bluuebunny/crossref_metadata_2025 huggingface dataset:
Magnetic fields from indoor transformer stations and risk of cancer in adults: a cohort study
Authors: Juutilainen Jukka , Khan Muhammad Waseem , Naarala Jonne , Roivainen Päivi
Published: November 2024

Objectives

Studies assessing the association of adult cancers with extremely low frequency (ELF) magnetic fields (MF) have provided inconclusive results, probably affected by limitations such as low exposure levels, confounding and various forms of bias. This study investigated the association between residential ELF MF exposure and adult cancer using a design that avoids the main limitations of previous studies.

Methods

Persons who have lived in buildings with indoor transformer stations during the period 1971–2016 formed the study cohort. Their MF exposure was assessed based on the location of their apartment in relation to the transformer room. Information on their cancer diagnoses was obtained from the Finnish Cancer Registry. SIR with 95% CI was calculated to investigate the association of MF exposure with overall cancer and specific cancers.

Results

The SIR for all primary sites was 1.01 (95% CI 0.93 to 1.09). An increased risk of digestive organ cancers was observed among the exposed persons, with a SIR of 1.23 (95% CI 1.03 to 1.46). The highest SIR was observed for gallbladder cancer (3.92, 95% CI 1.44 to 8.69). Increased risk of testicular cancer was observed among men exposed to MF during childhood, but this is likely to be due to confounding associated with living on the lowest floors. No other significant associations were observed for other primary cancer sites studied.

Conclusions

Overall cancer risk was not affected by residential MF exposure. The increased risk of digestive organ cancers among MF-exposed persons is a novel finding requiring confirmation in further studies.
Microbial exposure during recycling of domestic waste: a cross-sectional study of composition and associations with inflammatory markers
Authors: Hansen Karoline Kærgaard , Rasmussen Pil , Schlünssen Vivi , Broberg Karin , Østergaard Kirsten , Tranchant Emma Enshelm , Sigsgaard Torben , Kolstad Henrik A , Madsen Anne Mette
Published: November 2024

Objectives

This study aims to investigate (1) the microbial community composition by work characteristics and (2) the association between microbial genera level and inflammatory markers among recycling workers.

Methods

In this cross-sectional study, inhalable dust was collected with personal samplers from 49 production (86 samples) and 10 administrative workers (15 samples). Four groups of micro-organisms were identified down to species-level (aerobic and anaerobic bacteria, and fungi grown at 25°C and 37°C). Inflammatory markers were measured in serum collected at the end of the work shift. Microbial community composition was investigated using redundancy analysis and heatmaps. Associations between the most prevalent microbial genera and inflammatory markers were explored by mixed-effects regression.

Results

Community composition of all groups of micro-organisms except fungi (37°C) differed between production and administrative workers and by type of waste and season among the production workers. Overall,Bacillus,Staphylococcus,AspergillusandPenicilliumwere the most prevalent genera. CC16 concentrations increased withPenicilliumgenus level, C reactive protein and serum amyloid A withStaphylococcus, interleukin 2 (IL-2) and tumour necrosis factor withBacillus, and IL-8 withAspergillus. IL-1B decreased withStaphylococcusgenus level. Remaining analyses showed no statistically significant associations between microbial genera level and inflammatory markers.

Conclusions

Recycling workers are exposed to different compositions of microbial species than administrative workers depending on the type of waste handled and season. Specific systemic inflammatory effects were suggested for a limited number of microbial genera that need to be corroborated by future studies.
Composition of cardiac troponin release differs after marathon running and myocardial infarction
Authors: Airaksinen K E Juhani , Paana Tuomas , Vasankari Tuija , Salonen Selma , Tuominen Tuulia , Linko-Parvinen Anna , Pallari Hanna-Mari , Hellman Tapio , Teppo Konsta , Heinonen Olli J , Jaakkola Samuli , Wittfooth Saara
Published: November 2024

Objectives

Elevations of cardiac troponin T (cTnT) levels are common after strenuous exercise. We assessed whether the composition of cTnT release after marathon race differs from that of acute myocardial infarction (MI).

Methods

Troponin composition was analysed in plasma samples taken from 45 runners after marathon race and from 84 patients with type 1 MI. The concentration of long cTnT (intact and mildly fragmented cTnT) was measured with a novel upconversion luminescence immunoassay, total cTnT with a commercial high-sensitivity cTnT assay, and the ratio of long to total cTnT (troponin ratio) was determined as a measure of troponin fragmentation.

Results

Total cTnT exceeded the upper reference limit (>14 ng/L) in 37 (82%) runners. Troponin ratio was lower in runners ((IQR) 0.17 (0.11–0.24) vs 0.62 (0.29–0.96), p<0.001). With increasing troponin release the troponin ratio decreased (r=−0.497, p<0.001) in marathon runners and the concentration of long cTnT remained in all runners below 8.4 ng/L. In contrast to marathon runners, troponin ratio increased (r=0.565, p<0.001) with the increase of cTnT release in patients with MI. The median total and long cTnT concentrations were lower in marathon runners than in patients with MI (25 ng/L vs 835 ng/L and 4.1 vs 385 ng/L, p<0.001 for both).

Conclusion

In contrast to type 1 MI, only a small fraction of circulating cTnT exists as intact cTnT or long molecular forms after strenuous exercise and the difference in troponin composition is more pronounced in runners with higher troponin release.

Trial registration number

NCT06000930.
Arthritis progressors have a decreased frequency of circulating autoreactive T cells during the at-risk phase of rheumatoid arthritis
Authors: Turcinov Sara , Sharma Ravi Kumar , De Vries Charlotte , Cîrciumaru Alexandra , Gerstner Christina , Mathsson-Alm Linda , Raposo Bruno , Dubnovitsky Anatoly , Rönnblom Lars , Kwok William W , Chemin Karine , Malmström Vivianne , Hensvold Aase
Published: November 2024

Objectives

The aim of this study was to combine deep T cell phenotyping with assessment of citrulline-reactive CD4+T cells in the pre-rheumatoid arthritis (RA) phase.

Methods

20 anti-CCP2 positive individuals (HLA-DRB1*04:01) presenting musculoskeletal complaints without clinical or ultrasound signs of synovitis; 10 arthritis progressors and 10 matched non-arthritis progressors were included. Longitudinal samples (1–3 time points) of peripheral blood mononuclear cells were assessed using HLA-class II tetramers with 12 different citrullinated candidate autoantigens combined in a >20-colour spectral flow cytometry panel.

Results

The baseline CD4+T cell phenotype was similar between individuals who progressed to arthritis (ie, in the pre-RA phase) and the non-progressors, when studying markers associated with Th1, Th17, T-peripheral and T-regulatory cells as well as with T-cell activation. Citrulline-reactive CD4+T cells were present in both groups but at significantly lower frequency in the progressor group. CD4+T cells specific for citrullinated tenascin-C were the most frequently observed among the progressors, and their frequencies diminished during follow-up that is, closer to arthritis onset. Notably, PD-1 and CD95 expression on the memory cit-tenascin-C-specific T cells in this group indicated repeated antigen exposure.

Conclusions

Our data lend support to citrullinated tenascin-C as an interesting T cell antigen in RA. Moreover, lower frequency of circulating citrulline-specific cells in arthritis progressing individuals suggest an initiated homing of these cells to the joints and/or their associated lymph nodes in the pre-RA phase and a possible window of opportunity for therapeutic preventive interventions.
Potential new treatment for inferior vena cava injury using extracorporeal membrane oxygenation applying flow diversion effect
Authors: Maruhashi Takaaki , Saku Keita , Maruki Hideo , Oi Marina , Asari Yasushi
Published: November 2024

Background

Retrohepatic inferior vena cava (IVC) injuries remain among the most lethal and serious liver injuries. Gauze packing is currently the first choice for IVC injuries; however, laparotomy itself poses the risk of circulatory collapse. Thus, less invasive treatment strategies are needed.

Methods

In this study, we conducted an animal experiment to replicate and validate successful treatments for an actual case of retrohepatic IVC injury that we had encountered.

Results

A woman in her 80s presented to our hospital due to cardiac arrest caused by a pulmonary artery embolism. Venoarterial extracorporeal membrane oxygenation (ECMO) was introduced, and the patient was resuscitated. After resuscitation, contrast-enhanced CT revealed liver and retrohepatic IVC injuries, possibly caused by chest compressions. Liver injury was treated using transarterial embolization of the left hepatic artery. To treat the retrohepatic IVC injury, ECMO flow was increased to enhance the negative drainage pressure. The extravasation of the contrast medium had resolved in IVC angiography, and we opted for nonoperative management. The patient’s hemodynamic status gradually stabilized, and ECMO was withdrawn on day 6. We confirmed these findings in a dog model of retrohepatic IVC injury.

Conclusions

Our findings from the patient and the animal model suggest that the flow diversion effect of ECMO can effectively manage active bleeding from the IVC by inserting a drainage cannula across the injured lesion. We think this procedure represents a novel treatment option for retrohepatic IVC injuries.
Synthetic data generation with hybrid quantum-classical models for the financial sector
Authors: Pires Otto M. , Nooblath Mauro Q. , Silva Yan Alef C. , da Silva Maria Heloísa F. , Galvão Lucas Q. , Albino Anton S.
Published: November 2024

Data integrity and privacy are critical concerns in the financial sector. Traditional methods of data collection face challenges due to privacy regulations and time-consuming anonymization processes. In collaboration with Banco BV, we trained a hybrid quantum-classical generative adversarial network (HQGAN), where a quantum circuit serves as the generator and a classical neural network acts as the discriminator, to generate synthetic financial data efficiently and securely. We compared our proposed HQGAN model with a fully classical GAN by evaluating loss convergence and the MSE distance between the synthetic and real data. Although initially promising, our evaluation revealed that HQGAN failed to achieve the necessary accuracy to understand the intricate patterns in financial data. This outcome underscores the current limitations of quantum-inspired methods in handling the complexities of financial datasets.

Graphical abstract
Constraints on self-interaction cross-sections of dark matter in universal bound states from direct detection
Authors: Angloher G. , Banik S. , Benato G. , Bento A. , Bertolini A. , Breier R. , Bucci C. , Burkhart J. , Cipelli E. , Canonica L. , D’Addabbo A. , Di Lorenzo S. , Einfalt L. , Erb A. , Feilitzsch F. v. , Fichtinger S. , Fuchs D. , Garai A. , Ghete V. M. , Gorla P. , Guillaumon P. V. , Gupta S. , Hauff D. , Ješkovský M. , Jochum J. , Kaznacheeva M. , Kinast A. , Kuckuk S. , Kluck H. , Kraus H. , Langenkämper A. , Mancuso M. , Marini L. , Mauri B. , Meyer L. , Mokina V. , Olmi M. , Ortmann T. , Pagliarone C. , Pattavina L. , Petricca F. , Potzel W. , Povinec P. , Pröbst F. , Pucci F. , Reindl F. , Rothe J. , Schäffner K. , Schieck J. , Schönert S. , Schwertner C. , Stahlberg M. , Stodolsky L. , Strandhagen C. , Strauss R. , Usherov I. , Wagner F. , Wagner V. , Zema V. ,   CRESST Collaboration
Published: November 2024

$$\varLambda $$
Λ
-Cold Dark Matter ($$\varLambda $$
Λ
CDM) has been successful at explaining the large-scale structures in the universe but faces severe issues on smaller scales when compared to observations. Introducing self-interactions between dark matter particles claims to provide a solution to the small-scale issues in the $$\varLambda $$
Λ
CDM simulations while being consistent with the observations at large scales. The existence of the energy region in which these self-interactions between dark matter particles come close to saturating the S-wave unitarity bound can result in the formation of dark matter bound states called darkonium. In this scenario, all the low energy scattering properties are determined by a single parameter, the inverse scattering length $$\gamma $$
γ
. In this work, we set bounds on $$\gamma $$
γ
by studying the impact of darkonium on the observations at direct detection experiments using data from CRESST-III and XENON1T. The exclusion limits on $$\gamma $$
γ
are then subsequently converted to exclusion limits on the self-interaction cross-section and compared with the constraints from astrophysics and N-body simulations.
Gravitational waves from axion wave production
Authors: Li Mingqiu , Sun Sichun , Yan Qi-Shu , Zhao Zhijie
Published: November 2024

We consider a scenario with axions/axion-like particles Chern–Simons gravity coupling, such that gravitational waves can be produced directly from axion wave parametric resonance in the early universe after inflation. This axion gravity term is less constrained compared to the well-searched axion photon coupling and can provide a direct and efficient production channel for gravitational waves. Such stochastic gravitational waves can be detected by either space/ground-based gravitational wave detectors or pulsar timing arrays for a broad range of axion masses and decay constants.
Is a photon ring invariably a closed structure?
Authors: Wang Xiangyu , Wang Xiaobao , Zhang Hai-Qing , Guo Minyong
Published: November 2024

In this study, we investigate the image of a rotating compact object (CO) illuminated by a geometrically thin, optically thin disk on the equatorial plane. As the radius of the CO’s surface fluctuates, the CO may partially or entirely obscure the photon region. We observe that the perceived photon ring may exhibit discontinuities, deviating from a closed structure, and may even disappear entirely. We find that the disruption and disappearance of the photon ring are dependent on the observational angle-a novel phenomenon not previously observed in black hole imaging studies. Our study reveals that while the factors influencing this unique photon ring phenomenon are diverse and the outcomes complex, we can provide a clear and comprehensive explanation of the physical essence and variation trends of this phenomenon. We do this by introducing and analyzing the properties and interrelationships of three characteristic functions, $$\tilde{\eta }$$

η
~

, $$\eta _o$$

η
o

, and $$\eta _s$$

η
s

related to the photon impact parameters. Additionally, our analysis of the intensity cuts and inner shadows of the images uncovers patterns that differ significantly from the shadow curve.
Tripartite measurement uncertainty in Schwarzschild space-time
Authors: Dolatkhah Hazhir , Czerwinski Artur , Ali Asad , Al-Kuwari Saif , Haddadi Saeed
Published: November 2024

The effect of Hawking radiation on tripartite measurement uncertainty in a Schwarzschild black hole background is analyzed in this study. Two scenarios are examined. In the first, quantum memory particles approach a Schwarzschild black hole and are positioned near the event horizon, while the particle being measured remains in the asymptotically flat region. In the second scenario, the measured particle moves toward the black hole, and the quantum memories stay in the asymptotically flat region. This study considers two initial quantum states, namely GHZ and W states. Our findings reveal that in both cases, measurement uncertainty increases steadily with rising Hawking temperature. When comparing the GHZ and W states, the GHZ state initially exhibits lower measurement uncertainty at low Hawking temperatures than the W state, indicating greater resilience to Hawking radiation. Additionally, when the quantum memories remain in the asymptotically flat region while the measured particle falls toward the black hole, the uncertainties for GHZ and W states do not align at high temperatures. The GHZ state consistently demonstrates lower measurement uncertainty, showcasing its superior robustness against Hawking radiation.
A Stacking Ensemble Learning Algorithm for Anomaly Early Detection of Liquid Levels in High-Pressure Heaters of Turbine Reheat Systems
Authors: Xu Wei , Zhou Ti , Wang Yong , Wang Naibin , Qin Pan , Li Guoqing , Liang Shichang
Published: November 2024

In the steam turbine reheating system, maintaining the high-pressure heater liquid level is crucial to prevent equipment damage and safety accidents. High liquid levels may cause steam to enter the feed system, while low liquid levels may lead to overheating and damage to the heater tube bundle. In this study, the stacking ensemble learning model for combined data decomposition is proposed for high-precision liquid level prediction and early warning. Initially, outliers in the high-pressure heater liquid level data from the DSC system are processed. The Adaptive Local Iterative Filtering (ALIF) algorithm is used for the first data decomposition, followed by Empirical Mode Decomposition (EMD) for a second decomposition. Time-domain features such as summation, maximum value, skewness, and peak value are extracted to construct reconstructed data. Random Forest (RF) reduces the dimensionality of the reconstructed data. The prediction model combines Deep Belief Network (DBN), Deep Neural Networks (DNN), Light Gradient Boosting Machine (LGBM), and eXtreme Gradient Boosting (xGBoost) as base models, with Echo State Network (ESN) as the meta-learner. Finally, based on historical data, warning level values are set, and the system outputs warnings when the actual liquid level significantly deviates from the predicted value.
Threat Modeling Healthcare Privacy in the United States
Authors: McDonald Nora , Luo Alan , Moh Phoebe , Mazurek Michelle L. , Andalibi Nazanin
Published: November 2024

The landscape of digital privacy risks faced by individuals seeking abortions has grown increasingly complex following the overturn of Roe v. Wade. Reproductive healthcare providers are uniquely positioned to offer critical privacy guidance. We conducted interviews with 22 reproductive healthcare providers across the U.S. to explore their perceptions of privacy threats for abortion-seeking patients and the types of guidance they provide. Our findings show that providers are most concerned about privacy risks for vulnerable patients—minors, individuals seeking gender-affirming care, and those in abusive relationships—particularly regarding information that could be intercepted by people close to them, such as partners or relatives. However, providers generally do not perceive government surveillance or hostile actors as major threats to abortion-seeking patients. We conclude with an updated notion of
informed consent
and preliminary recommendations for ways healthcare providers can revise their threat models to better support the privacy of abortion-seeking patients.
Fuzzy Neural Logic Reasoning for Robust Classification
Authors: Lin Guo , Zhang Yongfeng
Published: November 2024

The efficacy of neural networks is widely recognized across a multitude of machine learning tasks, yet their black-box nature impedes the understanding of their decision-making processes. Such lack of explainability limits their use in high-stake fields such as medicine and finance, where transparent decision-making is essential. In contrast, traditional rule-based models offer clear input-output mappings, but often lag in performance when compared to their neural network counterparts. To address this challenge, this study introduces Fuzzy Neural Logic Reasoning (FNLR), a novel architecture that combines the best of both rule-based and deep learning models to achieve performance, interpretability, and noise robustness simultaneously. At its core, FNLR employs a “Symbolic Pre-Training

\(+\)

Neural Fine-Tuning” paradigm. Initially, the model adapts a pre-fitted binary decision tree. It then performs a “neuralization” process, replacing each node of the tree with a corresponding neural network equivalent. This transformation is facilitated through three shallow MLP modules, which are trained to emulate the relational operators intrinsic to decision trees. The model architecture is also extensible, allowing it to further boost expressiveness. Furthermore, FNLR incorporates fuzzy logic by proposing novel fuzzy relational operators, accounting for satisfaction degrees of propositions and thus eliminating rigid decision boundaries. This approach enhances model flexibility, enabling all paths of the decision tree to contribute to the target prediction in a weighted manner. Empirical evaluations on tabular datasets from various domains demonstrate that FNLR performs comparably to, or better than, state-of-the-art deep learning models designed for tabular data, while also exhibiting strong robustness to noise.
Sabotaging Material Extrusion-Based 3D Printed Parts through Low-Magnitude Kinetic Manipulation Attacks
Authors: Rais Muhammad Haris , Ahsan Muhammad , Ahmed Irfan
Published: November 2024

The increasing ubiquity of material-extrusion-based additive manufacturing is motivating cybersecurity researchers to explore its offensive and defensive landscape. Being a physical system, 3D printers have non-zero tolerance specifications for precision and trueness parameters. While a single-bit change in a digital data file is sufficient to fail its integrity and is easily detected through methods such as hashing, the printing process (and subsequently the printed object) remains compliant within the tolerance zone. This study systematically analyzes the material extrusion process and identifies four attack opportunities where low-magnitude kinetic cyberattacks exploit the physical process compliance zone to sabotage the printed part’s mechanical properties. The attacks are demonstrated on ASTM-compliant tensile and flexure bars through a man-in-the-middle attack scenario by hijacking the network layer communication between the 3D printer and the printer control machine. The physically stealthy attacks did not produce any evident deformation in the parts’ dimensions and mass, while the destructive tests confirm that they are still effective in modifying the tensile and bending strength by up to 25%. The effectiveness of the attacks in bypassing the defenses is assessed by implementing one of the leading detection schemes described in the current literature. The attacks were either not detected at all or detected with a significantly high false negative rate at various attack magnitudes.
Pacti: Assume-Guarantee Contracts for Efficient Compositional Analysis and Design
Authors: Incer Inigo , Badithela Apurva , Graebener Josefine B. , Mallozzi Piergiuseppe , Pandey Ayush , Rouquette Nicolas , Yu Sheng-Jung , Benveniste Albert , Caillaud Benoit , Murray Richard M. , Sangiovanni-Vincentelli Alberto , Seshia Sanjit A.
Published: November 2024

Contract-based design is a method to facilitate modular design of systems. While there has been substantial progress on the theory of contracts, there has been less progress on practical algorithms for the algebraic operations in the theory. In this article, we present (1) principles to implement a contract-based design tool at scale and (2) Pacti, a tool that can efficiently compute these operations. We illustrate the use of Pacti in a variety of case studies.
Control Software Engineering Approaches for Cyber-Physical Systems: A Systematic Mapping Study
Authors: Banerjee Amar , Choppella Venkatesh
Published: November 2024

Cyber-Physical Systems (CPS), robotics, the Internet of Things (IoT), and automotive systems are integral to modern technology. They are characterized by their safety criticality, accuracy, and real-time control requirements. Control software plays a crucial role in achieving these objectives by managing and coordinating the operations of various sub-systems. This article presents a novel Systematic Mapping Study (SMS) for control software engineering, analyzing 115 peer-reviewed papers. The study identifies, classifies, and maps existing solutions, providing a comprehensive and structured overview for practitioners and researchers. Our contributions include (1) a unique classification of literature into six research themes—engineering phases, engineering approaches, engineering paradigms, engineering artifacts, target application domains, and engineering concerns; (2) insights into the specificity of approaches to target technologies and phases; (3) the prominence of model-driven approaches for design and testing; (4) the lack of end-to-end engineering support in existing approaches; and (5) the emerging role of agile-based methods versus the dominance of waterfall-based methods. This article's significance lies in its thorough analysis and the high-level mapping of the solution space, offering new perspectives and a detailed roadmap for future research and innovation in control software engineering. The findings will guide advancements and best practices in the field, underscoring the article's impact.
Fast and Accurate Evacuation Planning Algorithm with Bayesian Optimization
Authors: Tokunaga Junpei , Kikukawa Yuki , Ebara Hiroyuki , Ueda Naonori
Published: November 2024

In this work, we propose a method for generating an evacuation plan at a high speed to realize safe and swift evacuation in the event of a large-scale disaster such as an earthquake and its accompanying tsunami. Existing conventional methods have several problems. Simulation-based methods that use agents and methods that use existing time expansion networks have high computational costs, which makes it difficult for evacuation routes to be immediately changed according to the effects of disasters such as collapsed buildings and roads. Although heuristics with reduced calculation costs are also being researched, they may result in very long evacuation completion times and cannot generate optimal evacuation plans. We guarantee the optimal solution by reducing the number of maximum flow problem calculations, which constitute the bottleneck for methods using the existing time expansion network, through the use of the Bayesian optimization machine learning method. This reduces the calculation cost of the entire algorithm. The performance of our method is evaluated from the two viewpoints of the evacuation completion time, which indicates the quality of the evacuation plan, and the time required for the generation by the solution of the algorithm in computer experiments under multiple scenarios. In addition, the impact of the number of evacuees and the locations of the sinks are analyzed. We show that our method can quickly generate an optimal evacuation plan.
Physics-Informed Neural Networks for Modeling Li-ion Batteries: Solving the Single Particle Model Without Labeled Data
Authors: Méndez-Corbacho Francisco J. , Larrarte-Lizarralde Beñat , Parra Rubén , Larrain Javier , del Olmo Diego , Grande Hans-Jürgen , Ayerbe Elixabete
Published: November 2024

Li-ion batteries are garnering significant attention due to the electrification of critical sectors. High-fidelity battery cell models have proven effective in assessing performance and optimizing design, alleviating the financial burden associated with extensive experimental procedures. However, the computational costs associated with such simulations can become prohibitive, particularly when numerous iterations are required or when integration into small devices, such as battery management systems, is necessary. To address these challenges and provide an alternative to traditional methods such as finite element and finite volume solvers, we propose the development of an algorithm that utilizes Physics-Informed Neural Networks (PINNs) to solve the Single Particle Model across multiple parameter ranges. A notable advantage of this machine learning approach is its capacity to generate competitive solutions post-training by relying solely on the governing equations, without the necessity for experimental or simulation data. Additionally, the lightweight nature of the model indicates its potential for embedding within small-scale devices.
Single-cell profiling of the peripheral blood immune landscape during mid- and late-stage pregnancy
Authors: Gao Fan , Li Xia , Wang Hongyan , Xu Zhen , Qian Wenjun , Bai Guiqin
Published: November 2024

There are significant differences in three subtypes of memory/effector T cells (HOPX+ CD4+ T cells, ZNF683+CD8+ T cells, and KLRB1+CD8+ T cells) between mid- and late pregnancy. In late pregnancy, intercellular interaction was enhanced and effector CD8+ T cells highly expressed infection-related pathways. A rare T cell subtype, “XIST+ T cells,” may be involved in the regulation of immune-related gene transcription and translation with a strong female bias.
Application Research of a Flexible River‐Blocking Line for UHV Large Span
Authors: Xiao Fengying , Yin Huajie , Huang Zhi , Zhu Risheng , Zhao Tengfei , Li Ying , Huang Mojia
Published: November 2024

The integrity of large crossing cables is frequently compromised by inland river ultra‐high sand dredgers, leading to severe safety incidents. To mitigate these risks, height‐limiting mechanisms, known as river‐blocking lines, are essential on both sides of the large crossing cable. Traditional river‐blocking lines are typically rigid structures, posing challenges for both the river‐blocking line and passing ships. To address this issue and enhance safety measures, this study introduces a novel flexible blocking device. Initially, the fundamental principles of suspension cable theory are employed to design the river‐blocking line. By leveraging energy conversion principles, heavy objects are suspended at the cable ends to convert the kinetic energy of oncoming ships into potential energy during collisions. Subsequently, a structural model for a three‐suspension cable river‐blocking line is developed. Finite element simulations confirm the model’s efficacy, aligning with theoretical expectations. Furthermore, a scaled prototype of the model is constructed to assess project feasibility and reliability. The results affirm that the flexible blocking device can serve as an effective height‐limiting mechanism, proficiently impeding passing ships, mitigating impact loads, safeguarding vessels, and offering direct physical protection for ultra‐high voltage (UHV) large‐span cables.
Effectiveness and Feasibility of Nonpharmacological Interventions for People With Parkinson’s Disease and Cognitive Impairment on Patient‐Centred Outcomes
Authors: Pigott Jennifer S. , Armstrong Megan , Tabassum Nujhat , Davies Nathan , Schrag Anette
Published: November 2024

Background: Cognitive impairment is common in Parkinson’s disease (PD) but has limited treatment options. Medication has shown some benefits but accompanied by risk of adverse events. We aimed to investigate effectiveness and feasibility of nonpharmacological interventions for people with PD and cognitive impairment on patient‐centred outcomes.

Methods: Systematic searches of five databases (MEDLINE, Embase, CINAHL, PsycINFO and Web of Science) were performed for studies evaluating nonpharmacological interventions for people with PD and cognitive impairment, reporting health‐related quality of life, function (activities of daily living) or wellbeing outcomes, published up to 15 May 2023. Two reviewers independently assessed full‐text articles and one reviewer extracted data, with a second reviewer reliability checking all data extraction. Randomised controlled trials (RCTs) were synthesised through meta‐analysis using a random‐effects meta‐analysis with restricted maximum likelihood method pooled estimate and observational studies through narrative synthesis.

Results: Eleven RCTs and three noncontrolled studies were included, studying a range of interventions: cognitive training, cognitive stimulation, cognitive rehabilitation, physical and cognitive exercise, goal management training, psychoeducation with mindfulness, broader rehabilitation programs and a psychological intervention. Feasibility was demonstrated. The majority showed effectiveness for their primary outcome. Meta‐analysis showed no significant improvement in HrQoL (seven RCTs: pooled effect, standardised mean difference, −0.20 [−0.57−0.18]) or function (four RCTs: 0.08 [−0.36, 0.52]), and wellbeing measurement was infrequent and indirect. Quality of evidence was judged as very low, limiting the conclusions drawn.

Conclusion: Whilst nonpharmacological trials for cognitive impairment in PD have shown promise, we found no evidence of effectiveness on HrQoL, function or wellbeing. However, this is based on very low‐quality evidence from a small number of diverse studies, not powered for these outcomes. Feasibility of a range of interventions has been demonstrated in both PD‐mild cognitive impairment and PD‐dementia. There is a need for more robust, adequately powered studies.
Staphylococcus aureus Dissemination Presenting With Encephalopathy and Epidural Abscess
Authors: Sundar Das Dhriti , Dey Anupam , Das Gurudip , Naik Suprava
Published: November 2024

Background: Staphylococcal infection is a common bacterial disease with common clinical features. Untreated infection, especially in immunosenescence cases, can affect other organs. This can lead to multiorgan dysfunction and cause increased morbidity and mortality. Unlike commonly presented features of pneumonia, dissemination of infection can pose diagnostic and therapeutic enigma. Therefore, any such presentation in common clinical practice can yield a conundrum of diagnoses.

Case Report: A 69‐year‐old elderly male presented to the Emergency Department with acute onset encephalopathy. Historically, cues were limited, and evaluation was negated for acute cerebrovascular event or seizure. Laboratory findings were suggestive of a severe sepsis. While clinical medicine workup and diagnostic dilemma were ongoing, possible sources of the sepsis were thoroughly sought including range of infectious causes. This patient’s presentation was one of its kind: staphylococcal bacteremia seeding to cause pneumonia and unusual epidural abscess in due course of illness.

Conclusion: The health outcome of the critically ill especially elderly patients depends mostly on the importance of clinical medicine to address the diagnostic enigma and virtue of supportive care delivered. Staphylococcus aureus infections are capable of developing distant infectious foci, as highlighted in this case, and that the clinician should be alert to this possibility. This particular case firmly posits an admonition for clinicians and the importance of clinical medicine for critical reasoning to improve the patient outcome.
Impacts of Land Use Types and Soil Depth on Soil Fertility Status in Dende Kebele, West Gojjam Zone, Ethiopia
Authors: Bayle Dilnesa
Published: November 2024

Land use change and soil depth lead to subsequent changes in soil physicochemical properties in the northern highlands of Ethiopia. These changes in soil properties indicate a shift in soil fertility levels. The purpose of this study was to examine the impacts of different land use types and soil depths on soil fertility status in Dende Kebele, West Gojjam Zone, Ethiopia. The study utilized a randomized complete block design (RCBD) experimental design, with various land use categories serving as treatments. The study area was purposefully divided into three land use types (Eucalyptus plantation, grazing, and cropped land) and two soil layers (0–20 and 20–40 cm) with three replications. A total of 18 composite soil samples were collected for laboratory analysis. The interaction between land use types and soil depth had a significant impact (p < 0.001) on particle size distribution, field capacity (FC), permanent wilting point (PWP), pH, organic carbon (OC), and exchangeable (K+ and Na+) and available micronutrients. The soil texture within the study area was classified as clay. The soil had low to medium bulk density (BD), very high total porosity (TP) and medium available water holding capacity (AWHC). The pH ranged from strongly to moderately acidic with low to medium levels of OC and TN. Available phosphorus (AP) was extremely low, while exchangeable Mg+2 and Ca+2 were high, and Na+ was low to medium. Levels of Fe, Mn, and Zn were high, while Cu levels were low to medium. Overall, the findings suggest that soil fertility was higher in Eucalyptus plantation soils compared to cropped land, highlighting the importance of soil fertility management for sustainable agriculture in the area.
Changes in Volatile Flavor Compounds of Vegetable Soybean Seeds With Different Preservation Processes Using Headspace‐Gas Chromatography–Ion Mobility Spectrometry
Authors: Yu Xiaomin , Fu Xujun , Yang Qinghua , Jin Hangxia , Zhu Longming , Yuan Fengjie
Published: November 2024

It is useful to analyze volatile flavor compounds and identify characteristic fingerprints for improving the aroma of vegetable soybean in the vegetable soybean breeding program. In this study, headspace‐gas chromatography–ion mobility spectrometry was applied to evaluate the volatile compounds of four vegetable soybean cultivars with different preservation processes. The results revealed that aldehydes, alcohols, and esters determined the basic volatile flavor of the vegetable soybean seeds. The peak intensities of most of the volatile compounds varied significantly between both cultivars and preservation methods. Compared with the preservation processes, the differences between cultivars would contribute more to the flavor. The characteristic fingerprints were further identified in the four cultivars, including Xiang Dou, Changxing Qingdou, Wu Pi Qing Ren, and Zhe Xian No. 84, with different preservation processes. Although the flavor could be affected by the preservation processing, the cultivar Zhe Xian No. 84 was the least affected among these cultivars and had great value in both promotion and application in vegetable soybean production. These results could provide useful information for improving the seed taste quality of vegetable soybean in China.
Supplementary Figure 2 from DAZAP1 Phase Separation Regulates Mitochondrial Metabolism to Facilitate Invasion and Metastasis of Oral Squamous Cell Carcinoma
Authors: Zhang Jiayi , Ni Zihui , Zhang Yu , Guo Yan , Zhai Rundong , Wang Mengqi , Gong Zizhen , Wang Mengyao , Zeng Fanrui , Gu Ziyue , Chen Qianming , Liu Laikui , Wang Zhiyong , Zhu Weiwen
Published: November 2024

Oxygen consumption rate (OCR) and Extracellular acidification rate (ECAR) analysis in DAZAP1-overexpression and -knockdown Cal27 and HN6 cell lines.
Enhancing internet of things attack detection using principal component analysis and kernel principal component analysis with cosine distance and sigmoid kernel
Authors: Elkhadir Zyad , Achkari Begdouri Mohammed
Published: November 2024

The widespread adoption of internet of things (IoT) devices has brought about unprecedented levels of connectivity and convenience. However, it has also introduced significant challenges, particularly in the areas of security and privacy. This study addresses the critical issue of intrusion detection within IoT environments, with a specific focus on analyzing the Iot-23 dataset. Our methodology involves employing principal component analysis (PCA) and kernel PCA for dimensionality reduction. Subsequently, we utilize the k-nearest neighbors (KNN) algorithm for classification purposes. To optimize the performance of the KNN algorithm, we experiment with various feature scaling techniques, such as StandardScaler, MinMaxScaler, and RobustScaler, utilizing different distance metrics. In our analysis, we discovered that employing the cosine distance metric in combination with KNN resulted in superior intrusion detection performance when utilizing PCA. Additionally, when utilizing kernel PCA, we evaluated multiple kernel functions and determined that the radial basis function and sigmoid kernel yielded the most favorable results.
Robust adaptive integral sliding mode control of a half-bridge bidirectional DC-DC converter
Authors: Cham Julius Derghe , Koffi Francis Lénine Djanna , Boum Alexandre Teplaira , Harrison Ambe
Published: November 2024

A novel approach to improving the dynamic response of a half-bridge bidirectional DC-DC converter is presented in this paper, particularly in the face of disturbances from internal or external sources. These converters, which are integral to the operation of DC microgrids, are responsible for stepping up or stepping down voltage as required. To optimize the converter's performance under varying conditions, we propose an adaptive integral sliding mode controller (AISMC) enhanced by particle swarm optimization (PSO). The proposed controller leverages the strengths of both super-twisting sliding mode control (STSMC) and adaptive control, providing a robust and responsive solution to the challenges posed by the converter's nonlinear dynamics. The system's stability is rigorously ensured through the application of Lyapunov stability criteria, which underpin the enhanced performance of the controller. Simulations conducted in the MATLAB/Simulink environment demonstrate that the AISMC-PSO outperforms conventional control strategies, offering superior stability, robustness, and precision. The results clearly indicate that the proposed approach minimizes errors and enhances the overall efficiency and reliability of the bidirectional half-bridge DC-DC converter, making it a highly effective solution for DC microgrid applications.
Deep learning for infectious disease surveillance integrating internet of things for rapid response
Authors: Sumithra Subramanian , Radhika Moorthy , Venkatesh Gandavadi , Lakshmi Babu Seetha , Jancee Balraj Victoria , Mohankumar Nagarajan , Murugan Subbiah
Published: November 2024

Particularly in the case of emerging infectious diseases and worldwide pandemics, infectious disease monitoring is essential for quick identification and efficient response to epidemics. Improving surveillance systems for quick reaction might be possible with the help of new deep learning and internet of things (IoT) technologies. This paper introduces an infectious disease monitoring architecture based on deep learning coupled with IoT devices to facilitate early diagnosis and proactive intervention measures. This approach uses recurrent neural networks (RNNs) to identify temporal patterns suggestive of infectious disease outbreaks by analyzing sequential data retrieved from IoT devices like smart thermometers and wearable sensors. To identify small changes in health markers and forecast the development of diseases, RNN architectures with long short-term memory (LSTM) networks are used to capture long-range relationships in the data. Spatial analysis permits the integration of geographic data from IoT devices, allowing for the identification of infection hotspots and the tracking of afflicted persons' movements. Quick action steps like focused testing, contact tracing, and medical resource deployment are prompted by abnormalities detected early by real-time monitoring and analysis. Preventing or lessening the severity of infectious disease outbreaks is the goal of the planned monitoring system, which would enhance public health readiness and response capacities.
Cloud computing environment based hierarchical anomaly intrusion detection system using artificial neural network
Authors: Vamsikrishna Mangalapalli , Latha Garapati Swarna , Babu Gajjala Venkata Ramesh , Giridhar Koppisetti , Alluri Lakshmeelavanya , Somasekhar Giddaluru , Sagar Bhimunipadu Jestadi Job Karuna , Dondapati Naresh
Published: November 2024

Nowadays, computer technology is essential to everyday life, including banking, education, entertainment, and communication. Network security is essential in the digital era, and detecting intrusion threats is the most difficult problem. As a result, the network is monitored for unusual activity using this hierarchical anomaly intrusion detection system, and when these actions are detected, an alert is generated. This hierarchical anomaly intrusion detection system, which uses artificial neural network (ANN) and is implemented on a cloud computing environment, analyzes data even in the high levels of traffic and protects computer networks and data from malicious activity. As a result, this system shows better detection, accuracy, and precision rates.
Technology performance of Indonesia tsunami early warning system: technometric approach
Authors: Utomo Satrio , Soehadi Gani , Setiadi Sadikin Budi , Mahmud Jaizuluddin , Santoso Irawan , Hardono Hardono , Tiolita Fitriana , Fifianny Helen
Published: November 2024

Indonesia tsunami early warning system (Ina TEWS) is used for detecting earthquakes and tsunamis. The system currently is still under development and the government really needs information regarding the technological capabilities of the system in order to provide protection to the community from the threat of tsunamis and earthquakes. To produce the system and its components on an industrial scale, it is important to assess the extent of performance and readiness for technological development. The assessment used the technometric method which groups technological components into technoware, humanware, infoware, and orgaware, integrated with analytic hierarchy process. The results showed that the highest technology component contribution is technoware, and the lowest is infoware. Based on the magnitude of the contribution of technology components and intensity of technology, the priority order is technoware, humanware, orgaware, and infoware. The technology contribution coefficient is 0.522 (Good). The technology level is semi modern. Infoware is the lowest technological component, therefore improvement efforts are needed, namely structuring an integrated document system and improving standard operating procedures for manufacturing operational activities, especially in the testing and data validation.
Optimal turning of a 2-DOF proportional-integral-derivative controller based on a chess algorithm for load frequency control
Authors: Buranaaudsawakul Techatat , Ardhan Kittipong , Audomsi Sitthisak , Sa-ngiamvibool Worawat , Dulyala Rattapon
Published: November 2024

Load frequency control is necessary for power system management. The power system must maintain a frequency range to ensure power supply stability. System faults and demand fluctuations may cause frequencies to change quickly. System stability and integrity suffer. We are optimizing the two-degree-of-freedom (2-DOF) proportional-integral-derivative (PID) controllers chess algorithm. This article addresses electrical load frequency regulation. We employ classical control theory and current adjustment. It aims for electrical system efficiency and dependability. It checks for errors using integral absolute error (IAE), integral squared error (ISE), integral of time multiply absolute error (ITAE), and integral time squared error (ITSE). Particle swarm algorithm (PSO) compares performance. The IAE of 0.03364, nearly identical to it, shows that chess trumps other algorithms in many scenarios. The chess algorithm's ISE was 0.00035, like PSO's 0.03363. The ISE was 0.00036, indicating PSO's error-reduction capabilities. For the chess algorithm, PSO is 0.07929, and ITAE is 0.07647. This indicates the PSO responds faster to system breakdowns and load changes. Finally, the chess algorithm's ITSE is 0.00072, below the PSO 0.00076. The chess algorithm is better at managing long-term load frequency.