bluuebunny/crossref_metadata_2025 huggingface dataset:
Advertising investments on television: real option estimation through Markov chains
Authors: D’Amico G. , De Blasis R. , Vigna V.
Published: April 2024

We propose the valuation of a real option in the telecommunications industry. According to the probabilistic present worth approach, we estimate the value of a contract between a television network and a company willing to advertise its business on this network. We assume that the value of the contract depends on a time-dependent variable, i.e., the number of viewers tuned into the network, which behaves like a Markov process. After discretizing and converting this number into a monetary value through a specific function, we compute the nth-order moment of the total discounted earnings. The knowledge of the moments, and the application of the maximum-entropy approach, allows to find the probability distribution of the payoff function and the consequential pricing of the real option. Finally, we apply the proposed model to the real television audience data.
How to lose your memory without losing your money: shifty epistemology and Dutch strategies
Authors: Bradley Darren
Published: April 2024

An objection to shifty epistemologies such as subject-sensitive invariantism is that it predicts that agents are susceptible to guaranteed losses. Bob Beddor (Analysis, 81, 193–198, 2021) argues that these guaranteed losses are not a symptom of irrationality, on the grounds that forgetful agents are susceptible to guaranteed losses without being irrational. I agree that forgetful agents are susceptible to guaranteed losses without being irrational– but when we investigate why, the analogy with shifty epistemology breaks down. I argue that agents with shifty epistemologies are susceptible to guaranteed losses in a way which is a symptom of irrationality. Along the way I make a suggestion about what it takes for an agent to be coherent over time. I close by offering a taxonomy of shifty epistemologies.
Uncovering the themes and trends in crowdfunding research using Latent Dirichlet Allocation
Authors: Rejeb Abderahman , Rejeb Karim , Appolloni Andrea , Treiblmaier Horst , Iranmanesh Mohammad
Published: April 2024

Crowdfunding (CF) has become a significant force in the entrepreneurial landscape, offering an innovative alternative to traditional financing channels for startups and projects. As the field expands, it is crucial to systematically analyze the existing literature to identify key themes, patterns, and emerging areas of interest. To achieve this goal, this study investigates the CF literature using latent Dirichlet allocation (LDA)-based topic modeling based on 1,678 publications extracted from the Scopus database. The review reveals significant growth in CF research, with top journals spanning diverse disciplines. Eight main topics are identified, including CF campaign success and financing, donation-based CF, social effects of CF, entrepreneurial projects and rewards in CF, financial and fintech aspects of CF, CF project success and performance, P2P lending models and credit risk assessment, and equity CF and venture capital. Several research directions are suggested for each topic to advance the CF field. The theoretical and practical implications are also discussed. To the authors’ best knowledge, this study represents the first systematic analysis of the CF literature using the LDA approach, offering a comprehensive and up-to-date overview of this field and highlighting emerging areas of interest and potential research directions.
Fatigue Testing Approach Utilising Machining Cutting Forces and Fixture Design
Authors: Okenyi V. , Afazov S. , Mansfield N. , Siegkas P. , Serjouei A. , Bodaghi M.
Published: April 2024

Background

Traditional fatigue testing methods can be expensive due to the need of specialised equipment for engineering materials and structures. Thus, a new fatigue testing approach utilising machining cutting forces to induce cyclic stresses, enabling fatigue life assessment of engineering materials and structures, has been developed.

Objective

This research aims to develop and verify a new testing approach using machining processes to enable the fatigue life assessment of engineering materials and structures. This is achieved by the utilisation of machining-induced cutting forces to generate cyclic stresses into welded samples used in applications of wind turbine monopile structures.

Methods

The methodology employes the development of a fixture encompassed with strain gauges and purposefully designed machining operations to mimic the cyclic stresses experienced in real applications. The machining-based fatigue testing approach was demonstrated on welded samples by replicating cyclic stresses of offshore wind turbine monopiles subject to in-service loads.

Results

The results show that rapid fatigue testing of engineering materials and structures is possible by utilising existing machine tools and centres, which are widely accessible to industry. Cyclic stresses were induced in welded structural steel samples proving the concept of this method.

Conclusion

This novel fatigue testing method showed that cyclic stresses can be induced by machining cutting forces to address real application needs. The key advantages are that this method can be quickly set up in industry, enabling fast fatigue testing that can lead to reduction of lead times for product and process development of industrial components.
Effect of composting and storage on the microbiome and resistome of cattle manure from a commercial dairy farm in Poland
Authors: Zalewska Magdalena , Błażejewska Aleksandra , Szadziul Mateusz , Ciuchciński Karol , Popowska Magdalena
Published: April 2024

Manure from food-producing animals, rich in antibiotic-resistant bacteria and antibiotic resistance genes (ARGs), poses significant environmental and healthcare risks. Despite global efforts, most manure is not adequately processed before use on fields, escalating the spread of antimicrobial resistance. This study examined how different cattle manure treatments, including composting and storage, affect its microbiome and resistome. The changes occurring in the microbiome and resistome of the treated manure samples were compared with those of raw samples by high-throughput qPCR for ARGs tracking and sequencing of the V3–V4 variable region of the 16S rRNA gene to indicate bacterial community composition. We identified 203 ARGs and mobile genetic elements (MGEs) in raw manure. Post-treatment reduced these to 76 in composted and 51 in stored samples. Notably, beta-lactam, cross-resistance to macrolides, lincosamides and streptogramin B (MLSB), and vancomycin resistance genes decreased, while genes linked to MGEs, integrons, and sulfonamide resistance increased after composting. Overall, total resistance gene abundance significantly dropped with both treatments. During composting, the relative abundance of genes was lower midway than at the end. Moreover, higher biodiversity was observed in samples after composting than storage. Our current research shows that both composting and storage effectively reduce ARGs in cattle manure. However, it is challenging to determine which method is superior, as different groups of resistance genes react differently to each treatment, even though a notable overall reduction in ARGs is observed.
Characterization and identification of long-chain hydrocarbon-degrading bacterial communities in long-term chronically polluted soil in Ogoniland: an integrated approach using culture-dependent and independent methods
Authors: Okoye Amara Ukamaka , Selvarajan Ramganesh , Chikere Chioma Blaise , Okpokwasili Gideon Chijioke , Mearns Kevin
Published: April 2024

Escalating oil consumption has resulted in an increase in accidental spills of petroleum hydrocarbons, causing severe environmental degradation, notably in vulnerable regions like the Niger Delta. Complex mixture of these hydrocarbons particularly long-chain alkanes presents unique challenges in restoration of polluted environment due to their chemical properties. This study aimed to investigate the long-chain hydrocarbon-degrading bacterial communities within long-term chronically polluted soil in Ogoniland, by utilizing both traditional cultivation methods and modern culture-independent techniques. Results revealed that surface-polluted soil (SPS) and subsurface soil (SPSS) exhibit significantly higher total organic carbon (TOC) ranging from 5.64 to 5.06% and total petroleum hydrocarbons (TPH) levels ranging from 36,775 ppm to 14,087 ppm, compared to unpolluted soil (UPS) with 1.97% TOC and 479 ppm TPH, respectively. Analysis of carbon chain lengths reveals the prevalence of longer-chain alkanes (C20-28) in the surface soil. Culture-dependent methods, utilizing crude oil enrichment (COE) and paraffin wax enrichment (PWE), yield 47 bacterial isolates subjected to a long-chain alkane degradation assay. Twelve bacterial strains demonstrate significant degradation abilities across all enriched media. Three bacterial members, namely Pseudomonas sp. (almA), Marinomonas sp. (almA), and Alteromonas (ladA), exhibit genes responsible for long-chain alkane degradation, demonstrating efficiency between 50 and 80%. Culture-independent analysis reveals that surface SPS samples exhibit greater species richness and diversity compared to subsurface SPSS samples. Proteobacteria dominates as the phylum in both soil sample types, ranging from 22.23 to 82.61%, with Firmicutes (0.2–2.22%), Actinobacteria (0.4–3.02%), and Acidobacteria (0.1–3.53%) also prevalent. Bacterial profiles at genus level revealed that distinct variations among bacterial populations between SPS and SPSS samples comprising number of hydrocarbon degraders and the functional predictions also highlight the presence of potential catabolic genes (nahAa, adh2, and cpnA) in the polluted soil. However, culture-dependent analysis only captured a few of the dominant members found in culture-independent analysis, implying that more specialized media or environments are needed to isolate more bacterial members. The findings from this study contribute valuable information to ecological and biotechnological aspects, aiding in the development of more effective bioremediation applications for restoring oil-contaminated environments.
Factors Impacting One-year Follow-up Visit Adherence after Bariatric Surgery in West China: A Mixed Methods Study
Authors: Liao Jing , Wen Yue , Yin Yiqiong , Qin Yi , Zhang Guixiang
Published: April 2024

Purpose

Quality follow-up (FU) is crucial after bariatric surgery. However, poor adherence after surgery is prevalent. This research aimed to explore the factors related to FU adherence after bariatric surgery in West China.

Materials and Methods

This study used a sequential explanatory mixed-methods research design. Participants (n = 177) were identified from the West China Hospital. Demographic information, disease profile, treatment information, and post-surgery FU information were obtained from the bariatric surgery database of the Division of Gastrointestinal Surgery of the West China Hospital. The survey data were analyzed using logistic regression. Semi-structured interviews with participants (n = 10) who had low adherence were conducted. The recording was transcribed verbatim and entered into qualitative data analysis software. Qualitative data were analyzed using a content analysis approach.

Results

Multiple logistic regression revealed that living in Chengdu (OR, 2.308), being employed (OR, 2.532), non-smoking (OR, 2.805), and having less than five years of obesity (OR, 2.480) were positive predictors of FU adherence within one year. Semi-structured interviews suggested that factors related to adherence to FU were lack of motivation, lack of opportunity, insufficient ability, and beliefs regarding consequences.

Conclusion

Factors impacting one-year FU visit adherence after bariatric surgery include not only demographic and disease-related factors but also social and family factors. These results will provide evidence to support healthcare professionals in developing personalized postoperative FU management strategies.

Graphical Abstract
The London money market and non-British bank lending during the first globalisation: evidence from Brazil
Authors: Kisling Wilfried , Molteni Marco
Published: April 2024

This study examines the relationship between the London money market (LMM) and the credit provision of non-British overseas banks in peripheral economies during the first wave of globalisation. Using monthly data between 1889 and 1913, we find a positive relationship between the amount of credit authorised by the German Brasilianische Bank für Deutschland in Brazil and the spread between the London market and floating rate. Our results suggest that increased demand for foreign bills and/or decreased borrowing costs in the LMM leads to an increase in credit supply. We use the impact of annual tax payments on the spread between the market and floating rate as an instrumental variable (IV) to show that this relationship is causal. Although there is a significant amount of literature on London’s historic role as a global financial centre and a growing number of studies on foreign banking history, little quantitative evidence is available about the connection between the two. This study bridges this gap.
Analysis of Cerebral Spinal Fluid Drainage and Intracranial Pressure Peaks in Patients with Subarachnoid Hemorrhage
Authors: Früh Anton , Truckenmüller Peter , Wasilewski David , Vajkoczy Peter , Wolf Stefan ,   for the Earlydrain Study Group
Published: April 2024

Background

After aneurysmal subarachnoid hemorrhage (aSAH), elevated intracranial pressure (ICP) due to disrupted cerebrospinal fluid (CSF) dynamics is a critical concern. An external ventricular drainage (EVD) is commonly employed for management; however, optimal strategies remain debated. The randomized controlled Earlydrain trial showed that an additional prophylactic lumbar drainage (LD) after aneurysm treatment improves neurological outcome. We performed a post hoc investigation on the impact of drainage volumes and critical ICP values on patient outcomes after aSAH.

Methods

Using raw patient data from Earlydrain, we analyzed CSF drainage amounts and ICP measurements in the first 8 days after aSAH. Outcomes were the occurrence of secondary infarctions and the score on the modified Rankin scale after 6 months, dichotomized in values of 0–2 as favorable and 3–6 as unfavorable. Repeated measurements were considered with generalized estimation equations.

Results

Earlydrain recruited 287 patients, of whom 221 received an EVD and 140 received an LD. Higher EVD volumes showed a trend to more secondary infarctions (p = 0.09), whereas higher LD volumes were associated with less secondary infarctions (p = 0.009). The mean total CSF drainage was 1052 ± 659 mL and did not differ concerning infarction and neurological outcome. Maximum ICP values were higher in patients with poor outcomes but not related to drainage volumes via EVD. After adjustment for aSAH severity and total CSF drainage, higher LD volume was linked to favorable outcome (per 100 mL: odds ratio 0.61 (95% confidence interval 0.39–0.95), p = 0.03), whereas higher EVD amounts were associated with unfavorable outcome (per 100 mL: odds ratio 1.63 (95% confidence interval 1.05–2.54), p = 0.03).

Conclusions

Findings indicate that effects of CSF drainage via EVD and LD differ. Higher amounts and higher proportions of LD volumes were associated with better outcomes, suggesting a potential quantity-dependent protective effect. Optimizing LD volume and mitigating ICP spikes may be a strategy to improve patient outcomes after aSAH.

Clinical trial registration: ClinicalTrials.gov identifier: NCT01258257.
The reliability and validity of the Affinity Altitude hypoxic generators in acute and chronic conditions
Authors: Willmott Ashley G. B. , Chung Henry C. , Roberts Justin D. , Maxwell Neil S.
Published: April 2024

This study investigated the reliability of the Affinity Altitude hypoxic generator within-day, between-day and between-generator under acute and chronic hypoxic conditions. An additional objective was to assess the validity of the fraction of inspired oxygen (FiO2) values in relation to the claimed manufacture’s reference values to ensure the accuracy and safety of the product. Three altitude generators (Affinity Altitude Ltd., Sussex, UK) were assessed across all available settings during a test–retest design for equivalent FiO2 and output volume. This consisted of two phases: 1) acute exposure (10 min per setting) and 2) chronic exposure (8 h per setting). FiO2 and volume data were calculated from 1 min collection samples using the Douglas bag method for acute and chronic exposures. There were low variations in FiO2 data across all settings within the acute exposure for within-day (coefficient of variation [CV] range: 0.0–2.6%), between-day (0.2–1.3%), and between-generator analysis (0.7–1.4%). This was similarly found for volume data within-day (0.1–3.7%), between-day (0.7–5.4%), and between-generator (1.2–3.0%). Equally, for chronic exposure trials, CV for FiO2 (< 4.0%) and volume (< 5.0%) across each of the generators presented low variations. The FiO2 values were similar to reference values, however, significant differences were found for settings 4 (−0.3% [17.6% vs. 17.9% reference value]) and 5 (−0.1% [15.8% vs. 15.9% reference value], both p < 0.05). A ‘good’ level of reliability (CV < 5%) and validity were found within and between the Affinity Altitude’s generators. However, a review of the reference values is warranted, and long-term experimental studies are required to determine the efficacy of this device for the purpose of physiological adaptations.
User gait biometrics in smart ambient applications through wearable accelerometer signals: an analysis of the influence of training setup on recognition accuracy
Authors: De Marsico Maria , Palermo Andrea
Published: April 2024

Gait recognition can exploit the signals from wearables, e.g., the accelerometers embedded in smart devices. At present, this kind of recognition mostly underlies subject verification: the incoming probe is compared only with the templates in the system gallery that belong to the claimed identity. For instance, several proposals tackle the continuous recognition of the device owner to detect possible theft or loss. In this case, assuming a short time between the gallery template acquisition and the probe is reasonable. This work rather investigates the viability of a wider range of applications including identification (comparison with a whole system gallery) in the medium-long term. The first contribution is a procedure for extraction and two-phase selection of the most relevant aggregate features from a gait signal. A model is trained for each identity using Logistic Regression. The second contribution is the experiments investigating the effect of the variability of the gait pattern in time. In particular, the recognition performance is influenced by the benchmark partition into training and testing sets when more acquisition sessions are available, like in the exploited ZJU-gaitacc dataset. When close-in-time acquisition data is only available, the results seem to suggest re-identification (short time among captures) as the most promising application for this kind of recognition. The exclusive use of different dataset sessions for training and testing can rather better highlight the dramatic effect of trait variability on the measured performance. This suggests acquiring enrollment data in more sessions when the intended use is in medium-long term applications of smart ambient intelligence.
Artificial Slags with Modulated Properties for Controlled Nickel Dissolution in Smelting Process
Authors: Urtnasan Erdenebold , Kumar Avneesh , Wang Jei-Pil
Published: April 2024

In pyrometallurgy process involving drying, calcination and reductive smelting steps, Saprolite ore is subjected in an electric arc furnace to produce Fe–Ni alloy and Ni matte. To optimize the nickel smelting step, the slag consisted of MgO–SiO2–FeO and having a lower viscosity and high fluidity is required. In this study, a number of new slag samples with varied degree of compositions are manufactured and used to examine the effect of their viscosity on nickel dissolution. Further correlation between the slag basicity and FeO addition on the smelting was also tested and simulated using Fe–Ni alloy from saprolite ore. As per the observations, the nickel dissolution increases with the increased content of FeO at a certain degree of activity which in turn can cause oxidation of nickel during the process. The slags with the ratio of MgO/SiO2 = 0.4–0.6, 30% fraction of FeO, and a viscosity of 1.8–1.06 poise in the olivine zone are found effective for the lowest dissolution rate of nickel with 0.021, 0.022, and 0.018 wt.%. The lowest dissolution rate is recorded for the slag with MgO/SiO2 ratio about 0.6. These slags with controlled amount of constituents could be used to recover precious metal from nickel smelter at a lower dissolution rate and thereby allowing to reduce the waste of the metal during smelting process.
Evaluation of NGS DNA barcoding for biosecurity diagnostic applications: case study from banana freckle incursion in Australia
Authors: Galaihalage Kalpani , Patel Shreya , Yadav Sonu
Published: April 2024

Molecular diagnostics in combination with morphological identification is the method of choice for several cryptic microbial plant pathogens. For some diagnostic applications, traditional sequencing techniques can be time consuming, making them ill-suited for biosecurity incursion responses, where accurate results are needed in real time. More rapid next generation sequencing tools must be tested and compared with traditional methods to assess their utility in biosecurity applications. Here utilizing 95 samples infected with fungal pathogen Phyllosticta cavendishii, from a recent incursion in Australia, we compare species identification success using Internal Transcribed Spacer (ITS) gene barcode on conventional Sanger and Oxford Nanopore MinION sequencing platforms. For Sanger sequencing, the average pairwise identity percentage score between generated consensus sequences and P. cavendishii sequence from holotype material on NCBI database was 99.9% ± SE 0.0 whereas for MinION sequencing the average pairwise identity percentage was 99.1% ± SE 0.1. Relatively larger consensus sequences (mean 486 bp ± SE 2.4) were generated by Sanger sequencing compared to MinION sequencing (mean 435 bp ± SE 4.6). Our results confirm that both sequencing methods can reliably identify P. cavendishii. MinION sequencing, provided quicker results compared to Sanger sequencing and demonstrated diagnostic competence, with the added advantage of being portable, for front-line “point of incursion” biosecurity applications.
The emerging role of CARM1 in cancer
Authors: Xie Zizhuo , Tian Yuan , Guo Xiaohan , Xie Na
Published: April 2024

Coactivator-associated arginine methyltransferase 1 (CARM1), pivotal for catalyzing arginine methylation of histone and non-histone proteins, plays a crucial role in developing various cancers. CARM1 was initially recognized as a transcriptional coregulator by orchestrating chromatin remodeling, transcription regulation, mRNA splicing and stability. This diverse functionality contributes to the recruitment of transcription factors that foster malignancies. Going beyond its established involvement in transcriptional control, CARM1-mediated methylation influences a spectrum of biological processes, including the cell cycle, metabolism, autophagy, redox homeostasis, and inflammation. By manipulating these physiological functions, CARM1 becomes essential in critical processes such as tumorigenesis, metastasis, and therapeutic resistance. Consequently, it emerges as a viable target for therapeutic intervention and a possible biomarker for medication response in specific cancer types. This review provides a comprehensive exploration of the various physiological functions of CARM1 in the context of cancer. Furthermore, we discuss potential CARM1-targeting pharmaceutical interventions for cancer therapy.
Ballast Settlement Accumulation in Zones with Unsupported Sleepers
Authors: Nabochenko Olga , Sysyn Mykola , Fischer Szabolcs
Published: April 2024

The high influence of impact and vibration on the behavior of crushed stone and ballast materials has been known for a long time. The zones with unsupported sleepers, which are always present in transition zones, crossings, welds, etc., are typically characterized by impact interaction, ballast full unloading, and additional preloading. However, no studies on ballast layer settlements consider impact vibration loading. Moreover, the influence of the cyclic loading on the ballast settlement intensity is considered ambiguously, with both decelerating and accelerating trends. The comprehensive literature review presents the influence of factors on settlement intensity. The present study aims to estimate the long-term processes of sleeper settlement accumulation depending on the loading factors: impact, cyclic loading, and preloading. The typical for a void zone ballast loading pattern was determined for various void sizes and the position along the track by using a model of vehicle-track interaction that was validated by experimental measurements. The loading patterns were parametrized with four parameters: maxima of the cyclic loading, impact loading, sleeper acceleration, and minimal preloading. A specially prepared DEM simulation model was used to estimate the ballast settlement intensity after initial settlement stabilization for more than 100 loading patterns of the void zone cases. The settlement simulation results clearly show that even a low-impact loading pattern causes many times increased settlement intensity than ordinary cyclic loading. Moreover, the initial preloading in the neighbor-to-void zones can cause even a decrease in the settlement intensity compared to the full ordinary or partial unloading. A statistical analysis using a machine learning approach and an analytic one was used to create the model for the intensity prediction regarding the loading patterns. The analytic approach demonstrates somewhat lower prediction quality, but it allows to receive plausible and simple analytic equations of the settlement intensity. The results show that the maximal cyclic loading has a nonlinear influence on the settlement intensity that corresponds to the 3–4 power function, and the impact loading is expressed by the linear to parabolic function. The ballast’s minimal preloading contributes to the reduction of the settlement intensity, especially for high cyclic loadings that are typical for neighbor-to-void zones. The results of the present study could be used for the complementing of the present phenomenological equations with the new factors and further application in the algorithms of the settlements accumulation prediction.
Combining transformer global and local feature extraction for object detection
Authors: Li Tianping , Zhang Zhenyi , Zhu Mengdi , Cui Zhaotong , Wei Dongmei
Published: April 2024

Convolutional neural network (CNN)-based object detectors perform excellently but lack global feature extraction and cannot establish global dependencies between object pixels. Although the Transformer is able to compensate for this, it does not incorporate the advantages of convolution, which results in insufficient information being obtained about the details of local features, as well as slow speed and large computational parameters. In addition, Feature Pyramid Network (FPN) lacks information interaction across layers, which can reduce the acquisition of feature context information. To solve the above problems, this paper proposes a CNN-based anchor-free object detector that combines transformer global and local feature extraction (GLFT) to enhance the extraction of semantic information from images. First, the segmented channel extraction feature attention (SCEFA) module was designed to improve the extraction of local multiscale channel features from the model and enhance the discrimination of pixels in the object region. Second, the aggregated feature hybrid transformer (AFHTrans) module combined with convolution is designed to enhance the extraction of global and local feature information from the model and to establish the dependency of the pixels of distant objects. This approach compensates for the shortcomings of the FPN by means of multilayer information aggregation transmission. Compared with a transformer, these methods have obvious advantages. Finally, the feature extraction head (FE-Head) was designed to extract full-text information based on the features of different tasks. An accuracy of 47.0% and 82.76% was achieved on the COCO2017 and PASCAL VOC2007 + 2012 datasets, respectively, and the experimental results validate the effectiveness of our method.
NLKFill: high-resolution image inpainting with a novel large kernel attention
Authors: Wang Ting , Xiang Dong , Yang Chuan , Liang Jiaying , Shi Canghong
Published: April 2024

The integration of convolutional neural network (CNN) and transformer enhances the network’s capacity for concurrent modeling of texture details and global structures. However, training challenges with transformer limit their effectiveness to low-resolution images, leading to increased artifacts in slightly larger images. In this paper, we propose a single-stage network utilizing large kernel attention (LKA) to address high-resolution damaged images. LKA enables the capture of both global and local details, akin to transformer and CNN networks, resulting in high-quality inpainting. Our method excels in: (1) reducing parameters, improving inference speed, and enabling direct training on 1024$$\times $$
×
1024 resolution images; (2) utilizing LKA for enhanced extraction of global high-frequency and local details; (3) demonstrating excellent generalization on irregular mask models and common datasets such as Places2, Celeba-HQ, FFHQ, and the random irregular mask dataset Pconv from NVIDIA.
Deep matrix factorization via feature subspace transfer for recommendation system
Authors: Wang Weichen , Wang Jing
Published: April 2024

The sparsity problem remains a significant bottleneck for recommendation systems. In recent years, deep matrix factorization has shown promising results in mitigating this issue. Furthermore, many works have improved the prediction accuracy of deep matrix factorization by incorporating the user’s and/or items’ auxiliary information. However, there are still two remaining drawbacks that need to be addressed. First, the initialization of latent feature representations has a substantial impact on the performance of deep matrix factorization, and most current models utilize a uniform approach to this initialization, constraining the model’s optimization potential. Secondly, many existing recommendation models lack versatility and efficiency in transferring auxiliary information from users or items to expand the feature space. This paper proposes a novel model to address the issues mentioned above. By using a semi-autoencoder, the pre-trained initialization of the latent feature representation is realized in this paper. Simultaneously, this model assimilates auxiliary information, like item attributes or rating matrices from diverse domains, to generate their latent feature representations. These representations are then transferred to the target task through subspace projection distance. With this, this model can utilize auxiliary information from various sources more efficiently and this model has better versatility. This is called deep matrix factorization via feature subspace transfer. Numerical experiments on several real-world data show the improvement of this method compared with state-of-the-art methods of introducing auxiliary information about items. Compared with the deep matrix factorization model, the proposed model can achieve 6.5% improvement at most in the mean absolute error and root mean square error.
Advances in All-Solid-State Lithium–Sulfur Batteries for Commercialization
Authors: Gicha Birhanu Bayissa , Tufa Lemma Teshome , Nwaji Njemuwa , Hu Xiaojun , Lee Jaebeom
Published: April 2024

Solid-state batteries are commonly acknowledged as the forthcoming evolution in energy storage technologies. Recent development progress for these rechargeable batteries has notably accelerated their trajectory toward achieving commercial feasibility. In particular, all-solid-state lithium–sulfur batteries (ASSLSBs) that rely on lithium–sulfur reversible redox processes exhibit immense potential as an energy storage system, surpassing conventional lithium-ion batteries. This can be attributed predominantly to their exceptional energy density, extended operational lifespan, and heightened safety attributes. Despite these advantages, the adoption of ASSLSBs in the commercial sector has been sluggish. To expedite research and development in this particular area, this article provides a thorough review of the current state of ASSLSBs. We delve into an in-depth analysis of the rationale behind transitioning to ASSLSBs, explore the fundamental scientific principles involved, and provide a comprehensive evaluation of the main challenges faced by ASSLSBs. We suggest that future research in this field should prioritize plummeting the presence of inactive substances, adopting electrodes with optimum performance, minimizing interfacial resistance, and designing a scalable fabrication approach to facilitate the commercialization of ASSLSBs.
Structural Engineering of Hierarchical Magnetic/Carbon Nanocomposites via In Situ Growth for High-Efficient Electromagnetic Wave Absorption
Authors: Liu Xianyuan , Zhou Jinman , Xue Ying , Lu Xianyong
Published: April 2024

Materials exhibiting high-performance electromagnetic wave absorption have garnered considerable scientific and technological attention, yet encounter significant challenges. Developing new materials and innovative structural design concepts is crucial for expanding the application field of electromagnetic wave absorption. Particularly, hierarchical structure engineering has emerged as a promising approach to enhance the physical and chemical properties of materials, providing immense potential for creating versatile electromagnetic wave absorption materials. Herein, an exceptional multi-dimensional hierarchical structure was meticulously devised, unleashing the full microwave attenuation capabilities through in situ growth, self-reduction, and multi-heterogeneous interface integration. The hierarchical structure features a three-dimensional carbon framework, where magnetic nanoparticles grow in situ on the carbon skeleton, creating a necklace-like structure. Furthermore, magnetic nanosheets assemble within this framework. Enhanced impedance matching was achieved by precisely adjusting component proportions, and intelligent integration of diverse interfaces bolstered dielectric polarization. The obtain Fe3O4-Fe nanoparticles/carbon nanofibers/Al-Fe3O4-Fe nanosheets composites demonstrated outstanding performance with a minimum reflection loss (RLmin) value of − 59.3 dB and an effective absorption bandwidth (RL ≤  − 10 dB) extending up to 5.6 GHz at 2.2 mm. These notable accomplishments offer fresh insights into the precision design of high-efficient electromagnetic wave absorption materials.
Exoatmospheric Evasion Guidance Law with Total Energy Limit via Constrained Reinforcement Learning
Authors: Yan Mengda , Yang Rennong , Zhao Yu , Yue Longfei , Zhao Xiaoru
Published: April 2024

Due to the lack of aerodynamic forces, the available propulsion for exoatmospheric pursuit-evasion problem is strictly limited, which has not been thoroughly investigated. This paper focuses on the evasion guidance in an exoatmospheric environment with total energy limit. A Constrained Reinforcement Learning (CRL) method is proposed to solve the problem. Firstly, the acceleration commands of the evader are defined as cost and an Actor-Critic-Cost (AC2) network structure is established to predict the accumulated cost of a trajectory. The learning objective of the agent becomes to maximize cumulative rewards while satisfying the cost constraint. Secondly, a Maximum-Minimum Entropy Learning (M2EL) method is proposed to minimize the randomness of acceleration commands while preserving the agent’s exploration capability. Our approaches address two challenges in the application of reinforcement learning: constraint specification and precise control. The well-trained agent is capable of generating accurate commands while satisfying the specified constraints. The simulation results indicate that the CRL and M2EL methods can effectively control the agent’s energy consumption within the specified constraints. The robustness of the agent under information error is also validated.
Raw milk cheeses from Beira Baixa, Portugal—A contributive study for the microbiological hygiene and safety assessment
Authors: Mendonça Rita , Furtado Rosália , Coelho Anabela , Correia Cristina Belo , Suyarko Elena , Borges Vítor , Gomes João Paulo , Pista Angela , Batista Rita
Published: April 2024

Due to specific bacterial microbiota, raw milk cheeses have appreciated sensory properties. However, they may pose a threat to consumer safety due to potential pathogens presence. This study evaluated the microbiological contamination of 98 raw milk cheeses from Beira Baixa, Portugal. Presence and enumeration of Coagulase Positive Staphylococci (CPS), Listeria monocytogenes, Salmonella spp., pathogenic Escherichia coli, and indicator microorganisms (non-pathogenic E. coli and Listeria spp.) was attained. E. coli antimicrobial resistance (AMR) was also evaluated. PCR and/or Whole genome sequencing (WGS) was used to characterize E. coli, Salmonella spp. and L. monocytogenes isolates. Sixteen cheeses (16.3%) were classified as Satisfactory, 59 (60.2%) as Borderline and 23 (23.5%) as Unsatisfactory/Potential Injurious to Health. L. monocytogenes, CPS > 104 cfu g−1, Extraintestinal pathogenic E. coli (ExPEC) and Salmonella spp. were detected in 4.1%, 6.1%, 3.1% and 1.0% of the samples, respectively. Listeria innocua (4.1%) and E. coli > 104 cfu g−1 (16.3%) were also detected. AMR E. coli was detected in 23/98 (23.5%) of the cheese samples, of which two were multidrug resistant. WGS identified genotypes already associated to human disease and Listeria spp. cluster analysis indicated that cheese contamination might be related with noncompliance with Good Hygiene Practices during cheese production.
Robot-assisted pedicle screw insertion versus navigation-based and freehand techniques for posterior spinal fusion in scoliosis: a systematic review and meta-analysis
Authors: Al-Naseem Abdulrahman O. , Al-Muhannadi Abdullah , Ramadhan Mohammad , Alfadhli Alwaleed , Marwan Yousef , Shafafy Roozbeh , Abd-El-Barr Muhammad M.
Published: April 2024

Purpose

The role of robotics in spine surgery remains controversial, especially for scoliosis correction surgery. This study aims to assess the safety and efficacy of robotic-assisted (RA) surgery specifically for scoliosis surgery by comparing RA to both navigation systems (NS) and conventional freehand techniques (CF).

Methods

As per the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) guidelines, a systematic review and meta-analysis were conducted via an electronic search of the following databases: MEDLINE, EMBASE, and the Cochrane Central Register of Controlled Trials (CENTRAL). All papers comparing RA to either NS or CF for posterior spinal fusion in scoliosis were included. Fixed and random effects models of analysis were utilised based on analysis heterogeneity.

Results

10 observational studies were included in total. RA had significantly greater odds of accurate pedicle screw placement relative to both NS (OR = 2.02, CI = 1.52–2.67, p < 0.00001) and CF (OR = 3.06, CI = 1.79–5.23, p < 0.00001). The downside of RA was the significantly greater operation duration relative to NS (MD = 10.74, CI = 3.52–17.97, p = 0.004) and CF (MD = 40.27, CI = 20.90, p < 0.0001). Perioperative outcomes including estimated blood loss, radiation exposure, length of hospital stay, cobb angle correction rate, postoperative SRS score, VAS pain score, JOA score, as well as rates of neurological injury and revision surgery, were comparable between the groups (p > 0.05).

Conclusion

RA offers significantly greater pedicle screw placement accuracy relative to NS and CF, however, surgery can take longer. In terms of perioperative outcomes, all three techniques are comparable.
The FDA Reclassification of Cervical Pedicle and Lateral Mass Screws: A Case Study in Regulatory History
Authors: Sussman Jonathan H. , Albayar Ahmed , Saylany Anissa , Budihal Bhargavi R. , Romeo Dominic , Xu Jason , Rosenow Joshua , Heary Robert F. , Welch William C.
Published: April 2024

The classification of medical devices by the Food and Drug Administration (FDA) involves rigorous scrutiny from specialized panels that designate devices as Class I, II, or III depending on their levels of relative risk to patient health. Posterior rigid pedicle screw systems were first classified by the FDA in 1984 and have since revolutionized the treatment of many spine pathologies. Despite this early classification by the FDA, posterior cervical pedicle and lateral mass screws were not reclassified from unclassified to Class III and then to Class II until 2019, nearly 35 years after their initial classification. This reclassification process involved a decades-long interplay between the FDA, formal panels, manufacturers, academic leaders, practicing physicians, and patients. It was delayed by lawsuits and a paucity of data demonstrating the ability to improve outcomes for cervical spinal pathologies. The off-label use of thoracolumbar pedicle screw rigid fixation systems by early adopters assisted manufacturers and professional organizations in providing the necessary data for the reclassification process. This case study highlights the collaboration between physicians and professional organizations in facilitating FDA reclassification and underscores changes to the current classification process that could avoid the prolonged dichotomy between common medical practice and FDA guidelines.
Exploring changes in building strength using seismic wave deconvolution
Authors: Chou Yu-Ting , Wen Strong , Liao Chun-Fu , Chen Ying Nien , Kuo Chun-Hsiang
Published: April 2024

In order to avoid the casualties caused by damaged buildings during strong earthquakes, it’s important and essential to understand the seismic design specifications of buildings. The motion of a building depends on the interferometry of building and ground motion, the coupling between building and ground, and the mechanical properties of the building. We applied deconvolution technique to the motion recorded to separate the building response and calculated the structural parameters (including traveling wave velocity, resonant frequency and attenuation parameter). Therefore, we can check medium’s state by tracking these parameters to avoid damaged building collapse in the next mega-earthquake. We deployed a seismograph array in the building to record the earthquakes and few months of ambient noise data. In addition, we divided the structure into lower and higher floors to calculate the layering velocity. We found that the wave velocities of the lower and higher floors are significantly different but the former exhibit stable variation. For the monitoring phase, we tried different stacking time-lengths and calculated the related traveling wave velocities which are similar to the analysis in earthquakes. From the above results, we concluded that the physical parameters of the building can monitor the healthy condition of structure. Finally, we hope these results would be helpful to build a long-term monitoring of building's healthy status and the assessment of seismic hazard.
Measuring concordance and discordance between selected individual characteristics and corresponding neighborhood-level social determinants of health
Authors: Ngai Ka Ming , Maroko Andrew , Bilal Saadiyah , Wilder Marcee , Gordon Lauren , Richardson Lynne D.
Published: April 2024

Background:

Individuals reside within communities influenced by various social determinants impacting health, which may harmonize or conflict at individual and neighborhood levels. While some experience concordant circumstances, discordance is prevalent, yet poorly understood due to the lack of a universally accepted method for quantifying it. This paper proposes a methodology to address this gap.

Methods:

We propose a systematic approach to operationalize concordance and discordance between individual and neighborhood social determinants, using household income (HHI) (continuous) and race/ethnicity (categorical) as examples for individual social determinants. We demonstrated our method with a small dataset that combines self-reported individual data with geocoded neighborhood level. We anticipate that the risk profiles created by either self-reported individual data or neighborhood-level data alone will differ from patterns demonstrated by typologies based on concordance and discordance.

Results:

In our cohort, it was revealed that 20% of patients experienced discordance between their HHIs and neighborhood characteristics. Additionally, 38% reside in racially/ethnically concordant neighborhoods, 23% in discordant ones, and 39% in neutral ones.

Conclusion:

Our study introduces an innovative approach to defining and quantifying the notions of concordance and discordance in individual attributes concerning neighborhood-level social determinants. It equips researchers with a valuable tool to conduct more comprehensive investigations into the intricate interplay between individuals and their environments. Ultimately, this methodology facilitates a more accurate modeling of the true impacts of social determinants on health, contributing to a deeper understanding of this complex relationship.
Topographic and hydrological controls on partial and full surges of Little Kluane Glacier, Yukon
Authors: Main Brittany , Copland Luke , Flowers Gwenn Elizabeth , Dow Christine Fiona , Van Wychen Wesley , Samsonov Sergey , Kochtitzky William
Published: April 2024

We demonstrate that a ~20 km long valley glacier in the St. Elias Mountains, Yukon, can experience both partial and full surges, likely controlled by the presence of a topographic constriction and the formation and drainage of supraglacial lakes. Based on analysis of air photos, satellite images and field observations since the 1940s, we identify a full surge of ‘Little Kluane Glacier’ from 2013 to 2018, and a partial surge of just the upper north arm between 1963 and 1972. Repeat digital elevation models and velocity profiles indicate that the recent surge initiated from the upper north arm in 2013, which developed into a full surge of the main trunk from 2017 to 2018 with peak velocities of ~3600 m a−1 and frontal advance of ~1.7 km from May to September 2018. In 2016, a mass movement from the north arm to the main trunk generated a surface depression in a region immediately downstream of a topographic constriction, which promoted the formation and rapid drainage of supraglacial lakes to the glacier bed, and likely established the conditions to propel the initial partial surge into a full surge. Our results underscore the complex interplay between glacier geometry, surface hydrology and topography required to drive full surges of this glacier.