bluuebunny/crossref_metadata_2025 huggingface dataset:
Androgen Exhibits a Protective Role Against Focal Erosions in Murine TNF-induced Inflammatory Arthritis
Authors: Chen Kiana , Kenney H Mark , Schwarz Edward , Rahimi Homaira
Published: October 2024

Rheumatoid arthritis (RA) is characterized by erosive pathology associated with joint inflammation and a sexual dimorphism with increased prevalence in females. Here, we aim to determine whether androgen is protective against inflammatory-erosive disease in TNF-transgenic (TNF-Tg) mice.

Wild-type (WT) and TNF-Tg male mice underwent sham (WT, n = 3; TNF-Tg, n = 7) or orchiectomy (WT, n = 3; TNF-Tg, n = 7) surgery at 1 month old to remove androgen production confirmed by serum testosterone concentration. Cohorts of orchiectomized TNF-Tg males were treated with either 5ɑ-dihydrotestosterone (.025 mg/day) (n = 3) or placebo (n = 3) via subcutaneous pellet insertion. Weekly clinical measures, along with mid-hindpaw bone volumes and ankle histology at 3 months old were evaluated for all groups.

Orchiectomies in TNF-Tg males significantly decreased serum testosterone (P < .05), weight gain (P < .001), and mid-hindpaw bone volumes (P < .05) in comparison to sham TNF-Tg mice. The cuboid bone also had increased synovitis by histology with the loss of androgen (P < .05). Treatment of orchiectomized TNF-Tg males with 5ɑ-dihydrotestosterone protected against the changes in weight gain (P < .01) and bone erosion (P < .05) associated with decreased osteoclast number in the cuboid (P < .01).

In the TNF-Tg model of chronic inflammatory arthritis, androgen is protective in erosive disease. The loss of endogenous androgen significantly accelerated the progression of inflammatory-erosive arthritis in male TNF-Tg mice to a similar severity as age-matched female mice. In addition, treatment with exogenous androgen prevented this observed bone loss in orchiectomized TNF-Tg males. Overall, androgen delays and limits bone erosion even in the presence of active inflammation and future studies are warranted to elucidate the associated mechanisms.
Electronic Consultations for Endocrine Conditions: A Scoping Review
Authors: Abdellatif Bahaa , Natarajan Varun , Leibowitz Alison J , Sitter Kailyn E , Vimalananda Varsha G
Published: October 2024

Electronic consultations (e-consults) are a mode of referral increasingly used to provide access to endocrine specialty care without the need for a patient in-person visit. This scoping review aimed to describe the models being used to deliver endocrine care via e-consult, what is known about outcomes of endocrine e-consult, and research gaps. The review was completed using an established methodological framework. PubMed, Embase, CINAHL, and Cochrane were searched for articles published in English between January 1, 2000, and March 21, 2024, that reported on e-consults for endocrine specialty care. The database search yielded 2522 articles, of which 19 underwent data extraction and synthesis. The overall body of endocrine e-consult literature is small and largely observational. Various models for endocrine e-consult programs exist. Findings on feasibility, acceptability, and timeliness are positive and consistent with the larger body of e-consult literature. Data on outcomes are limited but suggest that e-consults are no worse than other referral approaches to lowering A1C. Improvements in outcomes are greater for patients whose primary care providers implement e-consult recommendations. In summary, existing studies support the benefits of e-consults in various aspects of endocrine care quality, but the literature is nascent and there are significant research gaps. Future research should examine how e-consults can best address specific endocrine conditions, with a broad set of outcomes that addresses multiple quality dimensions. Advanced study designs and qualitative methods can help address unresolved questions about e-consults relevant to all specialties, including impact on care coordination and costs and best practices for reimbursement and workflow.
“Un prophète discret”
Authors: Earle Jason
Published: October 2024

This article analyzes how the French fascist and collaborationist author Pierre Drieu la Rochelle thought critically about the forms and the functions of the modern writer-prophet. In essays dedicated to the cases of D. H. Lawrence and Friedrich Nietzsche, as well as to his own ideological and literary trajectory in the interwar years, Drieu defined the true prophet as a writer of decadence, one compelled to narrate and embody the decline of the nation. By assuming this burden, the writer-prophet in turn becomes doomed to be misunderstood or ignored by his decadent contemporaries. This logic of prophecy’s ultimate failure provides a key to understanding the political and literary vision of Drieu’s autobiographical fascist bildungsroman Gilles (1939). Finally, this article considers how Drieu’s linking of prophecy and literary posterity can be seen as anticipating his recent controversial inclusion in the prestigious Bibliothèque de la Pléiade.
In the Dark (Antoine Volodine)
Authors: Hollister Lucas
Published: October 2024

This essay engages with the inclination of recent ecocritical thought to disqualify postapocalyptic fiction and, often by extension, dystopian fiction from the ranks of those responses to the Anthropocene deemed helpful or appropriate. After a survey of recent antidystopian pronouncements and a discussion of their implications, this essay argues that Antoine Volodine’s dark fiction offers tools for imagining why one might assert the validity—if not the productivity—of dark modes of thought. Specifically, it shows how Volodine’s darkness explores literary intransitivity and defiant nonpurposiveness, where stark pessimism about the ecological impacts of human life allows for the cultivation of hospitable ideas of a posthuman future.
Glottopolitics of Prophecy
Authors: Picón Natalia Castro
Published: October 2024

This article examines the secular use of prophecy and its appearances in Spain during the 2020 pandemic. It argues that prophecy functions as a cultural and political gesture with prescriptive potential over the future. As a glottopolitical tool (i.e., one that imbricates language and politics), it is contested between elites and subalterns articulating class warfare. Here, prophecy from above is analyzed through the concept of the “new normal,” which allowed Spanish politicians to predefine immediate future and safeguard the integrity of the capitalist system. As an example of prophecy from below, the article analyzes Lengua o muerte, a poetic text by Dani Zelko and the Movimiento por la lengua, which denounces the death of Mohammed Hossein during the pandemic as a case of linguistic discrimination. The author concludes that the discursive undertaking deployed in the book involves prophecy as a strategy of resistance against dispossession and projects a future of inclusion and emancipation.
Blog Serials: Digital Literature, Seriality, Readerly Performativity
Authors: Cho Heekyoung
Published: October 2024

The 1990s and the first decade of the 2000s were a period that witnessed dramatic changes in the literary scene in South Korea. Literary production’s engagement with digital media was one of the major changes in this period, when the divide, collision, and merge between pre-digital and digital came to affect almost every aspect of the society. This article explores the divergent strands in the development of digital literature, the new affordances of the digital environment that affected the literary serial vis-à-vis the print serial, and a virtual and actual community that readers and writers created through their engagement with literary texts and beyond. Through close analysis of a blog serial of Hwang Sŏgyŏng’s Evening Star (February 27–July 21, 2008; 102 episodes in all), which represents one of the significant moments in the early digital serialization of South Korean literature, this article discusses various types of performativity and participatory culture generated by and connected to the blog serial and participants.
Setting Treadmill Intensity for Rat Aerobic Training Using Lactate and Gas Exchange Thresholds
Authors: INOUE KOSHIRO , SOYA HIDEAKI , MURAKUMO KEI , ARAKI YUSUKE , HIRAGA TAICHI , SOYA SHINGO , OKAMOTO MASAHIRO
Published: October 2024

Purpose

To open up new horizons of translational research, we studied the lactate threshold (LT)–dependent physiological responses and adaptations to exercise in rats, highlighting the importance of intensity-specific studies for optimizing exercise prescriptions. LT is physiologically related to the noninvasive gas exchange threshold (GET), and both thresholds are moderate–heavy-intensity boundary indices in determining an effective intensity of aerobic training in humans. Although their practical utility is presumed to extend to rats, the actual existence of GET, the thresholds’ relations to maximal oxygen consumption (V̇O2max), and whether aerobic adaptations by training differ around the LT intensity remain uncertain.

Methods

This study sought to identify the GET using our previously established rat LT model by combining the use of a metabolic chamber and the V-slope method, and to confirm the thresholds’ relations to V̇O2max. We investigated changes in the thresholds and V̇O2max following 6 wk of endurance training at below or above LT intensity.

Results

GET and LT were significantly correlated and agreed with high precision, although with a fixed bias. Untrained rats exhibited GET and LT at 56% and 52% of their V̇O2max, respectively. Endurance training at supra-, but not below-, the LT intensity significantly improved V̇O2max and both thresholds; however, their %V̇O2max remained unaltered.

Conclusions

GET in rats is identifiable as a threshold associated with LT using the V-slope method. Furthermore, both thresholds can serve as moderate–heavy-intensity boundary indices for the aerobic training of rats. This study advances our understanding of exercise intensity regulation in rats, thereby contributing to the development of a more nuanced and effective model for exercise prescription, with implications for human health and fitness.
Interpreting the Projected Frontal Area in Front Crawl: Determining the Projected Frontal Area of Each Body Segment
Authors: WASHINO SOHEI , MURAI AKIHIKO , KADI TOMOYA , NARITA KENZO , MANKYU HIROTOSHI , YOSHITAKE YASUHIDE
Published: October 2024

Purpose

This study aimed to provide evidence for the interpretation of the projected frontal area (PFA) during front crawl. To achieve this goal, we developed a method for calculating the PFA of each body segment using digital human technology and compared the pressure drag under two calculation conditions: a combination of the PFA with and without accounting for the horizontal velocity of each body segment.

Methods

Twelve competitive male swimmers performed a 15-m front crawl at 1.20 m·s−1. The three-dimensional positions of the reflective markers attached to the swimmer’s body were recorded using an underwater motion-capture system. Based on the body shape of each swimmer obtained from the photogenic body scanner, individual digital human body models were created with the color of the model’s vertices divided into eight body segments. The time series of the volumetric swimming motion was reconstructed using inverse kinematics. The PFA of each body segment was then calculated by the automatic processing of a series of parallel frontal images. The pressure drag index, defined as the value excluding the drag coefficient while simultaneously considering the PFA and the horizontal velocity, was calculated under two conditions: the static condition (accounting for only the PFA of each body segment) and the dynamic condition (accounting for the PFA and horizontal velocity of each body segment).

Results

Notably, the pressure drag index was higher under the static condition than under the dynamic condition for the humerus, ulna, and hand segments (P < 0.001).

Conclusions

The results obtained using our methodology indicate that the PFA of the upper limb segments overestimates their contribution to pressure drag during front crawl under the static condition.
Theoretical Behavior Substitutions during the 24-h Day in Pregnancy and Infant Growth Outcomes
Authors: BADON SYLVIA E. , FERRARA ASSIAMIRA , GABRIEL KELLEY PETTEE , DOOLEY ERIN E. , QUESENBERRY CHARLES P. , AVALOS LYNDSAY A. , HEDDERSON MONIQUE M.
Published: October 2024

Purpose

The purpose of this study is to assess associations of theoretically reallocating time from sleep, sedentary behavior, or light-intensity physical activity (LPA) to moderate/vigorous-intensity physical activity (MVPA) during pregnancy with infant growth outcomes.

Methods

We used data from a cohort of pregnant individuals with overweight or obesity (n = 116). At 9 to 15 and 30 to 36 wk of gestation, waking movement was measured using wrist-worn accelerometers and sleep duration was self-reported. Outcomes were obtained from delivery electronic health records (birth) and study visits (12 months). We used compositional isotemporal substitution models.

Results

In early pregnancy, reallocating 10 min of sleep, sedentary behavior, or LPA to MVPA was associated with 20% (relative risk [RR] = 0.80; 95% CI, 0.75–0.85), 21% (RR = 0.79; 95% CI, 0.75–0.84), and 25% (RR = 0.75; 95% CI, 0.70–0.81) lower risk of large-for-gestational age (LGA) birthweight, respectively, and 17% (RR = 0.83; 95% CI, 0.75–0.91), 18% (RR = 0.82; 95% CI, 0.75–0.91), and 22% (RR = 0.78; 95% CI, 0.70–0.88) lower risk of rapid infant growth (birth to 12 months), respectively. In late pregnancy, reallocating 10 min to MVPA was associated with 18% to 22% lower risk of LGA birthweight, but was not associated with rapid infant growth. Reallocating time to MVPA in early or late pregnancy was not associated with high newborn body fat percentage.

Conclusions

Our findings suggest beneficial associations of theoretically reallocating time from sleep, sedentary behavior, or LPA to MVPA, especially during early pregnancy, for reducing LGA birthweight and rapid infant growth.
Longitudinal Associations of Aerobic Activity, Muscle-Strengthening Activity, and Adiposity with Cardiorespiratory Fitness
Authors: MEERNIK CLARE , SCHEINOWITZ MICKEY , LEONARD DAVID , BARLOW CAROLYN E. , LEONARD TAMMY , PETTEE GABRIEL KELLEY , PAVLOVIC ANDJELKA , BERRY JARETT D. , DEFINA LAURA F. , SHUVAL KEREM
Published: October 2024

Purpose

Cardiorespiratory fitness (CRF) declines with age, and greater declines increase the risk for adverse health outcomes. Understanding factors that attenuate age-related decreases in CRF can help extend healthy life. We sought to determine the longitudinal associations of aerobic physical activity, muscle-strengthening activity (MSA), and adiposity with CRF.

Methods

Study participants were enrolled in the Cooper Center Longitudinal Study and had three or more preventive medical examinations at the Cooper Clinic (Dallas, Texas) during 1987–2019. Aerobic activity and MSA were self-reported, and three measures of adiposity were clinically assessed: body mass index (BMI), body fat percentage, and waist circumference. CRF, expressed as metabolic equivalents (METs), was estimated by a maximal treadmill test. The longitudinal associations of aerobic activity, MSA, and adiposity with CRF were estimated using multivariable mixed linear regression models.

Results

The study included 6105 participants who were followed for a median of 7.1 yr. Most participants were men (83.6%), and their average age at baseline was 47.0 (SD = 8.7) yr. Mean CRF at baseline was 12.2 (SD = 2.3) METs. Increasing aerobic activity (per 500 MET·min·wk−1, β = 0.069, 95% confidence interval = 0.064–0.074 METs) or MSA (per days per week, β = 0.066, 95% confidence interval = 0.058–0.073 METs) was associated with increased CRF over time. Increasing BMI, body fat percentage, or waist circumference were each associated with decreased CRF over time.

Conclusions

These data offer longitudinal evidence on how changes in aerobic activity, MSA, and various measures of adiposity (beyond BMI) independently correlate with CRF over time. Healthy lifestyle behaviors that include increasing aerobic and MSA and limiting adiposity may positively influence the retention of fitness with age and improve downstream health outcomes.
Obesity Promotes Marrow-Derived Myeloid Cell Accumulation While Exercise Reduces Proliferative Signaling in Colon Cancer
Authors: VANHIE JAMES J. , ORLOFF LISA EK , TATE ALICE , GOODE COLE , COLLAO NICOLAS , PISANKO ANASTASIA , POWER KRISTA A. , DE LISIO MICHAEL
Published: October 2024

Purpose

Obesity increases colon cancer risk that has been previously linked to marrow-derived myeloid cells. We previously demonstrated that exercise training (EX) prevents colon cancer initiation, potentially through reduced myelopoiesis. However, it remains unknown whether early myeloid cell accumulation and inflammation in the colon precedes carcinogenesis with high-fat diet (HFD)-induced obesity, and if EX can attenuate these effects. We hypothesized that obesity would promote colon carcinogenesis that was preceded by myeloid cell accumulation and inflammation that would be attenuated by EX.

Methods

C57BL/6 mice were randomized to a HFD or control (CON) diet for 8 weeks. The HFD mice switched to CON diet and all mice were given intraperitoneal injections of azoxymethane (AOM) to induce colon cancer and randomized into EX or sedentary (SED) conditions.

Results

HFD mice developed more aberrant crypt foci (ACF), a marker for early carcinogenesis, compared with CON (P < 0.01), and EX developed fewer ACF compared with SED (P < 0.0001). Marrow-derived (P < 0.001) CD206+ macrophages were elevated in HFD compared with CON at study week 16 (P < 0.01). Marrow-derived CD206− macrophages (P < 0.05) and marrow-derived (P < 0.05) CD206+ macrophages were more abundant in HFD compared with CON at study week 42. EX did not alter colon immune cell populations. β-catenin protein was higher in HFD compared with CON at study week 42 (P < 0.05), and STAT3 protein content was lower at study week 28 with EX compared with SED (P < 0.05).

Conclusions

The results suggest that obesity promotes colon ACF formation, potentially through early inflammatory myeloid cell accumulation. Despite attenuating ACF, EX did not alter myeloid cell accumulation in the colon, suggesting that EX inhibits ACF formation through alternative mechanisms which may include reduced β-catenin and STAT3 signaling.
“Meu INSS”: o avanço tecnológico em um cenário de exclusão digital
Authors: Alcântara Bruno Marcel , Figueiredo Caio Maia , Xavier Christiane do Espírito Santo , Freitas Joselito Rodrigues de
Published: October 2024

Este artigo aborda o uso da tecnologia no serviço público. O foco é o Instituto Nacional do Seguro Social (INSS), que passa por um momento de transformação digital, diminuindo o atendimento presencial aos seus usuários e ofertando os diversos serviços por meio da plataforma “Meu INSS”, que ampliou o acesso aos cidadãos, levando o instituto aos municípios menores onde não existia uma agência física. Porém, depara-se com a realidade brasileira na qual grande parte da população não tem habilidade com tecnologia e há quem sequer tenha acesso à internet. Ao se debruçar em diversos trabalhos acadêmicos que versam sobre o tema, procura-se propor uma solução inovadora que possa diminuir o impacto da ausência do atendimento presencial.
Vai ter creche hoje?
Authors: Rickli Anne Caroline , Santos Carlos Alfredo Lopes Vieira dos , Moura Rachel Désirée de Barros e Silva , Santos Rafael Mendes dos , Alvim Roberta Pires , Vieira Sabrina Nunes
Published: October 2024

Este caso de ensino aborda o dilema do cumprimento da promessa de campanha do prefeito Prometeu acerca de garantir vaga na creche para todas as crianças do município de Criança Feliz. Nesse processo, o gestor municipal depara com três alternativas distintas para a solução do problema: contratação temporária de profissionais, realização de um novo concurso público para incremento dos servidores de carreira ou terceirização dos serviços com o custeio de vagas na rede privada, por meio de parceria com as instituições particulares. O caso apresenta características marcantes do modelo burocrático weberiano, do gerencialismo e do hollow state. Abordam-se ainda os impactos de cada modelo de organização na prestação de serviços públicos e a influência da política na tomada de decisões administrativas.
O “viaduto da controvérsia” em Tucupi: uma reflexão sobre responsabilidade e transparência na administração pública
Authors: Numao Fábio Hideo , Souza João Victor Levinthal Oliveira , Oliveira Luciano Simões , Mendes Luiz Henrique Pereira , Ribeiro Maria Angélica de Jesus
Published: October 2024

Neste caso de ensino, analisa-se a controversa construção de um viaduto na cidade imaginária de Tucupi, na Amazônia, marcada por sinais de corrupção e violação de leis. O objetivo do estudo é estimular uma reflexão crítica sobre as consequências da corrupção na administração pública, o papel dos mecanismos de responsabilização e a relevância da transparência e ética na gestão pública. O prefeito Zé da Manga, famoso por seu populismo, está envolvido em um escândalo de contratos sem licitação. O viaduto, uma promessa eleitoral e vital para o desenvolvimento regional, conectará a ilha de Açaí ao continente. A construção foi categorizada como emergencial, o que dispensou a necessidade de licitação, e a seleção do empreiteiro Toninho Construção sugere interesses ocultos. O caso ressalta a importância da transparência e da responsabilização na administração pública.
Projeto Educa+ no Igarapé
Authors: Nepomuceno Anderson Pinheiro , Maquiné Junior José Raimundo , Viana Júlio Alan dos Santos , Santos Junior Luis Carlos de Miranda , Leite Stanley Scherrer de Castro
Published: October 2024

Informamos que as notas de ensino dos casos possuem acesso restrito e são exclusivas para docentes e instrutores vinculados à uma instituição acadêmica. Saiba mais.
Predictors of mortality for blunt trauma patients in intensive care: A retrospective cohort study
Authors: Jennings Michael , Booker James , Addison Amy , Egglestone Rebecca , Dushianthan Ahilanandan
Published: October 2024

Background: Major trauma places substantial demand on critical care services, is a leading cause of death in under 40-year-olds and causes significant morbidity and mortality across all age groups. Various factors influence patient outcome and predefining these could allow prognostication. The aim of this study was to identify predictors of mortality from major trauma in intensive care. Methods: This was a retrospective study of adult trauma patients admitted to general intensive care between January 2018 and December 2019. We assessed the impact on mortality of patient demographics, patterns of injury, injury scores (Glasgow Coma Score (GCS), Charlson’s comorbidity index (CCI), Acute Physiology and Health Evaluation II (APACHE II), Injury Severity Score (ISS) and Probability of Survival Score (Ps19)), number of surgeries and mechanism of injury using logistic regression. Results: A total of 414 patients were included with a median age of 54 years (IQR 34–72). Overall mortality was 18.6%. The most common mechanism of injury was traffic collision (46%). Non-survivors were older, had higher ISS scores with lower GCS on admission and probability of survival scores. Factors independently predictive of mortality were age 70-80 (OR 3.047, p <0.05), age >80 (OR 24.969, p <0.001) and GCS <15 (OR 8.876, p <0.001). Ps19 was the best score for predicting mortality (p <0.001 for each score category), with an AUROC of 0.90. Conclusions: The significant mortality predictors were age, GCS <15 and Ps19. Contrary to previous studies CCI and APACHE II did not significantly predict mortality. Although Ps19 was found to be the best current prognostic score, trauma prognostication would benefit from a single validated scoring system incorporating both physiological variables and injury patterns.
Case Report: Post-operative mitral valve replacement complicating with a large cardiac mass and the role of TEE in Imaging
Authors: Pai D Narasimha , Nayak Chaithra , Kamath Padmanabh , Pasha Syed Waleem , Noronha Deepa
Published: October 2024

Background Postoperative complications are an integral part of valve surgery. Common complications include hematomas, bleeding, valve dehiscence, paravalvular leak, and acute PV thrombosis. With the available data from published articles, the rate of all valve-related complications is 0.7 to 3.5% per patient annually. [1] The pathology involved is multifactorial, often blood vessel injury leading to bleeding and hematoma. Although postoperative complications are evident, incidental diagnosis of a cardiac mass in an asymptomatic and hemodynamically stable patient postoperatively is crucial, requiring non-invasive imaging for immediate surgical action. Case presentation A woman in her 50s presented with chief complaints of worsening dyspnoea with suddenonset and chest pain. Clinical findings showed apex shifted downward and outward, wide split S2, and a mid systolic murmur radiating to the mid axillary line. Twelve-lead ECG showed LA enlargement, that aligned with X-ray findings. 2D Echocardiography revealed MVP with severe MR and a dilated LV. The patient underwent successful mitral valve replacement as per ACC/AHA class I recommendation. However, postoperative TTE showed a remarkably large mass measuring 5.6 cm*4.6 cm in the RA. Reexploration was performed, followed by mass excision. Plenty of organized clots were seen compressing the RA. TEE showed no evidence of mass. Following stabilization,the patient was discharged considering optimal INR values and prosthetic valve function assessed by echocardiography. The patient’s symptoms improved during the first follow-up. Conclusion Although postoperative cardiac complications are common, appropriate diagnosis with TTE and TEE has benefited surgeons. TEE-guided reexploration aids surgeons in decision-making and strategic approaches. Failure to diagnose such complications in asymptomatic patients can ultimately complicate the procedure. Henceforth, sonographers must be skilled in the detection and identification of unusual complications to guide redo interventions. Such an approach minimizes mortality, redo procedures, and avoids CPB hence reducing long-term prognosis and outcomes with valve replacement.
Redundant Telemetry Link System for Uncrewed Aerial Vehicles
Authors: Elbashir Ali , Sayar Ahmet
Published: October 2024

Thanks to their flexibility in research, prototyping, and development, Uncrewed Aerial Vehicles (UAV) are seeing increasing use in agriculture, healthcare, logistics, and other industries. With the increase in the use of UAV in civilian sectors, comes an increase in the possibility of errors in telemetry connections. Issues with the telemetry connection may be catastrophic and cause accidents and crashes and may also adversely affect the performance of the subsystems connected to the telemetry. In this study, a low-cost software-based redundant telemetry link system for UAV is proposed. The proposed system minimises the received packet loss using packet deduplication and is compared with a dynamic link switching method. In the proposed system, both fault tolerance and robustness are introduced into the UAV telemetry link. The analysis is performed using the ArduPilot UAV Simulation environment as a producer and a Golang implementation of the proposed methods.
Energy Harvesting Deadline Monotonic Approach for Real-time Energy Autonomous Systems
Authors: Chafi Safia Amina , Benhaoua Mohammed Kamal
Published: October 2024

This paper presents an innovative scheduling algorithm designed specifically for real-time energy harvesting systems, with a primary focus on minimizing energy consumption and extending the battery's lifespan. The algorithm employs a fixed priority assignment which is the deadline monotonic policy, we have chosen it for its optimality and superior performance compared to other fixed priority scheduling methods. To achieve a balance between energy efficiency and system performance, we incorporated a DVFS (Dynamic Voltage and Frequency Scaling) technique into the algorithm. This adaptive approach enables precise control over the processor's operating frequency, effectively managing energy consumption while ensuring satisfactory system functionality. The core objective of our scheduling algorithm centers on optimizing energy utilization in real-time energy harvesting systems, specifically tailored to extend the battery's operational life. Rigorous evaluations, including comprehensive comparisons against established fixed priority scheduling algorithms, validate the algorithm's efficacy in significantly reducing energy consumption while preserving the system's overall functionality. By combining the deadline monotonic policy and DVFS technique, our proposed algorithm emerges as a promising solution for energy-autonomous systems, contributing to the advancement of sustainable energy practices in real-time applications. As energy harvesting technologies continue to progress, our algorithm holds valuable potential to provide critical insights for enhancing the efficiency and reliability of future energy harvesting systems.
Mathematical Nonlinear Graph Theory Topology Layer Model for Photoelectric Tracking System
Authors: Li Jing , Shang Yunpeng
Published: October 2024

The performance of the photoelectric tracking system mainly depends on the tracking accuracy. In order to achieve the purpose of high precision tracking, controlling the power dragging device, the main component of the photoelectric tracking system, is the main means to achieve this purpose. In order to improve the tracking speed and accuracy of photoelectric tracking systems, the author proposed a mathematical nonlinear graph theory topology layer model for photoelectric tracking systems. The topology layer model and the motion node servo mechanism model of the photoelectric tracking system are studied, and the two-stage disturbance sources that affect the tracking stability are analyzed. The results show that the interference estimation error range designed by the author reaches 0.003 through comparison, it can be seen that the estimation error designed by the author is significantly smaller than the ESO estimation error, and the buffeting is small. Through comparison, it can be concluded that the estimation error of the algorithm and the disturbance observer designed by the author are significantly smaller than the estimation error of ESO, and the buffeting is small, and they have strong compensation ability for the disturbance of different frequencies. The interference observer can quickly and accurately estimate the interference and prove its stability. The effectiveness of the proposed observer and disturbance observer is fully demonstrated. The finite time integrated site selection mode disturbance observer (F-ISMDOB) is designed to quickly estimate and compensate for equivalent interference, and effectively improves the anti-interference performance of the system structure layer.
Enhancing Black Hole Attack Detection in VANETs: A Hybrid Approach Integrating DBSCAN Clustering with Decision Trees
Authors: Hong Seung-Phil
Published: October 2024

Ensuring the security of communication is crucial in Vehicular Ad Hoc Networks (VANETs) to protect the integrity of information sharing among cars. To implement VANET communication as an answer for the different uses, secure communication is necessary. The unreliability of VANET environments is caused by message delays or tampering in VANET applications. Finding the sweet spot between VANET security and performance and dependability is the primary goal. This project’s overarching goal is to fortify VANETs against Blackhole Routing Attacks and, by identifying and blocking harmful nodes, to mitigate the blackhole impact. This paper proposes a robust hybrid approach for the detection of black hole attacks in VANETs, leveraging the synergy between DBSCAN (Density-Based Spatial Clustering of Applications with Noise) clustering and Decision Trees. DBSCAN, a density-based clustering algorithm, is employed to identify spatial clusters of vehicles, while Decision Trees are utilized to discern normal communication patterns from malicious ones within these clusters. The integration of these two techniques enhances the accuracy and efficiency of black hole attack detection in the dynamic and resource-constrained VANET environment. Experimental results demonstrate the effectiveness of the proposed hybrid approach, providing a promising solution for bolstering the security of VANETs against emerging threats. Here in result 73.89% improvement is received in Packet Drop Rate using DBSCAN, also minor improvement over Throughput and Average end to end delay and major improvement in terms of Network Routing Load.
Machine Learning-based Human Resource Management Information Retrieval and Classification Algorithm
Authors: Li Wen , Zhou XiuKao
Published: October 2024

Efficient human resource management (HRM) is essential for company achievement in today’s fast-paced corporate world. Businesses must find effective ways to retrieve and categorise the ever-growing amount of HR-related knowledge. This work presents a new method for retrieving and classifying HRM data using machine learning. Modern natural language processing (NLP) and CNN methods are used by the algorithm we developed to handle unorganized human resources (HR) information, including worker records, job postings, and certificates. HR decision-making is facilitated by the system’s ability to derive insightful information from the information using sophisticated text mining and machine learning algorithms.The two main parts of the method are classification and data extraction. HR workers can more easily obtain the required knowledge thanks to information retrieval, which makes it possible to search HR data quickly and accurately. Contrarily, categorisation optimises the division of human resources information into pertinent classifications, including job positions, competencies, and achievement grades. We assess our algorithm’s effectiveness on various datasets from actual HR datasets. The outcomes show how well our strategy works to streamline HRM procedures, cut down on manual labour, and increase the precision of decisions. Furthermore, our technology is compatible with corporate human resources offices and educational settings because it complies with worldwide university requirements.This study belongs to the growing body of knowledge in HRM. It provides a useful tool for businesses looking to improve employee relations, simplify HR procedures, and attract and retain talent. The suggested method is a valuable resource for academics and businesses alike because of its versatility and compliance with global educational norms.
A Shared Economy Data Prediction Model Based on Deep Learning
Authors: Zhou Min
Published: October 2024

In the dynamic and intricate shared economy, efficient resource management and forecasting are still crucial. In this research, a new prediction model is presented that aims to improve the operational efficiency of several shared economy services. Our method creatively combines Long Short-Term Memory (LSTM) networks with Genetic Algorithms (GA) to assess user demand and optimize resource deployment. We apply this methodology especially to e-scooter sharing services, but the underlying ideas and methods can be applied to other shared economy platforms as well, like peer-to-peer lending services, car sharing, and vacation rentals. The model starts with a GA to adjust the hyperparameters of the LSTM network, making the network better suited to handle specific characteristics of common economic data. Capturing the complex temporal and spatial patterns of user behavior and demand on these platforms requires this optimization. The LSTM element then predicts changes in service demand due to its capacity to analyze sequential records. Further to being useful for analyzing sequential data and predicting destiny wishes, this predictive functionality is important for a shared economy platform to correctly manage stock, allocate assets, and predict personal wishes. We use a large dataset to check our technique, demonstrating the predictive accuracy of the model and demand and its potential to aid strategic choice-making. Compared to traditional fashions, the consequences show a large development in forecast accuracy and resource allocation efficiency. Our methodology creates a robust basis for statistics-driven insights that decorate customer happiness and decorate the long-term increase of the shared financial system. This work highlights the blended ability of GA and LSTM inside the shared economy and paves the manner for future enhancements in using modern-day gadget mastering techniques to optimize and alter various shared services. In quick, effective useful resource control and forecasting in the shared economic system is tough, however our specific forecasting model combining GA-LSTM gives a manner ahead. Our technique, as it should be, predicts fluctuations in provider demand; it was first refined the usage of GA to regulate the LSTM hyperparameters. The consequences show how correct and powerful our version is and spotlight how it can enhance customer pleasure and operational performance. This research paves the manner for future trends within the software of gadgets, gaining knowledge of methods and supports the continuing enlargement and development of shared economic system offerings.
Big Data Analysis and Information Sharing for Innovation and Entrepreneurship Education
Authors: Xie Qian
Published: October 2024

This study delves into the transformative potential of integrating big data analysis and information sharing in innovation and entrepreneurship education. Employing a comprehensive methodology encompassing K-Means Clustering, Decision Trees, Apriori Algorithm, and Neural Networks, the research investigates student engagement patterns, influential factors, collaborative relationships, and predictive modelling within educational settings. The findings reveal significant outcomes, with K-Means achieving a clustering precision of 75%, Decision Trees demonstrating an accuracy of 82%, Apriori Algorithm uncovering frequent itemsets with 68% support, and Neural Networks achieving a notable accuracy of 90%. Drawing insights from a diverse range of literature, including studies on big data management, demand prediction models, ecological approaches to entrepreneurship education, qualitative inquiries into startup strategies, applications of ICTs in education, and the impact of virtual gaming on SMEs’ growth, the research provides a robust foundation for understanding innovation and entrepreneurship education. This study contributes to both theoretical understanding and practical implications, guiding educators and policymakers in tailoring interventions and strategies to foster an adaptive and effective educational environment.
Risk Assessment of Vehicle Battery Safety based on Abnormal Features and Neural Networks
Authors: Wang Jiejia , Guo Zhiyang , Miao Xiaoyu
Published: October 2024

In this study, we evaluate a proactive battery EV safety assessment method using abnormal feature detection and neural networks. Four sophisticated algorithms —Isolation Timberland, One-Class SVM, Autoencoder and also LSTM— were performed to assess their applicability in detecting anomalous battery behavior. The Isolation Woodland algorithm showed a balanced accuracy recall trade-off of the values 0.85 and 0.92 respectively One class SVM demonstrated highly sharp results with an accuracy and recall values of 0.78 and 0.8, respectively. The autoencoder, that used a large amount of learning and won with 0.92 accuracy score and an F1-score – 0.89 The LSTM structure, programmed for sequential information, indicated a great execution with a 0.94 review and the F1-score of 0. A comparative study has shown that these algorithms can provide alot flexibility in sending based on the clear requirements.
Research on Collaborative Defense Method of Hospital Network Cloud based on End-to-end Edge Computing
Authors: Yang Huihong , Lin Shuijuni , He Qifan , Yu Qirong
Published: October 2024

This research introduces a groundbreaking collaborative defense mechanism that utilizes end-to-end edge computing to bolster the security of decentralized hospital cloud systems. By integrating intrusion detection systems, firewalls, anomaly detection, and threat intelligence in a unified manner through the efficiency of edge computing, this approach marks a significant advancement in healthcare cybersecurity. Through rigorous testing with a substantial dataset, the system demonstrated exceptional performance metrics, including a remarkable 95% accuracy in threat detection, a low false positive rate, and a swift response time of merely 0.25 seconds. Notably, the system effectively mitigates computational overhead, thereby optimizing resource utilization. Comparative analysis with existing methodologies underscores the superiority of this novel framework, particularly in terms of geolocation accuracy, the minimization of false positives, and expedited reaction capabilities. This study’s collaborative defense strategy, underpinned by end-to-end edge computing, presents a holistic and innovative solution to the escalating cyber threats facing healthcare infrastructures. By redefining the parameters of security in medical settings, it paves the way for a safer and more resilient healthcare information technology ecosystem.
Mobile Device Security: A Two-Layered Approach with Blockchain and Sensor Technology for Theft Prevention
Authors: Malsa Nitima , Jain Rachna , Goyal S.B.
Published: October 2024

In the backdrop of the escalating incidents of mobile device theft and associated security challenges, a resilient and innovative solution is imperative. The traditional security mechanisms, largely reliant on the International Mobile Equipment Identity (IMEI), have been fraught with vulnerabilities, leading to a surge in incidents of device theft and data breaches. Addressing this pressing issue, we present a novel, two-tiered approach integrating sensor technology and blockchain to bolster mobile device security. This work aims to create and assess a dual-layered security strategy that uses blockchain and sensor technologies in a complementary way. Based on a rigorous conceptual framework, it explores a two-tiered security model that intricately combines sensor and blockchain technologies. This collaborative integration aims to provide an effective solution to the widespread challenges of theft and security breaches in mobile devices. The methodology employs a sensor layer for real-time data collection and processing to detect potential thefts, and activating alerts. The blockchain layer, invoked upon these alerts, initiates secure, transparent, and decentralized transactions for verification and validation across network nodes. This dual mechanism ensures swift and secure anti-theft actions, supported by an enhanced encryption standard. Our result analysis reveals the proposed system’s superiority in computational time, energy consumption, and overall security levels when compared to existing protocols. The integration of real-time processing and blockchain’s immutable nature promises reduced false positives and enhanced data integrity. The findings indicate that this integrative approach not only mitigates theft but also ensures data security, marking a significant stride in mobile security technology. In conclusion, this two-layered system promises a scalable, efficient, and robust solution to mobile device theft and data breaches, with potential impacts transcending individual device security to influence broader data privacy and security paradigms, thus signifying a pivotal development in the field of mobile security.
Pre-DNNOff: On-Demand DNN Model Offloading Method for Mobile Edge Computing
Authors: Zuo Lin
Published: October 2024

Deep Neural Networks (DNNs) are critical for modern intelligent processing but cause significant latency and energy consumption issues on mobile devices due to their high computational demands. Moreover, different tasks have different accuracy demands for DNN inference. To balance latency and accuracy across various tasks, we introduce PreDNNOff, a method that offloads DNNs at a layer granularity within the Mobile Edge Computing (MEC) environment. PreDNNOff utilizes a binary stochastic programming model and Genetic Algorithms (GAs) to optimize the expected latency for multiple exit points based on the distribution of task inference accuracy and layer latency regression models. Compared to the existing method Edgent, PreDNNOff has achieved a reduction of about 10% in the expected total latency, and due to the consideration of different tasks’ varying requirements for accuracy, it has a broader applicability.
A Dynamic Sandbox Detection Technique in a Private Cloud Environment
Authors: Yang Zhangwei , Xiao Junyu
Published: October 2024

In specific private cloud scenarios, how to defend against malicious software and ensure data security is one of the current research hotspots, and sandbox is an important detection method. This paper proposes a dynamic behavior detection technique based on sandboxing, which real-time monitors and analyzes malicious software behavior. By improving the sandbox behavior weight, integrating virtual resources, and designing fine-grained access control, the detection accuracy and efficiency are enhanced based on zero trust access control system. The simulated attacks are identified on the testing platform, drawing knowledge graphs, achieving effective discovery and tracing. Meanwhile, this paper verified through experiments that the system consumption of the detection method is within an acceptable range, expanding the detection range and reducing the missed detection rate.
Multi-objective Optimization Algorithm of Cross-border E-commerce Social Traffic Network based on Improved Particle Swarm Optimization
Authors: Jin Wenjin , Li Yingyu
Published: October 2024

The optimization algorithm known as Particle Swarm Optimization (PSO) is based on swarm intelligence and was created by modeling the foraging behavior of bird flocks. This study using the generalized regression neural network to improve particle swarm optimization (PSO) algorithm, proposed a target for cross-border electricity social network optimization algorithm PSO-PNNG, the simulation experiment in multiple real social network data environment and algorithm comparison, and the basic operation of genetic algorithm into the particle swarm algorithm, enhance the particle swarm optimization algorithm’s performance, speed up the convergence speed. In this study, three social network datasets obtained by real reptiles were used to solve the proposed PSO-PNNG algorithm in a real social network data environment. The findings of the experiment indicate that the suggested multi-objective optimization algorithm for cross-border e-commerce social traffic network based on improved PSO has higher efficiency and accuracy than the traditional method.
Integration of LLM in Expiration Date Scanning for Visually Impaired People
Authors: Grumeza Theodor , Bozga Bogdan , Staniloiu Grigore-Liviu
Published: October 2024

In this study, the authors explore an approach to detect expiration dates of food products using a live feed stream and the integration with Large Language Models in order to improve accessibility for visually impaired people. The main objective is to enhance their capacity to engage in common tasks like grocery shopping autonomously. The novelty of this research lies in employing Meta LLAMA 2, a large language model, and experimenting with both traditional and a new OCR solution to find the expiration date using image processing. This approach offers audio information about whether the product has expired or when it will expire, helping in shopping and product recognition for visually challenged customers. The proposed solution consists of optical character recognition, mainly the EasyOCR library, fine-tuned on cropped images containing only the expiration dates and a validation phase that filters and checks the extracted data.