bluuebunny/crossref_metadata_2025 huggingface dataset:
Food Safety Knowledge, Attitude, and Practices of Restaurant Food Handlers in Jeddah City, Saudi Arabia
Authors: Alzhrani Wejdan F. , Shatwan Israa M.
Published: July 2024

Improper food handler (FH) practices in food service areas, such as restaurants, can result in foodborne diseases (FBD). To reduce FBD cases, the food safety knowledge, attitudes, and practices (KAP) of FHs working in restaurants in Jeddah City and the correlation between their sociodemographic information and KAP scores were investigated in this study. A self-administered questionnaire comprising four parts (socioeconomic information and food safety KAP) was distributed among 389 FHs. Out of the FHs surveyed, 94.1% were male and 68% were certified. The average scores were 9.3 ± 1.8, 9.3 ± 1.3, and 8.9 ± 1.1 for KAP, respectively. Most FHs (82.2%) belonged to the good KAP group, and the rest (17.7%) were in the poor KAP group. FHs aged 50 years and above (9.6 ± 0.6), those with over 10 years of working experience (9.2 ± 0.9), and are married (9.1 ± 1.1) had the highest scores for practice compared with other FHs (p = 0.01 for all). Salary is directly correlated with food safety attitudes (p = 0.04). The findings confirmed a high score for KAP among FHs working in Jeddah. Nevertheless, more focus must be given to training younger, single, and less-experienced FHs, necessitating education courses with practical and theoretical aspects. Further studies from all regions of Saudi Arabia are necessary to generalize the study findings.
Early Childhood Caries—Prevalence, Associated Factors, and Severity: A Hospital-Based Study in Riyadh, Saudi Arabia
Authors: Thirunavukkarasu Ashokkumar , Alaqidi Sultan Fadel
Published: July 2024

Early childhood caries (ECC) is not merely a problem of the tooth; rather, it has negative impacts on the child’s growth and development and oral health-related quality of life. The prevalence of ECC varies widely, and more variance is observed between countries than continents, and it changes over time. The current cross-sectional study aimed to determine ECC’s prevalence, associated factors, and severity. This study was conducted in the Dental Clinic Complex, North Riyadh, KSA. The study sample included 306 participants between 36 and 71 months of age. A questionnaire was developed to collect information from participants. The decaying and filled teeth (DMFT) index was used to estimate the prevalence of caries. The study results revealed the prevalence of ECC to be 76% among 36- to 71-month-old children in North Riyadh. ECC is influenced by socioeconomic factors like maternal occupation, education, oral hygiene habits, and dietary patterns. Also, it was found that exclusively breastfed children have lower ECC odds (68.1% vs. 83.6%, p = 0.001), while the intake of more than three between-meal sugar-containing snacks/beverages per day increases the likelihood of ECC (63.5% vs. 79.8%, p = 0.006). The researchers recommend encouraging breastfeeding as it correlates with lower ECC prevalence, in addition to establishing effective oral hygiene practices for preschool children.
NMR-Based Metabolomics of Blood Serum in Predicting Response to Induction Chemotherapy in Head and Neck Cancer—A Preliminary Approach
Authors: Boguszewicz Łukasz , Bieleń Agata , Jarczewski Jarosław Dawid , Ciszek Mateusz , Skorupa Agnieszka , Mrochem-Kwarciak Jolanta , Składowski Krzysztof , Sokół Maria
Published: July 2024

The role of induction chemotherapy (iCHT) in locally advanced head and neck squamous cell carcinoma (LA-HNSCC) is still to be established due to high toxicity and variable response rates. The aim of this retrospective study is to use NMR-based serum metabolomics to predict the response rates to iCHT from the pretreatment samples. The studied group consisted of 46 LA-HNSCC patients treated with iCHT. The response to the treatment was evaluated by the clinical, fiberoptic, and radiological examinations made before and after iCHT. The proton nuclear magnetic resonance (1H NMR) serum spectra of the samples collected before iCHT were acquired with a 400 MHz spectrometer and were analyzed using multivariate and univariate statistical methods. A significant multivariate model was obtained only for the male patients. The treatment-responsive men with >75% primary tumor regression after iCHT showed pretreatment elevated levels of isoleucine, alanine, glycine, tyrosine, N-acetylcysteine, and the lipid compounds, as well as decreased levels of acetate, glutamate, formate, and ketone bodies compared to those who did not respond (regression of the primary tumor <75%). The results indicate that the nutritional status, capacity of the immune system, and the efficiency of metabolism related to protein synthesis may be prognostic factors for the response to induction chemotherapy in male HNSCC patients. However, larger studies are required that would validate the findings and could contribute to the development of more personalized treatment protocols for HNSCC patients.
Global Warming and Its Effect on Binder Performance Grading in the USA: Highlighting Sustainability Challenges
Authors: Sepaspour Reza , Zebarjadian Faezeh , Ehsani Mehrdad , Hajikarimi Pouria , Moghadas Nejad Fereidoon
Published: July 2024

The mounting impacts of climate change on infrastructure demand proactive adaptation strategies to ensure long-term resilience. This study investigates the effects of predicted future global warming on asphalt binder performance grade (PG) selection in the United States using a time series method. Leveraging Long-Term Pavement Performance (LTPP) data and Superpave protocol model, the research forecasts temperature changes for the period up to 2060 and calculates the corresponding PG values for different states. The results reveal significant temperature increases across the majority of states, necessitating adjustments in PG selection to accommodate changing climate conditions. The findings indicate significant increases in average 7-day maximum temperatures across the United States by 2060, with 38 out of 50 states likely to experience rising trends. Oregon, Utah, and Idaho are anticipated to face the largest temperature increases. Concurrently, the low air temperature has risen in 33 states, with notable increases in Maine, North Carolina, and Virginia. The widening gap predicted between required high and low PG poses challenges, as some necessary binders cannot be produced or substituted with other grades. The study highlights the challenge of meeting future PG requirements with available binders, emphasizing the need to consider energy consumption and CO2 emissions when using modifiers to achieve the desired PG properties.
Dead on the Beach? Predicting the Drift of Whale Remains Improves Management for Offshore Disposal
Authors: Meynecke Jan-Olaf , Zigic Sasha , Perez Larissa , Dunn Ryan J. K. , Benfer Nathan , Gustafson Johan , Bosshard Simone
Published: July 2024

Whale mortality and strandings have increased in recent years, with deceased whales often brought to landfill. However, the disposal of whale remains offshore holds significant ecological importance and can be a culturally and ethically sensitive approach. Moreover, offshore disposal mitigates potential risks associated with onshore whale remains disposal, such as the spread of diseases and the logistical challenges of managing large carcasses. A challenge with offshore disposal is defining the best release location to avoid the remains drifting ashore or into shipping channels. Here we compared the drift model outputs using a drift forecast model (SARMAP) for a 14 m whale carcass that was moved offshore in southeast Queensland, Australia, and fitted with a satellite tracker over an observation period of 150 h until positioning signal ceased. The modelling was conducted using different ocean products (BLUElink, HYCOM, and Copernicus), which showed a good agreement with the tracked whale carcass, albeit with changing wind conditions and contrasting currents flowing northward along the coast and, further offshore, flowing south. This case study illustrated that wind was the foremost driver of carcass drift due to the surface area above the water surface. The drift forecast simulations allowed for a reliable prediction of the floating whale drift that can assist authorities with decision making. Offshore disposal of whale carcasses is a sustainable practice but requires good planning and scientific assessment.
Annual and Seasonal Variation of the Ocean Thermal Resources off the Mexican Coast
Authors: Carmona-Cedillo Carlos Melecio , Trasviña-Castro Armando , Chávez Valeria , Silva Rodolfo
Published: July 2024

A large amount of thermal energy is stored in the oceans between the tropics, available for conversion into electrical energy using OTEC technology. The aim of this study was to determine the annual and seasonal variability of the oceanic thermal resource in Mexico. Using the WOA18 database, we mapped surface temperature at a 10 m depth, deep cold water (<5 °C), vertical temperature difference (18 and 20 °C), and temperature anomalies. From the results, four areas were analyzed as being suitable for the installation of OTEC technology: Pacific (A), Los Cabos (B), Caribbean (C), and Gulf of Mexico (G). The optimal thermal resource (≥20 °C) was found between a 400 and 1000 m depth in all seasons in A and C, in spring, summer, and autumn in G, and only in summer and autumn in B. The suboptimal thermal resource (between 18 and 20 °C) was present between 400 and 800 m in all seasons in A, C, and G, and in summer and autumn in B. These results provide new information of utmost importance for future location and design considerations of OTEC plants on Mexican coasts, and the methodology can be used in other areas where there is a lack of field data and the development of OTEC technology is being considered.
Volatility Persistence and Spillover Effects of Indian Market in the Global Economy: A Pre- and Post-Pandemic Analysis Using VAR-BEKK-GARCH Model
Authors: Maharana Narayana , Panigrahi Ashok Kumar , Chaudhury Suman Kalyan
Published: July 2024

This study examines how the COVID-19 pandemic impacted stock market volatility and interconnectedness between India and other selected global economies. The analysis, using data from 2016 to 2024, reveals a substantial rise in volatility within both the Indian market and those of several other countries after the pandemic. Interestingly, the volatility transmission patterns also changed. While the Indian market’s volatility significantly influenced Brazil, China, and Mexico throughout the entire period, the influence of the US market became negligible post-pandemic. In contrast, Russia exhibited a weak but statistically significant impact on India’s volatility only after the pandemic. These findings highlight the lasting impact of the pandemic on global financial markets and emphasize the need for investors and policymakers to adapt. By understanding these new dynamics, investors can make more informed decisions, and policymakers can develop stronger risk management strategies and international coordination during periods of increased volatility. This study offers valuable insights for navigating the current financial landscape and the interconnectedness of emerging economies.
Study on the Influence of Temperature and Water Content on the Static Mechanical Properties of Sandstone
Authors: Zhang Xiaojun , He Maolin , Li Zhuo , Jia Yongsheng , Gao Wenxue
Published: July 2024

The area of permafrost worldwide accounts for approximately 20% to 25% of land area. In cold-climate regions of China, which are garnering international attention, the study of low-temperature and moisture effects on rock mass mechanical properties is of significant importance. China has a wide area of cold regions. This research can provide a foundation for China’s exploration activities in such extreme environments. This paper examines the mechanical behavior of rock specimens subjected to various low temperatures and water contents through uniaxial compression tests. The analysis encompasses failure modes, stress–strain relationships, uniaxial compressive strength (UCS), and elastic modulus (EM) of these specimens. Findings reveal that at lower temperatures, the rock specimens’ fracture patterns transition from compressive shear failure to cleavage failure, reflecting a shift from a plastic–elastic–plastic to a plastic–elastic response. Specifically, saturated rocks exhibit a 40.8% decrease in UCS and an 11.4% reduction in EM compared to their dry counterparts. Additionally, in cold conditions, an increased water content in rocks primarily leads to vertical cracking. Under such conditions, saturated rocks show a 52.3% decline in UCS and a 15.2% reduction in EM, relative to their dry state.
A Comparative Study of Pole–Slot Combination with Fractional Slot Concentrated Winding in Outer Rotor Permanent Magnet Synchronous Generator for Hybrid Drone System
Authors: Kim Jeongwon , Lee Ju , Kim Hyunwoo
Published: July 2024

This paper is a comparative study of pole–slot combinations with fractional slot concentrated windings in an outer rotor permanent magnet synchronous generator (ORPMSG) for a hybrid drone system. Fractional slot machines have been studied for automotive applications because of their high performance and simple winding manufacturing, compared with those of integer slot machines. In this study, four pole–slot combinations of ORPMSG with fractional slot concentrated windings were selected for comparison with the performance of the hybrid drone system. Based on the results of a finite element analysis (FEA), the machines were analyzed for cogging torque, back electromagnetic force (BEMF), torque, and electromagnetic loss under the same conditions as the machine specifications. Among the four pole–slot combinations of the ORPMSM, a one pole–slot model of the ORPMSG was selected, considering the performances of the machines. The selected pole–slot model of the ORPMSG was manufactured, and experiments were conducted on the manufactured model to verify the FEA results. Finally, the effectiveness of the comparative study of pole–slot combination with fractional slot concentrated winding in ORPMSM was verified by comparing the FEA and experimental results.
Dynamical Behaviors of a Stochastic Susceptible-Infected-Treated-Recovered-Susceptible Cholera Model with Ornstein-Uhlenbeck Process
Authors: Li Shenxing , Li Wenhe
Published: July 2024

In this study, a cholera infection model with a bilinear infection rate is developed by considering the perturbation of the infection rate by the mean-reverting process. First of all, we give the existence of a globally unique positive solution for a stochastic system at an arbitrary initial value. On this basis, the sufficient condition for the model to have an ergodic stationary distribution is given by constructing proper Lyapunov functions and tight sets. This indicates in a biological sense the long-term persistence of cholera infection. Furthermore, after transforming the stochastic model to a relevant linearized system, an accurate expression for the probability density function of the stochastic model around a quasi-endemic equilibrium is derived. Subsequently, the sufficient condition to make the disease extinct is also derived. Eventually, the theoretical findings are shown by numerical simulations. Numerical simulations show the impact of regression speed and fluctuation intensity on stochastic systems.
Fixed-Point Theorems for Fuzzy Mappings
Authors: de Andrade Allan Edley Ramos , Wasques Vinícius Francisco
Published: July 2024

Since the 1970s and 1980s, significant contributions have been made by Weiss, Butnariu, Heilpern, Chitra, Subrahmanyam, and others, extending fixed-point theorems to fuzzy mappings and topological spaces. This paper provides two generalizations of two important fixed-point theorems, one provided by Butnariu and the other provided by Chitra. The first generalization ensures that, under certain conditions, an acyclic fuzzy mapping has a fixed point. The second result ensures the existence of a point in the intersection of two or more fuzzy mappings considering contractible finite dimensional ANR spaces, which is a generalization of the statement provided by Chitra.
Data-Driven Method for Robust Recovery in 1-Bit Compressive Sensing with the Minimax Concave Penalty
Authors: Jia Cui , Zhu Li
Published: July 2024

With the advent of large-scale data, the demand for information is increasing, which makes signal sampling technology and digital processing methods particularly important. The utilization of 1-bit compressive sensing in sparse recovery has garnered significant attention due to its cost-effectiveness in hardware implementation and storage. In this paper, we first leverage the minimax concave penalty equipped with the least squares to recover a high-dimensional true signal x∈Rp with k-sparse from n-dimensional 1-bit measurements and discuss the regularization by combing the nonconvex sparsity-inducing penalties. Moreover, we give an analysis of the complexity of the method with minimax concave penalty in certain conditions and derive the general theory for the model equipped with the family of sparsity-inducing nonconvex functions. Then, our approach employs a data-driven Newton-type method with stagewise steps to solve the proposed method. Numerical experiments on the synthesized and real data verify the competitiveness of the proposed method.
Anti-Biofilm Extracts and Molecules from the Marine Environment
Authors: Caudal Flore , Roullier Catherine , Rodrigues Sophie , Dufour Alain , Artigaud Sébastien , Le Blay Gwenaelle , Bazire Alexis , Petek Sylvain
Published: July 2024

Pathogenic bacteria and their biofilms are involved in many diseases and represent a major public health problem, including the development of antibiotic resistance. These biofilms are known to cause chronic infections for which conventional antibiotic treatments are often ineffective. The search for new molecules and innovative solutions to combat these pathogens and their biofilms has therefore become an urgent need. The use of molecules with anti-biofilm activity would be a potential solution to these problems. The marine world is rich in micro- and macro-organisms capable of producing secondary metabolites with original skeletons. An interest in the chemical strategies used by some of these organisms to regulate and/or protect themselves against pathogenic bacteria and their biofilms could lead to the development of bioinspired, eco-responsible solutions. Through this original review, we listed and sorted the various molecules and extracts from marine organisms that have been described in the literature as having strictly anti-biofilm activity, without bactericidal activity.
Factors Influencing the Degree of Gastric Atrophy in Helicobacter pylori Eradication Patients with Drinking Habits
Authors: Ozeki Kayoko , Hada Kazuhiro , Wakiya Yoshifumi
Published: July 2024

Chronic gastritis caused by Helicobacter pylori (H. pylori) infection can lead to gastric atrophy. This study aimed to identify the factors associated with gastric atrophy in H. pylori eradication patients with drinking habits. Of the 250 patients who visited Hamamatsu University Hospital for H. pylori eradication and underwent eradication treatment between April 2017 and December 2020, 127 patients with drinking habits were included in this study. The degree of gastric atrophy of the patients was classified based on endoscopy. The relationship between patient attributes (sex, age, alcohol consumption, frequency of drinking, smoking status, and medication use) and a highly atrophic stomach was statistically analyzed. The results showed that gastric atrophy was significantly higher in males and in those aged 60 years or older and that gastric atrophy tended to be higher in those who drank 20 g or more per day and 5 days or more a week. There was also a trend toward higher atrophy in sake drinkers and lower atrophy in wine drinkers. This study provides useful knowledge for patient management and guidance after H. pylori eradication treatment and indicates the importance of comprehensive measures, including alcohol consumption control and lifestyle modification, especially for men and older people.
The Optimization of Numerical Algorithm Parameters with a Genetic Algorithm to Animate Letters of the Sign Alphabet
Authors: Hernandez-Mendez Sergio , Hernández-Mejía Carlos , Torres-Muñoz Delia , Maldonado-Mendez Carolina
Published: July 2024

At present, the development of animation-based works for human–computer interaction applications has increased. To generate animations, actions are pre-recorded and animation flows are configured. In this research, from two images of letters of the sign language alphabet, intermediate frames were generated using a numerical traced algorithm based on homotopy. The parameters of a homotopy curve were optimized with a genetic algorithm to generate intermediate frames. In the experiments performed, sequences where a person executes pairs of letters in sign language were recorded and animations of the same pairs of letters were generated with the proposed method. Subsequently, the similarity of the real sequences to the animations was measured using Dynamic Time Wrapping. The results obtained show that the images obtained are consistent with their execution by a person. Animation files between sign pairs were created from sign images, with each file weighing an average of 18.3 KB. By having sequences between pairs of letters it is possible to animate words and sentences. The animations generated by this homotopy-based animation method optimized with a genetic algorithm can be used in various deaf interaction applications to provide assistance. From several pairs of letters a file base was generated using the animations between pairs of letters; with these files you can create animations of words and sentences.
Carboxymethyl-Cellulose-Based Hydrogels Incorporated with Cellulose Nanocrystals Loaded with Vitamin D for Controlled Drug Delivery
Authors: Silva Nathália da Cunha , Silva Carla Jeany Teixeira , Gonçalves Max Pereira , Borsagli Fernanda G. L. Medeiros
Published: July 2024

Currently, the development of innovative materials for the treatment of various diseases is highly interesting and effective. Additionally, in recent years, environmental changes, including the search for a sustainable world, have become the main goal behind developing sustainable and suitable materials. In this context, this research produced innovative hydrogels that incorporate cellulose nanocrystals and nanofibres from underutilised fibres from a semiarid region of Brazil; the hydrogels were loaded with vitamin D to evaluate controlled drug release for the treatment of diverse diseases. Spectroscopic (FTIR, Raman, UV–VIS), X-ray diffraction, zeta potential and morphology (SEM, TEM) analyses were used to characterise these hydrogels. In addition, biocompatibility was assessed using a resazurin assay, and the in vitro kinetic accumulative release of vitamin D was measured. The results showed that nanocrystals and nanofibres changed the structure and crystallinity of the hydrogels. In addition, the chemical groups of the hydrogels were red- and blueshifted in the FTIR spectra when the nanocrystals, nanofibres and vitamin D were incorporated. Moreover, the nanocrystals and nanofibres were homogeneously spread into the hydrogel when vitamin D was loaded into the hydrogel matrix. Furthermore, the cytotoxicity was greater than 90%. Additionally, the in vitro accumulative kinetic data of vitamin D release were robust (close to 40 ng·mL−1), with equilibrium being reached in the first 30 min. These results confirm the potential of using these hydrogels as therapeutic biomaterials for diverse diseases and problems in humans, mainly in women, who are the most harmed by vitamin D deficiency.
Prediction of NOx Emissions in Thermal Power Plants Using a Dynamic Soft Sensor Based on Random Forest and Just-in-Time Learning Methods
Authors: He Kaixun , Ding Haixiao
Published: July 2024

Combustion optimization is an effective way to improve the efficiency of thermal power generation and reduce carbon and NOx emissions. Real-time and precise NOx emission prediction is the basis for combustion optimization control of thermal power plants. To construct an accurate NOx concentration prediction model, a novel just-in-time learning (JITL) method based on random forest (RF) is proposed in the present work. With this method, first, an improved permutation importance algorithm is proposed to extract important variables. In addition, a similarity index that incorporates temporal and spatial measures is defined to select a local training set representative of the process data. Moreover, considering the influence of model parameters on prediction performance under different working conditions, a process monitoring method based on a moving window (MW) is used to monitor the change in working conditions and guide online updating. The experimental results show that the proposed method has excellent prediction accuracy, with a coefficient of determination of 0.9319, a root-mean-square error of 3.6960 mg/m3, and an average absolute error of 2.7718 mg/m3 on the test set, making it superior to other traditional methods.
On-Device Semi-Supervised Activity Detection: A New Privacy-Aware Personalized Health Monitoring Approach
Authors: Roy Avirup , Dutta Hrishikesh , Bhuyan Amit Kumar , Biswas Subir
Published: July 2024

This paper presents an on-device semi-supervised human activity detection system that can learn and predict human activity patterns in real time. The clinical objective is to monitor and detect the unhealthy sedentary lifestyle of a user. The proposed semi-supervised learning (SSL) framework uses sparsely labelled user activity events acquired from Inertial Measurement Unit sensors installed as wearable devices. The proposed cluster-based learning model in this approach is trained with data from the same target user, thus preserving data privacy while providing personalized activity detection services. Two different cluster labelling strategies, namely, population-based and distance-based strategies, are employed to achieve the desired classification performance. The proposed system is shown to be highly accurate and computationally efficient for different algorithmic parameters, which is relevant in the context of limited computing resources on typical wearable devices. Extensive experimentation and simulation study have been conducted on multi-user human activity data from the public domain in order to analyze the trade-off between classification accuracy and computation complexity of the proposed learning paradigm with different algorithmic hyper-parameters. With 4.17 h of training time for 8000 activity episodes, the proposed SSL approach consumes at most 20 KB of CPU memory space, while providing a maximum accuracy of 90% and 100% classification rates.
Facile Preparation of TiO2NTs/Au@MOF Nanocomposites for High-Sensitivity SERS Sensing of Gaseous VOC
Authors: Wang Chunyan , Jiang Yina , Peng Yuyu , Huo Jia , Zhang Ban
Published: July 2024

Surface-enhanced Raman spectroscopy (SERS) is a promising and highly sensitive molecular fingerprint detection technology. However, the development of SERS nanocomposites that are label-free, highly sensitive, selective, stable, and reusable for gaseous volatile organic compounds (VOCs) detection remains a challenge. Here, we report a novel TiO2NTs/AuNPs@ZIF−8 nanocomposite for the ultrasensitive SERS detection of VOCs. The three-dimensional TiO2 nanotube structure with a large specific surface area provides abundant sites for the loading of Au NPs, which possess excellent local surface plasmon resonance (LSPR) effects, further leading to the formation of a large number of SERS active hotspots. The externally wrapped porous MOF structure adsorbs more gaseous VOC molecules onto the noble metal surface. Under the synergistic mechanism of physical and chemical enhancement, a better SERS enhancement effect can be achieved. By optimizing experimental conditions, the SERS detection limit for acetophenone, a common exhaled VOC, is as low as 10−11 M. And the relative standard deviation of SERS signal intensity from different points on the same nanocomposite surface is 4.7%. The acetophenone gas achieves a 1 min response and the signal reaches stability in 4 min. Under UV irradiation, the surface-adsorbed acetophenone can be completely degraded within 40 min. The experimental results demonstrate that this nanocomposite has good detection sensitivity, repeatability, selectivity, response speed, and reusability, making it a promising sensor for gaseous VOCs.
Monoolein-Based Wireless Capacitive Sensor for Probing Skin Hydration
Authors: Chaturvedi Vivek , Falk Magnus , Björklund Sebastian , Gonzalez-Martinez Juan F. , Shleev Sergey
Published: July 2024

Capacitive humidity sensors typically consist of interdigitated electrodes coated with a dielectric layer sensitive to varying relative humidity levels. Previous studies have investigated different polymeric materials that exhibit changes in conductivity in response to water vapor to design capacitive humidity sensors. However, lipid films like monoolein have not yet been integrated with humidity sensors, nor has the potential use of capacitive sensors for skin hydration measurements been fully explored. This study explores the application of monoolein-coated wireless capacitive sensors for assessing relative humidity and skin hydration, utilizing the sensitive dielectric properties of the monoolein–water system. This sensitivity hinges on the water absorption and release from the surrounding environment. Tested across various humidity levels and temperatures, these novel double functional sensors feature interdigitated electrodes covered with monoolein and show promising potential for wireless detection of skin hydration. The water uptake and rheological behavior of monoolein in response to humidity were evaluated using a quartz crystal microbalance with dissipation monitoring. The findings from these experiments suggest that the capacitance of the system is primarily influenced by the amount of water in the monoolein system, with the lyotropic or physical state of monoolein playing a secondary role. A proof-of-principle demonstration compared the sensor’s performance under varying conditions to that of other commercially available skin hydration meters, affirming its effectiveness, reliability, and commercial viability.
Localization for Dual Partial Discharge Sources in Transformer Oil Using Pressure-Balanced Fiber-Optic Ultrasonic Sensor Array
Authors: Liu Feng , Shi Yansheng , Zhang Shuainan , Wang Wei
Published: July 2024

The power transformer is one of the most crucial pieces of high-voltage equipment in the power system, and its stable operation is crucial to the reliability of power transmission. Partial discharge (PD) is a key factor leading to the degradation and failure of the insulation performance of power transformers. Therefore, online monitoring of partial discharge can not only obtain real-time information on the operating status of the equipment but also effectively predict the remaining service life of the transformer. Meanwhile, accurate localization of partial discharge sources can assist maintenance personnel in developing more precise and efficient maintenance plans, ensuring the stable operation of the power system. Dual partial discharge sources in transformer oil represent a more complex fault type, and piezoelectric transducers installed outside the transformer oil tank often fail to accurately capture such discharge waveforms. Additionally, the sensitivity of the built-in F-P sensors can decrease when installed deep within the oil tank due to the influence of oil pressure on its sensing diaphragm, resulting in an inability to accurately detect dual partial discharge sources in transformer oil. To address the impact of oil pressure on sensor sensitivity and achieve the detection of dual partial discharge sources under high-voltage conditions in transformers, this paper proposes an optical fiber ultrasonic sensor with a pressure-balancing structure. This sensor can adapt to changes in oil pressure environments inside transformers, has strong electromagnetic interference resistance, and can be installed deep within the oil tank to detect dual partial discharge sources. In this study, a dual PD detection system based on this sensor array is developed, employing a cross-positioning algorithm to achieve detection and localization of dual partial discharge sources in transformer oil. When applied to a 35 kV single-phase transformer for dual partial discharge source detection in different regions, the sensor array exhibits good sensitivity under high oil pressure conditions, enabling the detection and localization of dual partial discharge sources in oil and winding interturn without obstruction. For fault regions with obstructions, such as within the oil channel of the transformer winding, the sensor exhibits the capability to detect the discharge waveform stemming from dual partial discharge sources. Overall, the sensor demonstrates good sensitivity and directional clarity, providing effective detection of dual PD sources generated inside transformers.
A Novel Tongue Coating Segmentation Method Based on Improved TransUNet
Authors: Wu Jiaze , Li Zijian , Cai Yiheng , Liang Hao , Zhou Long , Chen Ming , Guan Jing
Published: July 2024

Background: As an important part of the tongue, the tongue coating is closely associated with different disorders and has major diagnostic benefits. This study aims to construct a neural network model that can perform complex tongue coating segmentation. This addresses the issue of tongue coating segmentation in intelligent tongue diagnosis automation. Method: This work proposes an improved TransUNet to segment the tongue coating. We introduced a transformer as a self-attention mechanism to capture the semantic information in the high-level features of the encoder. At the same time, the subtraction feature pyramid (SFP) and visual regional enhancer (VRE) were constructed to minimize the redundant information transmitted by skip connections and improve the spatial detail information in the low-level features of the encoder. Results: Comparative and ablation experimental findings indicate that our model has an accuracy of 96.36%, a precision of 96.26%, a dice of 96.76%, a recall of 97.43%, and an IoU of 93.81%. Unlike the reference model, our model achieves the best segmentation effect. Conclusion: The improved TransUNet proposed here can achieve precise segmentation of complex tongue images. This provides an effective technique for the automatic extraction in images of the tongue coating, contributing to the automation and accuracy of tongue diagnosis.
Predicting Emotional Experiences through Eye-Tracking: A Study of Tourists’ Responses to Traditional Village Landscapes
Authors: Ye Feng , Yin Min , Cao Leilei , Sun Shouqian , Wang Xuanzheng
Published: July 2024

This study investigates the relationship between eye-tracking metrics and emotional experiences in the context of cultural landscapes and tourism-related visual stimuli. Fifty-three participants were involved in two experiments: forty-three in the data collection phase and ten in the model validation phase. Eye movements were recorded and the data were analyzed to identify correlations between four eye-tracking metrics—average number of saccades (ANS), total dwell fixation (TDF), fixation count (FC), and average pupil dilation (APD)—and 19 distinct emotional experiences, which were subsequently grouped into three categories: positive, neutral, and negative. The study examined the variations in eye-tracking metrics across architectural, historic, economic, and life landscapes, as well as the three primary phases of a tour: entry, core, and departure. Findings revealed that architectural and historic landscapes demanded higher levels of visual and cognitive engagement, especially during the core phase. Stepwise regression analysis identified four key eye-tracking predictors for emotional experiences, enabling the development of a prediction model. This research underscores the effectiveness of eye-tracking technology in capturing and predicting emotional responses to different landscape types, offering valuable insights for optimizing rural tourism environments and enhancing visitors’ emotional experiences.
On the Performance of a Horizontally Mounted ADCP in an Energetic Tidal Environment for Floating Tidal Turbine Applications
Authors: Dillenburger-Keenan Jan , Miller Calum , Sellar Brian
Published: July 2024

Incident flow measurement is key in the tidal industry for conducting power performance assessments. This paper explores the use of a horizontally mounted Nortek Signature 500 Acoustic Doppler Current Profiler (ADCP) as a means for incident flow measurement onboard a utility-scale tidal turbine. This study shows that the measurement range of an ADCP mounted horizontally in highly dynamic tidal flow (up to 4 m/s) is less than the maximum range stated by the manufacturer. The ability for the horizontal ADCP to accurately resolve velocities in a multi-beam configuration is also analysed. Effects from both vertical shear and beam selection result in incident flow velocities that differ from a single horizontal beam recording. The maximum measurement range of the instrument is found to depend on current speed and on the proportion of data loss that is acceptable to the user. The ability of the ADCP to record data from the free-stream velocity two equivalent diameters upstream of the O2, as set out by IEC TS 62600-200, is considered. It is found that at this distance, there is 90% data loss. Accepting only 10% data loss across all flow speeds resulted in a maximum range of 31 m for a Nortek Signature 500 in this study. While some limitations of an ADCP deployed horizontally in highly energetic tidal flow are identified, the benefits of mounting the sensor close to the rotor facing horizontally into the incoming flow mean that valuable data are still produced for tidal turbine operators.
Reconstruction of the Chemical Gas Concentration Distribution Using Partial Convolution-Based Image Inpainting
Authors: Kang Minjae , Son Jungjae , Lee Byungheon , Nam Hyunwoo
Published: July 2024

An interpolation method, which estimates unknown values with constrained information, is based on mathematical calculations. In this study, we addressed interpolation from an image-based perspective and expanded the use of image inpainting to estimate values at unknown points. When chemical gas is dispersed through a chemical attack or terrorism, it is possible to determine the concentration of the gas at each location by utilizing the deployed sensors. By interpolating the concentrations, we can obtain the contours of gas concentration. Accurately distinguishing the contours of a contaminated region from a map enables the optimal response to minimize damage. However, areas with an insufficient number of sensors have less accurate contours than other areas. In order to achieve more accurate contour data, an image inpainting-based method is proposed to enhance reliability by erasing and reconstructing low-accuracy areas in the contour. Partial convolution is used as the machine learning approach for image-inpainting, with the modified loss function for optimization. In order to train the model, we developed a gas diffusion simulation model and generated a gas concentration contour dataset comprising 100,000 contour images. The results of the model were compared to those of Kriging interpolation, one of the conventional spatial interpolation methods, finally demonstrating 13.21% higher accuracy. This suggests that interpolation from an image-based perspective can achieve higher accuracy than numerical interpolation on well-trained data. The proposed method was validated using gas concentration contour data from the verified gas dispersion modeling software Nuclear Biological Chemical Reporting And Modeling System (NBC_RAMS), which was developed by the Agency for Defense Development, South Korea.
Examining Crop Yield Losses in Iğdır Plain Irrigation Systems in Türkiye Amidst Water Constraints
Authors: Karaaslan Yakup
Published: July 2024

Water stands as a crucial component of agricultural production. This study aims to propose water efficiency measures in agriculture as an essential element for climate change adaptation. In this study, yield losses in staple crops in agricultural regions are analyzed by using the most suitable methodologies, particularly in agriculture-reliant developing nations. Furthermore, this study seeks to determine the financial consequences of such losses. The methodology applied for this purpose was implemented in Türkiye’s Iğdır Plain, selected as the study site. As the first step, the yields of the first three most cultivated products in each product group were assessed under normal climatic conditions in terms of their crop water requirements and irrigation water requirements. Subsequently, the irrigation water supply was reduced by 10%, and the resulting yield losses were calculated per hectare. Then, the overall crop losses after applying the 10% water constraint were determined in the total cultivation area. Among the crops cultivated in the region, the analysis reveals that clover from the field crops category exhibits the highest water dependence, while apricot demonstrates the least reliance on water resources. As a result, the recommended crop rotation for the Iğdır Plain under water constraints comprises wheat, apricot, watermelon, maize, melon, apple, tomato, peach, and clover. The following measures are proposed to ensure sustainable use of water resources and reduce exposure to climate change: increasing the water transmission efficiency and water use efficiency in irrigation areas, allocating more space to water-stress-resistant crops in the crop pattern in basins, and substituting crops requiring excessive water with less water-dependent crops.
Proposing Machine Learning Models Suitable for Predicting Open Data Utilization
Authors: Jeong Junyoung , Cho Keuntae
Published: July 2024

As the digital transformation accelerates in our society, open data are being increasingly recognized as a key resource for digital innovation in the public sector. This study explores the following two research questions: (1) Can a machine learning approach be appropriately used for measuring and evaluating open data utilization? (2) Should different machine learning models be applied for measuring open data utilization depending on open data attributes (field and usage type)? This study used single-model (random forest, XGBoost, LightGBM, CatBoost) and multi-model (stacking ensemble) machine learning methods. A key finding is that the best-performing models differed depending on open data attributes (field and type of use). The applicability of the machine learning approach for measuring and evaluating open data utilization in advance was also confirmed. This study contributes to open data utilization and to the application of its intrinsic value to society.
Using Zebrafish to Screen Developmental Toxicity of Per- and Polyfluoroalkyl Substances (PFAS)
Authors: Britton Katy N. , Judson Richard S. , Hill Bridgett N. , Jarema Kimberly A. , Olin Jeanene K. , Knapp Bridget R. , Lowery Morgan , Feshuk Madison , Brown Jason , Padilla Stephanie
Published: July 2024

Per- and polyfluoroalkyl substances (PFAS) are found in many consumer and industrial products. While some PFAS, notably perfluorooctanoic acid (PFOA) and perfluorooctanesulfonic acid (PFOS), are developmentally toxic in mammals, the vast majority of PFAS have not been evaluated for developmental toxicity potential. A concentration–response study of 182 unique PFAS chemicals using the zebrafish medium-throughput, developmental vertebrate toxicity assay was conducted to investigate chemical structural identifiers for toxicity. Embryos were exposed to each PFAS compound (≤100 μM) beginning on the day of fertilization. At 6 days post-fertilization (dpf), two independent observers graded developmental landmarks for each larva (e.g., mortality, hatching, swim bladder inflation, edema, abnormal spine/tail, or craniofacial structure). Thirty percent of the PFAS were developmentally toxic, but there was no enrichment of any OECD structural category. PFOS was developmentally toxic (benchmark concentration [BMC] = 7.48 μM); however, other chemicals were more potent: perfluorooctanesulfonamide (PFOSA), N-methylperfluorooctane sulfonamide (N-MeFOSA), ((perfluorooctyl)ethyl)phosphonic acid, perfluoro-3,6,9-trioxatridecanoic acid, and perfluorohexane sulfonamide. The developmental toxicity profile for these more potent PFAS is largely unexplored in mammals and other species. Based on these zebrafish developmental toxicity results, additional screening may be warranted to understand the toxicity profile of these chemicals in other species.
Prevalência da Síndrome de Burnout em estudantes do curso de Medicina no Brasil: uma revisão de escopo
Authors: Andrade Pereira de Lima Arthur Henrique , Brarymi Dias Davi , Pinto de Barros Davi Miguel , Santiago Demetrius Felipe , Brarymi Dias Gabriel , Gomes Wanderley Gabriel , D'Amorim  Barretto Rodrigo Danzi , Etelvina de Macedo Alves Mylena , De Souza e Silva Hugo Rafael
Published: July 2024

Introdução. A síndrome de Burnout é prevalente em estudantes de medicina devido à pressão acadêmica. No Brasil, preocupa pela alta prevalência e seus efeitos na saúde mental. Essa questão demanda discussão e análise para abordar seus impactos na população brasileira. Objetivo. Investigar a prevalência da Síndrome de Burnout entre estudantes de medicina no Brasil, analisando diversos estudos recentes. Compreender os fatores associados ao surgimento da SB nessa população. Método. Utilizando a estratégia PCC (População, Conceito e Contexto), realizamos uma revisão de escopo seguindo as diretrizes PRISMA-ScR. Incluímos artigos que abordavam a prevalência de Burnout em estudantes de medicina no Brasil nos últimos 10 anos. A estratégia de busca foi aplicada nas bases de dados MEDLINE/PubMed, Web of Science e Embase, resultando em 8 artigos elegíveis. Resultado. Os estudos selecionados revelaram uma prevalência de Burnout variando de 9,5% a 57,5%, com diferentes fatores associados, como ano do curso, falta de suporte social, pensamentos suicidas e falta de tempo para atividades de lazer. Os resultados apontaram para uma alta prevalência da síndrome, especialmente nos anos clínicos do curso. Na discussão, os autores sugeriram a necessidade de intervenções para promover o bem-estar dos estudantes, enfatizando a importância do suporte socioemocional. Conclusão. A síndrome de Burnout representa um desafio significativo para a saúde mental dos estudantes de medicina brasileiros, exigindo medidas preventivas e estratégias para garantir um ambiente acadêmico saudável e produtivo.
DETERMINATION AND APPLICATION OF FORECASTING METHOD FOR MEDICINE CONSUMPTION IN HEALTHCARE ORGANIZATION
Authors: Çeliker Gökçen , Öztürk Nazife , Ersoyoğlu Rabia Nilüfer
Published: July 2024

The aim of this research is to estimate the consumption of a selected medicine in a public hospital for the next 1-year period by choosing the most appropriate fore casting method. Kaptoril 5-mg tablets from January 2018 to December 2022 were examined. In this research, time series methods were applied to the existing data using the Minitab 18 program. Moving average, exponentials moothing, and Holt-Winters fore casting methods were used in this study. Error measures such as mean absolute error, mean absolute percent age error, and mean squared error were used to compare the methods. For Kaptoril 5 mg, the most appropriatedem and fore casting method according to error measures is the Multiplicative Holt-Winters Method. According to this method, the mean absolute percent age error is 37.23. According to the multiplic ative Holt-Winters model, the total medicine consumption in 2023 was found to be 145 tablets. This research shows that time series fore casting methods can be applied to help reliable decision making in stock management of medicines by making a sample application on a selected medicine in a public hospital.