bluuebunny/crossref_metadata_2025 huggingface dataset:
A Review of Research on Progress in the Theory and Practice of Value Realization of Eco-Products
Authors: LIU Jiemin , Su Xuejiao , LIU Yuanmeng , SHUI Wei
Published: January 2024

The realization of value in eco-products represents a rapidly advancing research theme. However, the clarity of relevant concepts, types and operational mechanisms remains elusive. A comprehensive review of current-stage research outcomes in eco-product value realization not only provides a novel approach to rejuvenating regional economies but also proactively contributes to the enhancement of local ecological environments. This article conducts a systematic literature review on the fundamental concepts, research scope and pathways for realizing the value of eco-products. The conclusions are as follows: (1) To clarify the basic concepts of ecosystem services and eco-products, the interrelationship between the two and the research scope of eco-products. (2) To elaborate the method for accounting the value of eco-products. (3) To demonstrate governmental path, market path and collaborative path for realizing the value of eco-products. (4)To review cases pertaining to the realization of value in public eco-products, quasi-public eco-products and operational eco-products both domestically and internationally. Building on this research foundation, the article identifies shortcomings in past research results and proposes key directions for future research progress.
Toward Production of Cost Effective Modification of SmCo5 -Type Alloys Suitable for Permanent Magnets (Review Paper)
Authors: Gjoka Margarit , Sarafidis Charalampos
Published: January 2024

SmCo5 constitutes one of the strongest class of permanent magnets, which exhibit enormous uniaxial magnetocrystalline anisotropy energy and have a high Curie temperature. However, the performance of SmCo5 permanent magnets is hindered by a limited energy product and relatively high supply risk. Sm is a moderately expensive element among its group, and Co is a material more expensive than Fe, making SmCo5 magnets the most expensive class of permanent magnets. Subsequently, the need for new materials, with less content in critical and thus expensive resources, is obvious. A promising path of producing new compounds that meet these requirements is the chemically modification of established materials used in PM towards the reduction of expensive resources, for example reducing Co content with transition metals (like Fe, Ni) or using as substitutes raw rare earth materials with abundance greater than their demand, like Ce and La. Important instrument to achieve these goals are theoretical calculations, like Ab-initio methods, and especially DFT based calculations in predicting the possible stable RE-TM intermetallic compounds and their magnetic properties. This review aims to present the progress of recent years in the production of improved SmCo5 type magnets.
Circular Economy Business Model in the Construction Industry: A Systematic Review
Authors: Jayakodi Shashini , Senaratne Sepani , Perera Srinath
Published: January 2024

The construction business environment is becoming competitive and intense, and Business Models (BMs) are receiving considerable attention as potential sources of sustainable survival and growth. Converse to the linear economy model that industry follows, the Circular Economy (CE) has the potential to create a sustainable construction industry, while creating a sustainable business environment in construction organisations. Nevertheless, in traditional BMs, value is often concentrated on the financial value for the stakeholders; in Circular Economy Business Models (CEBMs), value is seen more broadly by considering a comprehensive range of value chain partners, the environment, and the society. Hence, implementing CE in the construction industry requires reforms in each value chain. Thus, the aim of this paper is to develop a conceptual model for construction organisations' adoption of CEBMs for their organisations. To accomplish the aim, the objectives of this paper are twofold: (i) to define the concept of CEBMs, and (ii) to map the Conceptual CEBMC (Circular Economy Business Model Canvas) as applicable in the construction organisational context. This research adopted a systematic document review, including a content analysis of selected papers. The study contributes significantly by proposing a CEBMC, in which construction organisations could successfully transform their businesses from linear to circular environments.
Iron-Imine Cocktail in Drug Development: A Contemporary Update
Authors: Anane Judith , Owusu Esther , Rivera Gildardo , Bandyopadhyay Debasish
Published: January 2024

Organometallic drug development is still in its early stage, but recent studies show that organometallics having iron as the central atom have the possibility of becoming good drug candidates because iron is an important micro-nutrient, and it is compatible with many biological systems, including the human body. Being an eco-friendly Lewis acid, iron can accept the lone pair of electrons from imino(sp2)-nitrogen, and the resultant iron-imine complexes with iron as a central atom have the possibility of interacting with several proteins and enzymes in humans. Iron-imine complexes have demonstrated significant potential as anticancer, bactericidal, fungicidal, and other medicinal activities in recent years. This article systematically discusses major synthetic methods and pharmacological potentials of iron-imine complexes having in vitro activity to significantly affect clinical performance from 2016 to date. In a nutshell, this manuscript offers a simplistic view of iron complexes in medicinal inorganic chemistry: for instance, iron is presented as an "eco-friendly non-toxic" metal (as opposed to platinum) that will lead to non-toxic pharmaceuticals. The abundant literature on iron chelators shows that many iron complexes, particularly if redox-active in cells, can be quite cytotoxic, which can be beneficial for future targeted therapies. While we made every effort to include all the related papers, any omission is purely unintentional.
Estimating Parameters of the Exponentially-Modified Logistic Distribution by the Maximum likelihood: Grey Wolf Optimization
Authors: Kasap Pelin , Faouri Adi Omaia
Published: January 2024

Generalized distributions have been studied a lot recently because of their flexibility and relia-bility in modeling lifetime data. The two-parameter exponentially-modified logistic distribution is a new flexible modified distribution that was introduced recently. It is regarded as a strong competitor for widely used classical symmetrical and non-symmetrical distributions such as normal, logistic, lognormal, and log-logistic. In this study, the unknown parameters of the dis-tribution are estimated using the maximum likelihood method. The Grey Wolf Optimization al-gorithm, which is a new meta-heuristic algorithm, is applied in order to solve the nonlinear like-lihood equations of the study model. The performance of the Grey Wolf Optimization method is compared to that of the other meta-heuristic algorithms used in this study, which include the Whale Optimization Algorithm, the Sine Cosine Algorithm, and the Particle Swarm Optimiza-tion Algorithm. The efficiencies of maximum likelihood estimates for all algorithms are com-pared via an extensive Monte-Carlo simulation study. The likelihood estimates for the location α and scale β parameters of the exponentially-modified logistic distribution developed with the Grey Wolf Optimization algorithm are the most efficient among others, according to simulation findings. Four real datasets are analyzed to show the flexibility of this distribution.
Estimation of Weighted Extropy with a Focus on Its Use in Reliability Modeling
Authors: Irshad Muhammed Rasheed , Krishnakumar Archana , Maya Radhakumari , Longobardi Maria
Published: January 2024

In the literature, estimation of weighted extropy are least considered. In this paper some non parametric estimators of weighted extropy are given. The validation and comparison of the estimators are implemented with the help of simulation study and data illustration. The usefulness of the estimators are demonstrated using real data sets.
Can We Still Get Bose-Einstein Condensates If the Noise Is Not Thermal?
Authors: Bier Martin
Published: January 2024

Bosons are particles that can occupy the same quantum state. Identical classical particles are considered distinguishable, but with bosons this is no longer the case. With bosons 1 and 2 in states α and β, the compound state function, ΨS=(1/2)Ψα(1)Ψβ(2)+Ψβ(1)Ψα(2), does not change when the particles are interchanged, i.e., there is one state where classically there would be two. This leads to boson statistics, which is different from Boltzmann statistics in that it favors putting more particles in the same state. For a sufficiently low temperature, it is possible that all bosons aggregate in the ground state where they effectively form a single megaparticle. We cover boson statistics and show how and why Bose-Einstein condensation can occur. We also show how growing networks like the WWW, the economy, or citation networks in scientific literature can follow boson statistics. Analyses of Bose-Einstein condensation have generally assumed a bath with a temperature, i.e., a thermal equilibrium where random collisions lead to a Gaussian-noise-term. However, many setups in physics involve conversion or transport of energy, i.e., nonequilibrium. Nonequilibrium noise is commonly characterized by the frequent occurrence of large kicks, i.e., outliers. We model this by implementing α-stable noise. No temperature exists in that case. We find that Bose-Einstein condensation is still theoretically possible, but probably much harder to engineer. Social and economic traffic is also often best modeled as nonequilibrium and our results may be significant in these contexts.
Role of Epiregulin on Lipopolysaccharide-Induced Hepatocarcinogenesis as a Mediator in the Cancer Microenvironment
Authors: Kubo Takahiro , Nishimura Norihisa , Kaji Kosuke , Tomooka Fumimasa , Shibamoto Akihiko , Iwai Satoshi , Suzuki Junya , Kawaratani Hideto , Namisaki Tadashi , Akahane Takemi , Yoshiji Hitoshi
Published: January 2024

Lipopolysaccharides (LPS) have been reported to be important factors in promoting the progression of hepatocellular carcinoma (HCC), but the corresponding molecular mechanisms remain to be elucidated. We hypothesize that epiregulin (EREG), an epidermal growth factor (EGF) family member derived from hepatic stellate cells (HSCs) and activated by LPS stimulation, is a crucial mediator of HCC progression in the tumor microenvironment. we used a mouse xenograft model of Huh7 cells mixed with half the number of LX-2 cells, with/without intraperitoneal LPS injection, to elucidate the role of EREG in LPS-induced HCC. In the mouse model, LPS administration significantly enlarged the size of xenografted tumors and elevated the expression of EREG in tumor tissues compared with those in negative controls. Moreover, CD34 immunostaining and the gene expressions of angiogenic markers by reverse transcription polymerase chain reaction revealed higher vascularization, with increased IL-8 expression in the tumors of the mice group treated with LPS compared to those without LPS. Our data collectively suggested that EREG plays an important role in the cancer microenvironment under the influence of LPS to increase not only tumor cell growth and migration/invasion of HCC cells but also tumor neovascularization via IL-8 signaling.
The Potential of Strategies Used to Mitigate Enteric Methane Emissions in Nutrition Studies of Ruminants: A Review
Authors: Hassen Abubeker , Tesfamariam Eyob Nangamso , Pepeta Bulelani Nangamso , Tasfamariam Eyob Nangamso
Published: January 2024

This study explored techniques with enteric methane (CH4) abating potential, ranging from chemical additives including 3-NOP, nitrate, and halogenated compounds to natural feed additives such as tannins, oils, seaweed extracts, and probiotics. Most of the evaluated strategies exert their influence either by promoting the growth of ruminal microbes that compete with methanogens for metabolic hydrogen as their resource or by directly consuming CH4. Additionally, externally supplied probiotics, including propionate-forming bacteria and methane-oxidizing bacteria, are a promising tool. However, challenges persist in implementing these strategies effectively. High doses of chemical additives and plant based secondary metabolites often achieve significant CH4 reduction, but they are associated with negative impact on dry matter intake and digestibility, thus compromising overall animal performance. On the other hand, using lower doses to maintain animal performance may not achieve beneficial CH4 reduction. Furthermore, concerns about the impact of certain additives on human health have led to a shift towards more natural alternatives, but their efficacy and feasibility in vivo necessitate further evaluation. Comprehensive research that integrates data from diverse global experiments on ruminants through meta-analytical approaches is essential. Such research will help in identify strategies that either reduce methane emissions and/or enhance production performance, ensuring sustainable livestock farming practices. In conclusion, mitigating CH4 emissions from ruminant livestock is a complex challenge with wide-ranging implication.
Patient with Foreign Accent Syndrome in Post Infarct Thrombotic Stroke: A Case Report
Authors: Tiffany Firstiafina , Ristinawati Ira , Nur Hanifa Shafira
Published: January 2024

Background: Stroke is the second leading cause of death in the world and the leading cause of disability worldwide. One of the disabilities is language disorder. Foreign Accent Syndrome (FAS) is a clinical condition associated with a noticeable change of accent, affecting verbal communication and social interaction skills. By identifying this case earlier, we hope the patient’s daily living is less disrupted.
Case: A 54-year-old woman came with complaints of weakness and tingling in the right limbs accompanied by a change in language accent. Weakness in the right limbs was felt since 4 years ago, then 1 year later the patient began to experience a change in the speech accent, from a Javanese accent to a Madurese accent, where previously the patient had never lived or studied Madurese. The results of the neurobehavior examination showed the patient had disturbances in the language and memory domain. MRI revealed cerebral infarction in the bilateral hippocampi, small vessel ischemic in the bilateral frontal lobes, and corona radiata.
Discussion: The accent change, known as FAS experienced by the patients, is thought to be due to neurological causes, which was stroke. In a study using a lesion network mapping approach, it was concluded that the lesion causing accent disorder was located within a single network in the bilateral frontal lobe. In line with the existing theory, the MRI results in this patient showed ischemia in the blood vessels of the bilateral frontal lobes.
Cerebral Proliferative Angiopathy as the Cause of Symptomatic Epilepsy in a Young Adult Male: A First Case Report from Indonesia
Authors: Swatan Jovian Philip , Sani Achmad Firdaus , Islamiyah Wardah Rahmatul , Kurniawan Dedy , Fatimah Ersifa
Published: January 2024

Background: Cerebral proliferative angiopathy (CPA) is a rare and distinct vascular malformation that was once considered a subset of cerebral arteriovenous malformation (AVM). Due to its relatively benign course with no distinctive clinical feature, CPA may often be overlooked and misdiagnosed with other diseases. We report a case of CPA as the underlying cause of symptomatic epilepsy.
Case: A 31-year-old male presented to the outpatient clinic with a history of focal to bilateral tonic-clonic seizure for 2 years. Following conservative management with an oral antiepileptic agent, the seizure frequency significantly decreased from once daily to once or twice monthly. The patient was lost to follow-up; however, he was incidentally referred back to our clinic two years later for further evaluation. A head Magnetic Resonance Imaging and Magnetic Resonance Angiography revealed a suspicion of giant AVM in the left hemisphere. Cerebral digital subtraction angiography (DSA) was performed and revealed a CPA in the left frontal area. The patient was managed conservatively and during the 6-month follow-up period, the patient did not have any seizures.
Discussion: In young adults, seizures may be caused by an underlying vascular abnormality. Cerebral DSA remained the gold standard for distinguishing various etiologies of vascular malformation, including CPA. Conservative treatment using oral antiepileptic agents was effective in controlling the seizure frequency in CPA. However, a complete diagnostic evaluation is still warranted to determine the most appropriate treatment, revealing some peculiar and unexpected etiologies in the process.
The Correlation Between the Number of Vascular Risk Factors and the Onset of Hemodialysis with Cognitive Impairement in Chronic Kidney Disease (CKD) Patients
Authors: Utami Priyanka Ganesa , Sundawiyani Wiwin
Published: January 2024

Background:Cognitive impairment is common in chronic kidney disease (CKD) patients, affecting at least 50% due to ischemia linked to impaired vascular function. Changes during dialysis, like water loss and ultrafiltration, disrupt brain blood supply, worsening ischemia and cognitive function impairment.
Objective: Analyze the correlation between vascular risk factors, dialysis onset, and cognitive impairment in CKD patients.
Methods: This analytic observational study sampled CKD patients on hemodialysis between February-July 2023, using purposive sampling. Inclusion and exclusion criteria were applied, and cognitive function was assessed using MOCA-INA. Spearman's rho statistical analysis was employed at a 95% confidence level (p <0.05).
Results: Spearman Rho revealed a correlation between hemodialysis for over 5 years and impaired cognitive function (P=0.02). Affected cognitive domains, in sequence, are memory/delayed recall, visuospatial, abstraction, language, attention, and naming. However, the number of vascular risk factors did not correlate with cognitive impairment (P=1.38).
Conclusion:CKD patients on hemodialysis for more than 5 years show a correlation with impaired cognitive function, primarily affecting memory/delayed recall, visuospatial, abstraction, language, attention, and naming. Vascular risk factors, however, exhibit no correlation with cognitive impairment.
Buku Ajar Psikologi Pendidikan Profesi Bidan
Authors: Azizah Nurul
Published: January 2024

Segenap puji dan syukur kami panjatkan kepada Allah SWT, yang dengan rahmat dan karunia-Nya, kami dapat menyelesaikan karya ini. Dalam momen yang penuh berkah ini, dengan rendah hati kami mempersembahkan buku Ajar Psikologi Kebidanan kepada para pembaca yang terhormat. Buku ini disusun dengan tujuan utama, yaitu memberikan pengetahuan yang mendalam kepada mahasiswa pendidikan profesi bidan, serta menjadi panduan berharga bagi para dosen yang mengampu mata kuliah psikologi kebidanan. Kami berharap karya ini dapat menjadi sumbangan berarti dalam meningkatkan kualitas pembelajaran dan membantu mahasiswa dalam mengikuti proses pembelajaran psikologi kebidanan, serta memberikan manfaat yang tak terhingga bagi para profesional kesehatan, khususnya bidan.
A mechanism to explain soil liquefaction environmental effects during an earthquake
Authors: Teixeira Fernando
Published: January 2024

Soil liquefaction has potentially devastating consequences for infrastructures and endangers human lives. The mechanisms used to explain the above-ground environmental effects of soil liquefaction during and after an earthquake stops require a set of conditions seldom observed in situ(e.g. in the water degassing mechanism). The nature of the high-pressure heads observed remains unknown. Herein, a conceptual model is presented based on the interplay between the pressure heads of the liquefied soil and water (or of a slurry). Water or a slurry of low density, for example, from water springs flowing out of bedrock fissures, is a requirement of the model. A simulation of a simplified system shows that a pressure head of the slurry well over the required to reach the soil surface is obtained in most of the function's domain. The flow velocity at the surface will depend on the soil and water mixture ratio, which is expected to be correlated with the soil characteristics through the profile- the potential for the walls of the cracks to collapse. The inexistence of active water springs prevents the ejection of water mixtures at the soil surface.
Direct Electrolytic Extraction of Lithium Metal from Brines Based on Sandwich-structured Garnet Electrolyte
Authors: Duan Huanan , Zhang Nan , Zheng Hongpeng , Li Guoyao , Ouyang Cheng , Yang Yu , Zhu Hong , Zhang Rongzi , Sun Haidong , Lin Yuhan , Liu Hezhou
Published: January 2024

Lithium metal as an important strategic resource has diverse industrial applications such as glass, ceramics, and alloys. With the increasing demand for lithium, lithium extraction from brines has become the focus of attention. However, due to the similarity of the radii of Li+ and Mg2+, conventional lithium extraction methods are inefficient for lithium extraction from brines with a high Mg/Li ratio. Herein we propose a direct electrolytic extraction of lithium (DEEL) system based on a sandwich-structured LLZTO (Li6.5La3Zr1.5Ta0.5O12) electrolyte with excellent water stability. The lanthanum zirconate (La2Zr2O7) on the surface of this sandwich-structured LLZTO can achieve high stability to water and air while still maintaining high ionic conductivity (7.5 × 10− 4 S cm− 1) and low lithium interface resistance (40 Ω cm2). This sandwich-structured LLZTO has been successfully demonstrated to stably extract lithium from simulated brines and natural brines. The current density for lithium extraction can reach 0.5 mA cm− 2, corresponding to a lithium extraction rate of 126 µg cm− 2 h− 1; the electrochemical extraction devices can stably work for 48 hours; the current efficiency can reach 97.71%. The extracted lithium metal exhibits a purity of 99.475 wt%. This work provides a highly efficient approach for extracting lithium from brines by electrolysis.
Residual Diffusion Modeling for Km-scale Atmospheric Downscaling
Authors: Mardani Morteza , Brenowitz Noah , Cohen Yair , Pathak Jaideep , Chen Chieh-Yu , Liu Cheng-Chin , Vahdat Arash , Kashinath Karthik , Kautz Jan , Pritchard Mike
Published: January 2024

Predictions of weather hazard require expensive km-scale simulations driven by coarser global inputs. Here, a cost-effective stochastic downscaling model is trained from a high-resolution 2-km weather model over Taiwan conditioned on 25-km ERA5 reanalysis. To address the multi-scale machine learning challenges of weather data, we employ a two-step approach Corrector Diffusion (CorrDiff), where a UNet prediction of the mean is corrected by a diffusion step. Akin to Reynolds decomposition in fluid dynamics, this isolates generative learning to the stochastic scales. CorrDiff exhibits skillful RMSE and CRPS and faithfully recovers spectra and distributions even for extremes. Case studies of coherent weather phenomena reveal appropriate multivariate relationships reminiscent of learnt physics: the collocation of intense rainfall and sharp gradients in fronts and extreme winds and rainfall bands near the eyewall of typhoons. Downscaling global forecasts successfully retains many of these benefits, foreshadowing the potential of end-to-end, global-to- km-scales machine learning weather predictions.
Deep generative modeling of the human proteome reveals over a hundred novel genes involved in rare genetic disorders
Authors: Marks Debora , Orenbuch Rose , Kollasch Aaron , Spinner Hansen , Shearer Courtney , Hopf Thomas , Franceschi Dinko , Dias Mafalda , Frazer Jonathan
Published: January 2024

Identifying causal mutations accelerates genetic disease diagnosis, and therapeutic development. Missense variants present a bottleneck in genetic diagnoses as their effects are less straightforward than truncations or nonsense mutations. While computational prediction methods are increasingly successful at prediction for variants in known disease genes, they do not generalize well to other genes as the scores are not calibrated across the proteome1-6. To address this, we developed a deep generative model, popEVE, that combines evolutionary information with population sequence data7 and achieves state-of-the-art performance at ranking variants by severity to distinguish patients with severe developmental disorders8 from potentially healthy individuals9. popEVE identifies 442 genes in patients this developmental disorder cohort, including evidence of 123 novel genetic disorders, many without the need for gene-level enrichment and without overestimating the prevalence of pathogenic variants in the population. A majority of these variants are close to interacting partners in 3D complexes. Preliminary analyses on child exomes indicate that popEVE can identify candidate variants without the need for inheritance labels. By placing variants on a unified scale, our model offers a comprehensive perspective on the distribution of fitness effects across the entire proteome and the broader human population. popEVE provides compelling evidence for genetic diagnoses even in exceptionally rare single-patient disorders where conventional techniques relying on repeated observations may not be applicable.
Impact of hydrological connectivity on rainfed annual crops in Mediterranean hilly landscapes: a numerical exploration
Authors: Dhouib Mariem , Molénat Jérôme , Prévot Laurent , Mekki Insaf , Zitouna-Chebbi Rim , Jacob Frédéric
Published: January 2024

Within hilly agricultural landscapes, topography induces lateral transfers of runoff water, so-called interplot hydrological connectivity. Runoff water generated in upstream plots can infiltrate downstream plots, thus influencing the water content in the root zone that drives crop functioning. As the impact of runoff on crop functioning in the context of Mediterranean rainfed annual crops has been understudied thus far, the current study aims to comprehensively investigate this effect. To quantify how the downstream infiltration of upstream runoff affects crop functioning, we conduct a numerical experiment using the AquaCrop crop model and consider two hydrologically connected plots. The experiment explores a range of upstream and downstream agro-pedo-climatic conditions: crop type, soil texture and depth, climate forcing, and the area of the upstream plot. The experiment relies on data collected over the last 25 years within the Kamech catchment, an environment research observatory in northeastern Tunisia. Overall, the results show that the downstream infiltration of upstream runoff has a positive impact on crop functioning in a moderate number of situations, ranging from 16% (wheat) to 33% (faba bean) as the average across aboveground biomass and yield. This positive impact is mostly found for soils with higher available water capacity and under semiarid and dry subhumid climate conditions, with a significant impact of rainfall intra-annual distribution in relation to crop phenology. These results need to be deepened by considering both a wider range of crops and future climate conditions, for which larger occurrences of semiarid climate conditions are expected.
Structure and mechanism of the Zorya anti-phage defense system
Authors: Taylor Nicholas , Hu Haidai , Hughes Thomas , Popp Philipp , Roa-Eguiara Aritz , Martin Freddie , Rutbeek Nicole , Hendriks Ivo , Payne Leighton , Yan Yumeng , Sousa Victor , Wang Yong , Nielsen Michael , Berry Richard , Erhardt Marc , Jackson Simon
Published: January 2024

Zorya is a recently identified and widely distributed bacterial immune system, which protects against phage invasion. It consists of a predicted membrane-embedded complex (ZorAB) and soluble components that differ among Zorya subtypes, notably ZorC and ZorD, in type I Zorya systems. Here, we reveal the molecular basis of the Zorya defense system using cryo-electron microscopy, mutagenesis, fluorescence microscopy, proteomics, and functional studies. We demonstrate that ZorAB shares the stoichiometry of other 5:2 inner membrane ion-driven rotary motors. Additionally, ZorA5B2 features a dimeric ZorB peptidoglycan binding domain and a pentameric α-helical coiled-coil tail made of ZorA that projects approximately 700 Å into the cytoplasm. We further characterize the structure and function of the soluble Zorya components, ZorC and ZorD, and find that they harbour DNA binding and nuclease activity, respectively. Comprehensive functional and mutational analyses demonstrates that all Zorya components work in concert to protect bacterial cells against invading phages. We present evidence that ZorAB operates as an ion-driven motor that becomes activated and anchors to the cell wall upon sensing of cell envelope perturbations during phage invasion. Subsequently, ZorAB transfers the phage invasion signal through the ZorA cytoplasmic tail to the soluble effectors, which function to prevent phage propagation. In summary, our study elucidates the foundational mechanisms of Zorya function and reveals a novel triggering signal for the rapid activation of an anti-phage defense system.
Socioeconomic Inequalities in Avoidable Mortality in Italy: Results From a Nationwide Longitudinal Cohort
Authors: Petrelli Alessio , Ventura Martina , Napoli Anteo , Pappagallo Marilena , Simeoni Silvia , Frova Luisa
Published: January 2024

BACKGROUND
Disparities in avoidable mortality have never been evaluated in Italy at the national level.
The present study aimed to assess the association between socioeconomic status, measured by individual education level, and avoidable mortality.
METHODS
The nationwide closed cohort of the 2011 Census of Population and Housing was followed up for 2012-2019 mortality. Outcomes of preventable and of treatable mortality were separately evaluated among people aged 30-74. Education level (elementary school or less, middle school, high school diploma, university degree or more) and residence macro area (North-West, North-East, Center, South-Islands) were the exposures, for which adjusted mortality rate ratios (MRRs) were calculated through multivariate quasi-Poisson regression models, adjusted for age at death. Relative index of inequalities was estimated for preventable, treatable, and non-avoidable mortality and for some specific causes.
RESULTS
The cohort consisted of 35,708,459 residents (48.8% men, 17.5% aged 65-74), 34% with a high school diploma, 33.5% living in the South-Islands; 1,127,760 deaths were observed, of which 65.2% for avoidable causes (40.4% preventable and 24.9% treatable).
Inverse trends between education level and mortality were observed for all causes; comparing the least with the most educated groups, a strong association was observed for preventable (males MRR=2.39; females MRR=1.65) and for treatable causes of death (males MRR=1.93; females MRR=1.45).
The greatest inequalities were observed for HIV/AIDS and alcohol-related diseases (both sexes), drug-related diseases and tuberculosis (males), and diabetes mellitus, cardiovascular diseases, and renal failure (females). Excess risk of preventable and of treatable mortality were observed for the South-Islands.
CONCLUSIONS
Socioeconomic inequalities in mortality persist in Italy, with an extremely varied response to policies at the regional level, representing a possible missed gain in health and suggesting a reassessment of priorities and definition of health targets.
Stress-Testing USB Accelerators for Efficient Edge Inference
Authors: Fischer Raphael , Staay Alexander van der , Buschjäger Sebastian
Published: January 2024

Several manufacturers sell specialized USB devices for accelerating machine learning (ML) on the edge. While being generally promoted as a versatile solution for more efficient edge inference with deep learning models, extensive practical insights on their usability and performance are hard to find. In order to make ML deployment on the edge more sustainable, our work investigates how resource efficient these USB accelerators really are. For that, we first introduce a novel and theoretically sound methodology. It allows for comparing intricate model performance in terms of quality and resource consumption across different execution environments. We then put it into practice by studying the usability and efficiency of Google's Coral edge tensor processing unit (TPU) and Intel's neural compute stick 2 (NCS). In total, we benchmark over 30 models across nine hardware configurations, which reveals intricate trade-offs. Our work demonstrates that USB accelerators are indeed capable of reducing the energy consumption by a factor up to ten, however this improvement cannot be observed for all configurations - more than 50% of the investigated models cannot be run on accelerator hardware, and in several other cases, the power draw is only marginally improved. Our experiments show that the NCS improves efficiency in a more stable way, while the TPU shows further benefits in specific cases but performs less predictable. We hope that our paper provides valuable insights for practitioners that want to deploy ML on the edge in the most efficient and sustainable way.
The 2022 Menyuan Mw6.7 Earthquake Triggered by 2016 M6.4 Earthquake and Stress Transfer on Surrounding Fault Systems in NE margin of Tibetan Plateau, China
Authors: lei dongning , Qiao Yueqiang , Yang Gang , Kong Yuyang
Published: January 2024

The Tuolaishan-Lenglongling fault, situated on the northeastern margin of Tibetan plateau, is characterized by highly seismic activity. Over the past four decades, three moderate earthquakes with a magnitude exceeding M6.0 have occurred around this fault. The most recent event, the 2022 Menyuan Mw6.7 earthquake has inspired the interest to its seismic hazard since the 2016 M6.4 and the 1984 M6.4 earthquake. Based source fault models we conducted the calculation of static Coulomb stress change for the recent two earthquake events. Our studies show that the 2022 M6.7 earthquake including its aftershocks has been triggered by the previous event of the 2016 M6.4 earthquake. Utilizing source fault models, we conducted calculations of static Coulomb stress change for the two recent earthquake events. Our analysis indicates that the 2022 M6.7 earthquake, along with its aftershocks, was triggered by the preceding 2016 M6.4 earthquake. Additionally, we performed calculations to assess the stress transfer resulting from the 2022 Mw6.7 earthquake on the adjacent fault systems, identifying the fault segments experiencing increased stress. Furthermore, the Tuolashan-Lenglongling fault exhibits the highest expected seismicity rate among the calculated fault systems. Consequently, increased attention should be directed towards these fault segments experiencing heightened stress to evaluate potential seismic hazard.
WITHDRAWN: Doxorubicin Involves in Cardiomyocyte Ferroptosis by Inhibiting STAT3 Phosphorylation and Reducing GPX4 Expression
Authors: Gao Ge , Shen Cheng , Wang Manman , Ji Cuiling , Fang Fang , Liu Zhichao , Chen Wenqiang , Zhang Jinguo
Published: January 2024

Doxorubicin (DOX) is a commonly employed cancer-fighting medication in medical settings, and its adverse effects on the heart pose a significant challenge in the treatment of tumors. The programmed cell death, encompassing cell death linked with apoptosis, pyroptosis, necroptosis, ferroptosis and autophagy/mitophagy, can be witnessed in the advancement of doxorubicin-induced cardiotoxicity, yet the precise mechanism remains uncertain. Recent studies have tended to ferroptosis as the predominant form of DOX-induced cell death. To identify differentially expressed genes (DEGs), the GSE193861 dataset obtained from the GEO database through high throughput sequencing was downloaded. GO and KEGG pathway enrichment analysis was performed on the ferroptosis-related differentially expressed genes (FRDEGs) obtained by screening the ferroptosis-related genes (FRGs) downloaded from the FerrDb database. Among the screened hub genes, transcription factor STAT3 was significantly associated with ferroptosis. Through our prediction, we discovered that STAT3 attaches to the promoter area of glutathione peroxidase 4 (GPX4), an essential controller of ferroptosis. In order to investigate the impact of DOX on ferroptosis in cardiomyocytes through the regulation of STAT3 and GPX4 expression, we created both in vivo and in vitro cardiotoxicity models utilizing DOX. The results demonstrated that DOX inhibited the phosphorylation and nuclear translocation of STAT3 and the expression of its downstream target gene GPX4, but had no effect on STAT3 expression. Notably, when the STAT3 was overexpressed, DOX lost its ability to inactivate GPX4. Consequently, activation of STAT3 ameliorated DOX-induced ferroptosis in cardiomyocytes. In summary, DOX induces ferroptosis in cardio- myocyte by inhibiting the phosphorylation of STAT3 and decreasing the expression of GPX4.
Autonomous Driving Comfort Prediction: Improve Comfort through Global Path Planning
Authors: Huang Chaosheng , Chen Zhengxian , Liu Yuqi , Xu Boyang , Ling Zihan , Li Zheyu , Yu Wenhao , Shen Yang , Wang Huanan , Li Jun , Yao Feiyu
Published: January 2024

Comfort is a pivotal consideration in the realm of autonomous driving, prompting researchers to address methods of enhancing driving comfort for autonomous driving vehicles. A central focus of current research revolves around the judicious planning of driving trajectories with a primary emphasis on comfort. Given the transient nature of comfort, prevailing path planning strategies predominantly center on local considerations. Nevertheless, it is imperative to recognize that macroscopic factors, including traffic flow and road conditions, wield a substantial influence on comfort. For instance, complex traffic scenarios elevate the likelihood of emergency braking, thereby affecting comfort. Consequently, investigating the intricate interplay between comfort and global path planning becomes essential. In the context of autonomous driving, this paper introduces a methodology and framework for predicting driving comfort by leveraging road information. The study establishes a comprehensive road information-comfort dataset and devises predictive models for driving comfort, employing a multi-head attention approach. The ensuing discussion elucidates the practical application of the model in path selection and planning through illustrative examples. Finally, real-road vehicle tests were conducted. Following the utilization of the path optimized by the model, the autonomous driving car exhibited an approximate 17.3% reduction in the mean longitudinal jerk and a 9.7% reduction in the mean lateral jerk. These results serve to validate the effectiveness and significance of the proposed model. Notably, this research marks the pioneering integration of driving comfort with global path planning, which is of immense importance for autonomous driving navigation systems, offering an insightful and valuable reference for related study.
Phytochemical Profiling and Molecular Docking Investigation of Avocado (Persea Americana Mill. Cultivar Hass) Leaves and Seeds: Implications for Antioxidant Activity and Health Benefits
Authors: Olufemi Ademoyegun , Rabiat Ahmed Shola , Raphael David Olamide , Mustapha Balikis Oluwakemi , Salau Shina
Published: January 2024

Despite the rising interest in avocado leaves and seeds' health benefits, little or no research has been conducted on both their phytochemical profiles in conjunction with molecular docking investigations, particularly in relation to its antioxidant activity. Utilizing phytochemical screening, molecular docking, and ADMET predictions, this study, investigates the antioxidant properties of avocado leaves (AVL) and seeds (AVS). Results show that AVS has a high presence of flavonoids (+++), terpenoids (+++), but a low presence of phenols (+), while AVL has a high presence of tannins (+++) and phenols (++). Molecular docking studies validate two AVL (L01 and L02) and two AVS (S02 and S03) compounds based on binding affinity and interactions with 2rgu.pdb, 3mng.pdb, and 2vwi.pdb protein targets. ADMET studies indicate that AVL and AVS extracts have favourable bioavailability and health safety characteristics. Additionally, DPPH, ABTS and FRAP radical inhibition (%) results show that AVL (46.36%/DPPH, 6.67%/ABTS, 11.07%/FRAP) has lesser antioxidant activity than AVS (70.97%/DPPH, 47.35%/ABTS, 3.66%/FRAP).
Fracture toughness of dried oil paints
Authors: Janas Arkadiusz , Avgerou Nefeli , Charalambides Maria , Fuster-López Laura , Bratasz Łukasz
Published: January 2024

The fracture toughness in opening mode for selected dried oil paints was determined filling in this way a critical gap in the knowledge required for the analysis of fracturing processes in paintings. The experimental work was made possible by the adaptation of the ASTM D5528–13 standard method to the size of available oil paints for a selection of oil paints from Mecklenburg's Paint Reference Collection now after approximately 30 years of drying. The careful regluing procedure allowed to improve the statistics of the measurements but also to get insight into in-depth profiles of the fracture toughness of some paints. The fracture toughness GIC values are the lowest for zinc white, raw umber and lead white with litharge, approximately 15 J/m2 and the highest for sap green. The variable homogeneity of some tested paints – lead white, zinc white, sap green and verdigris – is reflected in the large range of the values obtained. This study demonstrated that some paints were more brittle than glue-based ground layers and as a consequence more vulnerable to cracking
Financial Time-Series Forecasting: Towards Synergizing Performance And Interpretability Within a Hybrid Machine Learning Approach
Authors: Liu Shun , Wu Kexin , Jiang Chufeng , Huang Bin , Ma Danqing
Published: January 2024

In the realm of cryptocurrency, the prediction of Bitcoin prices has garnered substantial attention due to its potential impact on financial markets and investment strategies. This paper propose a comparative study on hybrid machine learning algorithms and leverage on enhancing model interpretability. Specifically, linear regression(OLS, LASSO), long-short term memory(LSTM), decision tree regressors are introduced. Through the grounded experiments, we observe linear regressor achieves the best performance among candidate models. For the interpretability, we carry out a systematic overview on the preprocessing techniques of time-series statistics, including decomposition, auto-correlational function, exponential triple forecasting, which aim to excavate latent relations and complex patterns appeared in the financial time-series forecasting. We believe this work may derive more attention and inspire more researches in the realm of time-series analysis and its realistic applications.
Autonomous Navigation for Personal Mobility Vehicles Considering Passenger Tolerance against Approaching Pedestrians
Authors: Omori Motonobu , Yoshitake Hiroshi , Shino Motoki
Published: January 2024

There are high expectations for autonomous personal mobility vehicles (PMVs) to support the mobility of older people. Autonomous navigation systems are being developed to assist mobility in public areas with mixed pedestrian traffic, such as airports and shopping malls. For autonomous navigation of PMVs, achieving both efficiency and comfort is important. In this study, we focused on the characteristic of passenger tolerance, in which a passenger’s discomfort is relatively small concerning an approaching pedestrian. The objective was to propose an efficient and comfortable autonomous navigation method considering the characteristics of passenger tolerance. First, the passenger tolerance characteristics were clarified through data analysis of a previous study’s dataset and a newly collected dataset. Next, a path planning method considering the characteristics was proposed, and the proposed method was evaluated by numerical simulations. The evaluation results showed that the proposed method can achieve efficient and comfortable autonomous navigation, especially in crowded environments.
Full Immersive Telepresence Through Integration of a VR-Headset with a Teleoperated Humanoid Robotic Head Featuring Real-Time Stereoscopic Vision and Binaural Auditory Perceptions
Authors: Dugan Anthony H , Sadykov Rustem , Roozbahani Daniel , Alizadeh Marjan , Handroos Heikki
Published: January 2024

The rapid evolution of technology has created new opportunities for teleoperation, especially with the emergence of affordable Virtual Reality (VR) systems originally designed for entertainment and gaming. Teleoperation in robotics has a long history, however, its integration with VR for controlling robotic systems is currently gaining significant attention. In this study, a novel real-time platform is developed that integrates teleoperation with a VR system, providing immersive control of a biomimetic robotic head. A 3D model of the robotic head was designed and built to mimic the movements of the operator's head in real-time using the operator’s kinematics. The fabricated robotic head is equipped with a pair of cameras and a binaural microphone to provide vision and audio perception. Additionally, two servomotors are employed to track the motion of the operator’s neck. To control the biomimetic robotic head, a game was developed in Unity to deliver 3D visual and stereophonic audio inputs to the headset, which ensures a smooth viewing and listening experience for the operator. The designed platform is operated through a custom-written C++ program that facilitates continuous tracking of the headset and establishes constant communication with the servomotors. High precision in the positional data and accurate tracking of the head-mounted display (HMD), along with efficient communication with the motors, were achieved. The integration of the VR system and teleoperation developed in this study advances VR technology beyond traditional entertainment-oriented applications and makes it applicable in diverse industrial fields, simultaneously elevating teleoperation in human-robot interactions to new levels of productivity.