bluuebunny/crossref_metadata_2025 huggingface dataset:
A Randomized Controlled Pilot Study of the Food Order Behavioral Intervention in Prediabetes
Authors: Shukla Alpana P. , Karan Ampadi , Hootman Katie C. , Graves Maya , Steller Ian , Abel Brittany , Giannita Ashley , Tils Jamie , Hayashi Lauren , O’Connor Madlen , Casper Anthony J. , D’Angelo Debra , Aronne Louis J.
Published: October 2023

(1) Background: Prior research in individuals with overweight/obesity and prediabetes or type 2 diabetes has shown that the ingestion of protein-rich food and non-starchy vegetables before concentrated carbohydrates (a carbohydrate-last food order) led to lower postprandial glucose excursions over 180 min, compared to eating the same foods in the reverse order. To expand upon this research, we sought to examine the feasibility and impact of carbohydrate-last food order behavioral intervention on glucose tolerance (GT), HbA1c, weight, and nutrient intake in adults with prediabetes in the real world over a 16-week span. (2) Methods: A total of 45 adults with overweight/obesity and prediabetes were randomized to receive 4-monthly standard nutritional counseling (C) or standard nutritional counseling plus carbohydrate-last food order counseling (FO) sessions (NCT# NCT03896360). (3) Results: The FO group decreased in body weight (−3.6 ± 5.7 lbs, p = 0.017), and trended toward lower HbA1c (−0.1 ± 0.2, p = 0.054). The C group weight trended lower (−2.6 ± 6.8 lbs, p = 0.102) without altering HbA1c (−0.03 ± 0.3, p = 0.605). GT was unchanged in both groups after 16 weeks. Changes in weight, HbA1c, and GT were similar between groups. Sensitivity analysis of pre-COVID participants showed significant weight loss in the FO group (−5.9 ± 5.3 lbs, p = 0.003) but not in C group (−1.0 ± 6.8 lbs, p = 0.608). After 16 weeks, the C group significantly reduced its daily intake of calories, fat, protein, and grains whereas the FO group increased its daily intake of vegetables and protein. There were 17 (94%) FO participants that reported high intervention adherence and 13 (72%) reported it was easy to eat protein/vegetables before carbohydrates. (4) Conclusions: A carbohydrate-last food order is a feasible behavioral strategy in individuals with prediabetes that improves diet quality, notably increasing protein and vegetable intake.
Considerations of Low Carbohydrate Availability (LCA) to Relative Energy Deficiency in Sport (RED-S) in Female Endurance Athletes: A Narrative Review
Authors: Lodge Melissa T. , Ward-Ritacco Christie L. , Melanson Kathleen J.
Published: October 2023

The purpose of this narrative review is to identify health and performance consequences associated with LCA in female endurance athletes. The intake of carbohydrates (CHO) before, during, and after exercise has been demonstrated to support sport performance, especially endurance activities which rely extensively on CHO as a fuel source. However, low energy availability (LEA) and low carbohydrate availability (LCA) are common in female athletes. LEA occurs when energy intake is insufficient compared to exercise energy expenditure, and LEA-related conditions (e.g., Female Athlete Triad (Triad) and Relative Energy Deficiency in Sport (RED-S)) are associated with a myriad of health and performance consequences. The RED-S model highlights 10 health consequences and 10 performance consequences related to LEA. The independent effect of LCA on health and performance has been under-researched, despite current CHO intake being commonly insufficient in athletes. It is proposed that LCA may not only contribute to LEA but also have independent health and performance consequences in athletes. Furthermore, this review highlights current recommendations for CHO intake, as well as recent data on LCA prevalence and menstrual cycle considerations. A literature review was conducted on PubMed, Science Direct, and ResearchGate using relevant search terms (i.e., “low carbohydrate/energy availability”, “female distance runners”). Twenty-one articles were identified and twelve met the inclusion criteria. The total number of articles included in this review is 12, with 7 studies illustrating that LCA was associated with direct negative health and/or performance implications for endurance-based athletes. Several studies included assessed male athletes only, and no studies included a female-only study design. Overall, the cumulative data show that female athletes remain underrepresented in sports science research and that current CHO intake recommendations and strategies may fail to consider female-specific adaptations and hormone responses, such as monthly fluctuations in estrogen and progesterone throughout the menstrual cycle. Current CHO guidelines for female athletes and exercising women need to be audited and explored further in the literature to support female athlete health and performance.
Lactobacillus paragasseri SBT2055 Activates Plasmacytoid Dendritic Cells and Improves Subjective Symptoms of Common Cold in Healthy Adults: A Randomized, Double-Blind, Placebo-Controlled Parallel-Group Comparative Trial
Authors: Kobatake Eiji , Iwama Yoshitaka , Arai Toshinobu , Tsukisaka Yuki , Kabuki Toshihide
Published: October 2023

This study investigated whether Lactobacillus paragasseri SBT2055 (LG2055) activates plasmacytoid dendritic cells (pDCs) and suppresses common cold symptoms in healthy adults. Cell-based experiments showed that a LG2055 treatment upregulated CD86 and HLA-DR expression in pDCs, indicating that LG2055 activates pDCs in vitro. In a subsequent randomized, double-blind, placebo-controlled, parallel-group comparative trial, 200 participants were randomly divided into two groups and consumed three capsules with or without LG2055 once daily for 12 weeks. The primary outcome was the score on a daily physical health questionnaire survey of common cold symptoms. Three participants discontinued the trial and six participants were excluded from the analysis, thus 191 participants (95 in the LG2055 group and 96 in the placebo group) were analyzed. The LG2055 group showed a significantly higher ratio of “without symptoms” responses for runny nose, plugged nose, sneezing, sore throat, hoarseness, and chill than the placebo group. Furthermore, a stratified analysis revealed that LG2055 intake enhanced CD86 and HLA-DR expression in the pDCs of the participants with low secretion rates of salivary secretory immunoglobulin A. These data suggest that LG2055 suppresses the subjective symptoms of the common cold by activating pDCs and improving the host’s immune system in healthy adults, especially in immune-weakened individuals (UMIN000049183).
Health Benefits of Bread Fortification: A Systematic Review of Clinical Trials according to the PRISMA Statement
Authors: Kaim Urszula , Goluch Zuzanna Sabina
Published: October 2023

Bread is among the most common foods for the world’s population. Therefore, it can be fortified to eliminate deficiencies of nutrients or be a carrier of other substances with a nutritional or physiological effect, bringing health benefits to its consumers, thus impacting sustainable health. This systematic review aimed to analyze clinical studies on the effects of bread fortification on human health. The study followed the PRISMA guidelines for transparency and utilized databases Scopus, Embase, PubMed, and Web of Science to search clinical trials focused on the effects of bread fortification on human health over the entire last decade. The methodological quality of selected studies was assessed using the Jadad scale. As a result, twenty-six studies meet the inclusion criteria. Clinical trials have shown health benefits from consuming bread fortified with vitamins (B9, C, D2, D3), minerals (K, P, Ca, Mg, Fe, Zn, Cr, Se), fiber, proteins, and polyphenolic compounds. Conclusions reveal that mandatory fortification aligns with the sustainable development goals. Thus, collaborative partnerships are essential for successful implementation and accessibility, fostering public health advances and progress developing into sustainable health.
Association of Plasma Zinc and Copper with Body Composition, Lipids and Inflammation in a Cross-Sectional General Population Sample from Germany
Authors: Övermöhle Cara , Rimbach Gerald , Waniek Sabina , Strathmann Eike A. , Liedtke Tatjana , Stürmer Paula , Both Marcus , Weber Katharina S. , Lieb Wolfgang
Published: October 2023

We aimed to relate circulating plasma zinc and copper to a broad spectrum of adiposity-related traits in a cross-sectional Northern German study (n = 841, 42% female, age: 61 ± 12 years). Zinc and copper were measured by inductively coupled plasma–mass spectrometry. Subcutaneous (SAT) and visceral (VAT) adipose tissue and liver fat were derived from 534 and 538 participants, respectively, via magnet resonance imaging. Associations were assessed using multivariable-adjusted linear regression analysis. An increase per one standard deviation (SD) in zinc was associated with direct linear increases in body mass index (BMI) (1.17%; 95% confidence interval (95%CI) 0.15–2.20%), waist circumference (0.85%; 95%CI 0.04–1.67%) and waist-to-hip ratio (0.64%; 95%CI 0.18–1.09%). A 1-SD increment in copper was directly associated with BMI (1.64%; 0.41–2.88%) and waist circumference (1.22%; 95%CI 0.25–2.20%) but not waist-to-hip ratio. Independent of fat intake, zinc displayed associations with VAT (5.73%; 95%CI 2.04–9.56%) and with liver fat (3.84%; 95%CI 1.49–6.25%), the latter association being also independent of BMI. Copper was directly associated with SAT (4.64%; 95%CI 0.31–9.15%) before accounting for BMI, but showed no association with VAT or liver fat. Observed associations suggest a possible relevance of zinc and copper to adiposity. Particularly zinc displayed associations with traits of abdominal adiposity and liver fat.
Interleukin-6 and Its Soluble Receptor Complex in Intensive Care Unit COVID-19 Patients: An Analysis of Second Wave Patients
Authors: Di Spigna Gaetano , Spalletti Cernia Daniela , Covelli Bianca , Vargas Maria , Rubino Valentina , Iacovazzo Carmine , Napolitano Filomena , Postiglione Loredana
Published: October 2023

In December 2019, a SARS-CoV-2 virus, coined Coronavirus Disease 2019 (COVID-19), discovered in Wuhan, China, affected the global population, causing more than a million and a half deaths. Since then, many studies have shown that the hyperinflammatory response of the most severely affected patients was primarily related to a higher concentration of the pro-inflammatory cytokine interleukin-6, which directly correlated with disease severity and high mortality. Our study analyzes IL-6 and its soluble receptor complex (sIL-6R and sgp130) in critically ill COVID-19 patients who suffered severe respiratory failure from the perspective of the second COVID wave of 2020. A chemiluminescent immunoassay was performed for the determination of IL6 in serum together with an enzyme-linked immunosorbent assay to detect serum levels of sIL-6R and sgp130, which confirmed that the second wave’s serum levels of IL-6 were significantly elevated in the more severe patients, as with the first 2019 COVID-19 wave, resulting in adverse clinical outcomes. At present, considering that no specific treatment for severe COVID-19 cases in its later stages exists, these molecules could be considered promising markers for disease progression, illness severity, and risk of mortality.
In Vitro Synergistic Inhibitory Effects of Plant Extract Combinations on Bacterial Growth of Methicillin-Resistant Staphylococcus aureus
Authors: Jeong Jae-Young , Jung In-Geun , Yum Seung-Hoon , Hwang You-Jin
Published: October 2023

Methicillin-resistant Staphylococcus aureus (MRSA) is one of the most common pathogens of healthcare-associated infections. Medicinal plants have long been used in the traditional treatment of diseases or syndromes worldwide. Combined use of plant extracts could improve the effectiveness of pharmacological action by obtaining synergism, acting on multiple targets simultaneously, reducing the doses of individual components, and minimizing side effects. We aimed to investigate the synergistic inhibitory effects of selected medicinal plants (Caesalpinia sappan L. (CS), Glycyrrhiza uralensis Fisch. (GU), Sanguisorba officinalis L. (SO), and Uncaria gambir Roxb. (UG)) on the bacterial growth of MRSA and its clinical isolates. SO and UG extracts generated the best synergistic interaction as adjudged by checkerboard synergy assays. MICs of the individual extracts decreased 4-fold from 250 to 62.5 μg/mL, respectively. The SO + UG combination was further evaluated for its effects on bacterial growth inhibition, minimum bactericidal/inhibitory concentration (MBC/MIC) ratio, and time-kill kinetics. The results indicate that the SO + UG combination synergistically inhibited the bacterial growth of MRSA strains with bactericidal effects. SO + UG combination also exhibited more potent effects against clinical isolates. In multistep resistance selection experiments, both standard and isolates of MRSA showed no resistance to the SO + UG combination even after repeated exposure over fourteen passages. Our data suggest that using plant extract combinations could be a potential strategy to treat MRSA infections.
The Cocrystal of Ubiquinol: Improved Stability and Bioavailability
Authors: Zhang Qi , Xia Mengyuan , Zheng Chenxuan , Yang Yinghong , Bao Junjie , Dai Wenjuan , Mei Xuefeng
Published: October 2023

Coenzyme Q10 (CoQ10) exists in two forms, an oxidized form and a reduced form. Ubiquinol is the fully reduced form of CoQ10. Compared to the oxidized form, ubiquinol has a much higher biological absorption and better therapeutic effect. However, ubiquinol has an important stability problem which hampers its storage and formulation. It can be easily transformed into its oxidized form—ubiquinone—even at low temperature. In this work, we designed, synthesized, and characterized a new cocrystal of ubiquinol with vitamin B3 nicotinamide (UQ-NC). Compared to the marketed ubiquinol form, the cocrystal exhibited an excellent stability, improved dissolution properties, and higher bioavailability. The cocrystal remained stable for a long period, even when stored under stressed conditions. In the dissolution experiments, the cocrystal generated 12.6 (in SIF) and 38.3 (in SGF) times greater maximum ubiquinol concentrations above that of the marketed form. In addition, in the PK studies, compared to the marketed form, the cocrystal exhibited a 2.2 times greater maximum total coenzyme Q10 concentration and a 4.5 times greater AUC than that of the marketed form.
Clinical Pharmacology of Vinpocetine: Properties Revisited and Introduction of a Population Pharmacokinetic Model for Its Metabolite, Apovincaminic Acid (AVA)
Authors: Petric Zvonimir , Paixão Paulo , Filipe Augusto , Guimarães Morais José
Published: October 2023

This paper examines the use of vinpocetine in the context of clinical pharmacology. The main and active metabolite of vinpocetine is apovincaminic acid (AVA). Due to the scarce information in the literature on AVA pharmacokinetics, we propose a population pharmacokinetic (PopPK) model for AVA based on a study in healthy volunteers with three different formulations of vinpocetine. The suggested PopPK model (and simulations) could be helpful in ensuring the more effective and safer use of the vinpocetine in the future given the increasing range of suggested indications for its use.
Ethnopharmacological Survey, Phytochemical Screening and Antimicrobial Activities of Medicinal Plants Used in the Treatment of Diarrhea in Southeastern Gabon
Authors: Yala Jean Fabrice , Mabika Mabika Rolande , Ikabanga Davy U. , Mounioko Franck , Mokouapamba Prince Rodrigue , Lepengue Alexis Nicaise , Souza Alain
Published: October 2023

Diarrhea is a condition that particularly affects children under five the age of years old in developing countries. The aim of this study was to evaluate the efficacy of medicinal extracts plants used in treatment and to characterize their inhibitory capacity in the growth of bacteria isolates in childhood diarrhea in the southeast region of Gabon. An ethnobotanical survey on the plants used in the treatment of diarrhea in southeastern Gabon was carried out and a phytochemical screening of the collected plants was performed. The antimicrobial activity of aqueous extracts was evaluated; 33 plant species were identified, representing 28 genera and 19 families. Bark (39.5%) was the most commonly used plant part, followed by powder formulations (28.9%). The preferred method of preparation and chewing (44.7%), together with drinking (36.8%), were the most prevalent modes of administration. Phytochemical screening showed a plethora of secondary metabolites (terpenoids, alkaloids and phenolic compounds), and a marked level of anti-diarrheal activity was found with Sida acuta and Santiria. ebo against Shigella spp. (16.22 ± 0.7 mm and 14.05 ± 1.4 mm) and Yersinia pestis (16.35 ± 0.5 mm and 15.51 ± 0.5 mm). The high diversity of secondary plant metabolites and their inhibitory ability against enteric pathogens would justify their use at the local level to treat diarrheal infections.
Genetic Variability in Seed Longevity and Germination Traits in a Tomato MAGIC Population in Contrasting Environments
Authors: Bizouerne Elise , Ly Vu Joseph , Ly Vu Benoît , Diouf Isidore , Bitton Frédérique , Causse Mathilde , Verdier Jérôme , Buitink Julia , Leprince Olivier
Published: October 2023

The stable production of high vigorous seeds is pivotal to crop yield. Also, a high longevity is essential to avoid progressive loss of seed vigour during storage. Both seed traits are strongly influenced by the environment during seed development. Here, we investigated the impact of heat stress (HS) during fruit ripening on tomato seed lifespan during storage at moderate relative humidity, speed (t50) and homogeneity of germination, using a MAGIC population that was produced under optimal and HS conditions. A plasticity index was used to assess the extent of the impact of HS for each trait. HS reduced the average longevity and germination homogeneity by 50% within the parents and MAGIC population. However, there was a high genetic variability in the seed response to heat stress. A total of 39 QTLs were identified, including six longevity QTLs for seeds from control (3) and HS (3) conditions, and six plasticity QTLs for longevity, with only one overlapping with a longevity QTL under HS. Four out of the six longevity QTL co-located with t50 QTL, revealing hotspots for seed quality traits. Twenty-one QTLs with intervals below 3 cM were analyzed using previous transcriptome and gene network data to propose candidate genes for seed vigour and longevity traits.
Enhancement of Computational Efficiency for Deadlock Recovery of Flexible Manufacturing Systems Using Improved Generating and Comparing Aiding Matrix Algorithms
Authors: Pan Yen-Liang , Tseng Ching-Yun , Chen Ju-Chin
Published: October 2023

After the fourth industrial evolution, precision and automatic manufacturing have become increasingly widely accepted in production. With highly variable productivity and flexibility, flexible manufacturing systems (FMS) lower production costs and increase efficiency. Due to its resource shareability, unexpected system deadlock may occur in some specific situations. Many existing works use deadlock prevention as the primary control methodology in research on system deadlock control, while this type of control policy would constrain the transportation resources and reduce the system’s liveness. This paper adopts a new transition-based deadlock recovery policy as the direct control strategy, which uses generating and comparing aiding matrix (GCAM) to determine the optimal control transition. We also improve the existing GCAM-based method by reducing the computational redundancy. This kind of control strategy and its benefit could be demonstrated through two typical systems of simple sequential processes with resource (S3PR) nets and their Petri nets model.
Avocado Waste Biorefinery: Towards Sustainable Development
Authors: Sandoval-Contreras Teresa , González Chávez Fernando , Poonia Amrita , Iñiguez-Moreno Maricarmen , Aguirre-Güitrón Lizet
Published: October 2023

The increasing demand for avocado consumption has led to a vast generation of waste products. Despite the high nutritional value of avocados, the waste generated from their processing poses a significant environmental challenge. Therefore, the development of a sustainable approach to avocado waste management is a major concern. Biorefinery presents a promising approach to the valorization of avocado waste components, including the seed, peel, and pulp residues. This paper explores the potential of avocado waste biorefinery as a sustainable solution to produce bio-based products. Several approaches, including extraction, hydrolysis, fermentation, and biodegradation, to obtain valuable products such as starch, oil, fiber, and bioactive compounds for food or feed goods have been proposed. The review also highlights the approaches towards addressing challenges of energy security and climate change by utilizing avocado waste as a source to produce biofuels such as biogas, biodiesel, and bioethanol. In conclusion, the development of avocado waste biorefinery presents a promising avenue for sustainable development. This process can efficiently convert the avocado waste components into valuable bio-based products and clean energy sources, contributing to the attainment of a circular economy and a more sustainable future.
Enhanced Gait Recovery in Chronic Post-COVID-19 Stroke: The Role of Combined Physical Rehabilitation
Authors: Fodor Hunor Pál , Dávid Hunor , Czont Attila , Miklóssy Ildikó , Orbán Kálmán-Csongor , Tar Gyöngyi , Fodor Abony , Kovács Zita , Albert Beáta , Salamon Pál
Published: October 2023

Background: Rehabilitation programs applied in cases of COVID-19-related stroke should counteract not only the effects of the stroke but also the effects of long-term COVID-19. As the molecular processes underlying these cases are still not fully understood, and evidence-based clinical outcomes are scarcely documented, there is a valid need to gather information and develop rehabilitation strategies for these patients. The risks, already clarified in the case of stroke, need to be assessed taking into account the coincidence of the two diseases. Endothelial injuries and emboli that develop after the hypercoagulable state of COVID-19 may take longer to heal, and complications may occur during exercise. This case study attempts to determine what the rehabilitation of a COVID-19-related stroke patient should include. The participant was a 64-year-old male with ischemic right middle cerebral artery stroke, left-side hemiplegia, and middle cerebral artery stenosis, and the CT showed a well-defined area of hypoattenuation in the basal ganglia territory involving the right lentiform nucleus, the anterior and posterior limbs of the internal capsule, and the dorsal part of the external capsule. His NIHSS score was 14, and he registered 15 points on the Barthel index. The patient had a COVID-19 infection two weeks before the stroke event. Methods: Conventional physical therapy was combined with adaptive ballistic strength training, a high-intensity interval training regimen, and manual treatment for myofascial release throughout the chronic recovery phase. Our primary goals were gait rehabilitation, muscle strengthening, weakness management, as well as spasticity reduction, while three different rehabilitation approaches were adopted in a single rehabilitation program to improve the outcome and long-term functional recovery of the patient. Results: The patient progressed in almost every aspect of the assessment criteria. This combined approach’s main success was improved gait speed, gait quality, and improved cardiovascular fitness. Take-away message: In the case of a stroke caused by COVID-19, where the endothelium cells are compromised, HIIT may be questionable due to the poor vascular condition. Based on our results, the low-volume HIIT approach proved appropriate and effective.
Optimized Deep Learning Model for Flood Detection Using Satellite Images
Authors: Stateczny Andrzej , Praveena Hirald Dwaraka , Krishnappa Ravikiran Hassan , Chythanya Kanegonda Ravi , Babysarojam Beenarani Balakrishnan
Published: October 2023

The increasing amount of rain produces a number of issues in Kerala, particularly in urban regions where the drainage system is frequently unable to handle a significant amount of water in such a short duration. Meanwhile, standard flood detection results are inaccurate for complex phenomena and cannot handle enormous quantities of data. In order to overcome those drawbacks and enhance the outcomes of conventional flood detection models, deep learning techniques are extensively used in flood control. Therefore, a novel deep hybrid model for flood prediction (DHMFP) with a combined Harris hawks shuffled shepherd optimization (CHHSSO)-based training algorithm is introduced for flood prediction. Initially, the input satellite image is preprocessed by the median filtering method. Then the preprocessed image is segmented using the cubic chaotic map weighted based k-means clustering algorithm. After that, based on the segmented image, features like difference vegetation index (DVI), normalized difference vegetation index (NDVI), modified transformed vegetation index (MTVI), green vegetation index (GVI), and soil adjusted vegetation index (SAVI) are extracted. The features are subjected to a hybrid model for predicting floods based on the extracted feature set. The hybrid model includes models like CNN (convolutional neural network) and deep ResNet classifiers. Also, to enhance the prediction performance, the CNN and deep ResNet models are fine-tuned by selecting the optimal weights by the combined Harris hawks shuffled shepherd optimization (CHHSSO) algorithm during the training process. This hybrid approach decreases the number of errors while improving the efficacy of deep neural networks with additional neural layers. From the result study, it clearly shows that the proposed work has obtained sensitivity (93.48%), specificity (98.29%), accuracy (94.98%), false negative rate (0.02%), and false positive rate (0.02%) on analysis. Furthermore, the proposed DHMFP–CHHSSO displays better performances in terms of sensitivity (0.932), specificity (0.977), accuracy (0.952), false negative rate (0.0858), and false positive rate (0.036), respectively.
Particle Size Distributions and Extinction Coefficients of Aerosol Particles in Land Battlefield Environments
Authors: Gao Lijuan , Chen Huimin , Chen Guang , Deng Jiahao
Published: October 2023

In land battlefield environments, aerosol particles can cause laser beams to undergo attenuation, thus deteriorating the operational performance of military laser devices. The particle size distribution (PSD) and extinction coefficient are key optical properties for assessing the attenuation characteristics of laser beams caused by aerosol particles. In this study, we employed the laser diffraction method to measure the PSDs of graphite smoke screen, copper powder smoke screen, iron powder smoke screen, ground dust, and soil explosion dust. We evaluated the goodness of fit of six common unimodal PSD functions and a bimodal lognormal PSD function employed for fitting these aerosol particles using the root mean square error (RMSE) and adjusted R2, and selected the optimal PSD function to evaluate their extinction coefficients in the laser wavelength range of 0.249~12 μm. The results showed that smoke screens, ground dust, and soil explosion dust exhibited particle size ranges of 0.7~50 µm, 1~400 µm, and 1.7~800 μm, respectively. The lognormal distribution had the best goodness of fit for fitting the PSDs of these aerosol particles in the six unimodal PSD functions, followed by the gamma and Rosin–Rammler distributions. For the bimodal aerosol particles with a lower span, the bimodal lognormal PSD functions exhibited the best goodness of fit. The graphite smoke screen exhibited the highest extinction coefficient, followed by the copper and iron powder smoke screens. In contrast, the ground dust and soil explosion dust exhibited the lowest extinction coefficients, reaching their minimum values at a wavelength of approximately 8.2 μm. This study provides a basis for analyzing and improving the detection and recognition performance of lasers in land battlefield environments.
Advancements and Applications of Drone-Integrated Geographic Information System Technology—A Review
Authors: Quamar Md Muzakkir , Al-Ramadan Baqer , Khan Khalid , Shafiullah Md , El Ferik Sami
Published: October 2023

Drones, also known as unmanned aerial vehicles (UAVs), have gained numerous applications due to their low cost, ease of use, vertical takeover and landing, and ability to operate in high-risk or hard-to-reach areas. The contribution of this review is that of building the bridge between drone technology and its application and advancements in the field of Geographic Information System (GIS). The integration of drones and GIS is valuable as it reduces costs and improves accessibility for geospatial data collection. Traditional methods involving aircraft for aerial photography are expensive, requiring the hiring of aircraft, pilots, and photographers. Drones equipped with advanced cameras and artificial intelligence software can replace the conventional technique and at the same time, be economical and time-efficient. The integration of drones and GIS is expected to bring revolutionary benefits in the fields of precision agriculture, urban planning, emergency health response, disaster management, the development of smart cities, food delivery, etc. In this paper, a state-of-the-art review of the deployment of drone-integrated GIS applications in different fields is presented. Numerous techniques and associated challenges related to their development, formulation, implementation, and regulation are highlighted. It has been concluded that drone-integration solutions in GIS improve efficiency and accuracy, enhance the decision-making process, and facilitate better real-time monitoring. The findings of this review paper are intended to help and benefit researchers, business developers, emergency service providers, industrialists, and policymakers.
YOLO-Crater Model for Small Crater Detection
Authors: Mu Lingli , Xian Lina , Li Lihong , Liu Gang , Chen Mi , Zhang Wei
Published: October 2023

Craters are the most prominent geomorphological features on the surface of celestial bodies, which plays a crucial role in studying the formation and evolution of celestial bodies as well as in landing and planning for surface exploration. Currently, the main automatic crater detection models and datasets focus on the detection of large and medium craters. In this paper, we created 23 small lunar crater datasets for model training based on the Chang’E-2 (CE-2) DOM, DEM, Slope, and integrated data with 7 kinds of visualization stretching methods. Then, we proposed the YOLO-Crater model for Lunar and Martian small crater detection by replacing EioU and VariFocal loss to solve the crater sample imbalance problem and introducing a CBAM attention mechanism to mitigate interference from the complex extraterrestrial environment. The results show that the accuracy (P = 87.86%, R = 66.04%, and F1 = 75.41%) of the Lunar YOLO-Crater model based on the DOM-MMS (Maximum-Minimum Stretching) dataset is the highest and better than that of the YOLOX model. The Martian YOLO-Crater, trained by the Martian dataset from the 2022 GeoAI Martian Challenge, achieves good performance with P = 88.37%, R = 69.25%, and F1 = 77.65%. It indicates that the YOLO-Crater model has strong transferability and generalization capability, which can be applied to detect small craters on the Moon and other celestial bodies.
A Computationally Efficient Approach for Resampling Microwave Radiances from Conical Scanners to a Regular Earth Grid
Authors: Mears Carl , Manaster Andrew , Wentz Frank
Published: October 2023

Satellite-borne microwave imagers are often operated as “conical scanners”, which use an off-axis paraboloid antenna that spins around an Earth-directed axis. As a result, individual measurements are arranged in curved “scans” on the Earth. Each measurement footprint is generally elliptical, with a range of alignments relative to fixed directions on the Earth. Taken together, these geometrical features present a challenge for users who want collocate microwave radiances with other sources of information. These sources include maps of surface conditions (often available on a regular latitude–longitude grid), information from other satellites (which will have a different, non-aligned scan geometry), or point-like in situ information. Such collocations are important for algorithm development and validation activities. Some of these challenges associated with collocating microwave radiances would be eliminated by resampling satellite data onto circular footprints on an Earth-fixed grid. This is because circular footprints help enable accurate collocations between satellite sensors on different platforms whose native footprints are usually ellipses canted at varying angles. Here, we describe a computationally efficient method to accurately resample microwave radiances onto circular footprints, facilitating comparisons and combinations between different types of geophysical information.
Local Differential Privacy Based Membership-Privacy-Preserving Federated Learning for Deep-Learning-Driven Remote Sensing
Authors: Zhang Zheng , Ma Xindi , Ma Jianfeng
Published: October 2023

With the development of deep learning, image recognition based on deep learning is now widely used in remote sensing. As we know, the effectiveness of deep learning models significantly benefits from the size and quality of the dataset. However, remote sensing data are often distributed in different parts. They cannot be shared directly for privacy and security reasons, and this has motivated some scholars to apply federated learning (FL) to remote sensing. However, research has found that federated learning is usually vulnerable to white-box membership inference attacks (MIAs), which aim to infer whether a piece of data was participating in model training. In remote sensing, the MIA can lead to the disclosure of sensitive information about the model trainers, such as their location and type, as well as time information about the remote sensing equipment. To solve this issue, we consider embedding local differential privacy (LDP) into FL and propose LDP-Fed. LDP-Fed performs local differential privacy perturbation after properly pruning the uploaded parameters, preventing the central server from obtaining the original local models from the participants. To achieve a trade-off between privacy and model performance, LDP-Fed adds different noise levels to the parameters for various layers of the local models. This paper conducted comprehensive experiments to evaluate the framework’s effectiveness on two remote sensing image datasets and two machine learning benchmark datasets. The results demonstrate that remote sensing image classification models are susceptible to MIAs, and our framework can successfully defend against white-box MIA while achieving an excellent global model.
A Machine-Learning-Based Access Point Selection Strategy for Automated Guided Vehicles in Smart Factories
Authors: Ohori Fumiko , Yamaguchi Hirozumi , Itaya Satoko , Matsumura Takeshi
Published: October 2023

Automated Guided Vehicles (AGVs) are becoming popular at many manufacturing facilities. To ensure mobility and flexibility, AGVs are often controlled by wireless communication, eliminating the constraints of physical cables. These AGVs require multiple Access Points (APs) to ensure uninterrupted coverage across the site. As AGVs move, they need to switch between these APs seamlessly. A primary challenge is that the communication downtime during this link-switching process must be minimal for effective AGV monitoring and control. Current AP selection strategies based on observed Received Signal Strength Indicator (RSSI) often fail in manufacturing environments due to RSSI’s inherent instability. This paper introduces a new AP selection technique for AGVs navigating these sites. Our approach harnesses the distinct movement patterns of AGVs and uses machine learning techniques to learn location-, trajectory-, and orientation-specific RSSI from the APs. Real-world factory data from our unique dataset revealed that our method extends the potential communication duration per route by 1.34 times compared to the prevalent signal strength-based switching methods commonly implemented in current drivers provided by chipset vendors or open-source Wi-Fi drivers. These results indicate that the automatic evaluation and tuning of the wireless environment using the proposed method is beneficial in reducing the time and effort required to investigate the detailed propagation paths needed to adapt AGV to existing APs.
Interconnect for Dense Electronically Scanned Antenna Array Using High-Speed Vertical Connector
Authors: Valizade Shahmirzadi Nooshin , Nikolova Natalia K. , Chen Chih-Hung
Published: October 2023

We present the design and the performance evaluation of a new interconnect for large-scale densely packed electronically scanned antenna arrays that utilize a high-speed digital board-to-board vertical connector. The application targets microwave tissue, imaging in the frequency range from 3 GHz to 8 GHz. The tissue-imaging arrays consist of hundreds of active antenna elements, which require low-reflection, low-loss, and low-crosstalk connections to their respective receiving and transmitting circuits. The small antenna size and the high array density preclude the use of coaxial connectors, which are also expensive and mechanically unreliable. Modern board-to-board high-speed connectors promise bandwidths as high as 12 GHz, along with high pin density, mechanical robustness, and low cost. However, their compatibility with the various transmission lines leading to/from the miniature printed antenna elements and microwave circuitry is not well studied. Here, we focus on the design of the transitions from coplanar waveguide transmission lines to/from a high-speed vertical connector. The performance of the interconnect is examined through electromagnetic simulations and measurements. Comparison is carried out with the expensive sub-miniature push-on sub-micro coaxial connectors commonly used in miniature radio-frequency electronics. The results demonstrate that high-speed vertical connectors can provide comparable performance in the UWB frequency range.
An Electrochemical Sensor of Theophylline on a Boron-Doped Diamond Electrode Modified with Nickel Nanoparticles
Authors: Jiwanti Prastika Krisma , Sari Anis Puspita , Wafiroh Siti , Hartati Yeni Wahyuni , Gunlazuardi Jarnuzi , Putri Yulia M. T. A. , Kondo Takeshi , Anjani Qonita Kurnia
Published: October 2023

Theophylline is a drug with a narrow therapeutic range. Electrochemical sensors are a potentially effective method for detecting theophylline concentration to prevent toxicity. In this work, a simple modification of a boron-doped diamond electrode using nickel nanoparticles was successfully performed for a theophylline electrochemical sensor. The modified electrode was characterized using a scanning electron microscope and X-ray photoelectron spectroscopy. Square wave voltammetry and cyclic voltammetry methods were used to study the electrochemical behavior of theophylline. The modified nickel nanoparticles on the boron-doped diamond electrode exhibited an electrochemically active surface area of 0.0081 cm2, which is larger than the unmodified boron-doped diamond’s area of 0.0011 cm2. This modified electrode demonstrated a low limit of detection of 2.79 µM within the linear concentration range from 30 to 100 µM. Moreover, the modified boron-doped diamond electrode also showed selective properties against D-glucose, ammonium sulfate, and urea. In the real sample analysis using artificial urine, the boron-doped diamond electrode with nickel nanoparticle modifications achieved a %recovery of 105.10%, with a good precision of less than 5%. The results of this work indicate that the developed method using nickel nanoparticles on a boron-doped diamond electrode is promising for the determination of theophylline.
On Indoor Localization Using WiFi, BLE, UWB, and IMU Technologies
Authors: Leitch Samuel G. , Ahmed Qasim Zeeshan , Abbas Waqas Bin , Hafeez Maryam , Laziridis Pavlos I. , Sureephong Pradorn , Alade Temitope
Published: October 2023

Indoor localization is a key research area and has been stated as a major goal for Sixth Generation (6G) communications. Indoor localization faces many challenges, such as harsh wireless propagation channels, cluttered and dynamic environments, non-line-of-sight conditions, etc. There are various technologies that can be applied to address these issues. In this paper, four major technologies for implementing an indoor localization system are reviewed: Wireless Fidelity (Wi-Fi), Ultra-Wide Bandwidth Radio (UWB), Bluetooth Low Energy (BLE), and Inertial Measurement Units (IMU). Sections on Data Fusion (DF) and Machine Learning (ML) have been included as well due to their key role in Indoor Positioning Systems (IPS). These technologies have been categorized based on the techniques that they employ and the associated errors in localization. A brief comparison between these technologies is made based on specific performance metrics. Finally, the limitations of these techniques are identified to aid future research.
Deep-Sea Biological Detection Method Based on Lightweight YOLOv5n
Authors: Ding Zhongjun , Liu Chen , Li Dewei , Yi Guangrui
Published: October 2023

Deep-sea biological detection is essential for deep-sea resource research and conservation. However, due to the poor image quality and insufficient image samples in the complex deep-sea imaging environment, resulting in poor detection results. Furthermore, most existing detection models accomplish high precision at the expense of increased complexity, and leading cannot be well deployed in the deep-sea environment. To alleviate these problems, a detection method for deep-sea organisms based on lightweight YOLOv5n is proposed. First, a lightweight YOLOv5n is created. The proposed image enhancement method based on global and local contrast fusion (GLCF) is introduced into the input layer of YOLOv5n to address the problem of color deviation and low contrast in the image. At the same time, a Bottleneck based on the Ghost module and simAM (GS-Bottleneck) is developed to achieve a lightweight model while ensuring sure detection performance. Second, a transfer learning strategy combined with knowledge distillation (TLKD) is designed, which can reduce the dependence of the model on the amount of data and improve the generalization ability to enhance detection accuracy. Experimental results on the deep-sea biological dataset show that the proposed method achieves good detection accuracy and speed, outperforming existing methods.
Edge Bleeding Artifact Reduction for Shape from Focus in Microscopic 3D Sensing
Authors: Park Sang-Ho , Park Ga-Rin , Baek Kwang-Ryul
Published: October 2023

Shape from focus enables microscopic 3D sensing by combining it with a microscope system. However, edge bleeding artifacts of estimated depth easily occur in this environment. Therefore, this study analyzed artifacts and proposed a method to reduce edge bleeding artifacts. As a result of the analysis, the artifact factors are the depth of field of the lens, object texture, brightness difference between layers, and the slope of the object. Additionally, to reduce artifacts, a weighted focus measure value method was proposed based on the asymmetry of local brightness in artifacts. The proposed reduction method was evaluated through simulation and implementation. Edge bleeding artifact reduction rates of up to 60% were shown in various focus measure operators. The proposed method can be used with postprocessing algorithms and reduces edge bleeding artifacts.
Entropy-Based Machine Learning Model for Fast Diagnosis and Monitoring of Parkinson’s Disease
Authors: Belyaev Maksim , Murugappan Murugappan , Velichko Andrei , Korzun Dmitry
Published: October 2023

This study presents the concept of a computationally efficient machine learning (ML) model for diagnosing and monitoring Parkinson’s disease (PD) using rest-state EEG signals (rs-EEG) from 20 PD subjects and 20 normal control (NC) subjects at a sampling rate of 128 Hz. Based on the comparative analysis of the effectiveness of entropy calculation methods, fuzzy entropy showed the best results in diagnosing and monitoring PD using rs-EEG, with classification accuracy (ARKF) of ~99.9%. The most important frequency range of rs-EEG for PD-based diagnostics lies in the range of 0–4 Hz, and the most informative signals were mainly received from the right hemisphere of the head. It was also found that ARKF significantly decreased as the length of rs-EEG segments decreased from 1000 to 150 samples. Using a procedure for selecting the most informative features, it was possible to reduce the computational costs of classification by 11 times, while maintaining an ARKF ~99.9%. The proposed method can be used in the healthcare internet of things (H-IoT), where low-performance edge devices can implement ML sensors to enhance human resilience to PD.
Scene Uyghur Recognition Based on Visual Prediction Enhancement
Authors: Liu Yaqi , Kong Fanjie , Xu Miaomiao , Silamu Wushour , Li Yanbing
Published: October 2023

Aiming at the problems of Uyghur oblique deformation, character adhesion and character similarity in scene images, this paper proposes a scene Uyghur recognition model with enhanced visual prediction. First, the content-aware correction network TPS++ is used to perform feature-level correction for skewed text. Then, ABINet is used as the basic recognition network, and the U-Net structure in the vision model is improved to aggregate horizontal features, suppress multiple activation phenomena, better describe the spatial characteristics of character positions, and alleviate the problem of character adhesion. Finally, a visual masking semantic awareness (VMSA) module is added to guide the vision model to consider the language information in the visual space by masking the corresponding visual features on the attention map to obtain more accurate visual prediction. This module can not only alleviate the correction load of the language model, but also distinguish similar characters using the language information. The effectiveness of the improved method is verified by ablation experiments, and the model is compared with common scene text recognition methods and scene Uyghur recognition methods on the self-built scene Uyghur dataset.