bluuebunny/crossref_metadata_2025 huggingface dataset:
Low-Volume Electrochemical Sensor Platform for Direct Detection of Paraquat in Drinking Water
Authors: Poudyal Durgasha C. , Samson Manish , Dhamu Vikram Narayanan , Mohammed Sera , Tanchez Claudia N. , Puri Advaita , Baby Diya , Muthukumar Sriram , Prasad Shalini
Published: August 2024

Direct testing of pesticide contaminants in drinking water is a challenge. Portable and sensitive sensor platforms are desirable to test water contaminants directly at farm and consumer levels. In this study, we have demonstrated the feasibility of an electrochemical sensor for the direct detection of paraquat (PQ) in drinking water samples. An immunoassay-based sensing platform was fabricated using PQ-specific antibody immobilized on the surface of the electrochemically reduced graphene oxide (rGO) modified screen-printed carbon electrode (rGO-SPCE). Using non-faradaic electrochemical impedance spectroscopy (EIS) as a detection tool, the sensor platform demonstrated a dynamic response for PQ concentration in drinking water ranging from 0.05 ng/mL to 72.9 ng/mL (0.19 to 243.8 nM), with a coefficient of determination (r2) of 0.997 and a limit of detection of 0.05 ng/mL (0.19 nM). Percentage recovery within ±20% error was obtained, and the sensor cross-reactivity test showed a selective response against glyphosate antigen. With the flexibility to use single-frequency EIS and low sample volume, the developed sensor demonstrated testing in water samples directly without any sample pre-processing. This low-volume electroanalytical sensor platforms can be translated into portable testing tools for the detection of various water contaminants.
Learning by Demonstration of a Robot Using One-Shot Learning and Cross-Validation Regression with Z-Score
Authors: Duque-Domingo Jaime , García-Gómez Miguel , Zalama Eduardo , Gómez-García-Bermejo Jaime
Published: August 2024

We introduce a One-Shot Learning system where a robot effectively learns how to manipulate objects by relying solely on the object’s name, a single image, and a visual example of a person picking it up. Once the robot has mastered picking up a new object, an audio command is all that is needed to prompt it to perform the action. Our approach heavily depends on synthetic data generation, which is crucial for training various detection and regression models. Additionally, we introduce a novel combined regression model called Cross-Validation Regression with Z-Score (CVR-ZS), which improves the robot’s grasp accuracy. The system also features a classifier that uses a cutting-edge text-encoding technique, allowing for flexible user prompts for object retrieval. The complete system includes a text encoder and classifier, an object detector, and the CVR-ZS regressor. This setup has been validated with a Niryo Ned robot.
Isolated Gate Driver for Medium Voltage Applications Using a Single Structure
Authors: Miraglia Dante , Aguilar Carlos , Arau Jaime
Published: August 2024

According to the International Electrotechnical Commission, medium voltage ranges from 1 kV to 36 kV. In this voltage range, the field of power electronics has been focusing on developing power converters with high efficiency. Converters for such applications include solid-state transformers, energy storage systems for vehicle charging, electric aircraft, etc. Power ranges could reach tens to hundreds of kilowatts at relatively high frequency (10–50 kHz). Currently, there are no high-frequency power semiconductors capable of switching these voltage levels. Instead of using a single power switch, a string of power switches is used. The upper switches in the string require special attention because they need the highest isolation capabilities and a floating control signal and power supply for the gate driver. Many techniques have been proposed to accomplish this, but they commonly use separate circuits for the control signal and the power supply, increasing the cost, size, and complexity of the gate driver. This paper presents a gate driver for medium voltage with high-voltage isolation capabilities in a single structure for the control signal and the power supply. The proposed gate driver uses a resonant converter that transmits power within the gate driver information. A demodulator separates the gate driver information from the power signal, obtaining the power supply and the control signal for the switch. The paper includes simulation and experimental results that demonstrate the viability of the proposal. The experimental results show the principal features of the gate driver, achieving improvements in complexity, isolation capabilities, and both rise and fall times for large input capacitances of power semiconductor switches. The proposed gate driver presents a rise time of 44 ns and a fall time of 46 ns for the gate input capacitance of currently available SiC MOSFETs. The isolation barrier uses a 25 mm air gap, achieving an isolation capability of approximately 68.2 kV, which exceeds the requirements for MV applications.
Research on Characteristics Matching of Micro-LED Devices
Authors: Chen Yufeng , Zheng Xifeng , Mao Xinyue , Cao Hui , Wang Yang , Xu Zicheng , Chen Junchang , Liu Fengxia , Huang Deju , Chen Yu
Published: August 2024

This paper presents the design of a 40 × 40 micro-light-emitting (micro-LED) test array based on a 20 mm × 20 mm substrate. A study of the relationship between luminous brightness, driving current, and driving voltage revealed that the data voltages of the red, green, and blue micro-LED array from 0 to 255 grey levels are 0.31 V, 0.29 V, and 0.30 V, respectively, under the condition that the target brightness of the white field is 1000 nits and the color temperature is 9300 K. The brightness range of the red micro-LED array is 64.8–101.2%, the brightness range of the green micro-LED array is 66.5–102.8%, and the brightness range of the blue micro-LED array is 53.5–129.2%. In order to overcome the luminance nonuniformity, a grey level depth of 12 -bit is required. A 10T3C pixel driver circuit based on low-temperature polysilicon (LTPS) with a depth of 12 bits greyscale is designed and fabricated into the micro-LED display. A brightness uniformity of 84.1–97.1% can be achieved by brightness correction combined with a 12-bit greyscale depth system for micro-LED display. This provides a valuable reference point for subsequent improvements in the quality of micro-LED displays.
Underwater Robot Target Detection Algorithm Based on YOLOv8
Authors: Song Guangwu , Chen Wei , Zhou Qilong , Guo Chenkai
Published: August 2024

Although the ocean is rich in energy and covers a vast portion of the planet, the present results of underwater target identification are not sufficient because of the complexity of the underwater environment. An enhanced technique based on YOLOv8 is proposed to solve the problems of low identification accuracy and low picture quality in the target detection of current underwater robots. Firstly, considering the issue of model parameters, only the convolution of the ninth layer is modified, and the deformable convolution is designed to be adaptive. Certain parts of the original convolution are replaced with DCN v3, in order to address the issue of the deformation of underwater photos with fewer parameters and more effectively capture the deformation and fine details of underwater objects. Second, the ability to recognize multi-scale targets is improved by employing SPPFCSPC, and the ability to express features is improved by combining high-level semantic features with low-level shallow features. Lastly, using WIoU loss v3 instead of the CIoU loss function improves the overall performance of the model. The enhanced algorithm mAP achieves 86.5%, an increase of 2.1% over the YOLOv8s model, according to the results of the testing of the underwater robot grasping. This meets the real-time detection needs of underwater robots and significantly enhances the performance of the object detection model.
Evaluating Photovoltaic Conversion Performance under Artificial Indoor Lighting
Authors: Guillén Cecilia
Published: August 2024

Several photovoltaic technologies, based on different semiconductor absorbers with band-gap energy in the range Eg = 1.0–1.5 eV are currently sharing the market for outdoor applications. These photovoltaic cells are designed to achieve an optimal photovoltaic conversion under solar illumination (represented by the standard AM1.5 global spectrum), but their performance changes under different artificial indoor lights. Here, the detailed balance principle that was first applied for an ideal photovoltaic absorber under solar radiation is now used by considering the actual spectra of four typical indoor lamps: incandescent, halogen, metal halide and white LED. For each particular illumination source, the theoretical maximum for short-circuit current, open-circuit voltage and maximum power point have been calculated and represented as a function of the absorber band-gap energy. Furthermore, the optical absorption spectra of some semiconductors with optimal solar conversion efficiencies are used to estimate their comparative performance under the various artificial light sources. It has been found that wide band-gap absorbers (Eg~1.9 eV) are needed to achieve a light-to-electricity conversion efficiency of 60% under LED illumination or 31% with metal halide bulbs, while a lowest band-gap energy of about 0.8 eV is required to obtain a maximum efficiency of 24% with incandescent and halogen lamps.
Design of a PID Controller for Microbial Fuel Cells Using Improved Particle Swarm Optimization
Authors: Wang Chenlong , Zhu Baolong , Ma Fengying , Sun Jiahao
Published: August 2024

The microbial fuel cell (MFC) is a renewable energy technology that utilizes the oxidative decomposition processes of anaerobic microorganisms to convert the chemical energy in organic matter, such as wastewater, sediments, or other biomass, into electrical power. This technology is not only applicable to wastewater treatment but can also be used for resource recovery from various organic wastes. The MFC usually requires an external controller that allows it to operate under controlled conditions to obtain a stable output voltage. Therefore, the application of a PID controller to the MFC is proposed in this paper. The design phase for this controller involves the identification of three parameters. Although the particle swarm optimization (PSO) algorithm is an advanced optimization algorithm based on swarm intelligence, it suffers from issues such as unreasonable population initialization and slow convergence speed. Therefore, this paper proposes an improved particle swarm algorithm based on the Golden Sine Strategy (GSCPSO). Using Circle chaotic mapping to make the distribution of the initial population more uniform, and then using the Golden Sine Strategy to improve the position update formula, not only improves the convergence speed of the population but also enhances convergence precision. The GSCPSO algorithm is applied to execute the described design process. The results of the simulation show that the designed control method exhibits smaller steady-state error, overshoot, and chattering compared with sliding-mode control (SMC), backstepping control, fuzzy SMC (FSMC), PSO-PID, and CPSO-PID.
An Area-Efficient and Configurable Number Theoretic Transform Accelerator for Homomorphic Encryption
Authors: Huang Jingwen , Kuo Chiayi , Liu Sihuang , Su Tao
Published: August 2024

Homomorphic Encryption (HE) allows for arbitrary computation of encrypted data, offering a method for preserving privacy in cloud computations. However, efficiency remains a significant obstacle, particularly with the polynomial multiplication of large parameter sets, which occupies substantial computing and memory overhead. Prior studies proposed the use of Number Theoretic Transform (NTT) to accelerate polynomial multiplication, which proved efficient, owing to its low computational complexity. However, these efforts primarily focused on NTT designs for small parameter sets, and configurability and memory efficiency were not considered carefully. This paper focuses on designing a unified NTT/Inverse NTT (INTT) architecture with high area efficiency and configurability, which is more suitable for HE schemes. We adopt the Constant-Geometry (CG) NTT algorithm and propose a conflict-free access pattern, demonstrating a 16.7% reduction in coefficients of storage capacity compared to the state-of-the-art CG NTT design. Additionally, we propose a twiddle factor generation strategy to minimize storage for Twiddle Factors (TFs). The proposed architecture offers configurability of both compile time and runtime, allowing for the deployment of varying parallelism and parameter sets during compilation while accommodating a wide range of polynomial degrees and moduli after compilation. Experimental results on the Xilinx FPGA show that our design can achieve higher area efficiency and configurability compared with previous works. Furthermore, we explore the performance difference between precomputed TFs and online-generated TFs for the NTT architecture, aiming to show the importance of online generation-based NTT architecture in HE applications.
Gas Sensor Physically Unclonable Function-Based Lightweight Bidirectional Authentication Protocol for Gas Sensor Networks
Authors: Hu Shuang , Wang Pengjun , He Haonan , Zhang Yuejun , Li Xiangyu
Published: August 2024

In gas sensor networks, users can access the data collected by the sensor nodes, but there is a risk of data leakage during transmission. This paper proposes a lightweight bidirectional authentication protocol based on gas sensor physically unclonable functions (GS-PUFs) with authentication technology to guarantee the reliability of data from sensor nodes. A sensor PUF array is constructed by preparing gas sensors to enhance the data security of the physical layer and reduce hardware resource consumption. The authentication part of the protocol mainly uses lightweight encryption methods, consisting of PUF data, one-way cryptographic hash functions, and iso-or functions, to reduce the computational overhead of the authentication process. The protocol security is enhanced by encrypting the GS-PUF response as an irreversible hash value and verifying the hash value by the user, server, and sensor node to complete bidirectional authentication. The test results demonstrate that the protocol, verified through the ProVerif formal tool, can resist impersonation, replay, node tampering, and cloning attacks. Among the compared schemes, this protocol offers the highest security and the least resource overhead, making it effectively applicable in the Internet of Things and other fields.
Bayesian Modeling of Travel Times on the Example of Food Delivery: Part 1—Spatial Data Analysis and Processing
Authors: Gibas Justyna , Pomykacz Jan , Baranowski Jerzy
Published: August 2024

Online food delivery services are rapidly growing in popularity, making customer satisfaction critical for company success in a competitive market. Accurate delivery time predictions are key to ensuring high customer satisfaction. While various methods for travel time estimation exist, effective data analysis and processing are often overlooked. This paper addresses this gap by leveraging spatial data analysis and preprocessing techniques to enhance the data quality used in Bayesian models for predicting food delivery times. We utilized the OSRM API to generate routes that accurately reflect real-world conditions. Next, we visualized these routes using various techniques to identify and examine suspicious results. Our analysis of route distribution identified two groups of outliers, leading us to establish an appropriate boundary for maximum route distance to be used in future Bayesian modeling. A total 3% of the data were classified as outliers, and 15% of the samples contained invalid data. The spatial analysis revealed that these outliers were primarily deliveries to the outskirts or beyond the city limits. Spatial analysis shows that the Indian OFD market has similar trends to the Chinese and English markets and is concentrated in densely populated areas. By refining the data quality through these methods, we aim to improve the accuracy of delivery time predictions, ultimately enhancing customer satisfaction.
3D-CNN Method for Drowsy Driving Detection Based on Driving Pattern Recognition
Authors: Lee Jimin , Woo Soomin , Moon Changjoo
Published: August 2024

Drowsiness impairs drivers’ concentration and reaction time, doubling the risk of car accidents. Various methods for detecting drowsy driving have been proposed that rely on facial changes. However, they have poor detection for drivers wearing a mask or sunglasses, and they do not reflect the driver’s drowsiness habits. Therefore, this paper proposes a novel method to detect drowsy driving even with facial detection obstructions, such as masks or sunglasses, and regardless of the driver’s different drowsiness habits, by recognizing behavioral patterns. We achieve this by constructing both normal driving and drowsy driving datasets and developing a 3D-CNN (3D Convolutional Neural Network) model reflecting the Inception structure of GoogleNet. This binary classification model classifies normal driving and drowsy driving videos. Using actual videos captured inside real vehicles, this model achieved a classification accuracy of 85% for detecting drowsy driving without facial obstructions and 75% for detecting drowsy driving when masks and sunglasses are worn. Our results demonstrate that the behavioral pattern recognition method is effective in detecting drowsy driving.
Exploration of Traffic Accident-Based Pilot Zones for Autonomous Vehicle Safety Validation
Authors: Kim Siyoon , Cho Minje , Lee Yonggeol
Published: August 2024

Recently, the commercialization of autonomous vehicles has increased the importance of verifying vehicle safety through autonomous trials. Autonomous driving trials are conducted in limited areas within artificially constructed test roads and pilot districts and directly explore road sections and areas with similar environments to ensure the safety of AVs driving on real roads. Many previous studies have evaluated the complex response potential of AVs by deriving edge scenarios to ensure their safety. However, difficulties arise in exploring real roads with traffic accident factors and configurations similar to those in edge scenarios, making validation on real roads uncertain. This paper proposes a novel method for exploring pilot zones using traffic accident data to verify the safety of autonomous vehicles (AVs). The method employs a CNN + BiGRU model trained on DMV dataset data to classify traffic accidents as AV- or human-caused. The model’s classification accuracy was evaluated using recall, precision, F1 score, and accuracy, achieving 100.0%, 97.8%, 98.9, and 99.5%, respectively. The trained model was applied to the KNPA dataset, identifying 562 out of 798 cases as AV-like, indicating potential areas of high accident density due to AV operation. Outlier detection and DBSCAN clustering were utilized to identify compact pilot zones, effectively reducing the area size compared to raw data clusters. This approach significantly lowers the cost and time associated with selecting test roads and provides a viable alternative for countries lacking real AV accident data. The proposed method’s effectiveness in identifying pilot zones demonstrates its potential for advancing AV safety validation.
Deep Learning for Cybersecurity Classification: Utilizing Depth-Wise CNN and Attention Mechanism on VM-Obfuscated Data
Authors: Han Sicheng , Yun Heeheon , Park Yongsu
Published: August 2024

With the increasing use of sophisticated obfuscation techniques, malware detection remains a critical challenge in cybersecurity. This paper introduces a novel deep learning approach to classify malware obfuscated by virtual machine (VM) code. We specifically explore the application of depth-wise convolutional neural networks (CNNs) combined with a spatial attention mechanism to tackle VM-protected cybersecurity datasets. To address the scarcity of obfuscated malware samples, the dataset was generated using VMProtect to ensure the models were trained on real examples of modern obfuscated malware. The effectiveness of our approach is demonstrated through extensive experiments on both regular malware and obfuscated malware, where our model achieved accuracies of nearly 100% and 93.55% in classifying the regular malware and the obfuscated malware, respectively.
Temporal Variations in Methane Emissions from a Restored Mangrove Ecosystem in Southern China
Authors: Tian Pengpeng , Li Xianglan , Xu Zhe , Wu Liangxu , Huang Yuting , Zhang Zhao , Chen Mengna , Zhang Shumin , Cai Houcai , Xu Minghai , Chen Wei
Published: August 2024

The role of coastal mangrove wetlands in sequestering atmospheric carbon dioxide has been increasingly investigated in recent years. While studies have shown that mangroves are weak sources of methane (CH4) emissions, measurements of CH4 fluxes from these ecosystems remain scarce. In this study, we examined the temporal variation and biophysical drivers of ecosystem-scale CH4 fluxes in China’s northernmost mangrove ecosystem based on eddy covariance measurements obtained over a 3-year period. In this mangrove, the annual CH4 emissions ranged from 6.15 to 9.07 g C m−2 year−1. The daily CH4 flux reached a peak of over 0.07 g C m−2 day−1 during the summer, while the winter CH4 flux was negligible. Latent heat, soil temperature, photosynthetically active radiation, and tide water level were the primary factors controlling CH4 emissions. This study not only elucidates the mechanisms influencing CH4 emissions from mangroves, strengthening the understanding of these processes but also provides a valuable benchmark dataset to validate the model-derived carbon budget estimates for these ecosystems.
Carbon Sequestration and Storage of Urban Trees in a Polluted Semiarid City
Authors: Rasoolzadeh Reihaneh , Mobarghaee Dinan Naghmeh , Esmaeilzadeh Hassan , Rashidi Yousef , Marcu Marina Viorela , Sadeghi Seyed Mohammad Moein
Published: August 2024

Cities play a critical role in anthropogenic CO2 emissions, which exacerbate climate change and impact urban populations. Urban green infrastructure, such as urban trees, provides essential ecosystem services, including reducing atmospheric CO2 levels. However, there is a significant knowledge gap regarding the impact of urban trees on climate change in semiarid, polluted cities like Tehran, the capital and largest metropolis of the Middle East. This study assesses the carbon sequestration and storage potential of Tehran’s urban infrastructure using the i-Tree Eco model. A randomized cluster sampling method was employed, collecting data on species composition, diameter at breast height (DBH), and total tree height. The results indicate that Tehran’s urban trees sequester approximately 60,102 tons of carbon per year, equivalent to 220,393 tons of CO2. The net carbon storage in urban trees is about 254,579 tons, equivalent to 933,455 tons of CO2. Parks and urban green spaces demonstrate the highest rate of carbon sequestration per hectare, followed by urban services land use. Prioritizing the planting of species with high sequestration rates like Cupressus arizonica (Arizona cypress) and Cupressus sempervirens L. var. horizontalis (Mediterranean cypress) could enhance carbon sequestration efforts in Tehran. These data provide valuable insights into the carbon sequestration potential and environmental impact of different land use types, and may aid in the development of effective environmental policies and land management strategies in semiarid urban areas and other cities in similar settings.
Nitrogen Deposition Reduces the Rate of Leaf Litter Decomposition: A Global Study
Authors: Xi Jiazhen , Wang Jiayi , Zhu Yufan , Xu Miaoping
Published: August 2024

The litter decomposition of plant leaves is a vital process in carbon (C) and nitrogen (N) cycling in global terrestrial ecosystems. However, previous assessments of the key determinants of the N deposition effects of litter decomposition have been more controversial. In this meta-analysis, we compared the overall effects of N addition on the litter decomposition rates, litter nutrient content (C and N), and litter constituent (lignin and cellulose) residual rates using a log response ratio approach. Our results showed that exogenous N addition increased the N content and inhibited lignin degradation in litter. N deposition decreased the leaf litter decomposition rate by increasing the lignin and N residues and decreasing the litter C content and soil pH. The analysis also concluded that the initial litter C/N ratio, lignin content, and soil pH were main factors in mediating the effect of N deposition on litter decomposition rate. Overall, the results of this study indicate that N deposition can slow decomposition rates by inhibiting N release and lignin degradation of litter. Notably, these results emphasize that the effect of N deposition on litter decomposition mainly depends on the endogenous quality of the litter and soil pH in the decomposition environment.
Personalized Visualization of the Gestures of Parkinson’s Disease Patients with Virtual Reality
Authors: Sakkas Konstantinos , Dimitriou Eirini Georgia , Ntagka Niki Eleni , Giannakeas Nikolaos , Kalafatakis Konstantinos , Tzallas Alexandros T. , Glavas Evripidis
Published: August 2024

Parkinson’s disease is a neurological disorder characterized by motor and non-motor symptoms. Assessment methods, despite the many years of existence of the disease, lack individualized visualization. On the other hand, virtual reality promises immersion and realism. In this paper, we develop an integrated system for visualizing the gestures of Parkinson’s disease patients in a virtual reality environment. With this application, clinicians will have information about the unique motor patterns and challenges they must address in each individual patient’s case, while the collected data can travel and be easily and instantly visualized in any location. At the beginning of this research, the current terms of immersive technologies in conjunction with data visualization and Parkinson’s disease are described. Through an extensive systematic literature review, the technological developments in the field of Parkinson’s data visualization are presented. The findings of the review lead to the experimental procedure and implementation of the application. The conclusions drawn from this work fuel future extensions on the contribution of immersive technologies to various diseases.
Thermal Recycling Process of Carbon Fibers from Composite Scrap—Characterization of Pyrolysis Conditions and Determination of the Quality of Recovered Fibers
Authors: Szatkowski Piotr , Twaróg Rafał
Published: August 2024

In this study, we took a closer look at the thermal recyclability of CFRP composites used in the manufacture of high-pressure cylinders. Thermal analysis was used to determine the minimum temperature at which stable resin decomposition begins. The aim was to find temperature parameters and retention times with which the pyrolysis process is as economically viable as possible, and the recovered fibers retain optimum mechanical properties. The surface morphology of fibers annealed in both inert and oxidizing atmospheres was examined. In addition, the mechanical strengths under static as well as dynamic conditions of the newly manufactured laminates containing the recovered fibers were investigated. During research, it was found that reusing fibers is very difficult. The recycled carbon fibers were successfully compressed in an epoxy matrix in the form of a pre-impregnated carbon mat with the presence of air. The presence of oxygen during the thermal degradation of the composite severely damaged the surface and structure of the carbon fiber, causing composites made from these fibers to be mechanically weaker by more than 247%.
Comparative Analysis and Evaluation of Modeling Methods for Nuclear-Grade HEPA Filters
Authors: Al Dabbas Ali , Al-Azba Mohammed , Kopecskó Katalin , Fawaier Mohammad , Alshebli Ahmad , Al-Hyari Laith , Joubert Aurélie
Published: August 2024

High-Efficiency Particulate Air (HEPA) filtration plays a crucial role in maintaining air quality in critical environments such as lean rooms, hospitals, and nuclear facilities. The point of this study is to look into how well nuclear-grade HEPA filters work and behave by looking at the main ways they catch particles using two modeling methods to figure out how well the filters work overall. This study encompasses particles with diameters ranging from 0.05 to 5.00 µm and a density of 1500 kg/m3. The current study systematically examined key parameters such as particle size, fiber diameter, and filtration velocity, which revealed their significant influence on the HEPA filter efficiency. Notably, the most penetrating particle size (MPPS) is identified within the expected range of 0.1–0.3 µm for both approaches. A critical threshold in fiber diameter is discovered when it exceeds 0.85 µm, resulting in a substantial shift in particle penetration and overall collection efficiency. This study also explored the impact of filtration velocity on filter performance, demonstrating increasing deviations as velocity rises, following a polynomial trend. The current study also rigorously validated the model predictions against experimental data from uranine particle filtration tests, confirming the model’s accuracy and applicability. These findings provide essential insights for optimizing the design and operation of nuclear HEPA filters, emphasizing the necessity of considering the particle size, fiber diameter, and filtration velocity. Both modeling approaches exhibit a negligible 0.04% deviation in the MPPS efficiency, which increases polynomially with the filtration velocity. Importantly, both approaches consistently identified the same MPPS regardless of the filtration velocity. Additionally, the model reinforces the substantial impact of fiber size on filter efficiency. A comprehensive comparison with the experimental data yielded closely aligned results with a maximum deviation of 1.14%. This validation strengthens the model’s ability to elucidate the underlying physical phenomena governing the influence of filtration velocity on efficiency, making it a valuable tool in nuclear HEPA filter research and development.
Effects of High-Speed Shearing Treatment on the Physical Properties of Carbohydrate-Binder Mixture during Gelatinization for Preparing Freeze-Dried Soup Products
Authors: Lee Ga-Yang , Jung Min-Jeong , Kim Byoung-Mok , Kim Ha Ram , Jun Joon-Young , Kim Nam Hee
Published: August 2024

Modernization has led to a large convenience food market, and the demand for freeze-dried (FD) soup products is increasing in the Republic of Korea. FD soup products are easy to eat without cooking and can be stored for long periods. However, it is often difficult to ensure sensory satisfaction after rehydration of FD soup products; in particular, the ingredients are not evenly dispersed. Therefore, a stable dispersion or reconstitution of the FD soup products is required after rehydration. Here, the effects of high-speed shearing homogenization on the physical properties of a carbohydrate-binder mixture comprising maltodextrin, potato starch, and rice flour were investigated during hydrothermal gelatinization. To find a suitable treatment condition, different homogenization eras, speeds, and concentrations of the binder mixture were considered; in particular, the homogenization eras were set by considering the hydrothermal property of the binder mixture profiled using differential scanning calorimetry. The viscosity of the binder mixture and the compression strength and microstructure of the FD binder block, including the dispersion stability after rehydration, were evaluated. The quality of the FD binder block was improved by homogenization above 5000 rpm when the core temperature of the binder mixture reached approximately To at 14.5–21.8% concentrations. The improved FD binder block exhibited a fine surface and tiny porous microstructure compared with the control (with continuous agitation at 250 rpm). The control block was divided into two phases, whereas the improved block maintained the initial dispersion stability at 50 °C for 1 h. These results are expected to be referenced for the purpose of improving the quality of the FD soup products.
Functional Characterization of Different Fructilactobacillus sanfranciscensis Strains Isolated from Chinese Traditional Sourdoughs
Authors: Yang Huanyi , Lin Jiaqi , Han Xueyuan , Bi Juguo , Dong Lijia , Sun Jianqiu , Shen Chi , Xu Ying
Published: August 2024

Fructilactobacillus sanfranciscensis, the dominant species of lactic acid bacteria in sourdoughs, impacts the microstructure and flavor of steamed bread through exopolysaccharide production, acidification, proteolysis, and volatile compound generation. The aim of this study is to investigate the phenotypic diversity and technological traits of 28 F. sanfranciscensis strains of different genotypes isolated from Chinese traditional sourdoughs. The results showed that F. sanfranciscensis strains exhibited substantial variation in proteinase and peptidase activities and the amount of acidification and volatiles in fermented sourdoughs. However, we observed no significant differences in exopolysaccharide production among the strains. The strains Sx14 and Ts1 were further chosen for transcriptomics to gain a deep insight into their intraspecies diversity in sourdough fermentation. Significant transcriptome differentiations between these two strains after 12 h fermentation in sourdoughs were revealed. According to the results, the strain Sx14 possessed higher dipeptidase and aminopeptidase activities, galactose utilization, and lactic and acetic acid production abilities, whereas Ts1 showed higher transmembrane transport of substrates and fructose utilization.
The Influence of Substrates on Blow Fly (Diptera: Calliphoridae) Development
Authors: Moore Kenya , Bagsby Kelly , Hans Krystal R.
Published: August 2024

Blow flies (Diptera: Calliphoridae) can be used in forensic entomology to provide information, including an estimate of the time of colonization and minimum postmortem interval, based on insect development. This study examined the development of third instar Calliphora terraenovae Macquart in four substrates: pine shavings, soil, sand, and clay. Pupation time, survival to adult, and wing vein length were measured to examine the substrate influence. There was a significant difference in the time to pupation (F3,36 = 11.87, p < 0.0001) and the number of flies that eclosed (F3,36 = 4.716, p = 0.007) among the substrates. Blow flies pupated faster in pine shavings and eclosed as adult flies faster in sand than in other substrates. Adults eclosed in the sand in 21.9 days, followed by pine shavings (22.8), clay (24.2), and soil (26.6). Although overall survivorship was low (10–46%), the greatest number of flies eclosed in sand, and the fewest in clay. Understanding the factors that impact blow fly development can help forensic entomologists improve rearing protocols and apply this information to death investigations, especially in cases with buried remains.
Nash Equilibria and Undecidability in Generic Physical Interactions—A Free Energy Perspective
Authors: Fields Chris , Glazebrook James F.
Published: August 2024

We start from the fundamental premise that any physical interaction can be interpreted as a game. To demonstrate this, we draw upon the free energy principle and the theory of quantum reference frames. In this way, we place the game-theoretic Nash Equilibrium in a new light in so far as the incompleteness and undecidability of the concept, as well as the nature of strategies in general, can be seen as the consequences of certain no-go theorems. We show that games of the generic imitation type follow a circularity of idealization that includes the good regulator theorem, generalized synchrony, and undecidability of the Turing test. We discuss Bayesian games in the light of Bell non-locality and establish the basics of quantum games, which we relate to local operations and classical communication protocols. In this light, we also review the rationality of gaming strategies from the players’ point of view.
The Impact of the G6PD Gene Mutations in Patients with Chronic Hepatitis C Infection Treated with Direct-Acting Antivirals: A Multicenter Observational Study
Authors: Smirne Carlo , Crobu Maria , Gerevini Chiara , Berton Alessandro , Rapetti Rachele , Pasini Barbara , Ravanini Paolo , Pirisi Mario
Published: August 2024

Following the advent of direct-acting antivirals (DAAs), the treatment of hepatitis C virus (HCV) infection is now rarely challenging. However, data are still limited concerning DAA use in patients affected by glucose-6-phosphate dehydrogenase deficiency (G6PDd). Based on these considerations, the goal of this study was to evaluate the effectiveness and safety of DAAs in this subpopulation. A retrospective multicenter observational study (2015–2023) was conducted on all 2754 consecutive HCV-positive patients treated with first- and second-generation all-oral DAAs, and with a G6PDd diagnosis confirmed by quantitative testing (n = 38). At the treating clinician’s discretion, an enhanced clinical and laboratory follow-up was performed, generally on a monthly basis both during treatment and up to six months after the end of it. Concerning hematochemical parameters, no significant differences were found between any considered time point. In all cases, no treatment-related adverse events were reported, and virologic response rates were as expected without G6PDd. In conclusion, in a large experience which, to the best of our knowledge, is unprecedented in the literature, the treatment of HCV hepatitis with nearly all available DAAs in patients with G6PDd as a comorbidity—a common occurrence in countries such as Italy—proved to be highly effective and safe.
Impact of NSD1 Alternative Transcripts in Actin Filament Formation and Cellular Division Pathways in Fibroblasts
Authors: Conteduca Giuseppina , Cangelosi Davide , Baldo Chiara , Arado Alessia , Testa Barbara , Wagner Ryan T. , Robertson Keith D. , Dequiedt Franck , Fitzsimmons Lane , Malacarne Michela , Filaci Gilberto , Coviello Domenico A.
Published: August 2024

Germline variants in the NSD1 gene are responsible for Sotos syndrome, while somatic variants promote neoplastic cell transformation. Our previous studies revealed three alternative RNA isoforms of NSD1 present in fibroblast cell lines (FBs): the canonical full transcript and 2 alternative transcripts, termed AT2 (NSD1 Δ5Δ7) and AT3 (NSD1 Δ19–23 at the 5′ end). The precise molecular pathways affected by each specific isoform of NSD1 are uncharacterized to date. To elucidate the role of these isoforms, their expression was suppressed by siRNA knockdown in FBs and protein expression and transcriptome data was explored. We demonstrate that one gene target of NSD1 isoform AT2 is ARP3 actin-related protein 3 homolog B (ACTR3B). We show that loss of both canonical NSD1 and AT2 isoforms impaired the ability of fibroblasts to regulate the actin cytoskeleton, and we observed that this caused selective loss of stress fibers. Our findings provide novel insights into NSD1 function by distinguishing isoform function and demonstrating an essential role of NSD1 in regulating the actin cytoskeleton and stress fiber formation in fibroblasts.
Cornulin as a Key Diagnostic and Prognostic Biomarker in Cancers of the Squamous Epithelium
Authors: Shankavaram Varun , Shah Dean , Alashqar Aseel , Sweeney Jackson , Arnouk Hilal
Published: August 2024

The prevalence of squamous cell carcinoma is increasing, and efforts that aid in an early and accurate diagnosis are crucial to improve clinical outcomes for patients. Cornulin, a squamous epithelium-specific protein, has recently garnered attention due to its implications in the progression of squamous cell carcinoma developed in several tissues. As an epidermal differentiation marker, it is involved in skin anchoring, regulating cellular proliferation, and is a putative tumor suppressor. The physiologically healthy squamous epithelium displays a considerable level of Cornulin, whereas squamous cell carcinomas have marked downregulation, suggesting that Cornulin expression levels can be utilized for the early detection and follow-up on the progression of these types of cancer. Cornulin’s expression patterns in cervical cancer have been examined, and findings support the stepwise downregulation of Cornulin levels that accompanies the progression to neoplasia in the cervix. Additional studies documented a similar trend in expression in other types of cancer, such as cutaneous, esophageal, and oropharyngeal squamous cell carcinomas. The consistent and predictable pattern of Cornulin expression across several squamous cell carcinomas and its correlation with key clinicopathological parameters make it a reliable biomarker for assessing the transformation and progression events in the squamous epithelium, thus potentially contributing to the early detection, definitive diagnosis, and more favorable prognosis for these cancer patients.
Global Distribution and Diversity of Haloarchaeal pL6-Family Plasmids
Authors: Dyall-Smith Mike , Pfeiffer Friedhelm
Published: August 2024

Australian isolates of Haloquadratum walsbyi, a square-shaped haloarchaeon, often harbor small cryptic plasmids of the pL6-family, approximately 6 kb in size, and five examples have been previously described. These plasmids exhibit a highly conserved gene arrangement and encode replicases similar to those of betapleolipoviruses. To assess their global distribution and recover more examples for analysis, fifteen additional plasmids were reconstructed from the metagenomes of seven hypersaline sites across four countries: Argentina, Australia, Puerto Rico, and Spain. Including the five previously described plasmids, the average plasmid size is 6002 bp, with an average G+C content of 52.5%. The tetramers GGCC and CTAG are either absent or significantly under-represented, except in the two plasmids with the highest %G+C. All plasmids share a similar arrangement of genes organized as outwardly facing replication and ATPase modules, but variations were observed in some core genes, such as F2, and some plasmids had acquired accessory genes. Two plasmids, pCOLO-c1 and pISLA-c6, shared 92.7% nt identity despite originating from Argentina and Spain, respectively. Numerous metagenomic CRISPR spacers matched sequences in the fifteen reconstructed plasmids, indicating frequent invasion of haloarchaea. Spacers could be assigned to haloarchaeal genera by mapping their associated direct repeats (DR), with half of these matching Haloquadratum. Finally, strand-specific metatranscriptome (RNA-seq) data could be used to demonstrate the active transcription of two pL6-family plasmids, including antisense transcripts.
A Multidisciplinary Geophysical Approach to Characterize a Fracture Zone: The Southern Limit of the Mining District of Linares-La Carolina, Spain
Authors: Rey Javier , Mendoza Rosendo , Vilchez José , Hidalgo M. Carmen , Fernández Isla , Berman Sara
Published: August 2024

In many cases, the lateral extension of economically valuable mineral deposits is linked to fractures. Knowing the characteristics of these tectonic structures is crucial for determining the continuity of mineralization in the subsoil and, consequently, for planning their exploitation. To address this challenge, a multidisciplinary study was carried out using various geophysical techniques and direct field observations to analyze the effectiveness of each technique in the investigation of fractures. For this purpose, the mining district of Linares-La Carolina in southeastern Spain, known for hosting Philonian deposits of galena (PbS), was chosen. At the southern boundary of this mining district, the seams stopped being exploited when they lost their continuity due to the presence of a regional normal fault. This fault zone was responsible for hiding the seams under a thick sedimentary cover. Detailed geological mapping allowed us to deduce the presence of two fracture systems. The geophysical techniques of electrical resistivity tomography and the time domain electromagnetic method were used, allowing us to identify the positions and the vertical displacement of these faults. Furthermore, using magnetometry, the presence of a third system of fractures was deduced. The results showed that this multidisciplinary analysis provides information of interest concerning the complex structure that configures the limits of this mining district.
Wounding Citrus Peel By-Products as Abiotic Stress to Induce the Synthesis of Phenolic Compounds?
Authors: Zapata Rosa , Martínez-Zamora Lorena , Cano-Lamadrid Marina , Artés-Hernández Francisco
Published: August 2024

Wounding stress stimulates secondary metabolism and induces the phytochemical accumulation of fresh-cut fruit and vegetables. This research aims to study the biosynthesis of secondary metabolites in citrus peel by-products after different wounding intensities. Orange, grapefruit, and lemon peels were cut into rectangular shapes of 8.5 × 2 cm (CTRL), 1 × 1 cm dices (D), and 0.25 cm grates (G). Samples were stored at 15 °C and 65% relative humidity. Their total phenolic content (TPC), total antioxidant capacity (TAC), and individual organic and phenolic compounds were analyzed after 0, 4, 8, 24, 28, and 36 h. The results showed that the metabolite content decreased with the incubation time, so it is recommended to sample by-products between the first 8 h to achieve the maximum content. Grating, the most severe abiotic stress, was not a suitable technique to induce the synthesis of biocompounds because it allowed the full recovery of flavedo, but partial of albedo. However, it was different for CTRL and D, whose TPCs were ~170 and ~200%, ~98 and ~78%, and ~8 and ~36% higher for orange, grapefruit, and lemon, respectively, compared to G. A principal component analysis confirmed differences between the fruit species and cutting shapes combining all factors. Wounding citrus peels induces the accumulation of phytochemical compounds, but the layer of peel recovered in the cut is crucial in the concentration of phytochemicals extracted. It could be an innovative tool to revalorize these inedible parts of citrus, but further research is still needed.
Influence of Solvent Concentration on the Amount of Individual Phenolic Compounds in Apple and Sour Cherry Pomace
Authors: Mandache Maria Bianca , Stoenescu Ana-Maria , Cosmulescu Sina
Published: August 2024

Apple and sour cherry pomace, by-products resulting from fruit processing for juice production, were analysed using high-performance liquid chromatography (HPLC) to identify and quantify individual phenolic compounds. In order to determine the most efficient method of extracting these phenolic compounds from pomace, different concentrations of ethanol were used as the organic solvent. The following phenolic compounds were analysed: gallic acid, neochlorogenic acid, (+)-catechin, (−)-epicatechin, chlorogenic acid, vanillic acid, caffeic acid, ferulic acid, sinapic acid, salicylic acid, ellagic acid, rutin, and myricetin. The amounts of these compounds varied depending on the concentration of ethanol used in the extraction process. Neochlorogenic acid, a potent antioxidant, was quantified in apple and sour cherry pomace extracts, showing significant variation with solvent concentration. In apple pomace, the highest amount was found in ethanol 100% (46.44 mg 100 g−1 DW), followed by ethanol 75% (32.09 mg 100 g−1 DW) and ethanol 50% (7.66 mg 100 g−1 DW). In sour cherry pomace, the highest amount was also extracted into ethanol 100% (45.20 mg 100 g−1 DW) and the lowest in ethanol 50% (29.12 mg 100 g−1 DW). Catechin was detected exclusively in cherry pomace, with a maximum yield observed in 75% ethanol (137.86 mg 100 g−1 DW), which was significantly higher compared to the other phenolic compounds analysed. The obtained results demonstrate the importance of apple and sour cherry pomace as valuable by-products, with the potential to be used in the production of functional foods.