Digital Health Interventions (DHIs) have emerged as transformative tools for enhancing healthcare delivery in low- and middle-income countries (LMICs), addressing gaps in access, efficiency, and quality of care. However, their integration and sustainability face significant challenges related to policy, planning, and implementation. This systematic review synthesizes existing literature to identify these barriers and propose strategies for effective DHI adoption in LMICs. Following the PRISMA guidelines, the study analyzed peer-reviewed articles, institutional reports, and policy documents from 2015 to 2025. Databases such as PubMed, Scopus, and Web of Science were searched using keywords including ”Digital Health Interventions,” ”LMICs,” ”policy,” ”planning,” and ”implementation challenges.” Out of 5,000 initially identified studies, 12 met the inclusion criteria after rigorous screening. The findings reveal three major challenges: (1) Policy gaps, including fragmented governance, weak regulatory frameworks, and lack of standardized data protocols, leading to disjointed and donor-dependent initiatives; (2) Planning barriers, such as inadequate infrastructure, limited funding, and insufficient stakeholder engagement, restricting scalability; and (3) Implementation obstacles, including technological constraints, low digital literacy, and misalignment with existing healthcare workflows. Despite these challenges, successful case studies highlight the importance of context-sensitive strategies, robust policy frameworks, and sustained investments. The study concludes that addressing these systemic barriers requires inclusive governance, capacity building, and interoperable systems. By fostering multi-stakeholder collaboration and aligning DHIs with national health priorities, LMICs can harness digital health innovations to achieve universal health coverage and improved health outcomes. Future research should focus on scalable, locally adapted solutions to ensure equitable and sustainable DHI integration in resource-limited settings.

Onosma kuhsallanensis (Sect. Onosma, Subsect. Asterotricha (Boiss.) Gürke) is described as new endemic species from Iran. Morphological data and molecular analysis based on the internal transcribed spacer (ITS) region of nrDNA, was carried out. The morphological study reveals that Onosma kuhsallanensis can be similar to O. safai-fari, but its distinguished by the shape of sterile shoot leaves; shorter fruiting calyx; broad calyx lobes; corolla color, shorter corolla and the position of filaments arising from the corolla. Additionally, this study presents a distribution map, images of the holotype specimens, diagnostic characters, detailed photographs, and an identification key.

The energy storage-generation inverter system for electric vehicles, based on virtual synchro-nous machine control, will provide reliable support for the stable operation of new energy microgrids. However, the power coupling between the active power and reactive power outputs of the VSG control can lead to steady-state power deviation, dynamic oscillation, and even system instability. This paper presents an electromagnetic dynamic five-order model for the new type of two-stage cascaded three-phase bridge inverter system, which accurately reflects system stability even under varying control parameters. As the limitation of the traditional 'quasi-steady-state' three-order model that only considers the power loop and fails to adequately capture system stability in low inertia and low damping environments, the proposed model enhances accuracy while broadening its applicability. Based on the fifth-order electromagnetic dynamic model, a self-adaptive dynamic virtual impedance decoupling control strategy is proposed in this paper, which takes into account both the system's dynamic and transient processes. By dynamically adjusting the virtual impedance of the system and directly compensating for coupling between active loop and reactive loop, power decoupling of inverter system can be achieved, thereby resolving issues related to poor system stability and control performance caused by strong coupling. The correctness and rationality of the proposed adaptive dynamic virtual impedance decoupling control strategy are ultimately validated through rigorous simulations and experiments.

Introduction: The underlying mechanisms of transcranial pulsed current stimulation (tPCS), a non-invasive neuromodulation technique, have attracted significant interest in recent years. However, the effects of anodal tPCS (a-tPCS) applied at alpha (8-12 Hz), beta (13-30 Hz), and gamma (30-100 Hz) frequencies remain underexplored. This study aimed to investigate the a-tPCS effects at 10, 25, and 80 Hz on cortical outcomes and adverse side effects. Methods: This double-blinded, randomized, counterbalanced crossover trial involved 15 healthy participants selected based on a power analysis. All participants completed four experimental sessions with 2mA stimulation for 20 minutes at randomized frequencies. Single-pulse and paired-pulse transcranial magnetic stimulation (TMS) over the primary motor cortex (M1) were applied pre- and post-assessments. A minimum 48-hour washout period was implemented to prevent carry-over effects between sessions. Results: The results indicated that a single session of a-tPCS at frequencies of 10, 25, and 80 Hz enhanced corticospinal excitability (CSE) compared to sham stimulation ( p < 0.05). The CSE-increased changes at 10 Hz (64.58%) and 25 Hz (44.82%) showed concurrent modulation of intracortical facilitation (ICF) and mild side effects during stimulation. However, the CSE-increased changes at 80 Hz (27.66%) coincided with a reduction in short intracortical inhibition (SICI), alongside minimal side effects and no phosphene perception. Conclusion: The results indicate that alpha-gamma range frequencies of a-tPCS increased CSE through glutamate-mediated pathways, whereas gamma-frequency stimulation may engage counter-regulatory GABAergic mechanisms. Thus, tPCS, as a neuromodulation technique that modulates neuroplasticity through frequency-dependent mechanisms, may offer a safer and more affordable option for vulnerable populations with fewer adverse effects. However, further research and clinical validation are needed to establish the efficacy of tPCS relative to other non-invasive brain stimulation techniques.

Objectives: To conduct a critical review of the current applications, challenges, and future directions of three-dimensional bioprinting (3DBP) in otolaryngology with a focus on surgical education, personalized implants, and regenerative medicine. Design: Expert opinion based on a targeted literature review and clinical experience. Setting: Translational research relevance of academic otolaryngology. Main Outcome Measures: Assessment of bioprinting approaches and new materials, anatomical accuracy, overcoming limitations by pairing with enhanced technology as virtual and augmented reality, Results: 3DBP is fast becoming an asset to otolaryngology. These Stereolithography (SLA) models facilitate the use of high-fidelity temporal bone models for surgical simulation and training. Functional outcomes of patient-specific implants for ossiculoplasty and cochlear implantation are promising, albeit mostly in preclinical settings. Educators have turned to virtual and augmented reality platforms to improve classroom experiences. But significant hurdles remain, including biocompatibility, the cost of high-resolution technologies, and regulatory impediments to clinical translation. Conclusion: Numerous studies have reported on the transformative potential of 3DBP for surgical planning, education, and implementation of personalized treatment in otolaryngology. A balanced assessment of both its current limitations and future promise is essential for ethical integration. The translation of this technology into routine practice will require multidisciplinary collaboration and rigorous validation through clinical trials.

Brain tumors form when brain cells undergo DNA mutations and grow uncontrollably due to genetic, environmental, or combined factors. This study introduces novel distance-based topological indices derived from the proposed KP-polynomial, targeting drugs used in brain tumor treatment. The main goal of the QSPR (Quantitative Structure-Property Relationship) analysis is to link molecular descriptors with key drug properties like boiling point, flash point, and enthalpy of vaporization. A polynomial regression model assesses the predictive power of these indices, with correlation coefficients indicating the relationship between predicted and experimental values. This theoretical approach aids chemists and healthcare professionals in estimating drug properties without experimental testing and supports drug discovery and advanced decision-making techniques such as CRITIC, TOPSIS, VIKOR, and COPRAS for drug ranking and analysis.

Micro-expressions (MEs) are spontaneous and transient facial movements that reflect real internal emotions and have been widely applied in various fields. Recent deep learning-based methods have been rapidly developing in micro-expression recognition (MER).Still,it is typical to focus on the one-sided nature of MEs, covering only representational features or low-ranking Action Unit (AU) features. The subtle changes in MEs characterize its feature representation weak and inconspicuous,making it tough to analyze MEs only from a single piece or a small amount of information to achieve a considerable recognition effect. In addition, the lower-order information can only distinguish MEs from a single low-dimensional perspective and neglects the potential of corresponding MEs and AU combinations to each other. To address these issues, we first explore how the higher-order relations of different AU combinations correspond with MEs through statistical analysis. Afterward, based on this attribute, we propose an end-to-end multi-stream model that integrates global feature learning and local muscle movement representation guided by AU semantic information. The comparative experiments were performed on benchmark datasets, with better performance than the state-of-art methods. Also, the ablation experiments demonstrate the necessity of our model to introduce the information of AU and its relationship to MER.

Trial design:A randomized clinical trial was designed in which patients received either diclofenac transdermal patches, ketoprofen transdermal patches, or ibuprofen tablets for pain control following archwire placement. The results were reported in accordance with the Consolidated Standards of Reporting Trials.

Motion in-betweening is a flexible and efficient technique for generating 3-dimensional animations. In this paper, we propose a keyframe-driven method that effectively addresses the pose ambiguity issue and achieves robust in-betweening performance. We introduce a keyframe-driven synthesis framework. At each recursion, the key poses at both ends keep predicting the new one at the midpoint. The recursive breakdown reduces motion ambiguities by simplifying the in-betweening sequence as the integration of short clips. The hybrid positional encoding scales the hidden states to adapt to long-and-short-term dependencies. Additionally, we employ a temporal refinement network to capture the local motion relationships, thereby enhancing the consistency of the predicted pose sequence. Through comprehensive evaluations that include both quantitative and qualitative comparisons, the proposed model demonstrates its competitiveness in prediction accuracy and in-betweening flexibility.

Objectives: To provide further evidence on the outcomes associated with fetal malformations of cortical development (MCD), currently informed by data from symptomatic pediatric cohorts, providing a new classification system. Design: Multicenter retrospective cohort study. Setting: Fetal medicine units of three tertiary centers in United Kingdom and Italy. Population: 118 fetuses diagnosed with MCD by ultrasound and/or magnetic resonance imaging included. Methods: The cases were classified according to their presumed etiology (genetic, hemorrhage, dysgenesis, infection) and imaging findings (focal, diffuse, mantle, sulcation). Neurodevelopmental delay was classified as mild, moderate or severe. Cases with missing information on pregnancy or postnatal outcome were excluded. Main Outcome Measures: Postnatal neurodevelopmental outcome ascertained from the infant’s neurological assessments according to International performance scales, depending on the age. Results: There were 52 (44%) livebirths, 64 (54.2%) terminations of pregnancy (TOP) and two (1.6%) intrauterine demises . 24 of 46 cases (54.3 %, 95% CI 39.01 -79.10) that survived the neonatal period had a normal or mildly delayed neurological development. The commonest etiology was genetic and the most frequent radiological finding was reduced sulcation. The best neurological outcome was found in children with focal lesions, and those with diffuse hemispheric lesions had the worst one. Conclusion: This is the largest cohort of fetuses diagnosed with MCDs systematically classified using etiology and radiological findings. Fetal MCDs have a better than previously expected postnatal neurodevelopmental outcome that is related to both the etiological classification and radiological findings: these results should be considered when counselling for a prenatal diagnosis of MCD.

The task of high-density heterogeneous traffic flow scenarios presents several challenges, including insufficient monitoring accuracy, limited adaptability to complex environments, and low computational efficiency. Currently, most existing traffic flow monitoring models have enhanced their performance through optimized architectures and lightweight designs. However, they still struggle with issues, such as vulnerability to interference, instability in complex tracking scenarios, and inadequate cross-domain generalization. This paper proposes a novel approach that enhances object detection and tracking by integrating attention mechanisms and feature reuse strategies. Specifically, a Convolutional Block Attention Module (CBAM) is incorporated into a baseline object detection framework to dynamically weight channel and spatial features, thereby improving detection robustness under challenging conditions such as occlusion and low light. To further refine the tracking process, the backbone of the DeepSORT algorithm is replaced with a Densely Connected Convolutional Network (DenseNet), enabling multi-level feature reuse and richer target appearance representation. As a result, this approach effectively mitigates issues like identity switching and trajectory fragmentation when tracking vehicles and pedestrians. Although the proposed enhancements are demonstrated within a specific object detection and tracking framework, the fundamental contributions—namely, attention-enhanced detection and dense feature aggregation—are widely transferable and can be applied across various detection and tracking architectures. The experimental results indicate that the proposed model achieves an improvement in detection accuracy by 1.7% and a recall enhancement by 1.4%; while also significantly reducing the miss detection rate in scenarios characterized by multi-object overlap and complex urban environments. Our model strikes a balance between accuracy and real-time performance for traffic flow monitoring in intelligent transportation systems.

Water scarcity demands sustainable desalination technologies. Solar-driven methods, particularly Thermohaline Solar Membrane Distillation (TSMD), offer energy-efficient and eco-friendly solutions. TSMD leverages natural thermohaline convection, driven by temperature and salinity gradients, to enhance water production while preventing salt accumulation—a persistent challenge in traditional methods. By combining confined saline layers, hydrophobic membranes, and advanced solar absorbers, TSMD achieves continuous operation even under high salinity (up to 20% brine). Compared to conventional techniques like Multi-Stage Flash (MSF), Multi-Effect Distillation (MED), and Reverse Osmosis (RO), TSMD demonstrates superior energy efficiency, environmental sustainability, and long-term stability. Innovations such as interfacial solar absorbers and modular multistage designs enable water production rates of up to 4.74 kg/m²/hr with competitive solar-to-water efficiencies. Additionally, TSMD minimizes brine discharge and carbon emissions, addressing critical environmental concerns. While scaling TSMD for diverse climates remains a challenge, advancements in material engineering and hybrid systems show promise. Integration with nanophotonics and thermal energy storage could further improve performance in off-grid and variable sunlight conditions. This review highlights TSMD’s potential as a cost-effective, durable, and scalable solution to global freshwater shortages, positioning it as a transformative technology in sustainable desalination.

China is facing growing challenges in cultivated land protection and sustainable food production, as the decline in grain crop cultivation and the expansion of non-grain production (NGP) threaten national food security. This study uses Shandong Province—a major grain-producing region—as a case study to analyze the spatial characteristics and influencing factors of NGP from three dimensions: overall level ( PNGP), planting structure ( PNGPZ1), and agricultural production structure ( PNGPN1). The Multiscale Geographically Weighted Regression (MGWR) model was employed to reveal spatial heterogeneity in factor impacts across different scales. Results show that PNGP, PNGPZ1, and PNGPN1 account for 23.05%, 23.97%, and 20.54% respectively, with higher values in eastern hilly areas and lower values in western plains. Compared to OLS and GWR models, MGWR provides superior explanatory power by capturing multiscale spatial variations. Social factors such as location and production conditions, along with climatic variables among natural conditions, play dominant roles in shaping NGP patterns. The findings offer valuable insights for managing NGP risks, promoting sustainable agricultural development, and ensuring regional food security.

Bangladesh is endowed with rich water resources and favorable climate conditions, and ranks among the global leaders of fish production. However, its aquaculture industry focuses mainly on finfish, overlooking the potential of aquatic plants. In contrast, countries like China, Vietnam, and Malaysia utilize aquatic plants for ornamental use, biogas, and fodder production. Integrating aquatic plants into Bangladesh’s aquaculture could foster sustainability, innovation, and ecological balance. A six-month study was conducted using a 1m² standard quadrat across aquaculture, non-aquaculture, and derelict ponds to assess aquatic plant diversity and abundance. Employing a multiple linear regression (MLR) model, the study examined the relationship between plant abundance and environmental variables, including weather and water quality. Thirteen aquatic plant species from ten families were identified, with Pistia stratiotes (50%) being the most dominant, followed by Lemna minor (20%) and Spirodela polyrrhiza (7%). Free-floating species accounted for 78.2% of all plants observed, with the highest abundance found in derelict ponds. The MLR model had R2 values of 0.670, 0.780, and 0.922 for aquaculture, non-aquaculture, and derelict pond, respectively. The independent variable (number of plants/quadrat) showed a significant relation with dependent variables (air temperature, water temperature, water depth, pH, DO, transparency, and rainfall) for non-aquaculture and the derelict ponds, but not for the aquaculture pond. The findings highlight the ecological importance of aquatic plant diversity and its potential role in Integrated Multi-Trophic Aquaculture (IMTA) to sustain economic growth, presenting a sustainable pathway for enhancing aquaculture productivity and resilience in Bangladesh.

En esta clase, los alumnos identificarán el botón de encendido y apagado del CPU, comprenderán su función básica y practicarán de manera segura cómo encender y apagar una computadora correctamente, promoviendo el cuidado de los equipos.

This document demonstrates, through rigorous numerical analysis, how Emergent Quantum Field Theory (E-QFT) naturally resolves the fundamental problem of infinite divergences that require renormalization in standard QFT. Thanks to the non-factorizable structure of its global Hilbert space H G , E-QFT introduces an intrinsic regularization that emerges from the fundamental principles of the theory, without artificial parameters. We present systematic comparisons between the two approaches for five critical cases of divergences (logarithmic, quadratic, and infrared), revealing a decisive mathematical and conceptual advantage of E-QFT, particularly striking for the hierarchy problem related to the Higgs boson mass correction.

The Gravito-ElectroMagnetic (GEM) experiment aims to detect a possible frequencydependent gravitational deflection of photons, an effect not predicted by General Relativity (GR). In this work, we present enhanced theoretical predictions derived from the Emergent Quantum Field Theory (E-QFT) framework, which posits a non-factorizable global Hilbert space structure as the foundation of all fundamental interactions. E-QFT predicts that the deflection angle ∆θ of photons near a massive object will exhibit a subtle but measurable linear dependence on photon energy, arising from the topological characteristics of the underlying quantum structure. We provide detailed numerical predictions based on realistic GEM experimental parameters, estimate the minimum angular resolution required for detection, and propose experimental enhancement strategies. Detection of the E-QFT signature would constitute a groundbreaking confirmation of a non-factorizable structure in nature, with profound implications for our understanding of quantum gravity, spacetime, and fundamental physics. This document offers a complete theoretical analysis, quantitative predictions, and practical guidelines for future experimental validation.

This articles develops a method for obtaining an approximate solution for a Laplace partial differential equation through an application of the generalized method of lines. More specifically, we address the issue of setting a boundary condition on a non-circular part of the domain boundary, in a polar coordinates context.

The rich and unique whole genome sequencing (WGS) data in the UK’s 100,000 Genomes Project (100kGP) remain under-explored with low diagnostic yield for rare diseases (RDs), including rare musculoskeletal (MSK) disorders. Machine learning (ML) algorithms, a powerful method for discovering insights into novel genes, suffer from the long-standing curse of dimensionality, especially for MSK studies with small patient cohorts and enormous numbers of candidate variants. To this end, we propose an ML framework to hierarchically collapse evidence from multiple variant-level annotations into a gene-level representation crucial for revealing genetic causality insights and dispelling the dimensionality curse. We curated variant data from 449 patients of four MSK subtypes from 100kGP with 980 non-MSK control participants, and we trained an ML model to indicate MSK-risk candidate genes. Those top-ranked ML genes suggest that our ML approach could extract inspiring algorithmic insights into the genetic MSK and RD research.

Green manure-tobacco rotation system has proven to be an effective strategy for improving soil nutrients and alleviating soil-borne fungal diseases. However, the differential efficacy of various green manures against tobacco root rot and their underlying microbial regulatory mechanisms remains unclear. Through pot-based experiments, this study systematically evaluates the disease-suppressive effects of two green manure, smooth vetch ( Vicia villosa) and rape ( Brassica campestris), applied at two incorporation rates on tobacco root rot (caused by Fusarium solani). Results indicated that green manure incorporation significantly reduced pathogen abundance by 36.1-64.7%, decreased root rot disease incidence by 10-20%, with smooth vetch exhibiting superior disease suppression compared to rape. Smooth vetch incorporation enriched a higher abundance of Bacillus spp. (Bacillus niacini and Bacillus megaterium) in rhizosphere soil, increasing by more than 2 times compared to no incorporation . Co-occurrence network analysis identified four microbial modules, among which Module 0 exhibited a significant negative correlation with pathogen related abundance. Within Module 0, bacterial taxa, particularly Bacillus spp., occupied central positions with extensive node interactions, while fungi maintained higher relative abundance. This module also contained other disease-resistant communities, including Paenibacillus, Lysobacter soli, Chaetomium sphaerale and Rhizopus arrhizus. Notably, smooth vetch treatment enhanced soil available nutrients, (especially alkaline nitrogen content) more effectively than rape treatment, favoring the increase of these disease-resistant communities. Collectively, smooth vetch demonstrates superior capacity in enhancing resistance to tobacco root rot and reducing disease incidence, offering an effective solution for tobacco soil-borne disease prevention and control.

This paper presents two fundamental principles that reframe our understanding of the nature of reality: electromagnetic phenomena are two-dimensional and follow the Cauchy distribution; there exists a non-integer variable dimension of spaces. Based on these principles, theoretical justification and methodology for an experiment aimed at testing a fundamental hypothesis about the nature of light are proposed. The study examines whether light propagation in experiments with shadows from thin objects follows the Cauchy distribution (which would be compatible with the exact twodimensionality D=2.0 of massless electromagnetic fields) or the sinc² function (which is traditionally expected in standard models). The work reveals the deep meaning of time through two interconnected questions: "What is the mechanism of synchronization and why is it the way it is?" and "Why don't we observe absolute synchronization and what causes desynchronization?" The proposed concept of variable dimension of spaces explains the nature of mass as a dimensional effect, arising only when deviating from the critical point D=2.0, offers a new interpretation of the relation E = mc 2 , and opens a path to resolving fundamental contradictions in modern physics. The results of the proposed reverse slit experiment could have revolutionary implications for understanding quantum mechanics, relativity theory, and the nature of space-time, potentially eliminating the need for concepts such as dark energy, inflationary cosmology, and the beginning of time. The work addresses the misunderstood lessons from the works of Hendrik and Ludwig Lorentz, showing how their original ideas, misinterpreted by subsequent generations, contained keys to a deeper understanding of the fundamental structure of reality. Keywords Dimension of electromagnetic field • Cauchy distribution • Nature of time • Information asymmetry • Masslessness of photon • Variable dimension of spaces

Digital literacy is an essential skill for navigating today's increasingly digital world. This research explores how Information and Communication Technology (ICT) can be leveraged to promote digital literacy among primary and secondary school students. Using a mixed-methods approach involving surveys and interviews across different educational institutions, the study identifies key factors influencing ICT integration, assesses the current digital skills of students, and examines barriers to effective implementation. The findings highlight the crucial role of teacher training, infrastructural support, and curriculum adaptation in fostering digital competencies. Recommendations are proposed to guide policymakers and educators in enhancing ICT-driven digital literacy programs in schools.

Urban gardens, including Family Allotment Gardens (FAGs) and community gardens (CGs), play an increasingly important role in the ecological and social structure of cities that are to be adapted to climate change. For this purpose, alongside recreational and productive benefits, regulatory ecosystem services are becoming increasingly important. The aim of this study, conducted in Warsaw, was to examine the relationship between five classified types of vegetation use in urban gardens—turf, flower, vegetable, orchard, and abandoned— in the terms of biodiversity and ecosystem services they provide. The study places particular emphasis on floristical biodiversity and regulating services (e.g., microclimate regulation, water retention, air quality improvement, and pollinator support), and introduces a novel consideration of abandoned plots with spontaneous vegetation, which have been marginally addressed in previous studies despite their growing presence in cities. Field data were collected from 44 garden plots. Results showed that while flower and orchard gardens provide the broadest range of regulating ecosystem services, abandoned plots exhibit the highest biodiversity and vegetation density, indicating their potential as future ecological hubs. However, their current contribution to ecosystem functioning remains limited due to their early successional stage. Overall, differences in ecosystem service provision between garden types were smaller than anticipated, suggesting the complexity of multiple factors influencing service quality. There was considerable within-group variation between the identified types, indicating that microscale management and vegetation structure may be more significant than formal garden classification. We argue that the observed, short-term and small-scale forms of greenery in urban gardens are insufficient to produce an impact at a supra-local scale.

This paper assumes an acquaintance with a new concept of time and gravity to provide the unified field theory. A new mathematical attempt intended to formalise the gravity current (𝐽 𝐴) and the speed of gravity signal (𝑉 𝑔) have been shown. The present author starts out from the"Theory of Original Gravity" [1] and presents to the unified field theory in a simple manner.This paper includes the formulations of quantum mechanics, pseudo-Riemannian manifold, fourgradient operator and Lorentz-invariant interval as a notable mathematical tools. 2 All of the ideas and matters presented in this paper are mainly based on quantum theory and manifold geometry.