researcher dwells at the margins), identity, its fragmentation, and its entanglement with questions of decoloniality, narrative and voice.
The Bangladeshi Diaspora in the United States: History and Portrait
Authors: Akhter Morsheda , Yang Philip Q.
Published: October 2023

Despite the rapid growth of the Bangladeshi diaspora in the USA, knowledge about this new diasporic community remains very limited. This study argues and demonstrates that the Bangladeshi diaspora in the USA is a fast-growing and sizable diasporic community that requires systematic research and better understanding. It delineates the history of the Bangladeshi diaspora to the USA in four periods and documents the phenomenal growth of the Bangladeshi diasporic community in the USA since 1981, using data from the US Department of Homeland Security (DHS). By taking into account the legal Bangladeshi immigration as well as the emigration and mortality rates of immigrants and undocumented Bangladeshi immigration, it estimates the current size of the Bangladeshi diasporic community in the USA at about 500,000 instead of a range of low-to-mid 200,000s normally cited. Additionally, using the pooled samples of the 2001–2019 American Community Surveys (ACS) and other ACS data, as well as the DHS data, this paper provides a demographic and socioeconomic portrait of the Bangladeshi diasporic community in the USA. The findings are generalizable to the population and fill some important gaps in the literature.
Sex Differences in the Efficacy of Mediterranean Diet Treatment: A Nutrigenomics Pilot Study
Authors: Di Renzo Laura , Gualtieri Paola , Frank Giulia , De Santis Gemma Lou , Cianci Rossella , Bigioni Giulia , De Lorenzo Antonino
Published: October 2023

The Mediterranean diet (MedD) has been shown to have beneficial effects on health, well-being, and mental status. It potentially modulates gene expressions linked to oxidative stress, contributing to its beneficial effects on overall health. The aim of this study was to assess the effects of MedD treatment in healthy human volunteers on the expression of ten genes related to oxidative stress and inflammation in women and men. Of 30 enrolled subjects, 17 were eligible, 10 women and 7 men. All of them received the same MedD treatment. Before and after 8 weeks of MedD treatment, an evaluation of body composition, blood tests, and anthropometric and clinical parameters was performed. Furthermore, 10 genes were amplified and analyzed. The study showed significant differences between females and males in body composition and biochemical parameters before and after MedD treatment. Significant differences between females and males in Resistance Force (p < 0.009) and Diastolic Blood Pressure (p < 0.04) before MedD treatment, and in High-Density Lipoprotein (p < 0.02) after MedD treatment, were observed. Moreover, a significant upregulation of Apolipoprotein E and Angiotensin I-Converting Enzyme in females has been shown. Sex differences impact MedD treatment response, and influence the genetic expression of genes related to oxidative stress; our findings may help to personalize diet therapy and contribute to overall health and well-being.
Double Heterozygous Pathogenic Variants in the LOX and PKD1 Genes in a 5-Year-Old Patient with Thoracic Aortic Aneurysm and Polycystic Kidney Disease
Authors: Ponińska Joanna Kinga , Pelczar-Płachta Weronika , Pollak Agnieszka , Jończyk-Potoczna Katarzyna , Truszkowska Grażyna , Michałowska Ilona , Szafran Emilia , Bilińska Zofia T. , Bobkowski Waldemar , Płoski Rafał
Published: October 2023

Familial thoracic aortic aneurysms and dissections may occur as an isolated hereditary trait or as part of connective tissue disorders with Mendelian inheritance, but severe cardiovascular disease in pediatric patients is extremely rare. There is growing knowledge on pathogenic variants causing the disease; however, much of the phenotypic variability and gene–gene interactions remain to be discovered. We present a case report of a 5.5-year-old girl with an aortic aneurysm and concomitant polycystic kidney disease. Whole exome sequencing was performed, followed by family screening by amplicon deep sequencing and diagnostic imaging studies. In the proband, two pathogenic variants were identified: p.Tyr257Ter in the LOX gene inherited from her mother, and p.Thr2977Ile in the PKD1 gene inherited from her father. All adult carriers of either of these variants showed symptoms of aortic disease. We conclude that the coexistence of two independent genetic variants in the proband may be the reason for an early onset of disease.
Ionospheric Total Electron Content (TEC) Anomalies as Earthquake Precursors: Unveiling the Geophysical Connection Leading to the 2023 Moroccan 6.8 Mw Earthquake
Authors: Nayak Karan , López-Urías Charbeth , Romero-Andrade Rosendo , Sharma Gopal , Guzmán-Acevedo German Michel , Trejo-Soto Manuel Edwiges
Published: October 2023

The study delves into the relationship between ionospheric total electron content (TEC) anomalies and seismic activity, with a focus on Morocco’s 6.8 Mw earthquake on 8 September 2023, lying within a tectonically active region at the convergence of the African and Eurasian Plates. To enhance the reliability of our findings, we incorporate space weather conditions, utilizing indices (Dst, Kp, and F10.7) to pinpoint periods of stable space weather. This minimizes the possibility of erroneously attributing natural ionospheric fluctuations to seismic events. Notably, our TEC analysis unveils positive and negative anomalies, with some occurring up to a week before the earthquake. These anomalies, exceeding predefined thresholds, provide compelling evidence of significant deviations from typical ionospheric conditions. Spatial mapping techniques employing both station-specific vTEC data and pseudorandom noise codes (PRNs) from multiple global navigation satellite system (GNSS) stations highlight a strong correlation between ionospheric anomalies and the earthquake’s epicenter. The integration of PRNs enhances coverage and sensitivity to subtle anomalies. Additionally, the analysis of satellite imagery and ground displacement data using Sentinel-1 confirms significant ground uplift of approximately 15 cm following the earthquake, shedding light on surface responses to seismic events. These findings underscore the potential of ionospheric science in advancing earthquake early warning systems and deepening our understanding of earthquake precursors, thus contributing to the mitigation of seismic event impacts and the protection of lives and infrastructure.
Basin Structure for Earthquake Ground Motion Estimates in Urban Los Angeles Mapped with Nodal Receiver Functions
Authors: Ghose Ritu , Persaud Patricia , Clayton Robert W.
Published: October 2023

We constrained sedimentary basin structure using a nodal seismic array consisting of ten dense lines that overlie multiple basins in the northern Los Angeles area. The dense array consists of 758 seismic nodes, spaced ~250–300 m apart along linear transects, that recorded ground motions for 30–35 days. We applied the receiver function (RF) technique to 16 teleseismic events to investigate basin structure. Primary basin-converted phases were identified in the RFs. A shear wave velocity model produced in a separate study using the same dataset was incorporated to convert the basin time arrivals to depth. The deepest part of the San Bernardino basin was identified near the Loma Linda fault at a depth of 2.4 km. Basin depths identified at pierce points for separate events reveal lateral changes in basin depth across distances of ~2–3 km near individual stations. A significant change in basin depth was identified within a small distance of ~4 km near the San Jacinto fault. The San Gabriel basin exhibited the largest basin depths of all three basins, with a maximum depth of 4.2 km. The high lateral resolution from the dense array helped to reveal more continuous structures and reduce uncertainties in the RFs interpretation. We discovered a more complex basin structure than previously identified. Our findings show that the basins’ core areas are not the deepest, and significant changes in basin depth were observed near some faults, including the San Jacinto fault, Fontana fault, Red Hill fault and Indian Hill fault.
Neuromyelitis Optica Spectrum Disorder with Sick Sinus Syndrome: Two Cases and a Literature Review
Authors: Lin Huiting , Duan Xinyi , Li Lina , Ye Jinhao , Xiao Haibing
Published: October 2023

Background and objective: Neuromyelitis optica spectrum disorder (NMOSD) is a rare immune-mediated demyelinating disease of the central nervous system (CNS). There is a lack of reports of sick sinus syndrome (SSS) associated with NMOSD; thus, we hereby report two cases of patients with NMOSD who developed SSS. Cases presentation: The patients were both male and presented with area postrema syndrome. Brain MRI showed lesions in the dorsal part of their medulla oblongata. They were diagnosed with NMOSD when aquaporin-4 antibodies were found in their serum. Slow heart rates and several episodes of syncope were also observed in case 1 during hospitalization, while Holter monitoring showed sinus pauses (10–11 s) and SSS was diagnosed. A pacemaker was fitted. Case 2 had a respiratory arrest followed by a subsequent cardiac arrest. He was successfully resuscitated with epinephrine injection and cardiopulmonary resuscitation. Through immunotherapy, their neurological functions became stable and heart rate and blood pressure returned to the baseline. Conclusions: Since sick sinus syndrome is a life-threatening complication, serious heart arrhythmias should be considered as a potential result of area postrema syndrome associated with NMOSD.