Digitalization is widely recognized as a transformative power for sustainable development. Careful alignment of progress made by digitalization with the globally acknowledged Sustainable Development Goals (SDGs) is crucial for inclusive and holistic sustainable development in the digital era. However, limited reference has been made in SDGs about harnessing the opportunities offered by digitalization capabilities. Moreover, research on inhibiting or enabling effects of digitalization considering its multi-faceted interlinkages with the SDGs and their targets is fragmented. There are only limited instances in the literature examining and categorizing the impact of digitalization on sustainable development. To overcome this gap, this paper introduces a new Digitainability Assessment Framework (DAF) for context-aware practical assessment of the impact of the digitalization intervention on the SDGs. The DAF facilitates in-depth assessment of the many diverse technical, social, ethical, and environmental aspects of a digital intervention by systematically examining its impact on the SDG indicators. Our approach draws on and adapts concepts of the Theory of Change (ToC). The DAF should support developers, users as well policymakers by providing a 360-degree perspective on the impact of digital services or products, as well as providing hints for its possible improvement. We demonstrate the application of the DAF with the three test case studies illustrating how it supports in providing a holistic view of the relation between digitalization and SDGs.

The prevailing challenge in our current era is climate change, a crisis recognized by the global community that has prompted immediate actions to confront and mitigate its consequences. There is a consensus among scientists that human activities, driven by developmental ambitions, are the driving force behind the ongoing climate crisis. “The well-documented aftermath, including the rise in sea levels, coastal erosion, reduced agricultural yields, compromised access to clean water, and health implications, emphasizes the intricate and wide-ranging effects of climate change.” Furthermore, there is an increasing recognition that climate change gives rise to distinct social, economic, and divergent consequences. “Although all countries worldwide will undergo the impacts of climate change, the distribution of these effects will differ among regions, generations, age groups, income levels, occupations, and genders.” Notably, the adverse consequences will disproportionately impact the impoverished, predominantly situated in developing nations. “This paper explores the effects of climate change on displacement and the ensuing refugee dilemma.” It aims to scrutinize the inclusion of climate-induced displacement in the present international framework, encompassing both climate change and refugee regimes. The paper promotes a human rights-centred approach to tackling displacement stemming from climate change.

The computing power of smartphones has not received considerable attention in the mainstream education system. Most of the education-oriented smartphone applications (apps) are limited to general purpose services like Massive Open Online Courses (MOOCs), language learning, and calculators (performing basic mathematical calculations). Greater potential of smartphones lies in educators and researchers developing their customized apps for learners in highly specific domains. In line with this, we present Fluid Dynamics, a highly accurate Android application for measuring flow properties in compressible flows. This app can determine properties across the stationary normal and oblique shock, moving normal shock and across Prandtl $-$ Meyer expansion fan. This app can also measure isentropic flows, Fanno flows, and Rayleigh flows. The functionality of this app is also extended to calculate properties in the atmosphere by assuming the International Standard Atmosphere (ISA) relations and also flows across the Pitot tube. Such measurements are complicated and time-consuming since the relations are implicit and hence require the use of numerical methods, which give rise to repetitive calculations. The app is an efficient semi-implicit solver for gas dynamics formulations and uses underlying numerical methods for the computations in a graphical user interface (GUI), thereby easing and quickening the learning of concerned users. The app is designed for the Android operating system, the most ubiquitous and capable surveillance platform, and its calculations are based on JAVA based code methodology. In order to check its accuracy, the app's results are validated against the existing data given in the literature.

Duchenne muscular dystrophy (DMD) is caused by genetic mutations in the cytoskeletal-sarcolemmal anchor protein dystrophin. Repeated cycles of sarcolemmal tearing and repair lead to a variety of secondary cellular and physiological stressors that are thought to contribute to weakness, atrophy, and fibrosis. Collectively, these stressors can contribute to a pro-inflammatory milieu in locomotor, cardiac, and respiratory muscles. Given the many unwanted side effects that accompany current anti-inflammatory steroid-based approaches for treating DMD (e.g., glucocorticoids), there is a need to develop new therapies that address inflammation and other cellular dysfunctions. Adiponectin receptor (AdipoR) agonists, which stimulate AdipoR1 and R2 isoforms on various cell types, have emerged as therapeutic candidates for DMD due to their anti-inflammatory, anti-fibrotic, and pro-myogenic properties in pre-clinical human and rodent DMD models. Although these molecules represent a new direction for therapeutic intervention, the mechanisms through which they elicit their beneficial effects are not yet fully understood, and DMD-specific data is limited. The overarching goal of this review is to investigate how adiponectin signaling may ameliorate pathology associated with dystrophin deficiency through inflammatory-dependent and -independent mechanisms, and to determine if current data supports their future progression to clinical trials.

In this paper, we introduce a new iteration scheme, named as the S**-iteration scheme, for approximation of fixed point of the nonexpansive mappings. This scheme is faster than Picard, Mann, Ishikawa, Noor, Agarwal, Abbas, Thakur, and Ullah iteration schemes. We show the stability of our instigated scheme and give a numerical example to vindicate our claim. We also put forward some weak and strong convergence theorems for Suzuki's generalized nonexpansive mappings in the setting of uniformly convex Banach spaces. Our results comprehend, improve, and consolidate many results in the existing literature.

Duchenne muscular dystrophy (DMD) is a progressive neuromuscular disease caused by mutations to the dystrophin gene - resulting in deficiency of dystrophin protein, loss of myofiber integrity in skeletal and cardiac muscle, and eventual cell death and replacement with fibrotic tissue. Pathologic cardiac manifestations occur in nearly every DMD patient, with development of cardiomyopathy - the leading cause of death - inevitable by adulthood. As early cardiac abnormalities are difficult to detect, timely diagnosis and appropriate treatment modalities remain a challenge. There is no cure for DMD – treatment is aimed at delaying disorder progression and alleviating symptoms. A comprehensive understanding of the pathophysiological mechanisms is crucial to development of targeted treatments. While established hypotheses of underlying mechanisms include sarcolemmal weakening, upregulation of pro-inflammatory cytokines, and perturbed ion homeostasis, mitochondrial stress has recently come into focus as a potential key contributor. Several experimental compounds targeting the skeletal muscle pathology of DMD are in development, but effects of such agents on cardiac function remain unclear. Synergistic integration of small molecule- and gene-target-based drugs with metabolic, immune, or ion balance-enhancing compounds into a combinatorial therapy offers potential for treating dystrophin deficiency-induced cardiomyopathy, making it crucial to understand the underlying mechanisms driving the disorder.

(1) Background: Type-2 Diabetes (T2DM) is a long-term medical disorder characterized by Insulin deficiency and high blood glucose levels. Among other medication to cure T2DM, the review of the literature found that various Pyrimidine derivative act as an agonist for G-protein-coupled receptor 119 (GPR119) was proposed to control blood glucose levels by enhancing the function of pancreatic beta-cells and its mechanism of action with fewer adverse effects.; (2) Methods: In the present research work, in-silico investigations were carried out to investigate the potential of Pyrimidine analogue as an agonists to the protein target GPR119 receptor. We performed exhaustive molecular modelling and protein modelling methodologies such as homology mod-elling, molecular docking along with various drug designing tools such as 3D-QSAR and pharmacophore mapping to ascertain the design of better GPR119 agonists.; (3) Results: On the basis of in-depth computa-tional studies, we designed new pyrimidine moiety and analyzed them for GPR-119 receptor agonist and fur-ther explored the ADME properties. Designed compounds found to exhibit better predicted activities as com-pared to reference compound; (4) Conclusions: The current research on pyrimidine derivatives, using molec-ular docking, 3D-QSAR and Pharmacophore mapping demonstrated that the obtained computational model has significant properties and the designed molecules and Dataset from this model, produced antidiabetic compound against the target GPR119 i.e., compound 1S, 1Z and 1D with the docking score of −11.696, -9.314 and -8.721 respectively. The pharmacokinetics and drug-likeness studies revealed that these compounds may be the future candidate for the treatment of diabetes acting via GPR119 agonist mechanism.

This paper investigates the efficacy of YOLOv8 variants for vehicle detection and license plate detection within smart parking applications, emphasizing performance under varying ambient lighting conditions. The proposed system is to seize full video frames, extracts regions of interest containing vehicles, and feeds them into separate, pre-trained YOLOv8 models – one dedicated to vehicle detection and another for license plate detection. Four YOLOv8 variants, nano, small, medium, and large, are evaluated. As a pre-processing step, the images are processed with the help of OpenCV and Pillow libraries to adjust the luminosity and increase the images’ DPI so that they would be easy to perceive by the Tesseract OCR engine. Sixteen potential combinations arise from pairing the four YOLOv8 models for vehicle and license plate detection tasks. To identify the most suitable combinations, we employ the Multi-Criteria Decision-Making method, specifically Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) analysis. This analysis considers 4 critical metrics: Precision, mean average precision at 95% Intersection of Union threshold, Recall and Total Inference Time. The objective is to achieve an optimal balance between high accuracy and real-time processing. Following the selection of optimal YOLOv8 combinations through TOPSIS analysis, we assess their performance under varying ambient light intensity (measured in lux). This evaluation aims to identify the most robust model combinations that ensure accurate vehicle and license plate recognition across the diverse lighting conditions encountered in real-world environment.

Digitalization is globally transforming the world with profound implications. It has enormous potential to foster progress toward sustainability. However, in its current form, digitalization also continues to enable and encourage practices with numerous unsustainable impacts affecting our environment, ingraining inequality, and degrading quality of life. There is an urgent need to identify such multifaceted impacts holistically. Impact assessment of digital interventions (DIs) leading to digitalization is important specifically for Sustainable Development Goals(SDGs). Action is required to understand the pursuit of short-term gains toward achieving long-term value-driven sustainable development. We need to understand the impact of DIs on various actors and in diverse contexts. A holistic understanding of the impact it creates will help us align it with visions of sustainable development and identify potential measures to mitigate negative short and long-term impacts. The recently developed Digitainability Assessment Framework (DAF) unveils the impact of DIs with an in-depth context-aware assessment and offers an evidence-based impact profile of SDGs at the indicator level. We performed the impact assessment of diverse technologies using DAF. This paper summarizes the insights from the Digitainable Spring School 2022 on "Sustainability with Digitalization and Artificial Intelligence," one of whose goals was to operationalize the DAF as a tool in the action learning process with diverse professionals in the field of digitalization and sustainability. The DAF guides a holistic context-aware process formulation for a given DI. An evidence-based evaluation within the DAF protocol benchmarks a specific DI’s impact against the SDG indicators framework. The operationalization of the DAF was carried out by looking at four different DIs: smart home technologies (SHT) for energy efficiency, blockchain for food security, artificial intelligence for land use cover and changes (LUCC), and big data for international law. Each of the four studies addresses different DIs for digitainability assessment using different techniques for a diverse group of indicators, demonstrating the potential of the DAF but also outlining the existing data gaps that limit a comprehensive analysis.

Chronic kidney disease (CKD) affects >10% of the adult population. Each year approximately 120,000 Americans develop end-stage kideny disease and initiate dialysis, which is costly and associated with functional impairments, worse health-related quality of life, and high early-mortality rates exceeding 20% in the first year. Recent declarations by the World Kidney Day and the U.S. Government Executive Order seek to implement strategies that reduce the burden of kidney failure by slowing CKD progression and controlling uremia without dialysis. Pragmatic dietary interventions may have a role in improving CKD outcomes and preventing or delaying dialysis initiation. Evidence suggests that a patient-centered plant-dominant low-protein diet (PLADO) of 0.6-0.8 g/kg/day comprised of >50% plant-based sources, administered by dietitians trained in non-dialysis CKD care, can be promising. The scientific premise of the PLADO is based on the observations that high protein diets with high meat intake are not only associated with higher cardiovascular disease risk but also higher CKD incidence and faster CKD progression due to increased intraglomerular pressure and glomerular hyperfiltration. Meat intake increases production of nitrogenous end-products, worsens uremia, and may increase the risk of hyperkalemia, given constipation from the typical low fiber intake. Plant-dominant, fiber-rich, low-protein diet may lead to favorable alterations in the gut microbiome, which can modulate uremic toxin generation and slow CKD progression, along with reducing cardiovascular risk in CKD patients. PLADO is a heart-healthy, safe, flexible, and feasible diet that could be the centerpiece of a conservative and preservative CKD-management strategy that challenges the prevailing dialysis-centered paradigm.