bluuebunny/crossref_metadata_2025 huggingface dataset:
Well-being in newly qualified doctors: exploring the value of peer-led reflective sessions
Authors: Van Den Berghe Christen , Jaffe Yvette , Lachmann Emily , Ladan Zahra , Buckland Amber , Laurent Sue , Needleman Sarah , Shanmugalingam Shanthi
Published: May 2024

Introduction

Following the COVID-19 pandemic and ongoing pressures within the National Health Service, there has been an increasing concern about the well-being of junior doctors in the UK. Newly qualified doctors are particularly vulnerable due to the significant shift in responsibility they experience.

Objectives

To implement peer-led reflective session for foundation year 1 (FY1) (first-year postgraduation) doctors and to create a dedicated space in which doctors could share difficult or challenging experiences.

Methods

In 2021 and 2022, peer-led reflective sessions were held for FY1s in a district general hospital in London, England. Feedback assessing well-being, perceived levels of support, as well as session benefits and areas for improvement, was obtained throughout the year. Qualitative feedback underwent thematic analysis to understand key benefits of the sessions.

Results

Feedback collected at the launch of the initiative revealed that over 80% of respondents agreed or strongly agreed that their mental health and well-being had been negatively impacted by work. The majority felt additional support was needed. All attendees found reflective sessions useful. Key benefits were as follows: the provision of a safe psychological space, sharing experiences and reflecting with peers, building relationships with colleagues and the reassurance that others struggled with similar challenges.

Conclusion

FY1 doctors reported that starting work negatively affected their well-being and that additional support was needed. Facilitated peer-led reflective sessions were beneficial and offered a supportive environment for FY1s to share and reflect. We propose the initiative can serve members of the wider healthcare team, including doctors of different grades and nurses.
Impact of age at ivacaftor initiation on pulmonary outcomes among people with cystic fibrosis
Authors: Merlo Christian A , McGarry Lisa J , Thorat Teja , Nguyen Catherine , DerSarkissian Maral , Muthukumar Aruna , Healy Joe , Brookhart M Alan , Rubin Jaime L
Published: May 2024

Background

Ivacaftor (IVA) improves lung function and other extrapulmonary outcomes in people with cystic fibrosis (CF). However, the effect of initiating IVA at earlier versus later ages has not been studied.

Methods

We conducted an observational cohort study of people in the US CF Foundation Patient Registry aged ≥6 years with ≥1 CF transmembrane conductance regulator–gating mutation to compare the effects of initiating IVA at earlier ages on per cent predicted forced expiratory volume in 1 s (ppFEV1) and pulmonary exacerbation (PEx) outcomes. People with CF were grouped by age at IVA initiation (ages 6–10, 11–15, 16–20 and 21–25 years) to perform three analyses of younger versus older IVA initiation (6–10 vs 11–15, 11–15 vs 16–20 and 16–20 vs 21–25 years). For each analysis, baseline characteristics assessed over 1-year periods at the same age prior to IVA initiation were balanced by standardised mortality/morbidity ratio (SMR) weighting. For each analysis, outcomes were compared over a 5-year outcome assessment period when both groups were in the same age range and receiving IVA.

Findings

Baseline characteristics were well balanced between younger and older IVA initiator groups after SMR weighting. In the outcome assessment period, younger IVA initiators had significantly higher mean ppFEV1than older initiators across all comparisons, and those initiating IVA between ages 6–10 and 11–15 years had significantly lower PEx rates.

Interpretation

Study findings showed the importance of early IVA initiation in people with CF.
Distribution, frequency, and impact of herbicide-resistant weeds in Saskatchewan
Authors: Geddes Charles M. , Pittman Mattea M. , Sharpe Shaun M. , Leeson Julia Y.
Published: May 2024

Herbicide-resistant weeds threaten contemporary agriculture by reducing crop yields and quality. Monitoring of herbicide-resistant weeds is essential to the development of informed integrated weed management (IWM) strategies. In 2019 and 2020, a randomized-stratified preharvest survey of 419 fields in Saskatchewan, Canada, was conducted to determine the distribution, frequency of occurrence, and impact of herbicide-resistant weeds. Mature seeds were collected from uncontrolled weeds in each field. The samples were tested for resistance to acetyl-CoA carboxylase (ACCase) and/or acetolactate synthase (ALS)-inhibiting herbicides using whole-plant bioassays under a controlled-environment. In 2019/2020, herbicide-resistant weeds occupied 72% of the surveyed fields, corresponding to an estimated 6.2 million ha of annual cropland and a total field area of 11.4 million ha. Herbicide-resistant weeds cost Saskatchewan farmers an estimated $343 million CAD annually in reduced crop yields and quality, and increased weed control expenditures. Wild oat ( Avena fatua L.) had the greatest impact among grass weeds, with ACCase inhibitor resistance documented in 77% and ALS inhibitor resistance in 30% of fields where the weed seeds were collected and tested (47% and 18% of all fields surveyed, respectively). Multiple herbicide (ACCase and ALS inhibitor)-resistant wild oat were documented in 26% of the tested fields. Kochia ( Bassia scoparia (L.) A.J. Scott) had the greatest impact among broadleaf weeds, where 100% of the samples tested were ALS inhibitor-resistant (39% of all fields surveyed). The growing prevalence of herbicide-resistant weeds in Saskatchewan warrants further adoption of IWM where non-chemical tactics play an important role in stewardship of the remaining effective herbicides.
C17-833 camelina
Authors: Eynck C. , Zatylny A.M. , Klein-Gebbinck H.W. , Falk K.C.
Published: May 2024

Camelina ( Camelina sativa) elite germplasm C17-833 was developed at the Agriculture and Agri-Food Canada Saskatoon Research and Development Centre. It was developed through hybridization of camelina cultivar AAC 10CS0048 and F4 line 11CS0231-24-10 and subsequent pedigree selection. C17-833 yields significantly higher (114%) than the check cultivar AAC 10CS0048. It also has significantly higher seed oil content (42.6% vs. 41.9%) and significantly larger seeds (126%). C17-833 has very good resistance to downy mildew disease caused by Hyalonospora camelinae and is adapted to all soil zones of the Canadian Prairies.
Efficacy of Ni2+ on modification the structure, ultrasonic, optical, and radiation shielding behaviors of potassium lead borate glasses
Authors: Abd-Allah W. M. , Afifi M. , Abdelhamied M. M. , Ramadan W. , Rayan D. A. , Mesady I. A. El
Published: May 2024

This paper presents the method of preparing (60 − x) B2O3–20 K2O–20 PbO–x NiO, coded as (NiO x), and x = (0–10 mol%) glass systems fabricated through the melt-quench technique. The prepared glass was characterized through X-ray diffraction spectra (XRD); the mechanical behavior of the glass samples was investigated using the ultrasonic technique, Fourier transform infrared (FTIR) spectra, the optical reflectance R(λ), refractive index (n), optical conductivity (σopt), the dispersion parameters of the studied samples were deduced using Wemple and Di-Domenico models. The results obtained were reported in detail. One of the fundamental parameters used to evaluate the interaction of radiation with shielding material was the mass attenuation coefficient (μm), which was obtained using Phy/X software and PHITS code program. It was used to calculate radiation interaction parameters, e.g., linear (μL) attenuation coefficient, effective atomic number (Zeff), half value layer HVL, mean free path (MFP) and the average atomic cross section, σt. Comparing the shielding behavior of the glass samples revealed that (NiO 10) glass demonstrated the highest μm and μL compared to the other samples. The maximum μm values equal 48.13, 48.73, 49.42, 50.59, and 51.08 cm2/g for (NiO 0) to (NiO 10), recorded at 0.015 MeV, respectively. This study shows that increasing the amount of NiO in the preferred glass samples leads to achieving high-performance radiation shielding materials.

Graphical abstract
Older People’s Capacity to Work in Indonesia
Authors: SURIASTINI NI WAYAN , WIJAYANTI IKA YULIA , OKTARINA DWI
Published: May 2024

This study aimed to estimate the work capacity of older Indonesians based on their health condition and other characteristics. Two analytic methods were used in this analysis—the Milligan–Wise method and the Cutler–Meara–Richards-Shubik method—and they found relatively small and substantial additional work capacities, respectively. The results showed that mortality alone may not be able to explain the additional work capacity due to only small improvements in the health conditions of older people. The effect of poverty, the dominance of the informal sector, low educational attainment and skills, and inadequate government social protection and assistance were also responsible for the continued employment of older people until serious health conditions halted their economic activities. Thus, several features were highlighted in this study: (i) the feasibility of extending the pensionable age, (ii) flexible terms for a pension scheme for informal workers, (iii) improved health conditions by expanding the noncontributory health insurance, and (iv) upgraded skills for older workers.
Health Capacity to Work among Older Malaysians
Authors: MANSOR NORMA , AWANG HALIMAH , PARK DONGHYUN
Published: May 2024

This paper uses two methodologies to explore the extent to which greater labor force participation among older Malaysians can expand Malaysia’s labor supply. The Milligan–Wise method estimates the potential to increase the labor force participation rate of older Malaysians by estimating how much they would work if they were to work as much as those with the same mortality rate in the past. The Cutler, Meara, and Richards-Shubik ( 2013 ) method estimates the same potential by estimating how much older Malaysians would work if they worked as much as their younger counterparts in similar health. We made further simulations to quantify the capacity of older Malaysians to work after they are 60 years old. The results show significant additional work capacity among older people in Malaysia, particularly males, urban dwellers, and those with low educational attainment.
Image Encryption Algorithm Based on 2D-Linear-Infinite-Collapse Chaotic Map and Improved Hilbert Curve
Authors: Tang Can , Chen Jiale , Wang Jun
Published: May 2024

In recent years, many image encryption schemes have adopted Hilbert curves for encryption. In this approach, the Hilbert curve is used to encrypt grayscale images by traversal scrambling. However, the correlation between pixels has not been fully considered and those algorithms are not safe enough. To solve this problem, a new image encryption algorithm based on a new chaotic system of 2D-LICM (Two-Dimensional Linear-Infinite-Collapse Chaotic Map) and an improved Hilbert curve is proposed in this paper. First, we propose a new 2D-chaotic system to address the shortcoming that the commonly used chaotic systems are too simple in scope and complexity. Then, a new image encryption algorithm is proposed using the newly designed 2D-LICM and the improved Hilbert curve. The proposed algorithm uses Hilbert curve to reduce the correlation between adjacent pixels of the image at the pixel and bit levels and increase the scrambling and diffusion effects. Simulation and security analysis results show that the proposed scheme has high security and is superior to several advanced image encryption algorithms.
CMVT: ConVit Transformer Network Recombined with Convolutional Layer
Authors: Mao Chunxia , Li Jun , Hu Tao , Zhao Xuanyu
Published: May 2024

Vision transformers are deep neural networks applied to image classification based on a self-attention mechanism and can process data in parallel. Aiming at the structural loss of Vision transformers, this paper combines ConViT and Convolutional Neural Network (CNN) and proposes a new model Convolution Meet Vision Transformers (CMVT). This model adds a convolution module to the ConViT network to solve the structural loss of the transformer. By adding hierarchical data representation, the ability to gradually extract more image classification features is improved. We have conducted comparative experiments on multiple dataset, and all of them have been enhanced to improve the efficiency and performance of the model.
A Hybrid Deep BiLSTM-CNN for Hate Speech Detection in Multi-social media
Authors: Kumar Ashwini , Kumar Santosh , Passi Kalpdrum , Mahanti Aniket
Published: May 2024

Nowadays, means of communication among people have changed due to advancements in information technology and the rise of online multi-social media. Many people express their feelings, ideas, and emotions on social media sites such as Instagram, Twitter, Gab, Reddit, Facebook, and YouTube. However, people have misused social media to send hateful messages to specific individuals or groups to create chaos. For various governance authorities, manually identifying hate speech on various social media platforms is a difficult task to avoid such chaos. In this study, a hybrid deep-learning model, where bidirectional long short-term memory (BiLSTM) and convolutional neural network (CNN) are used to classify hate speech in textual data, is proposed. This model incorporates a GLOVE-based word embedding approach, dropout, L2 regularization, and global max pooling to get impressive results. Further, the proposed BiLSTM-CNN model has been evaluated on various datasets to achieve state-of-the-art performance that is superior to the traditional and existing machine learning methods in terms of accuracy, precision, recall, and F1-score.
EOGT: Video Anomaly Detection with Enhanced Object Information and Global Temporal Dependency
Authors: Pi Ruoyan , Wu Peng , He Xiangteng , Peng Yuxin
Published: May 2024

Video anomaly detection (VAD) aims to identify events or scenes in videos that deviate from typical patterns. Existing approaches primarily focus on reconstructing or predicting frames to detect anomalies and have shown improved performance in recent years. However, they often depend highly on local spatio-temporal information and face the challenge of insufficient object feature modeling. To address the above issues, this article proposes a video anomaly detection framework with
E
nhanced
O
bject Information and
G
lobal
T
emporal Dependencies
(EOGT)
and the main novelties are: (1) A
L
ocal
O
bject
A
nomaly
S
tream
(LOAS)
is proposed to extract local multimodal spatio-temporal anomaly features at the object level. LOAS integrates two modules: a
D
iffusion-based
O
bject
R
econstruction
N
etwork
(DORN)
with multimodal conditions detects anomalies with object RGB information; and an
O
bject
P
ose
A
nomaly Refiner
(OPA)
discovers anomalies with human pose information. (2) A
G
lobal
T
emporal
S
trengthening
S
tream
(GTSS)
with video-level temporal dependencies is proposed, which leverages video-level temporal dependencies to identify long-term and video-specific anomalies effectively. Both streams are jointly employed in EOGT to learn multimodal and multi-scale spatio-temporal anomaly features for VAD, and we finally fuse the anomaly features and scores to detect anomalies at the frame level. Extensive experiments are conducted to verify the performance of EOGT on three public datasets: ShanghaiTech Campus, CUHK Avenue, and UCSD Ped2.
Role of Na/K-ATPase α1 caveolin-binding motif in adipogenesis
Authors: Huang Minqi , Wang Xiaoliang , Chen Yiliang , Pessoa Marco T. , Terrell Kayleigh C. , Zhang Jue , Tian Jiang , Xie Zijian , Pierre Sandrine V. , Cai Liquan
Published: May 2024

A Na/K-ATPase α1 caveolin-binding motif regulates adipogenesis. Mutation of this binding motif in the mouse leads to reduced fat with increased extracellular matrix production and inflammation. RNA-seq analysis and pharmacological interventions in human iPSC-derived adipocytes revealed that TGF-β signal, rather than Na/K-ATPase-mediated ion transport, is a key mediator of NKA regulation of adipogenesis.
Acetoacetate and d- and l-β-hydroxybutyrate have distinct effects on basal and insulin-stimulated glucose uptake in L6 skeletal muscle cells
Authors: Khouri Hannah , Roberge Mathilde , Ussher John R. , Aguer Céline
Published: May 2024

A limited number of studies investigate the effect of ketone bodies at concentrations reflective of both typical fasting and ketoacidosis. We tested a mix of physiologically relevant concentrations of ketone bodies, which allowed us to highlight the differential effects of d- and l-β-hydroxybutyrate and acetoacetate on skeletal muscle cell glucose uptake. Our findings will assist in better understanding the mechanisms that contribute to muscle insulin resistance and provide guidance on recommendations regarding ketogenic diets.
A descending pathway from the lateral/ventrolateral PAG to the rostroventral medulla mediating the vasomotor response evoked by social defeat stress in rats
Authors: Matsuyama Mio , Horiuchi Jouji
Published: May 2024

The sympathetic vasomotor pathway of an acute psychological stress-induced cardiovascular response is mediated via neurons in the RVM indirectly from the hypothalamus. In this study, we showed the relaying area of the efferent sympathetic vasomotor pathway from the hypothalamus to the RVM. The results suggested that the pressor response during psychological stress is mediated via neurons in the lateral/ventrolateral PAG to the RVM.
Empirical Study of the Effect of Nanocoolant Particles on Corrosion Rate of 316 Stainless Steel
Authors: Asmara Yuli Panca , Raman Jeya gopi , Suparjo  , Herlina Firda , Wei Yap Chun
Published: May 2024

The advancement of nanotechnology has had an impact on the use of heat exchangers. Nanocoolants, which offer higher thermal efficiency than traditional coolants, have paid significant attention. These innovative fluids, which contain nanomaterials, not only have better heat efficiency but also improve energy efficiency compared to regular coolants. However, the presence of solid nanoparticles in the coolant may cause corrosion and erosion of tubes, leading to massive degradation of those parts. To evaluate the effectiveness of nanocoolant particles, this research was conducted by studying the impact of using nanocoolant on erosion-corrosion occurring on metal surfaces. The study focused on the erosion-corrosion of stainless steel (AISI 316) in coolant solutions containing nanoparticles. The experiments utilized a rotating cylinder electrode (RCE) with rotational speeds ranging from 0 to 1800 rpm and a temperature range of 30°C-70°C. The corrosion rate was determined using the linear polarization resistance (LPR) method, while the erosion was measured by calculating the average surface roughness of the samples. The design of the experiment (DOE) was utilized to find the mathematical expressions of the effects of the nanocoolant on erosion and corrosion. The findings revealed that the corrosion rate and surface roughness of the samples increased with an increase in temperature and rotation speed. Furthermore, the erosion-corrosion effects of the nanocoolant were less significant in stagnant conditions than in flow conditions, and significant differences were observed when compared with conventional coolant. Additionally, synergistic erosion and corrosion processes were detected at higher temperatures and higher rotation speeds for both types of coolants.
Mechanism and Application of Attitude and Orbit Coupling Dynamics for Spacecraft Proximity Relative Motion
Authors: Hao An , Jin Li , Tianzhe Wang , Jie Zang , Xianliang Zhang , Yong Hao
Published: May 2024

This paper analyzes the root causes of attitude-orbit coupling effects of spacecraft proximity relative motion in space precision collaborative tasks from three aspects: mathematical representation, physical definition, and engineering applications. At first, taking mathematical representation as the context, spacecraft proximity relative motion representations such as particle relative dynamic model, extended particle relative dynamic model, and dual-spiral-based relative dynamic model are investigated in detail. On this basis, the mechanism of attitude-orbit coupling effects originating from different mathematical representations is further investigated. Second, spiral theory–based attitude-orbit coupling relative dynamics is developed. The innovation of this work is extending the dual number representation from rigid body to flexible body, which makes it possible to describe the proximity relative motion between two rigid-flexible coupling spacecraft. Third, the application value of attitude-orbit coupling relative dynamic model in precision collaborative mission such as precision formation, rendezvous and docking, space manipulation, and on-orbit assembly is provided. Finally, simulation results verify the engineering significance of the attitude-orbit coupling relative dynamic model.
Intelligent Diagnosis of Urban Underground Drainage Network: From Detection to Evaluation
Authors: Luo Daming , Du Kanglei , Niu Ditao
Published: May 2024

During the process of urban development, there is large‐scale laying of underground pipeline networks and coordinated operation of both new and old networks. The underground concrete drainage pipes have become a focus of operation and maintenance due to their strong concealment and serious corrosion. The current manual inspections for subterranean concrete drainage pipelines involve high workloads and risks, which makes meeting the diagnostic needs of intricate urban pipeline networks challenging. Through advanced information technology, it has reached a consensus to intelligently perceive, accurately identify, and precise prediction of the condition of urban subterranean drainage networks. The development process of detection and evaluation methods for underground concrete drainage pipe networks is the focus of this study. The study discusses common algorithms for classifying, locating, and quantifying pipeline defects by combining the principles of deep learning with typical application examples. The intelligent progression of information collection methods, image processing techniques, damage prediction models, and pipeline diagnostic systems is systematically elaborated upon. Lastly, prospects for future research of intelligent pipeline diagnosis are provided.
Combination Immunotherapy with Vaccine and Oncolytic HSV Virotherapy Is Time Dependent
Authors: Totsch Stacie K. , Ishizuka Andrew S. , Kang Kyung-Don , Gary Sam E. , Rocco Abbey , Fan Aaron E. , Zhou Li , Valdes Pablo A. , Lee SeungHo , Li Jason , Peruzzotti-Jametti Luca , Blitz Sarah , Garliss Christopher M. , Johnston James M. , Markert James M. , Lynn Geoffrey M. , Bernstock Joshua D. , Friedman Gregory K.
Published: May 2024

Oncolytic virotherapy or immunovirotherapy is a strategy that utilizes viruses to selectively infect and kill tumor cells while also stimulating an immune response against the tumor. Early clinical trials in both pediatric and adult patients using oncolytic herpes simplex viruses (oHSV) have demonstrated safety and promising efficacy; however, combinatorial strategies designed to enhance oncolysis while also promoting durable T-cell responses for sustaining disease remission are likely required. We hypothesized that combining the direct tumor cell killing and innate immune stimulation by oHSV with a vaccine that promotes T cell–mediated immunity may lead to more durable tumor regression. To this end, we investigated the preclinical efficacy and potential synergy of combining oHSV with a self-assembling nanoparticle vaccine codelivering peptide antigens and Toll-like receptor 7 and 8 agonists (referred to as SNAPvax),which induces robust tumor-specific T-cell immunity. We then assessed how timing of the treatments (i.e., vaccine before or after oHSV) impacts T-cell responses, viral replication, and preclinical efficacy. The sequence of treatments was critical, as survival was significantly enhanced when the SNAPvax vaccine was given prior to oHSV. Increased clinical efficacy was associated with reduced tumor volume and increases in virus replication and tumor antigen–specific CD8+ T cells. These findings substantiate the criticality of combination immunotherapy timing and provide preclinical support for combining SNAPvax with oHSV as a promising treatment approach for both pediatric and adult tumors.
Automating cloud virtual machines allocation via machine learning
Authors: Kamoun-Abid Ferdaous , Frikha Hounaida , Meddeb-Makhoulf Amel , Zarai Faouzi
Published: May 2024

In the realm of healthcare applications leveraging cloud technology, ongoing progress is evident, yet current approaches are rigid and fail to adapt to the dynamic environment, particularly when network and virtual machine (VM) resources undergo modifications mid-execution. Health data is stored and processed in the cloud as virtual resources supported by numerous VMs, necessitating critical optimization of virtual node and data placement to enhance data application processing time. Network security poses a significant challenge in the cloud due to the dynamic nature of the topology, hindering traditional firewalls’ ability to inspect packet contents and leaving the network vulnerable to potential threats. To address this, we propose dividing the cloud topology into zones, each monitored by a controller to oversee individual VMs under firewall protection, a framework termed divided-cloud, aiming to minimize network congestion while strategically placing new VMs. Employing machine learning (ML) techniques, such as decision tree (DT) and linear discriminant analysis (LDA), we achieved improved accuracy rates for adding new controllers, reaching a maximum of 89%, and used the K-neighbours classifier method to determine optimal locations for new VMs, achieving an accuracy of 83%.
Energy efficient slotted synchronization approach in LoRaWAN
Authors: Shayo Eva , Abdalla Abdi T. , Mwambela Alfred , Sutikno Tole
Published: May 2024

In recent years, long-range wide-area networks (LoRaWAN) have gained much attention as low-power wide-area networks. LoRaWAN uses ALOHA as the medium access control protocol, where the end devices transmit data randomly and retransmit it up to eight times if collisions occur. ALOHA is not energy efficient and works perfectly for a smaller network. Several techniques, including the use of synchronization and scheduling schemes, to deal with the limitations imposed by ALOHA in LoRaWAN have been reported in the literature. However, the existing synchronization and scheduling algorithms transmit synchronization messages randomly using one super frame with fixed time slots that accommodate devices using different spreading factors, which limit the LoRaWAN network's scalability. This work proposes a slotted synchronization mechanism for transmitting synchronization requests to the gateway. The performance of the slotted synchronization was evaluated through simulation using packet delivery ratio (PDR) and energy efficiency as the performance parameters. The results indicate that when the number of devices in the network increases, a time-slotted synchronization consumes less energy, on average, by about 0.2 mAh. The use of a slotted synchronization can improve the energy efficiency of the end devices as collisions are completely avoided, achieving a PDR of 100%.
Identification of soluble solid content and total acid content using real-time visual inspection system
Authors: Moorthy C. H. V. K. N. S. N. , Tripathi Mukesh Kumar , Hudagi Manjunath R. , Hadimani Lingaraj A. , Chavan Gayatri Sanjay , Angadi Sanjeevkumar
Published: May 2024

This paper presents the framework for identifying materials using a fused descriptor-based approach, leverage computer vision techniques. The system is structured into three phases: derivation, extraction, and portrayal. Initially, the system employs K-means gathering techniques for establishing derivation. Following derivation, the system utilizes variety, texture, and shape-based feature extraction methods to extract relevant features from the soluble solid content and total acid content using real-time visual inspection system. A “consolidating” fusion feature is explored in the final phase using classification algorithms like C4.5, support vector machines (SVM), and k-nearest neighbors (KNN). The performance evaluation of the recognition system demonstrates promising results, with accuracy rates of 97.89%, 94.60%, and 90.25% achieved by using C4.5, SVM, and KNN separately. This indicates that the proposed fusion strategy effectively supports accurately recognizing materials using a fused descriptor-based approach.
Improving non-line-of-sight situations in indoor positioning with ultra-wideband sensors via federated Kalman filter
Authors: Türker Mehmet Nasuhcan , Arsan Taner
Published: May 2024

Ultra-wideband (UWB) technology is renowned for its exceptional performance in fast data transmission and precise positioning. However, it faces sensitivity challenges when the tagged object is not in direct line of sight, resulting in position inaccuracies. Applying the federated Kalman filter (FKF), this research focuses on mitigating position deviation induced by non-line-of-sight (NLOS) scenarios in UWB technology. The utilization of the FKF in NLOS scenarios has demonstrated a noteworthy reduction in position deviation. This study uses the FKF to analyze measurements taken under line-of-sight (LOS) and NLOS conditions within indoor settings. The outcomes of this study provide a promising foundation for future research endeavors in the field of UWB technology, emphasizing the potential for improved performance and accuracy in challenging operational environments.
URL shortener for web consumption: an extensive and impressive security algorithm
Authors: Gochhait Saikat , Rathore Yogesh Singh , Leonova Irina , Pandey Mahima Shanker , Saraswat Bal Krishna , Maurya Santosh Kumar , Singh Hare Ram , Bansal Nidhi
Published: May 2024

URL stands for uniform resource locator are the addresses of the unique resources on the internet. We all need URLs to access any type of resource on the internet, such as any web page, and document. Sometimes URLs can be long, irrelative and unattractive and unable to send sometimes via email. So, for this, we proposed a URL shortener web application based on the Python-Django platform which is fast and makes your long URLs in the shortest form which you can share on social media platforms. It makes all the messy, unattractive URLs short and shareable. Writing paper proposed a premium section in our application that gives access to the customizable URLs and analytics of your shorten URLs. Customizable URLs are the URLs you create by your own keywords. By creating a premium profile with the application, you can create your own URLs by using your own keywords. We have considered security a major part of the application that prevents the short URLs from being hacked or redirected to any advertising website or content. We store all the data related to the URL to show you the best view of your analytics and update it regularly. Main contribution in this field that for web application that provides users with a fast, secure and shortest URL for their using long URLs. Comparatively to other services that are currently available, the application provides superior security, availability, and confidentiality.
Accurate detection of melanoma skin cancer using fuzzy based SegNet model and normalized stacked LSTM network
Authors: Chembian Woothukadu Thirumaran , Sankar Krishna Murthi , Koteeswaran Seerangan , Thinakaran Kandasamy , Raman Periyannan
Published: May 2024

Early detection of melanoma skin cancer (MSC) is critical in order to prevent deaths from fatal skin cancer. Even though the modern research methods are effective in identifying and detecting skin cancer, it is a challenging task due to a higher level of color similarity between melanoma non-affected areas and affected areas, and a lower contrast between the skin portions and melanoma moles. For highlighting the aforementioned problems, an efficient automated system is proposed for an early diagnosis of MSC. Firstly, dermoscopic images are collected from two benchmark datasets namely, international skin imaging collaboration (ISIC)-2017 and PH2. Next, skin lesions are segmented from dermoscopic images by implementing a fuzzy based SegNet model which is a combination of both deep fuzzy clustering algorithm and the SegNet model. Then, hybrid feature extraction (ResNet-50 model and local tri-directional pattern (LTriDP) descriptor) is performed to capture the features from segmented skin lesions. These features are given into the normalized stacked long short-term memory (LSTM) network to categorize the classes of skin lesions. The empirical evaluation reveals that the proposed normalized stacked LSTM network achieves 98.98% and 98.97% of accuracy respectively on the ISIC2017 and PH2 datasets, and these outcomes are more impressive than those of the conventional detection models.
Enhancing machine learning algorithm performance through feature selection for driver behavior classification
Authors: Bouhsissin Soukaina , Sael Nawal , Benabbou Faouzia , Soultana Abdelfettah
Published: May 2024

Machine learning (ML) techniques empower computers to learn from data and make predictions or decisions in various domains, while preprocessing methods assist in cleaning and transforming data before it can be effectively utilized by ML. Feature selection in ML is a critical process that significantly influences the performance and effectiveness of models. By carefully choosing the most relevant and informative attributes from the dataset, feature selection enhances model accuracy, reduces overfitting, and minimizes computational complexity. In this study, we leverage the UAH-DriveSet dataset to classify driver behavior, employing Filter, embedded, and wrapper methods encompassing 10 distinct feature selection techniques. Through the utilization of diverse ML algorithms, we effectively categorize driver behavior into normal, drowsy, and aggressive classes. The second objective is to employ feature selection techniques to pinpoint the most influential features impacting driver behavior. As a results, random forest emerges as the top-performing classifier, achieving an impressive accuracy of 96.4% and an F1-score of 96.36% using backward feature selection in 7.43 s, while K-nearest neighbour (K-NN) attains an accuracy of 96.29% with forward feature selection in 0.05 s. Following our comprehensive results, we deduce that the primary influential features for studying driver behavior include speed (km/h), course, yaw, impact time, road width, distance to the ahead vehicle, vehicle position, and number of detected vehicles.
Reviewing chronic ailments: predicting diseases with a multi-symptom approach
Authors: Oussous Aicha , Ez-Zahout Abderrahmane , Ziti Soumia
Published: May 2024

The integration of machine learning (ML) techniques is now indispensable in healthcare, especially in addressing the challenges posed by chronic illnesses, which present a significant global health concern due to their unpredictable nature. This study compares ML techniques employed in the diagnosis and treatment of chronic conditions such as diabetes, liver disease, thyroid disease, breast cancer, heart disease, Alzheimer’s disease, and others. Two primary criteria guided the selection of diseases under investigation. Firstly, those extensively studied with ML methods, and secondly, those leveraging ML models to resolve issues or yield promising results. The research concludes that in real-time clinical practice, there is no universally proven method for selecting the optimal course of action due to each method’s unique advantages and disadvantages. While a hybrid technique may exhibit slightly slower speed growth, it holds the potential to enhance the accuracy and performance of a model.
Emotion detection using EEG: hybrid classification approach
Authors: Kulkarni Deepthi D. , Dixit Vaibhav Vitthalrao , Deshmukh Shweta Shirish
Published: May 2024

The field of emotion research facilitates the development of several applications, all of which aim to precisely and swiftly identify emotions. Speech and facial expressions are the main focus of typical emotion analysis, although they are not accurate indicators of true feelings. Signal analysis, namely the electroencephalograph (EEG) of the brain signals, is the other area in which emotions are analyzed. When compared to other modalities, EEG offers precise and comprehensive data that facilitates the estimation of emotional states. In order to categories the emotions using an EEG signal, this work suggests a hybrid classifier (HC). The input EEG data is preprocessed using the wiener filtering approach to extract the original information from the noisy signal. The preprocessed signal is used to extract features, such as entropy and a new hybrid model that includes models such as Bi-directional long short-term memory (Bi-LSTM) and improved recurrent neural networks (IRNN), which trains using the retrieved features, is included as part of the classification process. Happy, sad, calm, and angry are the categorization findings; the suggested work demonstrates more accurate classification results than the traditional approaches. All these are done on DEAP dataset with 60%, 70%, 80%, and 90% training sets and also a new DOSE dataset is been created similar to DEAP dataset.
Performance evaluation of multiclass classification models for ToN-IoT network device datasets
Authors: Soni Soni , Remli Muhammad Akmal , Daud Kauthar Mohd , Al Amien Januar
Published: May 2024

Internet of things (IoT) technology has empowered tangible objects to establish internet connections, facilitating data exchange with computational capabilities. With significant potential across sectors like healthcare, environmental monitoring, and industrial control, IoT represents a promising technological advancement. This study explores datasets from ToN-IoT’s IoT devices, focusing on multi-class classification, including normal and attack classes, with an additional aim of identifying potential attack sub-classes. Datasets comprise various IoT devices, such as refrigerators, garage doors, global positioning systems (GPS) sensors, motion lights, modbus devices, thermostats, and weather sensors. Comparative analysis is conducted between two prominent multiclass classification models, extreme gradient boosting (XGBoost) and light gradient boosting machine (LightGBM), utilizing accuracy and computational time metrics as evaluation criteria. Research findings highlight that the LightGBM model achieves superior accuracy at 78%, surpassing XGBoost 74.31%. However, XGBoost demonstrates an advantage with a shorter computational time of 1.23 seconds, compared to LightGBM 6.79 seconds. This study not only provides insights into multiclass classification model selection but also underscores the crucial consideration of the trade-off between accuracy and computational efficiency in decision-making. Research contributes to advancing our understanding of IoT security through effective classification methodologies. The findings offer valuable information for researchers and practitioners, emphasizing the nuanced decisions needed when selecting models based on specific priorities like accuracy and computational efficiency.
Melanoma image synthesis: a review using generative adversarial networks
Authors: Ahmed Mohammed Altaf , Qureshi Mohammad Naved , Umar Mohammad Sarosh , Bedoui Mouna
Published: May 2024

Melanoma is a highly malignant skin cancer that may be fatal if not promptly detected and treated. The limited availability of high-quality melanoma images, which are needed for training machine learning models, is one of the obstacles to detecting melanoma. Generative adversarial networks (GANs) have grown in popularity as a strong technique for image synthesis. This research is also targeted at the sustainable development goal (SDG) for health care. In this study, we survey existing GAN-based melanoma image synthesis methods. In this work, we briefly introduce GANs and how they may be used for generating synthetic images. Ensuring healthy lifestyles and promoting well-being for everyone, regardless of age, is the main aim. A comparative study is carried out on how GANs are used in current research to generate melanoma images and how they improve the classification performance of neural networks. Various public and proprietary datasets for training GANs in melanoma image synthesis are also discussed. Lastly, we assess the examined studies' performance using measures like the Frechet Inception distance (FID), Inception score, structural similarity ındex (SSIM), and various classification performance metrics. We compare the evaluated findings and suggest further GAN-based melanoma image-creation research.
Real-time monitoring system for blood pressure monitoring based on internet of things
Authors: Ngurah Desnanjaya I Gusti Made , Aditya Nugraha I Made
Published: May 2024

Blood pressure is an important cardiovascular health indicator, with normal values set by the WHO at 140 mmHg for systole and 90 mmHg for diastole. Excess of these values indicates hypertension, which increases the risk of serious medical complications. This research developed an internet of things (IoT)-based blood pressure monitoring device, which facilitates digital blood pressure measurement and data transmission to widely accessible applications and websites. The device uses an MPX5050GP pressure sensor, Arduino Nano, and NodeMCU ESP32, as well as other components programmed using the Arduino IDE. Test results obtained from 10 subjects, the device showed an average difference in systole of 7.9 mmHg and diastole of 5.4 mmHg. This complies with recognized accuracy standards of a maximum error of 10 mmHg and indicates that the device operates effectively with the designed concept.
Development of virtual tour reality using 360-degree panoramic images and Leaflet JavaScript
Authors: Amali Lanto Ningrayati , Katili Muhammad Rifai , Sugeha Alif Perdana
Published: May 2024

This paper describes virtual reality (VR) development using a 360-degree panoramic and Leaflet JavaScript (Leaflet JS) to introduce campus buildings in real-time. The campus building of Universitas Negeri Gorontalo (UNG) in Bone Bolango Regency was chosen as a case study. It allows users to navigate and listen to background sound and narration, open the site map interactively, and read brief information about each location. Each panorama contains hotspots that allow users to explore further. All images are combined using a photo-stitching technique to produce a panoramic image. The research method used is the multimedia development life cycle (MDLC), which consists of six stages: concept, design, material collection, assembly, testing, and distribution. Based on the system usability scale (SUS) test, the virtual tour reality website application received feedback from users regarding its usability, satisfaction, and effectiveness, and it is interesting to use this application. The results show that the website application can visualize the campus building environment with various layers of information and can create a very realistic and detailed representation of the campus environment.
Restauração de ecossistemas: financiamento por meio de Blended Finance e Fundos de Biodiversidade
Authors:   Universidade de São Paulo. Escola Superior de Agricultura Luiz de Queiroz, Sousa Fernando Henrique , Chaves Rafael Barreiro ,   Secretaria de Meio Ambiente, Infraestrutura e Logística do Estado de São Paulo (Semil), Braga Alexandre de Gerard ,   Secretaria de Meio Ambiente, Infraestrutura e Logística do Estado de SP (Semil)
Published: May 2024

Esta Nota Técnico-Científica 2 integra a Série Biota Síntese e apresenta fontes de financiamento nacionais e internacionais para a restauração de ecossistemas e a conservação da biodiversidade, visando apoiar o desenvolvimento de políticas públicas de restauração de paisagens e ecossistemas. Foi elaborada em coprodução, a partir de levantamentos realizados no início de 2023 por pesquisadores do programa Biota Síntese e agentes públicos da Secretaria de Meio Ambiente, Infraestrutura e Logística do Estado de São Paulo (Semil-SP). Trata-se de um diagnóstico preliminar, não exaustivo, que, junto com outros estudos, pode subsidiar o debate, a análise e a construção de políticas públicas inovadoras alinhadas às metas de programas como o ReflorestaSP e o Plano de Ação Climática de SP.
Inhibition of Hmbox1 Promotes Cardiomyocyte Survival and Glucose Metabolism Through Gck Activation in Ischemia/Reperfusion Injury
Authors: Bei Yihua , Zhu Yujiao , Zhou Jingwen , Ai Songwei , Yao Jianhua , Yin Mingming , Hu Meiyu , Qi Weitong , Spanos Michail , Li Lin , Wei Meng , Huang Zhenzhen , Gao Juan , Liu Chang , van der Kraak Petra H. , Li Guoping , Lei Zhiyong , Sluijter Joost P.G. , Xiao Junjie
Published: May 2024

BACKGROUND:

Exercise-induced physiological cardiac growth regulators may protect the heart from ischemia/reperfusion (I/R) injury. Homeobox-containing 1 (Hmbox1), a homeobox family member, has been identified as a putative transcriptional repressor and is downregulated in the exercised heart. However, its roles in exercise-induced physiological cardiac growth and its potential protective effects against cardiac I/R injury remain largely unexplored.

METHODS:

We studied the function of Hmbox1 in exercise-induced physiological cardiac growth in mice after 4 weeks of swimming exercise. Hmbox1 expression was then evaluated in human heart samples from deceased patients with myocardial infarction and in the animal cardiac I/R injury model. Its role in cardiac I/R injury was examined in mice with adeno-associated virus 9 (AAV9) vector-mediated Hmbox1 knockdown and in those with cardiac myocyte–specific Hmbox1 ablation. We performed RNA sequencing, promoter prediction, and binding assays and identified glucokinase (Gck) as a downstream effector of Hmbox1. The effects of Hmbox1 together with Gck were examined in cardiomyocytes to evaluate their cell size, proliferation, apoptosis, mitochondrial respiration, and glycolysis. The function of upstream regulator of Hmbox1, ETS1, was investigated through ETS1 overexpression in cardiac I/R mice in vivo.

RESULTS:

We demonstrated that Hmbox1 downregulation was required for exercise-induced physiological cardiac growth. Inhibition of Hmbox1 increased cardiomyocyte size in isolated neonatal rat cardiomyocytes and human embryonic stem cell–derived cardiomyocytes but did not affect cardiomyocyte proliferation. Under pathological conditions, Hmbox1 was upregulated in both human and animal postinfarct cardiac tissues. Furthermore, both cardiac myocyte–specific Hmbox1 knockout and AAV9-mediated Hmbox1 knockdown protected against cardiac I/R injury and heart failure. Therapeutic effects were observed when sh-Hmbox1 AAV9 was administered after I/R injury. Inhibition of Hmbox1 activated the Akt/mTOR/P70S6K pathway and transcriptionally upregulated Gck, leading to reduced apoptosis and improved mitochondrial respiration and glycolysis in cardiomyocytes. ETS1 functioned as an upstream negative regulator of Hmbox1 transcription, and its overexpression was protective against cardiac I/R injury.

CONCLUSIONS:

Our studies unravel a new role for the transcriptional repressor Hmbox1 in exercise-induced physiological cardiac growth. They also highlight the therapeutic potential of targeting Hmbox1 to improve myocardial survival and glucose metabolism after I/R injury.