bluuebunny/crossref_metadata_2025 huggingface dataset:
Wi-Fi Drive: Wireless Handheld Storage Device
Authors: Rajan Deva
Published: August 2023

Recent advances in Internet-of-Thing’s technology have opened the doors to new scenarios for biosensor applications. Flexibility, portability, and remote control and access are of utmost importance to move these devices to people’s homes or enterprises. More external hard drives and pen drives appearing, more issues are reported by users. Also, many users reported that their external hard drive keeps disconnecting in Windows 10. Some specific models also have issues. In case you have some important data that has been affected by infected files, viruses or malware, you surely will need a good protection. Protect from file loss, malware, hardware failure. In this project, an innovative wireless handheld storage device maned Wi-Fi Drive is a wireless flash disk is presented. Wi-Fi Drive Model include machine learning workloads, query-intensive data warehouses, and ingestion and processing of IoT sensor data. When connected with power supply, can be used as wireless storage. Backup phone contacts and photos. It can back up data to cloud storage providers with the Dashboard software. With that program, one can access their files on the hard drive via the web. No longer have to connect your drive to your PCs to transfer data, since the device also supports both Android and iOS. Then users can also transfer files between other cloud services. You can also attach, detach, swap, and edit volumes. The hard drives give you remote file access, extra cloud storage and have handy web backup options. You can add as many accounts as you like, including multiple accounts of the same service. To keep your accounts and files safe, the program has a master password feature. Enable it to keep unauthorized users out. You can also encrypt data with a key before uploading.
Multiple Constraints Source to K- Terminal Reliability Optimization under Common Cause Failures
Authors: Hazarika Kalpana , Bhadoria Vikas Singh , Khemaria Manish
Published: August 2023

Many systems like Gas pipelines, Irrigation systems, Fire Fighting Systems, etc., their satisfactory performance or reliability does not only depend on proper connectivity but also on delivering a required demand. Hence, reliability Optimization (RO) of such networks with Multiple Constraints i.e. Cost, Capacity, and Connectivity (CCC) is a complex decision. The common practices of RO are mostly observed as optimization at the component level compared to other alternative possibilities like right of way, feasibility of structural changes and capital investment for it etc. Till now extensive works have been proposed by many authors on the RO of SKT networks with single or multiple constraints using Exact and Approximate Methods. But RO of SKT Network with multiple constraints under Common Cause Failure (CCF) is a barely studied area. To demonstrate the proposed methodology, a single source to five terminals irrigation network is considered here. This paper is presented in three successive sections, Section A- Mathematical Modelling of SKT network applying Exhaustive Search Method to enumerate the successful paths with intended delivery capacity. Section B- Heuristic Approach is used for Reliability Optimization of SKT network with s-identical redundant components under cost constraints. Section C - Demonstrate the effect of CCFs using Poison Failure processes. In Conclusion Reliability of the SKT network with and without CCF is compared to show the significant effects of CCF. For its analysis, author has considered the irrigation network and assuming the common tendency of network optimization with s-identical components to explain how unknowingly the affect CCF get introduced due to selection of s-identical components especially in an identical operating conditions.
An Automated Classification for Speaker Identification System Based on Regional Accent
Authors: Singh Mahesh
Published: August 2023

The authors have requested that this preprint be removed from Research Square.
Microbial interaction between human skin and Nukadoko, a fermented rice bran bed for pickling vegetables
Authors: Niwa Ryo , Chen Dominique , Seong Young ah , Jo Kazuhiro , Ito Kohei
Published: August 2023

The skin microbiome, which varies widely between individuals, plays a crucial role in human health. It also interacts with the environment in various ways, including during the preparation of fermented food. Nukadokois a pickle and traditional fermented food in Japan that utilizes lactic acid bacteria to ferment vegetables. The microbial composition of Nukadoko contributes to the flavor of pickled products. However, the origin of the microbes present in the rice bran bed has yet to be determined and no studies have examined how the skin microbiome affects the quality of Nukadoko. In this study, the effects of interaction of microbes between Nukadoko and the human skin during Nukadokomaintenance were studied. Three participants were asked to stir commercially available late-stage Nukadoko for 14 days and not stir it for the remaining 14 days to examine microbial settlement and shedding. Microbiome analysis was performed on human skin and Nukadoko. We found that microorganisms from rice bran beds can temporarily settle on human skin but are shed quickly. Stirring rice bran beds by hand may have short-term effects on the skin microbiome. This study provides insights into the communication between human and food microbiomes in traditional Japanese fermented foods.
BlueEdge: Application Design for Big Data Cleaning Processing using Mobile Edge Computing Environments.
Authors: Elmobark Nagwa Elmobark Nagwa , El-ghareeb Haitham El-ghareeb Haitham , Elhishi Sara Elhishi Sara
Published: August 2023

With the rapid growth of the Internet of Things (IoT) and the emergence of big data, handling massive amounts of data has become a major challenge. Traditional approaches involve sending raw data to cloud data centers for cleaning, processing, and interpretation using data warehouse tools. However, this study introduces BlueEdge, a fog edge mobile application that aims to shift the cleaning and preprocessing tasks from the cloud to the edge. We compare BlueEdge with four popular data cleaning tools (WinPure, DoubleTake, WizSame, and DQGlobal) that operate within data warehouse architectures, such as Hadoop servers. The comparison considers criteria such as time consumption, resource utilization (memory and CPU), and tool performance. BlueEdge utilizes Natural Language Processing (NLP) techniques via the Natural Language Toolkit (NLTK) and Python packages to connect with a real-time database. Our results demonstrate that BlueEdge successfully performs the same cleaning services in real time on the fog edge (mobile). It excels in handling data duplication elimination services, including different spelling and pronunciation, misspellings, name abbreviations, honorific prefixes, common nicknames, and split names. BlueEdge achieves high accuracy percentages, ranging from 90% to 100%, surpassing data warehouse applications running on cloud servers. Additionally, BlueEdge significantly reduces memory consumption to 5,000 bytes per edge on mobile devices, while data warehouses range from 10,000 to 60,000 bytes on Hadoop servers. Moreover, BlueEdge minimizes the data cleaning time to 1 second per edge, compared to the typical 4 to 30 seconds for data warehouses. Notably, BlueEdge can be executed by users on mobile devices without permission, and it is available free of charge.
Prevention and therapy of orchestra musicians’ playing-related musculoskeletal disorders with transcranial direct current stimulation: a pilot study
Authors: Krumpöck Paul , Ebenbichler Gerold , Knosp Christina , Roiger-Simek Ricarda-Samantha , Margreiter-Neuwirth Nicoletta , Neuwirth Wolfgang , Kasprian Gregor , Nenning Karl-Heinz , Schmidbauer Victor , Benca Emir , Sterz Fritz
Published: August 2023

Background
Orchestra musicians and music students complain about painful playing-related musculoskeletal disorders (PRMDs) without receiving appropriate therapeutical support. The aim of this pilot study was to provide a proof of concept of a new treatment regimen for PRMDs based on a comprehensive assessment. This regimen consisted of transcranial direct current stimulation (tDCS) in addition to physiotherapeutic exercises and psychological coaching. As a part of the assessment, we tried to demonstrate the feasibility of multiple new diagnostic tools that could potentially investigate the efficacy of the treatment regimen and elucidate pathophysiological mechanisms of PRMDs.
Methods
Musicians from the Orchestra Academy of the Vienna Philharmonic Orchestra were asked for their voluntary participation. Seven participated in their choice of self-administered tDCS sessions with concurrent physiotherapeutic exercises, physiotherapeutic and psychological assessments, the pain questionnaire, and medical imaging or 3D motion capture acquisitions. The feasibility of these methods was determined through observation of the investigators and participant feedback, as well as a safety questionnaire and a checklist.
Results
All pilot trials (n = 10) were completed successfully without any major issues. Some minor problems, such as lack of electrode connectivity or tingling/burning sensations of the scalp, were encountered by the participants during the tDCS sessions (n = 2/2 participants), the physiotherapeutic assessments (n = 1/3) and exercises (n = 1/2), the psychological assessments (n = 1/2) and the medical imaging (n = 1/2).
Conclusions
The trials showed very promising results for all investigated study parts, therefore demonstrating the feasibility and safety of studying this new treatment regimen in a randomized clinical trial with a larger sample of musicians. Also, the medical imaging and 3D motion capture acquisitions encourage further investigation to evaluate their potential diagnostic applicability for musicians’ PRMDs.
Mobile monitoring system to detect the disease activity pattern and predict clinical outcomes in patients with newly diagnosed Crohn’s disease
Authors: Lee Yoo Jin , Kwak Sang Gyu , Kim Eun Soo , Kim Sung Kook , Lee Hyun Seok , Chung Yun Jin , Jang Byung Ik , Kim Kyeong Ok , Kim Jeongseok , Jo Hyeong Ho , Kim Eun Young
Published: August 2023

We aimed to determine whether Crohn’s disease (CD) activity patterns depicted in a web-based symptom diary could help predict clinical outcomes in patients with newly diagnosed CD. Patients diagnosed with CD within 3 months were prospectively enrolled from four tertiary centres. They recorded their symptoms on a website using a smartphone at least once a week. The index outcomes were disease-related admissions and surgery during follow-up. The disease activity from enrolment to outcome or last follow-up was reviewed for pattern analysis. Cox regression was used to identify the predictors of disease outcomes. Among 135 patients with new CD, 102 were enrolled in the study. During a median follow-up period of 42 months, 25 (24.5%) and 6 (5.9%) patients required admission and surgery, respectively. Poor activity pattern was an independent predictor of disease-related hospitalisation (adjusted hazard ratio [aHR], 3.96; 95% confidence interval [CI], 1.5–10.45; p=0.005). A poor activity pattern (aHR, 19.48; 95% CI, 1.86–203.95; p=0.013) and female (aHR, 11.28; 95% CI, 1.49–85.01; p=0.018) were found to be independent predictors of bowel resection. CD disease activity patterns monitored through the mobile monitoring system could predict clinical outcomes, such as disease-related hospitalisation and surgery, in patients with newly diagnosed CD.
The Beauty Premium: Physicians’ Appearance and Patients’ Selection and Evaluation Behavior in Online Health Communities
Authors: Yang Yanbin , Ma Chengyu , Liu Haopeng
Published: August 2023

Background:With the development of information technology, online health communities (OHCs) are becoming an increasingly popular source of health information.
Objectives: While the impact of appearance has been extensively studied in various research fields, its role in online health communities has received little attention. This study aims to evaluate the effect of physicians’ appearance on patients’ selection and evaluation behavior in online health communities, and explore the difference in the beauty premium among different types of physicians.
Methods: Over 13,000 images of physicians and their relevant information were collected from Good Doctor website, which is now China’s leading OHC. We identified facial features in physicians’ photos based on deep learning and used Ordinary Least Squares (OLS) regression models to estimate the relationship between physicians’ appearance and patients’ behavior. We addressed the potential endogeneity issue by utilizing Propensity Score Matching (PSM) method.
Results:This study found that physicians’ appearance positively impacts patients’ selection and evaluation behavior. In addition, heterogeneity analysis showed that the appearance of male, high titles, longer service duration, and surgical diseases physicians have a greater influence on patients’ behaviors.
Conclusions: The beauty premium exists in online health communities. Therefore, this study provides new evidence on the impact of physicians’ facial appearance in online health communities, and provides useful insights for patients, physicians, and platforms about the relationship between physician’s structured or unstructured information and patients’ decision-making behaviors.
EuCARE-POSTCOVID Study: A Multicentre Cohort Study on Long-Term Post-COVID-19 Manifestations
Authors: Varisco Benedetta , Bai Francesca , De Benedittis Sara , Tavelli Alessandro , Cozzi-Lepri Alessandro , Sala Matteo , Miraglia Federica Gaia , Santoro Maria Mercedes , Ceccherini-Silberstein Francesca , Shimoni Yishai , Ravid Sivan , Kozlovski Tal , Köning Florian , Pfeifer Nico , Mommo Chiara , Marchetti Giulia
Published: August 2023

Background. Post-COVID-19 condition refers to persistent or new onset symptoms occurring three months after acute COVID-19, which are unrelated to alternative diagnoses. Symptoms include fatigue, breathlessness, palpitations, pain, concentration difficulties ("brain fog"), sleep disorders, and anxiety/depression. The prevalence of post-COVID-19 condition ranges widely across studies, affecting 10-20% of patients and reaching 50-60% in certain cohorts, while the associated risk factors remain poorly understood.
Methods. This multicentre cohort study, both retrospective and prospective, aims to assess the incidence and risk factors of post-COVID-19 condition in a cohort of recovered patients. Secondary objectives include evaluating the association between circulating SARS-CoV-2 variants and the risk of post-COVID-19 condition, as well as assessing long-term residual organ damage (lung, heart, central nervous system, peripheral nervous system) in relation to patient characteristics and virology (variant and viral load during the acute phase). Participants will include hospitalised and outpatient COVID-19 patients diagnosed between 01/03/2020 and 01/02/2025 from 8 participating centres. A control group will consist of hospitalised patients with respiratory infections other than COVID-19 during the same period.
Patients will be followed up at the post-COVID-19 clinic of each centre at 2-3, 6-9, and 12-15 months after clinical recovery. Routine blood exams will be conducted, and patients will complete questionnaires to assess persisting symptoms, fatigue, dyspnoea, quality of life, disability, anxiety and depression, and post-traumatic stress disorders.
Discussion. This study aims to understand post-COVID-19 syndrome's incidence and predictors by comparing pandemic waves, utilising retrospective data. Gender association, especially the potential higher prevalence in females, will be investigated. Symptom tracking via questionnaires and scales will monitor duration and evolution. Questionnaires will also collect data on vaccination, reinfections, and new health issues. Biological samples will enable future studies on post-COVID-19 sequelae mechanisms, including inflammation, immune dysregulation, and viral reservoirs.
Trial registration: This study has been registered with ClinicalTrials.gov under the identifier NCT05531773.
A novel, timely, cost-effective, and non-invasive photography-based technique for standard length measuring and individual identification of seahorses
Authors: Luzzatto Diego , Cussac Victor
Published: August 2023

This study aimed to develop a non-invasive and efficient method for measuring and identifying individual seahorses (Hippocampus patagonicus) in their natural habitat. A total of 976 seahorses were captured and photographed on a measuring board for standard length (Ls) measurements. Head photographs were also taken for individual recognition. Seahorses were tagged with visible implant elastomers (VIE) for calibration. The analysis showed no significant difference between left and right Ls measurements. The unique pattern of white dots on the heads served as individual fingerprints, consistent with VIE tagging. The recapture rate was 12%, with 89 individuals observed multiple times. Two distinct growth patterns were identified: males exhibited higher growth rates and a negative correlation with Ls compared to females. Released seahorses displayed different behaviors, with significant differences in Ls among these behaviors. Smaller seahorses tended to swim slowly towards nearby holdfasts, while larger seahorses escaped further or remained rigid before grasping a holdfast. The proposed methodology allowed for estimating individual seahorse growth rates, and the measurements were objective and precise. The results were obtained through quick and minimally invasive manipulation of the observed individuals.
VAPI: Vectorization of Algorithm for Performance Improvement
Authors: Yashar Mahmood , Rashid Tarik A.
Published: August 2023

This study presents the vectorization of metaheuristic algorithms as the first stage of vectorized optimization implementation. Vectorization is a technique for converting an algorithm, which operates on a single value at a time to one that operates on a collection of values at a time to execute rapidly. The vectorization technique also operates by replacing multiple iterations into a single operation, which improves the algorithm's performance in speed and makes the algorithm simpler and easier to be implemented. It is important to optimize the algorithm by implementing the vectorization technique, which improves the program's performance, which requires less time and can run long-running test functions faster, also execute test functions that cannot be implemented in non-vectorized algorithms and reduces iterations and time complexity. Converting to vectorization to operate several values at once and enhance algorithms' speed and efficiency is a solution for long running times and complicated algorithms. The objective of this study is to use the vectorization technique on one of the metaheuristic algorithms and compare the results of the vectorized algorithm with the algorithm which is non-vectorized.
Prevalence of high-risk HPV genotypes and associated factors among women living with HIV in Uganda: A facility-based cross-sectional study
Authors: Nakigozi Harriet , Ndejjo Rawlance , Bazeyo William , Nabaggala Annet , Achola Caroline , Iga Moses , Kalyesubula Simeon , Kanamwangi Ben , Mutungi Gerald , Batte Charles , Mukunya David , Sserwanga Lawrence , Gemageine Godfrey , Oyoo Charles Akiya , Nabadda Susan
Published: August 2023

Background: Women living with HIV are at risk for cervical dysplasia and cancer worldwide. In 2015, the World Health Organization (WHO) recommended that testing for high-risk HPV (hrHPV) infection be incorporated into cervical cancer screening programs using molecular nucleic acid tests (NATs) but this has not previously been done in Uganda. The country’s coverage for Human Papilloma Virus (HPV) screening remains low at less than 10% for women aged 25 - 49 years. This study determined the prevalence and associated factors of high-risk Human Papilloma Virus genotypes among women living with HIV in Uganda.
Methods: A descriptive cross-sectional study was conducted in 15 selected health facilities among participants who were on Antiretroviral therapy (ART). Participants who consented to participate were instructed on how to collect their own high vaginal swabs using a cervical brush for HPV molecular testing (HPV DNA or HPV RNA) and their demographics data was collected using a standard questionnaire. Laboratory diagnosis for HPV molecular testing was done using Gene xpert machines and Hologic Aptima Machine. Modified Poisson regression analysis was conducted to determine the associated factors.
Results: This study involved 5856 HIV positive participants on ART. A total of 2006 out of 5856 (34.3%) participants had high risk HPV genotypes. The prevalence of the individual genotypes were: HPV16 317(15.8%), HPV 18-45 308 (15.4%) and other high-risk HPV 1381 (68.8%). The independent factors associated with all hrHPV, and the individual genotypes (16, 18=45 and other hrHPV) were parity, education level, having more than one partner, and engaging in early sex. Smoking was associated with HPV 16, HPV 18-45 and other hrHPV. Age was associated with all hrHPV and HPV 18-45, marital status with all hrHPV and HPV 16, and occupation with HPV 16.
Conclusions. The prevalence of high-risk HPV genotypes among HIV positive women attending ART clinics in public facilities in Uganda was high. Other hrHPV genotype was the commonest compared to 18-45 and HPV 16. The integration of cervical cancer screening in ART programmes remains paramount to support the early detection of cervical cancer and Non-invasive self-collected urine and vaginal sampling for cervical cancer screening present an opportunity.
Dynamics analysis and image encryption application of Hopfield neural network with a novel multistable and highly tunable memeristor
Authors: Yao Wei , Liu Jiapei , Sun Yichuang , Zhang Jin , Yu Fei , Cui Li , Lin Hairong
Published: August 2023

Building neural network models and studying their dynamic behaviours is extremely important from both a theoretical and practical standpoint due to the rapid advancement of artificial intelligence. In addition to its engineering applications, this article concentrates primarily on the memristor model and chaotic dynamics of the asymmetric memristive neural network. First, we develop a novel-multistable, highly-tunable memristor model. Using this memristor model to build an asymmetric memristive neural network (AMNN), the chaotic dynamics of the proposed AMNN are investigated and analyzed using fundamental dynamics techniques such as equilibrium stability, bifurcation diagrams, and Lyapunov exponents. According to the findings of this study, the proposed AMNN possesses a number of complex dynamic properties, including scaling amplitude hyperchaos with coupling strength control, and coexisting unusual chaotic attractors with initial control and coupling strength control. Significantly, the neural networks demonstrate for the first time the phenomenon of infinitely persisting uncommon chaotic attractors. In the interim, a system for image encryption based on the proposed AMNN is constructed utilizing hyperchaotic attractors. By analyzing correlation, information entropy, and key sensitivity, the devised ciphering method reveals a number of benefits. The feasibility of the encryption system is validated through FPGA hardware experiments, and the proposed memristor and AMNN models have been translated into a Simulink model.
Method for Setting Speed Limit Value of Extra Long Tunnel
Authors: Zhaoyou MA , Zhigang DU , Junhua Wang , WANG Xiaoyan
Published: August 2023

Based on the feature analysis of the visual environment constituent elements of the urban expressway extra-long tunnel, the model test of the visual illusion effect of the driver on the visual information of the extra-long tunnel was designed to study the effect of different visual information conditions of the extra-long tunnel on the driver's illusion of speed. Through the simulation test of the operation safety and traffic efficiency comparison of the extra-long tunnel, the effect of different speed limit values on the operation safety and efficiency of the extra-long tunnel under different traffic flow conditions is compared. Comprehensively considering the driving environment of the extra-long tunnel and the driving needs of the driver, a method and process for determining the speed limit value based on the functional positioning of the extra-long tunnel and the linear technical indicators, legal speed limit requirements, the driver's speed perception needs, and the dynamic balance of operational safety and efficiency are proposed. Standardize and guide the setting of speed limit values for extra-long tunnels, and create a good driving environment and speed law enforcement environment.
2040 greenhouse gas reduction targets and energy transitions in line with the EU Green Deal
Authors: Rodrigues Renato , Pietzcker Robert , Sitarz Joanna , Merfort Anne , Hasse Robin , Hoppe Johanna , Pehl Michaja , Ershad Ahmad Murtaza , Baumstark Lavinia , Luderer Gunnar
Published: August 2023

The European Green Deal aims to guide the European Union towards achieving net-zero greenhouse gas emissions by implementing a comprehensive set of policy initiatives and legislation. While emission reduction targets and policies up to 2030 are mostly implemented, it is of high priority for EU legislation to spell out the further transformation to climate neutrality by defining interim policy targets for 2040. Here we explore, based on an integrated energy-economy-climate model with high sector detail, pathways to achieve climate neutrality in the EU under uncertainty about key energy system developments. Results suggest that emission reductions of 87–91% by 2040 relative to 1990 are consistent with a cost-efficient distribution of mitigation efforts over time, substantially exceeding the 78%-level implied by a linear interpolation between the 2030 and 2050 goals. Additionally, we identify a 5-7-fold upscaling of electricity generation from wind and solar, a 44–50% share of electricity in final energy supply and an upscaling of Carbon Capture and Storage to 120–330 Mt CO2/yr as crucial transformation milestones for 2040.
Toe brachial index and not ankle brachial index is a surrogate to CT angiography in the evaluation of peripheral arterial disease in type 2 diabetes
Authors: Singhania Pankaj , Das Tapas Chandra , Bose Chiranjit , Mondal Asif , Bhattacharjee Rana , Singh Archana , Mukhopadhyay Satinath , Chowdhury Subhankar
Published: August 2023

Background
Non-invasive clinic-based tools for assessing PAD are not without limitations. Therefore, costly tests like Doppler study, CT angiography and MR angiography must be resorted to. Ankle Brachial Index (ABI), commonly used for assessment of PAD, have high false positivity rates in sclerosed, calcified arteries which render them non-compressible. Toe Brachial Index (TBI) can be an alternative, as digital arteries are relatively unaffected by these changes.
Aim
To compare the reliability of ABI and TBI in diagnosing PAD using CT angiography (CTA) as the reference and identify the best surrogate to CTA.
Methods
A total of 350 limbs from 175 adults with T2D were selected. ABI &TBI were measured with an automated vascular Doppler XT 6 ports bilaterally for all subjects. For any subject, the limb with lower ABI and TBI was included for analysis. ABI < 0.9 & TBI < 0.6 were taken as evidence of PAD. CTA showing > 50% narrowing was taken as evidence of PAD.
Results
24% of our study subjects had CTA confirmed PAD. ABI has low sensitivity of 38% (95% CI: 0.21–0.52) compared to TBI being 90% (95% CI: 0.66–0.92). The specificity however was similar. ABI < 0.9 was able to detect CTA confirmed PAD, but ABI > 0.9, including the so-called normal ABI (0.9–1.3) was unable to detect PAD. ROC showed ABI at 1.005 has sensitivity 64.71% (95% CI: 0.48–0.79) and specificity 61.7% (95% CI: 0.53–0.69) and TBI at 0.6 has sensitivity 82.35% (95% CI: 0.66–0.92) & specificity 92% (95% CI:0.87–0.96). Utilizing Cohen’s Kappa, the reliability of ABI with respect to CTA showed fair agreement (K = 0.225, p = 0.001), whereas the reliability of TBI with respect to CTA showed substantial agreement (K = 0.759, p < 0.0001)
Conclusion
ABI < 0.9 detects PAD reliably, but presence of PAD in patients with ABI > 9.0 including the normal of ABI (0.9–1.3) can be confirmed with TBI, which correlated strongly with CTA. TBI is also non-inferior for PAD detection, when ABI < 0.9. TBI and not ABI can be utilized as an apt surrogate to CTA for assessment of PAD in subjects with T2D.
RAD-Seq analysis of wild Japanese garlic (Allium macrostemon Bunge) growing in Japan revealed that this neglected crop was previously actively utilized
Authors: Probowati Wiwit , Koga Shogo , Harada Kentaro , Nagano Yukio , Nagano Atsushi J. , Ishimaru Kanji , Ohshima Kazusato , Fukuda Shinji
Published: August 2023

Wild Japanese garlic (Allium macrostemon Bunge), commonly referred to as "no-biru" in Japan, is a widespread wild onion species found across the country. Despite being deeply entwined in ancient Japanese culture, as evidenced by numerous references in classical literature, it remains an underutilized crop in Japan, contrasting its culinary applications in China and Korea. Determining the origins of its domestic populations and understanding their genetic composition is crucial to highlighting the plant's historical significance in Japan. This study aims to bridge this knowledge gap by examining the genetic diversity of 47 A. macrostemon samples from various regions in Japan using restriction site-associated DNA sequencing (RAD-Seq). Our analysis distinguished unique population structures, dividing the samples into three distinct groups: A, B, and C. Notably, Groups A and B showed clear evidence of bulb propagation, while Group C diverged from this pattern, forming four subgroups C1, C2, C3, and C4. Hybridization between subgroup C1 and either Group A, B, or both, gave rise to subgroups C2, C3, and C4. Thus, Groups A, B, and C1 are posited as the ancestral populations. Additionally, our morphological observations indicated distinct differences among these three groups. Our findings also suggest that human migration may have influenced the plant's distribution, hinting at active usage in the past that later waned, causing its current underutilized status. Gaining insight into the genetic diversity of A. macrostemon has significant implications. It could improve its desirable traits, such as bulb and leaf quality for culinary purposes, potentially triggering a renaissance in its use as a food source in Japan.
Isolation and Structure Elucidation of Bis(2-ethylhexyl) terephthalate from barks of Acacia xanthophloea
Authors: Michael Cheloti , Phanice Wangila , Ambrose Kiprop , Arthur Onyuka , Solomon Derese , Mukavi Justus , Ingrid Wekesa , Emmanuel Kariuki , Gladys Mugo , Martin Murimi
Published: August 2023

A common and native tree of Eastern and Southern Africa, Acacia xanthophloea is a species of tropical plants and a member of the Fabaceae family.Although A. xanthophloea is known as a source of tannins, very little is known about its phytochemistry. In this context, the main objective of this work was to isolate and characterize the structures of secondary metabolites found in the bark of this plant. Substantial amounts of bis(2-ethylhexyl) terephthalate [DEHT/ dioctyl terephthalate (DOTP)] were isolated in a fraction of the bark extract while undertaking the isolation of phytochemicals from A. xanthophloea barks. The structure of this compound was confirmed by NMR, and mass spectroscopic data. DEHT is a terephthalic acid ester, the main plasticizer that is used to confer elasticity and flexibility to various fiber and plastic products. To the best of our knowledge, this is the first time DEHT has been isolated from the barks of A. xanthophloea. This study contributes to the growing evidence of plasticizers in our food and drug sources.
Effect of Content and Language Integrated Learning/ CLIL/ on EFL students’ reading motivation and comprehension in Ethiopia
Authors: Etefa Fanta , Yigzaw Abiy , Shifere Kassie , Tezera Melku
Published: August 2023

The purpose of this study was to examine the effect of content and language integrated learning/ CLIL/ on EFL students’ reading comprehension and reading motivation in Ethiopia. To this end, a quasi-experimental pretest –posttest design was used. Thus, sixty- nine EFL learners from two randomly selected sections of grade nine were used as the study’s participants. The sections were then randomly assigned as control (n=35) and experimental (n=34) groups. To collect data, a reading motivation questionnaire and reading comprehension test were utilized. Pretests were first given to both groups to examine if there were mean differences between the two groups. Then, treatment on CLIL reading strategies was given to the experimental group. Thenceforth, posttests were given to both groups. Data were analyzed using descriptive statistics, namely mean, standard deviation and inferential statistics of independent samples t-test, paired t-test, Mann-Whitney U Test and Wilcoxon Signed Rank Test. The findings from data analysis revealed that CLIL had a significant positive effect on students’ reading comprehension and motivation. Moreover, the findings signposted that CLIL approach needed to be incorporated in EFL teaching materials in Ethiopia
An efficient and reliable protocol on in vitro propagation of ‘Colt’ (Prunus avium F 299/2 x Prunus pseudocerasus Lind) rootstock of cherry
Authors: Mir M. A. , Manzoor Ikra , Kashani Syed Zainab , Attia Houneida , Bhat Momin Showkat , Abidi Ishfaq. A. , Alamer Khalid H. , Dar Showket Ahmad , Lone Showkat A , Iesa Mohamed A. M. , Al-Qarni Maha , Fekry Waleed M.E. , Sultan Rania Mohammad Sabri , Ainusairi Ghalia S.H. , Zoubi Omar Mahmoud Al , Alharbi Basmah M. , Moqbel Fahmi S , Youssef Rawa M
Published: August 2023

Background The 'Colt' is a triploid hybrid of P. avium and P. pseudocerasusL. grown as a cherry rootstock because of its resistance to cherry stem pitting (a debilitating virus disease), Phytophthora root rot, bacterial canker, and gopher damage.
ResultsThe current study was carried out using two types of explants: forced (F1) and unforced (F2) and five types of sterilants were used to sterilize explants: 10% sodium hypochlorite (NaOCl) for 10 minutes (S1), 0.1% mercuric chloride (HgCl2) for 5 minutes (S2), ethyl alcohol for 10 seconds (S3), (S1) + (S3) & (S2) + (S3) for sterilization. During culture establishment, six growth regulator regimes viz., BA (Benzyl adenine) (0.20), (0.40) & (0.60) and BA+IBA (Indole-3-butyric acid) (0.20+0.01), (0.40+0.01), and (0.60+0.01) mg/l, two media i.e., MS (Murashige and Skoog, 1962)& WPM (Woody Plant Medium)& two types of explants viz., forced (F1) & unforced shoot tips(F2) were utilized. During shoot proliferation, MS & WPM culture medium with growth regulators viz., BA (0.20), (0.40) & (0.60), BA+IBA (0.20+0.01), (0.40+0.01) and (0.60+0.01) mg/l were used respectively. During rhizogenesis using different media (MS & WPM) with various levels of IBA (0.50), (1.00) (1.50), and (2.00) mg/l, respectively were used. The superior rooted plants were hardened in sand & cocopeat respectively.
Conclusion In this experiment, maximum culture asepsis (%) obtained in unforced shoot tips(F2) with (S5),maximum explant survival (%) in forced shoot tips (F1) with (S4), and highest establishment (%) with MS medium, BA (0.6) mg/l & in forced shoot tips (F1). Significantly higher shoot proliferation is obtained with MS medium with BA (0.6) mg/l , maximum shoot number & length in WPM medium with BA (0.2) mg/l. A significantly higher rooting percentage was obtained in MS medium with IBA (1.00) mg/l with maximum ex vitro survival (%) in cocopeat.
Absolute and non-invasive determination of the electron bunch length in a Free Electron Laser using a Bunch Compressor Monitor
Authors: Orlandi Gian Luca
Published: August 2023

In a linac driven Free Electron Laser (FEL), the shot-to-shot and non-invasive monitoring of the electron bunch length is normally ensured by Bunch Compressor Monitors (BCMs). The bunch-length dependent signal of a BCM results from the detection and integration - over a given frequency band - of the temporal coherent enhancement of the radiation spectral energy emitted by the electron beam while experiencing a longitudinal compression. In this work, we present a method that permits to express the relative variation of the bunch length as a function of the relative statistical fluctuations of the BCM and charge signals. Furthermore, in the case of a BCM equipped with two detectors simultaneously operating in two distinct wavelength bands, the method permits an absolute determination of the electron bunch length. The proposed method is beneficial to a FEL since it permits to tune the machine compression feedback with respect to the absolute measurement of the bunch length instead of a bunch-length dependent signal. In a CW-superconducting-linac driven FEL, it can offer the precious opportunity to implement a fully non-invasive and absolute diagnostics of the bunch length.
A New Ensemble Method for Brain Tumor Segmentation
Authors: Laouali Souleymane Mahaman , Chebbah Mouna , Nakouri Haïfa
Published: August 2023

In the medical field, Magnetic resonance image (MRI) brain tumor segmentation
is highly crucial and important. Tumor segmentation can
help in diagnosis and prognosis, overall growth predictions, Tumor density
measures, and care treatment plans needed for patients. However,
this task is extremely challenging due to low contrast, noise in medical
images, and to the voluminous size of data. This work focuses on
improving medical image semantic segmentation using a time-efficient
ensemble learning approach. We propose a novel ensemble deep approach
using a Convolutional Neural Network and three Autoencoders to extract
relevant features from brain MRIs, reduce dimensionality, then apply
supervised learning for pixel-by-pixel binary classification to achieve
tumor segmentation. Experiments show promising results with our model
achieving a Dice Similarity Coefficient of 88.63%, a Jaccard Similarity
Index of 79.58% and a Recall of 84.34%, thus demonstrating
the model’s ability to identify the tumor’s pixels with precision while
consuming fewer resources. Therefore, the segmentation performance
is sufficiently addressed in terms of accuracy, reliability and speed.
Exergoeconomic analysis and optimization of wind power hybrid energy storage system
Authors: Wen Caifeng , Lv Yalin , Du Qian , Zhang Boxin , Lian Xuhui , Wang Qiang
Published: August 2023

Taking the thermal-electric hybrid energy storage wind power system with the effective combination of thermal energy storage and battery energy storage as the research object, the exergoeconomic characteristics of the system are studied experimentally. Firstly, RSM is used to optimize the power quality and exergoeconomic characteristics of the system, and the optimal operating conditions are obtained. Then, an optimization strategy is proposed based on MOPSO algorithm to explore the effective ways to improve the exergetic efficiency of each subsystem and reduce the system exergy loss and system exergetic cost. The results show that the exergo-economics can effectively evaluate the generation-energy storage characteristics of the new wind power system of ' wind power + energy storage '. It is confirmed that the proposed optimization strategy and algorithm can avoid the situation of low η2, η3 and η when λ is small. When λ is large, the unit exergy cost and current harmonic distortion are further reduced, and the power quality and system exergy efficiency are improved while the unit exergy cost is reduced, so as to realize the optimal configuration of the thermal-electric hybrid energy storage wind power system.
DeepSearch: A deep learning based document retrieval system
Authors: Malik M. Shoaib , Klakow Dietrich , Waltemath Dagmar
Published: August 2023

Deep learning has enabled machines to be used for many tasks, including computer vision, image recognition, natural language processing, self-driving cars, and predicting the contingencies of certain business transactions. Effective application of deep learning techniques can help develop intelligent applications. Many web-based tools get their information from generic search engines such as Google but also specialized search engines such as PubMed (for retrieving information on biomedical publications). In this study, we evaluate the performance of different deep learning architectures for the task of document retrieval. Our goal was to identify the optimal architecture for the document retrieval task. We therefore compare the deep architecture of feed-forward neural networks with recurrent neural networks, in particular the long short-term memory neural networks. We observe that the local context is not sufficient as it does not provide reliable predictions, which are, however, provided by long distance dependencies where long short-term memory neural networks are used. We first describe the algorithms used for our evaluation and then compare their performance. Our dataset contained two publicly available document sets from the 20-Newsgroup and BBC, respectively.
One-pot, degradable, silica nanocarriers with encapsulated oligonucleotides for mitochondrial specific delivery
Authors: Trayford Chloe , Wilhalm Alissa , Habibovic Pamela , Smeets Hubert , Tienen Florence , Rijt Sabine van
Published: August 2023

Mutations in nuclear and mitochondrial genes are responsible for severe chronic disorders such as mitochondrial myopathies (MM). Gene therapy using antisense oligonucleotides is a promising strategy to treat mitochondrial DNA (mtDNA) diseases by blocking the replication of the mutated mtDNA. However, transport vehicles are needed for intracellular, mitochondria-specific transport of oligonucleotides. Nanoparticle (NP) based vectors such as large pore mesoporous silica nanoparticles (LP) often rely on surface complexation of oligonucleotides exposing them to nucleases and limiting mitochondria targeting and controlled release ability. In this work, stable, fluorescent, hollow silica nanoparticles (HSN) that encapsulate and protect oligonucleotides in the hollow core were synthesized by a facile one-pot procedure. Our HSN were dual-doped with rhodamine B isothiocyanate (RITC) and bis[3-(triethoxysilyl)propyl]tetrasulfide (BTES) to enable cell tracing, intracellular-specific degradation and controlled oligonucleotide release. We also synthesized LP as a benchmark to compare the oligonucleotide loading and release efficacy of our HSN. Mitochondria targeting was enabled by NP functionalization with TPP and, for the first time, MITO-porter. HSN exhibited high oligonucleotide incorporation ratios and release dependent on intracellular degradation. Further, MITO-porter capping of our NP enabled delayed, glutathione (GSH) responsive oligonucleotide release and mitochondria targeting at the same efficiency as TPP functionalized NP. Overall, our NP are promising vectors for anti-gene therapy of mtDNA disease as well as many other monogenic disorders worldwide.
Pollution by presumed domestic sewage in storm drainage in separated sewer areas
Authors: Ozaki Noriatsu , Kindaichi Tomonori , Ohashi Akiyoshi
Published: August 2023

The deteriorating state of infrastructure poses a threat to developed countries, emphasizing the need for the development of effective management and maintenance techniques. Among various concerns, the leakage of aged domestic sewer pipelines has become a significant issue. In this study, we measured hydrophobic organic pollutants such as PAHs, fragrance substances, and caffeine in standing water samples collected from storm drains in five different urban areas that have separate sewer systems. The pollution levels of PAHs did not differ significantly between the districts, while notable differences were observed in fragrance substances and caffeine. Their pollution levels tended to increase with the age of the sewer systems, with caffeine concentrations being over two orders of magnitude higher in districts with sewage systems aged over 40 years compared to those with systems just ten years old. Furthermore, from a comparison of different older storm discharge areas, different high-concentration patterns for between caffeine and musk fragrance substances, possibly reflecting different leakage situations.
Logistics performance, transaction cost economics, trade liberalization, and agricultural food worldwide
Authors: Shikur Zewdie Habte , Bahta Yonas
Published: August 2023

The purpose of this study is to examine the relationship between multi-explanatory variables and food trade in a global context to fill a research gap using System General Method of Moments (GMM) framework analysis that, to the best of our knowledge, has not been applied regarding this issue, our aim is to contribute to the existing literature. The study provides additional insight by explaining the link between transaction cost economics, logistics performance, and food trade, which remains unknown. Therefore, the paper provides further insights compared to the earlier work by shedding light on how various dimensions of logistics performance determine food export and import in light of the current situation of food shortages. The model results indicated that trade liberalization has a negative impact on food exports and imports. The adoption of chemical fertilizers has a significant and negative impact on food exports but is insignificant on food imports due to a significant shift in consumer demand from inorganic to organic foods globally. The model's findings show that logistics services, shipments, customs and clearance, and infrastructure are all significant and positive drivers of agricultural food exports and imports except for timeliness and tracking and tracing. The size of agricultural land is found to be positively and significantly related to the food trade. Therefore, countries should integrate logistics performance enhancement, transaction cost economics, and an organic production system into their policy formulation and implementation to increase food trade. A framework makes the countries with low transaction costs more likely to trade food with other low transaction cost countries and vice versa for high transaction costs.
Deep Convolutional Architectures for Extrapolative Forecasts in Time-dependent Flow Problems
Authors: Bhatt Pratyush , Kumar Yash , Soulaïmani Azzeddine
Published: August 2023

Physical systems whose dynamics are governed by partial differential equations (PDEs) find numerous applications in science and engineering. The process of obtaining the solution from such PDEs may be computationally expensive for large-scale and parameterized problems. In this work, deep learning techniques developed especially for time-series forecasts, such as LSTM and TCN, or for spatial-feature extraction such as CNN, are employed to model the system dynamics for advection-dominated problems. This paper proposes a Convolutional Autoencoder(CAE) model for compression and a CNN future-step predictor for forecasting. These models take as input a sequence of high-fidelity vector solutions for consecutive time steps obtained from the PDEs and forecast the solutions for the subsequent time steps using auto-regression; thereby reducing the computation time and power needed to obtain such high-fidelity solutions. Non-intrusive reduced-order modeling techniques such as deep auto-encoder networks are utilized to compress the high-fidelity snapshots before feeding them as input to the forecasting models in order to reduce the complexity and the required computations in the online and offline stages. The models are tested on numerical benchmarks (1D Burgers’ equation and Stoker’s dam-break problem) to assess the long-term prediction accuracy, even outside the training domain (i.e. extrapolation). The most accurate model is then used to model a hypothetical dam break in a river with complex 2D bathymetry. The proposed CNN future-step predictor revealed much more accurate forecasting than LSTM and TCN in the considered spatiotemporal problems.