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Behavior of turnout sleepers in a large-scale ballast box test

Abstract Turnouts are essential components of the railway infrastructure. Their sleepers change in length leading to asymmetric loading and structural discontinuities. This accelerates ballast wear and sleeper settlement creating challenges for maintaining track geometry and safety. Understanding load transfer from sleepers to ballast is key to improve railway durability and performance. This study examines the settlement behavior of turnout sleepers and the pressure distribution beneath them in a large-scale laboratory ballast box test. Cyclic loading tests were conducted on a long concrete sleeper with and without under sleeper pads (USP) to compare their load transfer characteristics and settlement behavior. For this laser displacement sensors and pressure mapping sensors (Getzner Sensor Sleeper technology) were used. A vertical cyclic load, oscillating between 5 kN and 160 kN at frequencies of 3 Hz to 5 Hz, was applied to one side of the turnout sleeper, while a constant load of 10 kN was applied to the opposite side to represent the structural stiffness of the turnout. For each test configuration more than four million load cycles were carried out. The sleeper showed a degressive settlement pattern with asymmetric distribution. The use of USP resulted in more uniform and gradual settlement, more uniform pressure distribution and reduction of pressure peaks over time. Additionally, the sleeper deformation caused by the load was mitigated and the occurrence of voids underneath the sleeper was significantly reduced. The sleeper with elasto-plastic USP show larger initial oscillation amplitudes due to the pad’s elasticity, which stabilizes over time.

ACHealthChain blockchain framework for access control and privacy preservation in healthcare

Abstract Ensuring privacy and confidentiality in healthcare data management remains a critical challenge. Traditional centralized access control mechanisms are susceptible to security breaches, including unauthorized access, data leakage, and single points of failure, as well as privacy violations such as patient record exposure and improper data sharing. To address these issues, this paper proposes ACHealthChain, a blockchain-based framework leveraging Hyperledger Fabric for decentralized and transparent access control. The framework integrates the InterPlanetary File System (IPFS) for decentralized storage and ensures privacy through Hyperledger Fabric channels. ACHealthChain features PolicyChain for fine-grained access control and revocation, structuring patient health data into separate subchains for EHRs and diagnoses with permissioned access. Additionally, LogChain enhances auditing and accountability. A series of experiments evaluate ACHealthChain’s performance and scalability, considering metrics such as throughput, latency, and resource utilization. Results demonstrate that ACHealthChain improves throughput by 19.7% and reduces latency by 87%, outperforming existing frameworks built on the same platform. The scalability analysis further confirms the framework’s capability to handle increasing workloads within an expanding blockchain network. ACHealthChain presents a promising solution for secure and efficient healthcare data sharing with potential real-world applications.

Development and validation of heat resistant rolling and deformation absorbing nozzle necks for friction and reaction force reduction

Combined impacts of habitat degradation and cyclones on a community of small mammals

Abstract We determined the combined impacts of habitat degradation and recurrent cyclones on a community of small mammals in a rainforest landscape in Madagascar. We used capture-release and morphometry data of 609 individuals of shrew tenrecs, rodents, and nocturnal lemurs, and vegetation surveys from 360 plots in four sites with different degradation levels for four field seasons (2021–2023) separated by two cyclone events. Combined impacts of degradation and cyclones significantly affected small mammals’ diversity and capture abundance and only the body mass of the lesser tufted-tailed rat and brown mouse lemur. Diversity, capture abundance and body mass decreased immediately after the cyclones, and bounced back 4–5 months later, except in the forest fragment. We also examined the independent effects of habitat degradation using vegetation structure as it had more impacts than cyclones on small mammals. Plant diversity, canopy cover percentage, mean diameter at breast height, and estimated height significantly impacted small mammals’ diversity, capture abundance, and body-mass with species-specific variations. Our results suggest that recurrent cyclones may act as an intermediate disturbance factor, while habitat degradation might have permanent impacts on small mammals, emphasizing the importance of long-term monitoring of wild populations to understand their spatiotemporal dynamics and their effective conservation.

Enhanced medical image segmentation using novel level set evolution and efficient optimization

Marsupial embryos lack the epigenetic reset seen in placental mammals

A novel assessment system for osteoporotic vertebral compression fractures

Topological transitions, pinning and ratchets for driven magnetic hopfions in nanostructures

Transient transfection using 222 nm far UV-C irradiation

Outdoor thermal comfort benchmarks and optimization design for children in open parks of hot summer and cold winter region

Genomes from a four-generation family reveal the rate of new mutations

Comparison of trabeculotomy ab externo outcomes between resident physicians and supervising ophthalmologists using propensity score matching

A metaheuristic optimization-based approach for accurate prediction and classification of knee osteoarthritis

Abstract Knee osteoarthritis (KOA) is a severe arthrodial joint condition with significant global socioeconomic consequences. Early recognition and treatment of KOA is critical for avoiding disease progression and developing effective treatment programs. The prevailing method for knee joint analysis involves manual diagnosis, segmentation, and annotation to diagnose osteoarthritis (OA) in clinical practice while being highly laborious and a susceptible variable among users. To address the constraints of this method, several deep learning techniques, particularly the deep convolutional neural networks (CNNs), were applied to increase the efficiency of the proposed workflow. The main objective of this study is to create advanced deep learning (DL) approaches for risk assessment to forecast the evolution of pain for people suffering from KOA or those at risk of developing it. The suggested methodology applies a collective transfer learning approach for extracting accurate deep features using four pre-trained models, VGG19, ResNet50, AlexNet, and GoogleNet, to extract features from KOA images. The numeral of extracted features was reduced for identifying the most appropriate feature attributes for the disease. The binary Greylag Goose (bGGO) optimizer was employed to perform this task, with an average fitness of 0.4137 and a best fitness of 0.3155. The chosen features were categorized utilizing both deep learning and machine learning approaches. Finally, a CNN hyper-parameter algorithm was performed utilizing GGO. The suggested model outperformed previous models with accuracy, sensitivity, and specificity of 0.988692, 0.980156, and 0.990089, respectively. A comprehensive statistical analysis test was performed to confirm the validity of our findings.

Impact of posterior occlusal contact loss on cardiovascular disease using a Japanese claims database

Analysis of experiments with high frequency time series responses and the implications for power and sample size

Prediction and design of thermostable proteins with a desired melting temperature

Exploring the drivers of reef island shoreline change using machine learning models

Abstract Empirical records of reef island shoreline change show magnitude and styles of island change are highly variable over various spatio-temporal scales. However, the attribution of processes as drivers of observed change is poorly resolved. In this study, we develop machine-learning models to explore the drivers of shoreline and positional change of island footprints using multi-decadal records spanning the western-central Pacific. Our models identify a set of ‘important’ predictors, notably a combination of oceanographic, climatic, and local-scale morphological properties of islands and reef platforms. Additionally, we use the models to examine the interactions between these predictors. Results offer the first machine-learning models for reef island physical change, and highlight the complex relationships between a range of controls. While sea-level rise is considered a uniform threat across all islands, our results illustrate that the direct erosional response to high sea-level rise rates was attenuated in settings of ‘positive’ local-scale properties, such as broader reef platforms, and/or high vegetation density; underscoring the necessity for nuanced adaptation strategies that acknowledge local-scale variabilities. Results have implications for understanding attribution, developing vulnerability indices for small islands, and lay the groundwork for projections of island change as effects of climate change intensify over the coming decades.

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Biomechanical performance evaluation of S2AI combine with LC-2 screw for day II pelvic crescent fracture dislocation via finite element analysis

Abstract Plate fixation is a classic method for treating day II crescent fracture dislocation of the pelvic (CFDP). However, due to the advantages of minimally invasive techniques and reduced complications associated with internal fixation percutaneous cannulated screws have emerged as a promising alternative for treating Day II CFDP. In this study, we propose using an S2AI screw combined with an LC-2 screw (S2AI + LC-2) for the treatment of Day II CFDP. The aim of this study was to compare its biomechanical stability with that of two conventional fixation methods using finite element analysis (FEA). A finite element (FE) model of pelvic was developed and validated. Three fixation methods were applied: S1 sacroiliac (SI) screws combined with LC-2 screw (S1 + LC-2), S1 and S2 SI screws combined with LC-2 screw (S1 + S2 + LC-2), and S2AI + LC-2. A 500 N load was applied, and the displacement of the crescent fracture fragments, the stress distribution of the implants, the displacement of the SI joint, and the maximum stress on the bone surrounding the screws were analyzed across the three FE models. After loading 500 N stress, the maximum displacement of the crescent fracture fragment and the maximum stress of bone around the implant in the S2AI + LC-2 group were the smallest in three groups. The displacement of SI joint in S2AI + LC-2 group was less than that in S1 + LC-2 and S1 + S2 + LC-2 (P < 0.001). The maximum stress of implants in each group is smaller than the yield stress of titanium. The maximum stress of the bone around the screws at SI joint in all models lower than the yield strength of cortical bone. The maximum stress of the bone around LC-2 screws in all models lower than the yield strength of cancellous bone. The S2AI + LC-2 group can achieve reliable stability of the SI joint, and the stress on the bone around the screw could be reduced. The S2AI + LC-2 group has good biomechanical stability and can be considered as a new implant to treat Day II CFDP.

Application of effective microorganisms for Littoral zone restoration in eutrophic reservoirs

Abstract Inland waters play an important ecological, social, and economic role. However, they are exposed to various types of pollution, mainly from agriculture, industry, and urbanization. Therefore, it is important to take measures to restore their biological balance by supporting the natural processes of bioremediation. One of the methods for such measures is the use of effective microorganisms (EM). The objectives of this study are therefore: (i) to verify the temporal and spatial variability of the results of microbiological parameters (total heterotrophic bacteria - HBN, microscopic fungi - MF, coliform count - CI, dehydrogenase activity - DHA) in the sandy littoral substrate (beach) collected within the eutrophic Turawa reservoir (Southern Poland, Central Europe); ii) comparison of the results of microbiological parameters at control points and during the application of EM (spraying the shore surface with a liquid bio-preparation); iii) verification of the effectiveness of EM on the microbiological parameters of the substrate collected in the coastal zone of the reservoir and comparison with the results from other studies. The statistical analyses performed (PCA, HCA, correlation matrix) showed a high relationship and correlation (R from 0.88 to 0.92) between the study points and discrepancies between the parameters tested. Statistical significance was demonstrated for CI when the group of control points was compared with EM application - there was an average decrease in CI of 48% after EM application (decrease from 6.31 · 10 −3 g to 3.28 · 10 −3 g). The results obtained were consistent with the literature for HBN and MF (an increase of 14.74 and 10.81 medians in the group with EM application; 0.07045 · 10 6 CFU/g and 0.205 · 10 3 CFU/g, respectively) and differed for DHA (decrease, marginal difference, i.e. 2.41%; 41.5 mg TPF/kg·h). The results described represent one of the case studies related to the bioremediation of water reservoirs and the improvement of sanitary safety in the vicinity of water reservoirs. The research fits into strategies for rational land management governed by numerous national and international legal acts, strategies, and policies.