bluuebunny/crossref_metadata_2025 huggingface dataset:
Analisis faktor-faktor yang mempengaruhi indeks harga pangan di Indonesia tahun 2000-2023
Authors: Nugroho Farid , Afandi Akhsyim
Published: July 2024

Purpose – This study aims to explore the factors that influence the food price index in Indonesia
Methods – This study uses ARDL analysis and is conducted using monthly time series data from January 2000 until August 2023
Findings – The result shows that money supply had a positive and significant impact on the food price index in Indonesia both in the short-run and long-run.
Implication – The Indonesian government needs to formulate a policy that resists increasing food prices, especially during sudden spikes in money supply.
Originality – The findings of this research can be used as an additional reference for the stakeholders and policymakers to control the movement of the food price index in Indonesia.
 
Abstrak
Tujuan – Penelitian ini merupakan sebuah upaya untuk menyelidiki faktor apa saja yang mempengaruhi indeks harga pangan di Indonesia
Metode – Penelitian ini menggunakan analisis ARDL dan menggunakan data time series bulanan dari Januari 2000 sampai dengan Agustus 2023
Temuan – Hasil estimasi ARDL menunjukkan bahwa pada jangka pendek dan jangka panjang jumlah uang beredar memberikan dampak positif dan signifikan terhadap indeks harga pangan di Indonesia.
Implikasi – Pemerintah Indonesia hendaknya dapat merumuskan suatu kebijakan yang dapat meredam kenaikan tingkat indeks harga pangan khususnya pada saat terjadi kenaikan tajam pada jumlah uang beredar.
Orisinalitas – Hasil penelitian ini dapat dijadikan sebagai referensi tambahan oleh pemangku kepentingan dan pembuat kebijakan dalam  merumuskan kebijakan terkait indeks harga pangan di Indonesia.
An Approach to a Group Movie Recommender System using Matrix Factorization-based Collaborative Filtering
Authors: Darmawan Faiha Adzra , Baizal Z. K. A.
Published: July 2024

The growth of online movie streaming platforms has driven the demand for recommender systems that are able to deal with the daunting challenge of users finding movies that match their preferences. However, these recommender systems tend to focus on the needs of individual users, whereas in the real world, there are circumstances in which recommendations are needed for a group of users. Therefore, this study proposes a Group Recommender Systems (GRS) using Matrix Factorization (MF) with aggregation model to recommend movies for a group of users. We employ three Matrix Factorization methods to three distinct group sizes, which are small, medium, and large. Our goal is to identify the most effective approach for each group size. To evaluate the performance, we use precision and recall as measurement metrics. The results show that the MF method, After Factorization (AF) outperforms the other MF methods, i.e., Before Factorization (BF) and Weighted Bfore Factorization (WBF) in terms of precision parameters for small groups (2-4 users), which achieving a score of 0.86. Meanwhile, BF method surpassing both AF and WBF in precision parameters for medium groups (5-8 users) with a score of 0.81.
Feature Extraction vs Fine-tuning for Cyber Intrusion Detection Model
Authors: Sanmorino Ahmad , Suryati Suryati , Gustriansyah Rendra , Puspasari Shinta , Ariati Nining
Published: July 2024

This study investigates the effectiveness of feature extraction and fine-tuning approaches in developing robust cyber intrusion detection models using the Network-based Security Lab - KDD dataset (NSL-KDD). The role of cyber intrusion detection is pivotal in securing computer networks from unauthorized access and malicious activities. Feature extraction, involving methods such as PCA, LDA, and Autoencoders, aims to transform raw data into informative representations, while fine-tuning leverages pre-trained models for task-specific adaptation. The study follows a comprehensive research method encompassing data collection, preprocessing, model development, and experimental evaluation. Results indicate that LDA and Autoencoders excel in the feature extraction phase, demonstrating precision, high accuracy, F1-Score, and recall. However, fine-tuning a pre-trained Multilayer Perceptron model surpasses individual feature extraction methods, achieving superior performance across all metrics. The discussion emphasizes the complexity and flexibility of these approaches, with fine-tuned models showcasing higher adaptability. In conclusion, this study provides valuable insights into the comparative effectiveness of feature extraction and fine-tuning for cyber intrusion detection. The findings underscore the importance of leveraging pre-trained knowledge and adapting models to specific tasks, offering a foundation for further advancements in enhancing network security through advanced machine learning techniques.
Sistem Monitoring dan Controlling “Smart waste” berbasis Internet of Things menggunakan modul ESP 32
Authors: Lianawati Yosita , Mahendra Christy , Sugianto Gerson Manuel , Mendrofa Syukur Jaya , Setiani Angelica Lulu , Baraga Bernarditus Yordan
Published: July 2024

The development of technology is one of the things that is currently popular and has become an innovation for our daily lives. The Internet of Things is a technology that combines electronic devices with the Internet and becomes an innovation for various kinds of problems that often occur. The problem that continues to happen and is difficult to handle is waste. Every day, the amount of waste increases, and the waste processing process is still a job that takes a long time and often becomes a source of disease. So, through these developments and problems, researchers created an innovation for handling waste using the Internet of Things, where this system can monitor and control the sorting of waste based on the categories of organic waste and plastic waste. The waste sorting results will be put into the waste shredding process, and raw materials for fertilizer will be produced from organic waste and plastic pellets from shredding plastic waste. Users can control and receive notification results if the enumeration storage area weighs more than 4 kg. Testing of the "Smart waste" Monitoring and Controlling System based on the Internet of Things using the ESP 32 module was carried out 30 times and obtained a successful result of 86.67%.
Keywords: Internet of Things, Smart waste, Monitoring, Controlling, Esp 32
Prototype PPG (Photoplethysmography) Secara Real-time sebagai Pendeteksi Dini Gangguan Detak Jantung Dilengkapi dengan Visual Graph pada Android
Authors: Mulyani Sri , Zarory Hilman , Ullah Aulia , Jufrizel Jufrizel
Published: July 2024

Kesehatan jantung adalah faktor vital dalam menjaga kesehatan tubuh manusia. Salah satu teknologi terkini untuk pemantauan kesehatan jantung adalah PPG (Photoplethysmography), yang memberikan data esensial mengenai detak jantung dan kondisi kardiovaskular. Penelitian ini bertujuan mengembangkan prototype PPG secara real-time dengan visualisasi grafis di platform android. Perancangan perangkat keras melibatkan sensor MAX30102, modul AMS1117, Arduino Uno, dan modul ESP8266. Bagian perangkat lunak mencakup pengembangan antarmuka pengguna menggunakan Cocos2d-x dan Android Studio, memungkinkan visualisasi sinyal PPG, pengukuran BPM, serta statistik seperti rata-rata BPM, 10 periode sinyal, dan standar deviasi. Hasil pengujian menunjukkan bahwa prototype mampu menampilkan sinyal PPG dengan baik dan stabil dalam 4.07 detik setelah penempatan jari pada sensor, menghasilkan nilai BPM dalam 5.92 detik, nilai rata-rata BPM dalam 11.49 detik, dan memulihkan stabilitas sinyal dalam 3.27 detik setelah gangguan input. Error mencapai 2.91% untuk nilai BPM dan 2.13% untuk nilai rata-rata BPM. Penelitian ini menjanjikan penggunaan alat deteksi detak jantung secara real-time dengan respons dan akurasi yang baik.
Sustainable recovery of silver nanoparticles from electronic waste: applications and safety concerns
Authors: Onsy Mohamed Abdel Mohsen
Published: July 2024

Electronic waste (e-waste) poses a significant environmental challenge because of the rapid proliferation of electronic devices. One valuable component of e-waste is silver, often present in the form of nanoparticles (AgNPs) due to their extensive use in electronics for their excellent conductive properties. AgNPs have been used in other applications such as textiles, healthcare, plasmonic, sensors, catalysis, antimicrobial activities, and wastewater treatment. Recovering AgNPs from e-waste mitigates environmental pollution and provides a sustainable source of this valuable material. However, their traditional synthesis methods raise environmental concerns. This paper evaluates the likelihood of convalescing AgNPs from e-waste, as a sustainable alternative, using the traditional recovery processes (chemically based) and bio-based technology, and proposes sustainable multistage processes for recovering based metals, precious metals, AgNPs, and nanosilica. Moreover, the use of AgNPs as an antimicrobial agent (for medical applications) and the detection of heavy metals, pesticides, and dyes in wastewater (for the environmental management industry) are evaluated. Finally, the environmental and health risks posed by using AgNPs are discussed. The results indicate that there is a high potential of recovering Ag and producing AgNPs from e-waste. In addition, to further enhance the bio-based technology, potential strategies for large-scale recovery processes have been highlighted.
Lesson Learned from Post-Disaster Psychosocial Support for Children in Palu, Central Sulawesi—Indonesia
Authors: Sokang Yasinta Astin , Omega Petrayuna Dian , Novianty Anita , Garey Evans , Dawan Anil , Hutapea Rully D. , Siantoro Ayu
Published: July 2024

In the aftermath of the devastating Palu disaster in September 2018, communities, especially children, suffered deep psychological trauma. At the request of faith leaders, local NGO Wahana Visi Indonesia (WVI), in partnership with Krida Wacana Christian University (UKRIDA), conducted a psychosocial support training program for volunteers, including Qur’an recitation teachers and Sunday school teachers in Palu. These trained volunteers then initiated a psychosocial support initiative for children in the area. Years later, WVI and UKRIDA conducted a reflective study of the program, collecting data from 40 volunteers through focus group discussions and surveys. The study identified key themes, including the effectiveness of the training, reflections on program implementation, lessons learned, and recommendations for future programs. The study underscores the importance of multi-stakeholder involvement, particularly that of faith-based organizations, in providing psychosocial support in disaster-affected areas. Given the recurring threat of disasters in the region, the study advocates for transparent collaboration among various social stakeholders. To build community resilience, disaster management strategies need to be widely disseminated, and the need for continuous training to strengthen community preparedness is emphasized. In addition, the development of culturally sensitive training modules tailored to the community’s language, age, and religious background is essential. Such a comprehensive commitment will ensure the community’s ability to deal effectively with future disasters. Detailed findings are discussed further in this paper.
Historic Flood Events and Current Flood Hazard in Ulaanbaatar City, Central Mongolia
Authors: Narangerel Serdyanjiv , Suzuki Yasuhiro
Published: July 2024

Climate change will increase the frequency of extreme weather events, alter rainfall patterns, and exacerbate flood disasters in Ulaanbaatar City. Here we combine aerial and satellite imagery with cadastral data, to scrutinize the historical trajectory of rainfall patterns and flood disasters in Ulaanbaatar over the past six decades. The study focusses on the causative factors behind historical floods, current flood conditions, the geographical distribution of floods, land ownership in floodprone areas, and the spatial allocation of fences and buildings based on social conditions. Over the last 60 years, Ulaanbaatar received a total of 16,780 mm of precipitation, with a staggering 80.5% of this total occurring during the summer season. Over this period, the city has endured about ten significant flood disasters. The most severe and destructive events occurred in 1966, 1982, 1994, 2003, 2009, and 2023 as river basins and mountain flash floods. These flood events claimed at least 220 lives, affected around 46,000 households, and caused economic losses of ca. 3.3 million U.S. dollars. Our study identifies several flood hazard areas along the Tolgoit, Selbe, Uliastai, and Tuul River valleys, which define a flood buffer zone extending 200 m from their banks, encompassing 59 khoroos of 7 districts in Ulaanbaatar. There are 27,970 fences and 12,887 buildings in the 200 m buffer zone, which is 66.5% of all fence unit area, and 46.3% of the total building, situated within the identified flood risk areas. In response to these findings, we emphasize the urgent need for comprehensive long-term strategy for sustainable flood management based on disaster resilence.
AN ADAPTIVE METHOD FOR BUILDING A MULTIVARIATE REGRESSION
Authors: Pavlov Alexander , Holovchenko Maxim , Drozd Valeriia
Published: July 2024

We propose an adaptive method for building a multivariate regression given by a weighted linear convolution of known scalar functions of deterministic input variables with unknown coefficients. As, for example, when multivariate regression is given by a multivariate polynomial. In contrast to the general procedure of the least squares method that minimizes only a single scalar quantitative measure, the adaptive method uses six different quantitative measures and represents a systemically connected set of different algorithms which allow each applied problem to be solved on their basis by an individual adaptive algorithm that, in the case of an active experiment, even for a relatively small volume of experimental data, implements a strategy of a statistically justified solving. The small amount of data of the active experiment we use in the sense that, for such an amount, the variances of estimates of unknown coefficients obtained by the general procedure of the least squares method do not allow to guarantee the accuracy acceptable for practice. We also proposed to significantly increase the efficiency of the proposed by O. A. Pavlov. and M. M. Holovchenko modified group method of data handling for building a multivariate regression which is linear with respect to unknown coefficients and given by a redundant representation. We improve it by including some criteria and algorithms of the adaptive method for building a multivariate regression. For the multivariate polynomial regression problem, the inclusion of a partial case of the new version of the modified group method of data handling in the synthetic method proposed by O. A. Pavlov, M. M. Golovchenko, and V. V. Drozd, for building a multivariate polynomial regression given by a redundant representation, also significantly increases its efficiency.
MODELS OF REMOTE IDENTIFICATION OF PARAMETERS OF DYNAMIC OBJECTS USING DETECTION TRANSFORMERS AND OPTICAL FLOW
Authors: Nikulina Olena , Severyn Valerii , Kondratov Oleksii , Olhovoy Oleksii
Published: July 2024

The tasks of remote identification of parameters of dynamic objects are important for various fields, including computer vision, robotics, autonomous vehicles, video surveillance systems, and many others. Traditional methods of solving these problems face the problems of insufficient accuracy and efficiency of determining dynamic parameters in conditions of rapidly changing environments and complex dynamic scenarios. Modern methods of identifying parameters of dynamic objects using technologies of detection transformers and optical flow are considered. Transformer detection is one of the newest approaches in computer vision that uses transformer architecture for object detection tasks. This transformer integrates the object detection and boundary detection processes into a single end-to-end model, which greatly improves the accuracy and speed of processing. The use of transformers allows the model to effectively process information from the entire image at the same time, which contributes to better recognition of objects even in difficult conditions. Optical flow is a motion analysis method that determines the speed and direction of pixel movement between successive video frames. This method allows obtaining detailed information about the dynamics of the scene, which is critical for accurate tracking and identification of parameters of moving objects. The integration of detection transformers and optical flow is proposed to increase the accuracy of identification of parameters of dynamic objects. The combination of these two methods allows you to use the advantages of both approaches: high accuracy of object detection and detailed information about their movement. The conducted experiments show that the proposed model significantly outperforms traditional methods both in the accuracy of determining the parameters of objects and in the speed of data processing. The key results of the study indicate that the integration of detection transformers and optical flow provides reliable and fast determination of parameters of moving objects in real time, which can be applied in various practical scenarios. The conducted research also showed the potential for further improvement of data processing methods and their application in complex dynamic environments. The obtained results open new perspectives for the development of intelligent monitoring and control systems capable of adapting to rapidly changing environmental conditions, increasing the efficiency and safety of their work.
Childhood Parental Deportations, Immigration Enforcement Experiences, and Posttraumatic Stress Disorder Among US-Born Latino Adults, 2021
Authors: Pinedo Miguel , Escobar Christian
Published: July 2024

Objectives. To examine how having a parent deported in childhood and immigration enforcement encounters relate to posttraumatic stress disorder (PTSD) among a sample of US-born Latinos.

Methods. In 2021, a national sample of 1784 US-born Latinos was recruited to complete a questionnaire. The questionnaire elicited data on sociodemographics, mental health, and immigration-related experiences. The dependent variable was past-year symptoms of PTSD. Immigration-related variables included (1) having a parent deported during their childhood, (2) having a (nonparent) family member deported, (3) fear of having a parent or loved one deported, (4) fear of immigration enforcement encounters, and (5) having experienced an immigration raid. A multivariable logistic regression was conducted to examine independent associations between immigration-related variables and PTSD.

Results. Having a parent deported during childhood was associated with more than twice the odds of meeting criteria for PTSD symptoms. Having a loved one deported, fearing the deportation of a loved one, and having experienced an immigration raid were all associated with PTSD.

Conclusions. It is imperative to better understand the long-term implications of immigration policies in perpetuating health inequities among US-born Latinos. ( Am J Public Health. 2024;114(S6):S495–S504. https://doi.org/10.2105/AJPH.2024.307660 )

[Formula: see text]
Reservoir Sediment Deltas of the Southwestern United States: Challenges and Opportunities for Riparian Vegetation Management
Authors: Lott Casey , Fischer Richard
Published: July 2024

This technical note summarizes methods used to identify 58 reservoir sediment deltas in the Southwestern United States and California, behind 47 different dams, with potential regional importance as breeding or migratory stopover habitat for three species of riparian vegetation-dependent birds listed under the Endangered Species Act*: least Bell’s vireo (LBVI) (Vireo bellii pusillus), southwest willow flycatcher (SWFL) (Empidonax traillii extimus), and yellow-billed cuckoo (YBCU) (Coccyzus americanus). Information on inflow streams, drainage area, and management authority are provided for each delta. This technical note recommends follow-up research on the temporal dynamics of vegetation colonization, growth, and mortality on these deltas to explore the potential value of these geographically widespread hydrogeomorphic features on arid-land rivers for the conservation of riparian vegetation–dependent birds.
Full-Endoscopic Spinal Surgery for Older Patients With Degenerative Spinal Pathology: A Narrative Review
Authors: Eun Jongpil , Oh Youngmin
Published: July 2024

The aging population worldwide is experiencing an increasing incidence of degenerative spinal disorders, leading to significant morbidity and diminished quality of life. Traditional open surgical approaches for treating these conditions in older patients often entail substantial risks and prolonged recovery times. However, recent advancements in endoscopic surgical techniques have provided promising alternatives with potentially reduced morbidity and quicker recovery. This paper reviews the current literature regarding full-endoscopic spinal surgery (FESS) for degenerative spinal pathology in older patients. We discuss the evolution of endoscopic techniques, patient selection criteria, surgical indications, outcomes, and complications associated with FESS in older adults. Several studies have reported favorable outcomes of FESS in older patients, including reduced postoperative pain, shorter hospital stays, faster recovery, and comparable or even improved clinical outcomes compared with traditional open surgery. Moreover, FESS offers the advantages of minimal tissue disruption, preservation of spinal stability, and decreased blood loss, which are particularly advantageous in older patients with multiple comorbidities. However, challenges such as a steep learning curve for surgeons, limited visualization, and technical constraints in managing complex pathologies remain significant concerns. Additionally, the long-term durability and effectiveness of FESS in older patients require further investigation. In conclusion, FESS presents a promising minimally invasive option for treating degenerative spinal pathology in older adults. Further research and technological advancements are needed to optimize patient selection, refine surgical techniques, and improve long-term outcomes in this growing demographic category of patients.
Fully Endoscopic Transforaminal Approach for L5–S1 Foraminal Stenosis: A Narrative Review
Authors: Seo Jong Hun , Kim Dong Joon , Kim Pius , Ju Chang Il
Published: July 2024

Lumbar foraminal stenosis (LFS) is a fairly common degenerative condition of the spine that can often occur in the L5–S1 segment. Traditional surgical approaches for LFS can be categorized into microscopic foraminotomy via the Wiltse approach and interbody fusion. Microscopic foraminotomy via the Wiltse approach was previously considered the gold standard for treating LFS. However, with advancements in endoscopic equipment and techniques, fully endoscopic foraminotomy through various approaches is now widely performed for the treatment of LFS. Among these approaches, endoscopic foraminotomy via the transforaminal approach offers many advantages, but difficulties may be encountered when there are anatomical barriers, which are often present in the L5–S1 segment. This study aimed to explore the overall clinical outcomes and complications of transforaminal endoscopic lumbar foraminotomy, as well as the challenges specific to the L5–S1 segment and techniques to overcome them.
Bird Community Responses to Restoration along Las Vegas Wash, Nevada
Authors: Ballard Jennifer , Ammon Elisabeth M. , Van Dooremolen Deborah M.
Published: July 2024

In a 12-year study of seasonal bird use along Las Vegas Wash (Clark County, Nevada), an area undergoing intensive channel stabilization and riparian and wetland restoration, we found that total bird abundance and species richness significantly increased. In the late phase of the project, total bird abundance was greatest during the nonbreeding season and winter transition, and species richness was greatest during the fall transition. Species associated with riparian shrubs decreased, likely as a response to large-scale removal of non-native shrubs and the lag in recovery of a native shrub understory. Species associated with trees and the mid-story had a mixed response, with detections of those species previously most closely associated with non-native trees showing a negative trend, while those associated with native overstory plants showing an increasing or mixed trend. Detections of wetland and aquatic species increased significantly throughout the project. This study shows differential restoration responses of species based on their natural history, seasons of primary use of the site, and the lag time in recovery of particular vegetation elements that are used by various species assemblages.
Social and Sexual Behavior of Intersex Mallards: A Literature Review and a New Case Study
Authors: Hayes Floyd E. , Martins Drielly N. , Nunez Christian X.
Published: July 2024

Spontaneous sex reversal is a form of intersexuality in which one sex, usually a female, acquires traits of the other sex. We reviewed previously published accounts of intersex Mallards (Anas platyrhynchos) resulting from sex reversal, of which four individuals behaved as females and four behaved as males, and two of the latter mounted other females. Video analyses of an intersex Mallard in Napa, Napa County, California, revealed that it was less social, spending much more time alone than either sex of normal Mallards during the breeding season, and behaved like a female by spending much more time with males than with females. It is unknown why some intersex Mallards behave as females and others as males.
Unpaired Quad-Path Cycle Consistent Adversarial Networks for Single Image Defogging
Authors: Liu Wei , Huang Xiaoyu , Chen Cheng , Jiang Rui , Zhang Caiwang
Published: July 2024

Adversarial learning-based image defogging methods have been extensively studied in computer vision due to their remarkable performance. However, most existing methods have limited defogging capabilities for real cases because they are trained on the paired clear and synthesized foggy images of the same scenes. In addition, they have limitations in preserving vivid color and rich textual details in defogging. To address these issues, we develop a novel generative adversarial network, called quad-path cycle consistent adversarial network (QPC-Net), for single image defogging. QPC-Net consists of a Fog2Fogfree block and a Fogfree2Fog block. In each block, there are three learning-based modules, namely, fog removal, color-texture recovery, and fog synthetic, which sequentially compose dual-path that constrain each other to generate high quality images. Specifically, the color-texture recovery model is designed to exploit the self-similarity of texture and structure information by learning the holistic channel-spatial feature correlations between the foggy image with its several derived images. Moreover, in the fog synthetic module, we utilize the atmospheric scattering model to guide it to improve the generative quality by focusing on an atmospheric light optimization with a novel sky segmentation network. Extensive experiments on both synthetic and real-world datasets show that QPC-Net outperforms state-of-the-art defogging methods in terms of quantitative accuracy and subjective visual quality.
A Self-Supervised Learning Approach for High Throughput and High Content Cell Segmentation
Authors: Raphael Marc , Lam Van , Byers Jeff , Robitaille Michael , Kaler Logan , Christodoulides Joseph
Published: July 2024

In principle, AI-based algorithms should enable rapid and accurate cell segmentation in high-throughput settings. However, reliance on large training datasets, human input, computational expertise and limited generalizability, have prevented this goal of completely automated, high throughput segmentation from being achieved. To overcome these roadblocks, we introduce an innovative self-supervised learning method (SSL) for pixel classification that requires no parameter tuning or curated data sets, thus providing a more efficient cell segmentation approach for high-throughput, high-content research. We demonstrate that our algorithm meets the criteria of being fully automated with versatility across various magnifications, optical modalities and cell types. Moreover, our SSL algorithm is capable of identifying complex cellular structures and organelles which are otherwise easily missed, thereby broadening the machine learning applications to high-content imaging. Our SSL technique displayed consistently high F1 scores across segmented images, with scores ranging from 0.831 to 0.876; outperforming the popular Cellpose algorithm, which showed a greater F1 variance of 0.645 to 0.8815, mainly due to segmentation errors. This novel SSL methodology advances segmentation accuracy, requires no curated training imagery or computational expertise and eliminates cloud-based data security concerns, thereby streamlining the process of applying machine learning to large data sets.
Coincidence detection between apical and basal dendrites drives STDP in cerebellar Golgi cells
Authors: Prestori Francesca , Pali Eleonora , Masoli Stefano , Domenico Danila Di , Sorbo Teresa , D'Angelo Egidio
Published: July 2024

Cerebellar Golgi cells (GoCs) segregate parallel fiber (pf) and mossy fiber (mf) inputs on apical and basal dendrites. Computational modelling predicted that this anatomical arrangement, coupled with a specific ionic channel localization, could be instrumental to drive spike-timing dependent plasticity (STDP) at mf-GoC synapses. Here, we have tested this hypothesis with GoC patch-clamp recordings in acute mouse cerebellar slices. Indeed, repeated mf-pf pairing on the theta-band (1 Hz
Quantitative Assessment of Water Scales Impact on the Comprehensive State of Brain Using Virtual Reality
Authors: Zhang Yiming , Li Qingbin , Zhu Xuezhou
Published: July 2024

The aquatic environment has a significant impact on human physical and mental health. In order to accurately and quantitatively describe the direct effects of different water scales on the human brain, this study used virtual reality technology to conduct an immersive experience at different water scales and assessed the comprehensive state of the brain with the collected metrics of electroencephalogram (EEG) and heart rate variability (HRV) of the subjects. The study established a coupling relationship between the relative area of water with flow rate and the comprehensive brain state through Gaussian Process Regression (GPR) model. The classification model was also implemented to predict the different responses to water scales of populations.
Establishment of Post-Inflammatory Irritable Bowel Syndrome Animal Model Following Acute Colitis Recovery
Authors: Jeong Eui Sun , Jung Hye-Kyung , Choi Euno , Yun Kyeongeui , Lee Ayoung , Kim Yung Sung
Published: July 2024

Irritable bowel syndrome (IBS) is a prevalent disorder with an unclear pathophysiology. This study aimed to establish an experimental murine model of post-inflammatory IBS induced by acute severe colitis (acute model) or chronic mild repeated colitis (chronic model) to facilitate IBS analysis. The acute model was induced with 3% dextran sulfate sodium (DSS) for 5 days, followed by a 12-week recovery period. The chronic model involved administration of 0.5% DSS for 5 days, followed by a 5-day resting period, repeated thrice. We conducted comparative analyses to assess inflammation severity, intestinal motility, permeability, visceral hypersensitivity, and microbiome composition. In the acute model, mild leukocyte infiltration was observed, colonic transit time shortened at 12 weeks (P < 0.001), occludin expression decreased (P = 0.041), and inflammatory cytokines and transient receptor potential vanilloid 1 was upregulated in colonic mucosa (P < 0.050). In the chronic model, only mild inflammatory changes were noted. Microbiota analysis in the acute model revealed differences in microbial abundance and compositions (P = 0.001). The acute model effectively induced a post-inflammatory IBS model, characterized by low-grade inflammation that causes gut dysmotility, alters permeability, and increases visceral hypersensitivity with notable microbial composition changes.
Regular Red-Green-Blue InGaN quantum wells with In content up to 40% grown on InGaN nanopyramids
Authors: Dussaigne Amélie , Paillet Colin , Rochat Névine , Cooper David , Grenier Adeline , Vézian Stéphane , Damilano Benjamin , Michon Adrien , Hyot B
Published: July 2024

Full color micro-displays with a pixel pitch below 10 µm are needed for augmented and virtual reality applications. In the native emission approach, high efficiency Red-Green-Blue (RGB) pixels should be reached using monolithically integrated InGaN based micro-LEDs. We report here the growth of high optical quality RGB InGaN/InGaN quantum wells grown on InGaN nanopyramids of diameter less than 1 µm by metal organic vapour phase epitaxy (MOVPE). The nanopyramids are obtained by nanoselective area growth using an in situ patterned epitaxial graphene on SiC as an embedded mask. RGB emissions are measured at different locations on the sample and depend on the size of the InGaN nanopyramids. Advanced correlative analysis conducted on the same transmission electron microscopy lamella reveal an at least fully relaxed In0.13Ga0.87N core and very regular quantum wells emitting in the red range (620 nm) along the pyramid sidewalls with an In content up to 40%.
Anti-IL-5 treatment, but not neutrophil interference, attenuates inflammation in a mixed granulocytic asthma mouse model, elicited by air pollution
Authors: De Volder Joyceline , Bontinck Annelies , Haelterman Valerie , Boon Louis , Joos Guy F , Brusselle Guy G , Maes Tania
Published: July 2024

Introduction:
Diesel exhaust particles (DEP) have been proven to aggravate asthma pathogenesis. We previously demonstrated that exposure to house dust mite (HDM) and DEP in mice increases both eosinophils and neutrophils in bronchoalveolar lavage fluid (BALF) and also results in higher levels of neutrophil-recruiting chemokines and neutrophil extracellular trap (NET) formation. We aimed to evaluate whether treatment with anti-IL-5 can alleviate the asthmatic features in this mixed granulocytic asthma model. Moreover, we aimed to unravel whether neutrophils modulate the DEP-aggravated eosinophilic airway inflammation.
Material & methods
Female C57BL6/J mice were intranasally exposed to saline or HDM and DEP for 3 weeks (subacute model). Interference with eosinophils was performed by intraperitoneal administration of anti-IL-5. Interference with neutrophils and neutrophil elastase was performed by intraperitoneal anti-Ly6G and sivelestat administration, respectively. Outcome parameters included eosinophils subsets (homeostatic EOS and inflammatory EOS), proinflammatory cytokines, goblet cell hyperplasia and airway hyperresponsiveness.
Results
The administration of anti-IL-5 significantly decreased eosinophilic responses, affecting both inflammatory and homeostatic eosinophil subsets, upon subacute HDM + DEP exposure while BAL neutrophils, NET formation and other asthma features remained present. Neutrophils were significantly reduced after anti-Ly6G administration in BALF, lung and blood without affecting the eosinophilic inflammation upon HDM + DEP exposure. Sivelestat treatment tended to decrease BALF inflammation, including eosinophils, upon HDM + DEP exposure, but did not affect lung inflammation.
Conclusion
Inhibition of IL-5 signalling, but not neutrophil interventions, significantly attenuates eosinophilic inflammation in a mouse model of mixed granulocytic asthma, elicited by air pollution exposure.
Photobiomodulation Combined with Human Umbilical Cord Mesenchymal Stem Cells Modulates the Polarization of Microglia
Authors: Li Na , Zhu Hongyu , Cai Yuanhao , Guo Jun , Liu Pai , Han Zhibo , Wu Huancheng , Liu Yi , Chen Hongli
Published: July 2024

Neuroinflammation develops in neurodegenerative diseases. The severity of neuroinflammation is closely linked to microglial polarization. Different microglial phenotypes release inflammatory cytokines with distinct functions. Modulation of microglial polarization to alter neuroinflammation is a potential therapeutic strategy. Human umbilical cord mesenchymal stem cells (hUCMSCs) possess multiple stem cell properties that can be used to modulate inflammation. Various methods of pre-treatment of stem cells have shown enhanced efficacy in disease treatment. Photobiomodulation (PBM) is a non-invasive intervention that can reduce inflammation. Our in vitro experiments established a microglial cell inflammation model and demonstrated that PBM pre-treated hUCMSCs exhibited reduced the release of pro-inflammatory cytokines while promoting the expression of anti-inflammatory cytokines in microglia. This treatment significantly reduced the expression of Notch pathway-related genes in an inflammatory model, facilitated decreased M1 phenotype polarization, and increased M2 phenotype polarization in microglia. An animal inflammation model was established. In vivo studies showed that 808 nm light combined with hUCMSCs improved memory. and significantly reduced pro-inflammatory cytokines release in serum and brain tissue of male C57BL/6J mice, while promoting the expression of anti-inflammatory cytokines and M2 phenotype polarization of microglia. The results highlight the crucial role of 808 nm PBM in modulating microglial function and attenuating neuroinflammation through interaction with hUCMSCs. The findings offer novel insights into the molecular mechanisms of microglial polarization.
A Privacy Preserving Federated Learning BasedIoT Framework Using Cloud Computing
Authors: Ahmad Wasim , Almaiah Muhammad Amin , Ali Bakht Sher , Ali Aitizaz
Published: July 2024

This abstract explores the transformative impact of IoT on modern life, empha- sizing the integration of Federated Learning (FL), Edge Computing, and Secure Offloading in AI applications. The rapid evolution of IoT has revolutionized com- mercial operations and consumer interactions, driven by advanced sensing and computational capabilities in mobile devices. However, concerns over data privacy and limited computational resources hinder the deployment of compute-intensive applications. FL emerges as a distributed AI paradigm, ensuring privacy and saving network resources. Edge computing optimizes service delivery, reducing latency and energy consumption, supported by intelligent offloading algorithms and blockchain technology for secure and efficient edge services. Challenges like slow learning speeds persist but are addressed through ongoing advancements in neural networks.The proposed framework is compared with the benchmark mod- els and it was observed that the proposed framework suppress the benchmark models.
Investigating Antifungal Effect of Metal Oxide NPs and Carbon Nanodots Synthesized via Green Routes for Candida albicans
Authors: Singh Satya , Chauhan Himanshi , Yadav Himanshi , Hada Vivek , Singh Kumari Neha
Published: July 2024

Fascination using green synthesis methods is growing rapidly, attracting researchers to seek safer and more biocompatible processes for nanoparticle production. This study introduces a pioneering approach to crafting and investigating the antifungal effect of environmentally friendly carbon dots (organic materials) prepared from neem, rosemary, magnesium oxide (MgO), and zinc oxide (ZnO) nanoparticles (inorganic materials). MgO nanoparticles were synthesized using a Salvia hispanica (chia seed) extract. In contrast, ZnO nanoparticles were prepared from zinc acetate dihydrate and an aqueous extract of Dahlia pinnata leaves. Stable carbon nanodots were prepared from Azadirachta Indica leaves (commonly known as neem leaves) and Rosmarinus officinalis L. leaves (commonly known as rosemary leaves). This research delves into the intricate mechanisms governing their efficacy, offering insights into their potential therapeutic applications against microbial illnesses. The study illuminates their structural and functional attributes by employing various characterization methods, including FTIR, SEM, DLS, XRD, UV-vis-nir, and zeta potential. Moreover, it underscores the versatility of these materials in biomedical and environmental realms, highlighting their potential for various applications. Using the crystal violet bioassay method, we evaluated their antifungal potential against Candida albicans biofilm production. This study reveals the significant antifungal properties of these nanomaterials, hinting at their potential in combating fungal infections and advancing the fight against microbial threats. We collected three Candida albicans isolates recovered from clinical samples and examined the antifungal effect of carbon nanodots, MgO, and ZnO NPs by varying their concentrations. We coated the microtiter plate with 2,5-dimercapto-1,3,4-thiadiazole (DMcT) and then coated it with the nanodots. We observed a significant efficacy at higher concentrations. Our study unveils this unexplored area, which has been quite mysterious and more or less not understood scientifically, particularly at the molecular scale. However, it has been a traditional practice for many centuries in certain parts of the world, especially in India, where extracts prepared using plant parts, such as leaves, roots, stems, and flowers, have been used for healing and recovery from certain diseases, such as chicken pox and smallpox.
Exploring the Integration of a Global AI Model with Traditional Data Assimilation in Weather Forecasting
Authors: Xu Hongxiong , Duan Yihong , Xu Xiangde
Published: July 2024

Recent advancements in artificial intelligence (AI) have profoundly transformed weather forecasting, challenging traditional reliance on numerical weather prediction (NWP) models. Despite notable progress, AI models still depend heavily on traditional NWP systems and data assimilation methods to generate analysis fields, a dependency that increases computational demands and might limit forecast accuracy. This study explored the integration of Gridpoint Statistical Interpolation (GSI) with the Pangu-Weather AI forecasting model (GSI-Pangu), and assessed the potential for AI models to autonomously generate forecasts by leveraging mature data assimilation systems. Our experiments commenced by adopting ERA5 reanalysis data for the initial cycle, and then involved assimilation of simulated observations in subsequent cycles, spanning a month-long period. Results demonstrated notable enhancements in forecast accuracy, with reductions in the root mean square error across various atmospheric variables compared with the results of a control experiment without data assimilation. Additionally, the results highlighted GSI-Pangu’s ability to predict large-scale circulation patterns of extreme precipitation events, together with its effectiveness in driving regional models to accurately forecast precipitation intensity and distribution. Successful implementation of GSI within the Pangu-Weather framework underscores the transformative potential of hybrid forecasting systems, which merge conventional meteorological techniques with AI innovations, thereby facilitating accelerated adoption of AI in weather forecasting.
A cluster-based solution for Service Function Chain allocation in large scale infrastructure
Authors: Bezerra Diego de Freitas , Santos Guto Leoni , Rocha Élisson da Silva , Gonçalves Glauco Estacio , Moreira André , Marquezini Maria Valéria , Kelner Judith , Sadok Djamel
Published: July 2024

Changes in telecommunication services demand the development of a new infrastructure to attend new network applications' requirements. The traditional approach to network functions running over dedicated equipment can no longer handle all the dynamics of these new services. The network function virtualization (NFV) paradigm decouples a function from the underlying dedicated hardware thus making networks more flexible and agile. A set of virtual network functions (VNFs) can be deployed as virtual machines or containers across common servers, and orchestrated to compose a service function chain (SFC).Despite the many benefits of NFV, it raises several challenges. SFC placement is a complex task, since it requires taking into consideration the characteristics of VNFs, the SFC requirements, and the state of network infrastructure. This poses a challenge for large scale networks. Information regarding network resources are stored in a large database, and retrieving such data in order to perform SFC placement according to some strategy can be a problem. Limited memory capacity and the presence of a large number of disk operations can compromise the performance of SFC placement algorithms and make it unfeasible. In addition, the amount of memory available to run the allocation algorithm may not be sufficient to load the large amount of information that describe the network resources (occasionally in the hundreds of gigabytes). We address this problem by using a cluster based solution that stores and retrieves data for large scale infrastructures in order to perform SFC placement. The results demonstrate that the use of clusters in the preprocessing step can drastically reduce the size of the resulting database, as well as the execution time to select candidate nodes for a scalable SFC allocation.
Incidence and Management of Duodenal Trauma in a War Setting: Insights from a Military Hospital in Yemen
Authors: Obadiel Yasser Abdurabo , Saeed Mohammed abdulkarem , Jowah Haitham Mohammed , Al-Brashi Ali
Published: July 2024

Purpose: Duodenal injuries are a significant challenge in war trauma because of their complexity and potential for severe complications. This study evaluated the outcomes of duodenal injury management among patients with war trauma in Yemen, focusing on incidence, severity, surgical approaches, postoperative complications, and factors influencing success or failure.
Patients and Methods: A retrospective analysis was conducted on 520 exploratory laparotomy cases from June 2019 to December 2022 at a military hospital in Yemen. Twenty-seven patients with confirmed duodenal injuries were included. Data on demographic characteristics, injury characteristics, surgical management, and outcomes were collected and analyzed.
Results: The incidence rate of duodenal injuries was 5.2%. The cohort comprised predominantly young males, with a mean age of 21.93 years. Penetrating trauma accounted for 95.7% of cases. The mean Injury Severity Score (ISS) was 20.63, and most injuries were classified as AAST Grade II. The second duodenum part (D2) was the most commonly affected area. Associated injuries were frequent, particularly colonic (73.9%), chest (39.1%), and liver (29.6%) injuries. Surgical management primarily involved exploratory laparotomy (81.5%) and primary closure (66.7%). Postoperative complications were common, affecting 66.7% of patients, with sepsis (55.6%) being the most common. The success and mortality rates were 81.5%, and the mortality rate was 11.1%. Key risk factors for poor outcomes included higher ISS, grade III injury, damage control surgery, and shrapnel injury.
Conclusion: Duodenal injuries in war trauma settings pose significant management challenges, with high rates of associated injuries and postoperative complications. Early diagnosis, appropriate surgical techniques, and vigilant postoperative care is crucial for improved outcomes. Further multicenter research is needed to validate these findings and enhance management strategies in diverse settings.