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Our aim was to describe the numbers and distribution of patients with different types of thalassemia and to assess the standards of care in all thalassemia treatment centers throughout Sri Lanka and the success of the ongoing prevention programme. This cross-sectional island-wide survey was conducted by two trained medical graduates, who visited each thalassemia center to collect data from every patient, using a standardized form. Data was collected through review of patient registers and clinical records. We collected data on 1774 patients from 23 centers. 1219 patients (68.7%) had homozygous β-thalassemia, 360 patients (20.3%) had hemoglobin E β-thalassemia, and 50 patients (2%) had sickle β-thalassemia. There were unacceptably high serum ferritin levels in almost all centers. The annual number of births of patients with β-thalassaemia varied between 45-55, with little evidence of reduction over 19 years. Central coordination of the treatment and ultimately prevention of thalassemia is urgently needed in Sri Lanka. Development of expert centers with designated staff with sufficient resources will improve the quality of care and is preferred to managing patients in multiple small units.

Computed Tomography (CT) is a widely use medical image modality in clinical medicine, because it produces excellent visualizations of fine structural details of the human body. In clinical procedures, it is desirable to acquire CT scans by minimizing the X-ray flux to prevent patients from being exposed to high radiation. However, these Low-Dose CT (LDCT) scanning protocols compromise the signal-to-noise ratio of the CT images because of noise and artifacts over the image space. Thus, various restoration methods have been published over the past 3 decades to produce high-quality CT images from these LDCT images. More recently, as opposed to conventional LDCT restoration methods, Deep Learning (DL)-based LDCT restoration approaches have been rather common due to their characteristics of being data-driven, high-performance, and fast execution. Thus, this study aims to elaborate on the role of DL techniques in LDCT restoration and critically review the applications of DL-based approaches for LDCT restoration. To achieve this aim, different aspects of DL-based LDCT restoration applications were analyzed. These include DL architectures, performance gains, functional requirements, and the diversity of objective functions. The outcome of the study highlights the existing limitations and future directions for DL-based LDCT restoration. To the best of our knowledge, there have been no previous reviews, which specifically address this topic.

Alzheimer’s disease (AD), an irreversible neurodegenerative disorder that inflicts the majority cases of dementia, wherein patients suffer progressive memory loss and cognitive function decline. Despite having no drugs for curing, early detection of AD allows the provision of preventive treatment to control the disease progression. The objective of this study is to develop a computer-aided system based on Deep Learning model to identify AD from cognitively normal and its early stage, mild cognitive impairment (MCI), using only structural MRI (sMRI). To achieve this objective, we have proposed a multi-class classification using 3D T1-weight brain sMRI images from the ADNI database. Axial brain images were extracted from the 3D MRI and being fed as input to the Convolutional Neural Network (CNN) to perform multiclass classification. Three different models were being experimented namely a CNN from scratch, VGG-16, and ResNet-50. The convolutional base of VGG-16 and ResNet-50 trained on ImageNet dataset were used as a feature extractor. Additionally, a new densely connected classifier was added on top of the convolutional base to perform classification.

Noise is a definite degeneration of medical images that interferes with the diagnostic process in clinical medicine. Although many denoising algorithms have been developed to improve the visual quality of medical images, failure to noise adaptation has been identified as a critical limitation of many existing denoising algorithms. Therefore, the objective of this study is to conduct an in-depth review to investigate and classify the various self-adaptive approaches and techniques implemented in recent medical image denoising applications. The articles published from the year 2015 have been retrieved from the web of science core collection database focusing on four medical imaging modalities, such as radiography, magnetic resonance imaging, computed tomography, and ultrasound. The analysis of the applications has emphasized the unique algorithmic components used to achieve the self-adaptability in detailed. Moreover, the strengths and weaknesses of those applications have been reviewed according to the various adaptive denoising approaches. Finally, this review highlights the limitations of existing adaptive denoising algorithms and open research directions for further studies of the domain.

The martensitic transformation of austenite as well as the reversion of martensite to austenite has been reported to significantly improve mechanical properties of steels. In the present work, three dimensional (3D) elastoplastic phase-field simulations are performed to study the kinetics of martensite reversion in stainless steels at different annealing temperatures. The input simulation data are acquired from different sources, such as CALPHAD, ab initio calculations, and experiments. The results show that the reversion occurs both at the lath boundaries as well as within the martensitic laths, which is in good agreement with the experimental observations. The reversion that occurs within the laths leads to splitting of a single martensite lath into two laths, separated by austenite. The results indicate that the reversed austenite retains a large extent of plasticity inherited from martensite.

This paper investigates the validity of the hypothesis that suggests there is a link between fiscal deficits and inflation in developing countries and further explores this link in the absence of public sector wage expenditure. Sri Lanka, a developing country with a persistent fiscal deficit, a large public sector and increasing inflation, has been chosen for the empirical study. An auto-regressive distributed lag (ARDL) model is employed in the analysis, using annual data from 1959 to 2008. The results suggest that, in the long run, a one percentage point increase in the ratio of the fiscal deficit to narrow money is associated with about an 11 percentage point increase in inflation. This link becomes weaker in the absence of the public sector wage expenditure. The overall inference is that inflation is not only a monetary phenomenon in Sri Lanka and public sector wage expenditure is a key factor in explaining the deficit-inflation relationship.

The nosocomial pathogen Acinetobacter baumannii is a frequent cause of hospital-acquired infections worldwide and is a challenge for treatment due to its evolved resistance to antibiotics, including carbapenems. Here, to gain insight on A. baumannii antibiotic resistance mechanisms, we analyse the protein interaction network of a multidrug-resistant A. baumannii clinical strain (AB5075). Using in vivo chemical cross-linking and mass spectrometry, we identify 2,068 non-redundant cross-linked peptide pairs containing 245 intra- and 398 inter-molecular interactions. Outer membrane proteins OmpA and YiaD, and carbapenemase Oxa-23 are hubs of the identified interaction network. Eighteen novel interactors of Oxa-23 are identified. Interactions of Oxa-23 with outer membrane porins OmpA and CarO are verified with co-immunoprecipitation analysis. Furthermore, transposon mutagenesis of oxa-23 or interactors of Oxa-23 demonstrates changes in meropenem or imipenem sensitivity in strain AB5075. These results provide a view of porin-localized antibiotic inactivation and increase understanding of bacterial antibiotic resistance mechanisms. The bacterial pathogen Acinetobacter baumannii has evolved resistance to many antibiotics, including carbapenems. Here, Wu et al. show that the carbapenemase Oxa-23 interacts with the outer membrane porin CarO in an A. baumannii isolate, indicative of porin-localised antibiotic inactivation.

Underwater Sensor Networks (USNs) have received a lot of focus in terms of their use in environmental monitoring, ocean research and military surveillance. Current approaches to safe transmission of data in USNs are mainly based on the standard cryptographic methods, blockchain-based solutions, or energy-saving routing algorithms. Although such solutions enhance security and reliability, they tend to be poor in dealing with important underwater communication limitations including high propagation delay, low bandwidth, dynamic channel conditions and susceptibility to Byzantine attacks. Moreover, the prevailing approaches are generally isolated security mechanisms, which do not combine advanced cryptographic techniques with asynchronous consensus protocols. To overcome these constraints, this paper presents a blockchain-based framework using Asynchronous Byzantine Fault Tolerance (ABFT) along with cutting-edge cryptography, such as, Distributed Key Generation (DKG), Verifiable Secret Sharing (VSS), Multi-Party Computation (MPC), and Threshold Cryptography. The suggested solution is safe, trustworthy, and effective when data is transmitted in the underwater environment in real conditions. In order to justify the security of the suggested framework, formal and informal security analyses are taken into consideration. The framework uses established cryptographic primitives and ABFT consensus to guarantee resistance to Byzantine attacks, data corruption, and unauthorized access. Informal security analysis shows that the system meets the important security properties, such as confidentiality, integrity, authentication, and fault tolerance in adversarial conditions. The metrics that are used to carry out the performance evaluation include throughput, latency, packet loss, CPU utilization, and network lifetime. Experimental findings indicate that the suggested framework can be used to boost the security robustness and system efficiency of the current approaches.

With a focus on the Bay of Bengal, models ranging from a 1/12.5° global ocean model to a ¼° regional Indian Ocean model to a 2 km local high-resolution coupled model are used to simulate salinity exchanges in the Indian Ocean. Global Hybrid Coordinate Ocean Model simulations show a surprisingly large persistent flow of high-salinity water from the equatorial Indian Ocean into the Bay of Bengal during the northeast monsoon, although it is weaker than during the southwest monsoon. On average, salt is transported into the Bay of Bengal between 83°E and 95°E, and low-salinity water flows southward near the east coast of Sri Lanka and east of 95°E. The Regional Ocean Modeling System shows that knowledge of river input of freshwater is essential for modeling surface salinities correctly in the Bay of Bengal. High-resolution coupled model simulations are in agreement with recent observations and show that a strong subsurface current with a speed of about 1 m s⁻¹ intrudes into the Bay of Bengal beneath southward-flowing low-salinity water during the northeast monsoon. The subsurface high-salinity water, which originates in the northern Arabian Sea, spreads northward into the Bay of Bengal and downward along constant density surfaces. North of 10°N, the model simulation implies that mixing takes place on density surfaces at depths of 100–150 m after advection of cold, low-salinity water from the north, and subsequent stirring of the two water masses. Vertical diffusion plays an insignificant role in this mixing.

Water, a fundamental and indispensable resource necessary for the survival of living beings, has become a pressing issue in numerous regions worldwide due to scarcity. Urban areas, where the majority of the global population resides, witness a substantial consumption of blue water, particularly in commercial buildings. This study investigates the potential for enhancing water efficiency within an ongoing high-rise office building construction situated in a tropical climate. The investigation utilizes the green building guidelines of leadership in energy and environmental design (LEED) through a case-study-based research approach. Strategies included using efficient plumbing fixtures (such as high air–water ratio fixtures and dual-flush toilets), the selection of native plants, implementing a suitable irrigation system, introducing a rainwater harvesting system (RWHS) and improving the mechanical ventilation and air conditioning (MVAC) system. The results showed a 55% reduction in water use from efficient fixtures, a 93% reduction in landscaping water needs and a 73% overall water efficiency with a RWHS from the baseline design. Additionally, efficient cooling towers and the redirection of condensed water into the cooling tower make-up water tank improved the overall water efficiency to 38%, accounting for the water requirements of the MVAC system. The findings of this study can contribute to more sustainable and water-efficient urban development, particularly in regions facing water scarcity challenges. The significance of these findings lies in their potential to establish industry standards and inform policymakers in the building sector. They offer valuable insights for implementing effective strategies aimed at reducing blue water consumption across different building types.