The invasive vine Hiptage benghalensis (Malay trumpet vine) poses significant ecological threats to tropical and subtropical ecosystems due to its aggressive growth, rapid spread, and ability to outcompete native vegetation. This study examines the ecological impacts of H. benghalensis on biodiversity and ecosystem function in Southeast Asia and explores effective management strategies for its control. Through a combination of field surveys, ecological assessments, and experimental control methods, we evaluate the extent of H. benghalensis invasion in multiple habitats, including forest edges, riparian zones, and disturbed landscapes. Our findings reveal that H. benghalensis reduces native plant diversity, alters soil properties, and disrupts local wildlife habitats. We compare various control methods, including manual removal, chemical herbicides, and biological control agents, to assess their effectiveness and sustainability in long-term management. The study underscores the importance of an integrated approach, combining early detection, rapid response, and ongoing monitoring, to mitigate the spread of H. benghalensis. Additionally, we discuss the socioeconomic implications of invasive species management and the need for public awareness and policy intervention to support conservation efforts. This research contributes valuable insights for the development of targeted management strategies to preserve native biodiversity and restore ecosystem health in regions affected by H. benghalensis. Methodology Research Design This study utilized a mixed-methods research design, combining quantitative ecological surveys with qualitative stakeholder interviews and participatory assessments. The quantitative component focused on field data collection to assess the ecological impact of Hiptage benghalensis and evaluate the effectiveness of various control methods. The qualitative component involved interviews with local farmers, conservationists, and policy makers to understand the socioeconomic implications of H. benghalensis invasion and management practices. This approach allowed for a comprehensive understanding of the biological, environmental, and social dimensions of invasive species management. Participants or Subjects The study was conducted in multiple regions of Southeast Asia, particularly in areas severely affected by Hiptage benghalensis.

Sustainable agricultural development in Vietnam faces critical challenges related to pest management, biodiversity conservation, and ecological resilience. This study presents a multifaceted approach that integrates molecular taxonomy, biological control, and agroecological sustainability, offering a comprehensive perspective grounded in case studies and fieldwork across diverse Vietnamese agroecosystems. By applying DNA barcoding and phylogenetic analysis, we accurately identified key insect pests and their natural enemies, revealing hidden diversity and correcting misidentifications that have previously hindered effective biological control. The integration of molecular data with ecological monitoring enabled the targeted deployment of indigenous parasitoids and predators, leading to measurable reductions in chemical pesticide use and crop damage. Our findings underscore the value of molecular tools in improving the precision of biological control strategies and promoting more sustainable pest management. This interdisciplinary framework highlights the importance of biodiversityinformed agriculture and provides a replicable model for other regions facing similar ecological and agricultural pressures. The study advocates for continued investment in molecular diagnostics and conservation-based pest management as pillars of sustainable agriculture in Southeast Asia and beyond.

BWTaligner is a high-performance sequence alignment tool designed to efficiently map short and long DNA/RNA reads to reference genomes using the Burrows-Wheeler Transform (BWT) and FM-index data structures. By optimizing memory usage and incorporating advanced heuristics for handling mismatches and indels, BWTaligner achieves fast and accurate alignment suitable for large-scale genomic datasets. The tool supports multi-threaded execution, customizable alignment parameters, and output formats compatible with standard downstream analysis pipelines. Benchmarking against existing aligners demonstrates that BWTaligner provides competitive accuracy with significantly reduced computational overhead, making it a practical choice for both research and clinical genomics applications. BWTaligner is open-source and freely available at [tool URL].

The rapid advances in genomics have revolutionized the field of biocontrol, offering new and innovative strategies for managing pests and invasive species in agriculture. In Southeast Asia, where agriculture is heavily dependent on diverse ecosystems, the integration of genomics with traditional biocontrol methods presents a transformative approach to pest management. This article explores the role of genomic technologies in enhancing biocontrol practices, focusing on how molecular tools can be utilized to better understand pest behavior, resistance mechanisms, and ecological interactions. Through the use of gene editing, molecular diagnostics, and nextgeneration sequencing, scientists are now able to identify and manipulate pest genomes, offering more targeted and efficient control methods. Additionally, genomics enables the identification of natural predators and parasitoids, enhancing their use in integrated pest management (IPM) strategies. This review discusses the challenges and opportunities of implementing genomicdriven biocontrol in Southeast Asian agriculture, considering the region's unique ecological and socioeconomic context. Key issues include the need for regulatory frameworks, ethical considerations, and local farmer engagement to ensure the successful deployment of genomicbased biocontrol strategies. The paper concludes by highlighting the future potential of genomics in sustainable pest and invasive species management, offering a comprehensive overview of integrative approaches that can contribute to resilient and sustainable agricultural systems in the region.

I present an exposition of Sinai-Ruelle-Bowen (SRB) measures and the Ledrappier-Young dimension formula, which relates the Hausdorff dimension of an invariant measure to its Lyapunov exponents and entropies. Beginning with measure disintegration along stable foliations (Rokhlin's theorem) and the formalism of conditional entropy, I examine how entropy contributions in different invariant directions influence the dimension of the measure. I then state and discuss the Ledrappier-Young formula in full detail, highlighting how entropy and expansion rates interact through a block decomposition of unstable foliations. I conclude with a synthesis of these ideas, emphasizing how the formula unifies dynamical invariants with fractal geometry of measures.

Salmonella enterica serovar Typhimurium ( S. Tm) is a major cause of foodborne diarrhea. However, in healthy individuals, the microbiota typically restricts the growth of incoming pathogens, a protective mechanism termed colonization resistance (CR). To circumvent CR, Salmonella strains can utilize private nutrients that remain untapped by the resident microbiota. However, the metabolic pathways and environmental niches promoting pathogen growth are still not completely understood. Here, we investigate the significance of the gfr operon in gut colonization of S. Tm, which is essential for the utilization of fructoselysine (FL) and glucoselysine (GL). These Amadori compounds are present in heated foods with high protein and carbohydrate contents, particularly in Western-type diets. We detected FL in both mouse chow and the intestinal tract of mice and showed that gfr mutants are attenuated during the initial phase of colonization in the murine model. Experiments in gnotobiotic mice and competition experiments with Escherichia coli suggest that gfr-dependent fitness advantage is context-dependent. We conclude that dietary Amadori products like FL can support S. Tm gut colonization, depending on the metabolic capacities of the microbiota.

Achieving sustainable agriculture in the face of environmental degradation and climate change demands innovative and integrative approaches. This article explores the convergence of biodiversity conservation, biological control strategies, and modern genomic tools to enhance agroecosystem resilience in Vietnam and comparable regions. We examine the role of native biodiversity in supporting natural pest regulation, the application of classical and augmentative biological control methods, and how genomic technologies-such as DNA barcoding, environmental DNA (eDNA), and gene editing-can improve pest and natural enemy identification, monitoring, and deployment. Through case studies from Vietnam, we demonstrate how locally adapted, biodiversity-based solutions combined with genomics can reduce pesticide reliance, protect ecosystem services, and contribute to climate-smart agricultural practices. The article highlights the importance of interdisciplinary collaboration and capacity building to scale these innovations across Southeast Asia and globally.

In this paper, we discuss the convergence of solutions to a class of parabolic chemotaxis model combined with the Navier-Stokes equations { ∂ t n ε + u ε · ∇ n ε = ∆ n ε − ∇ · ( n ε S ( x , n ε , c ε ) · ∇ c ε ) − b n ε l ε ∂ t c ε + u ε · ∇ c ε = ∆ c ε − c ε + n ε , ∂ t u ε + κ ( u ε · ∇ ) u ε = ∆ u ε + ∇ P ε + n ε ∇ ϕ , ∇ · u ε = 0 to solutions of the parabolic-elliptic counterpart formally derived in the limit as ε↘0. Let N≥2, Ω ⊂ R N is a smoothly region in physical space. For l ∈ ( 0 , N N − 1 ) , under suitable assumptions, we establish a general result that guarantees certain strong and pointwise convergence properties, provided that the assumed bounds on ∇ c ε and u ε hold in L λ ( ( 0 , T ) ; L q ( Ω ) ) and L ∞ ( ( 0 , T ) ; L r ( Ω ) ) , separately, for some λ∈(2 ,∞], q>N and r>N satisfying 1 λ + N 2 q < 1 2 .

In Southeast Asia, agriculture plays a critical role in the economy, yet it faces significant challenges from pests, invasive species, and environmental degradation. Effective pest management and agroecological innovation are essential for improving crop yields and ensuring sustainable agricultural practices. This article explores multi-scale approaches that integrate genetic, ecological, and socioeconomic perspectives to address pest management and agroecological innovation in the region. By combining advances in pest taxonomy, ecological monitoring, and cutting-edge technologies, the study highlights innovative strategies for pest control and management, ranging from molecular techniques to field-based interventions. We emphasize the importance of understanding pest dynamics at various scales, from the genetic level of pest resistance to field-level practices that promote biodiversity and soil health. Furthermore, the article discusses the role of local knowledge in shaping effective, contextspecific solutions and the need for cross-disciplinary collaboration among scientists, farmers, and policy-makers. The study concludes by outlining key opportunities for integrating pest management with broader agroecological strategies, including the promotion of agro-biodiversity, ecosystem services, and sustainable practices that ensure food security and environmental resilience in Southeast Asia.

The intensification of agriculture has led to increased reliance on chemical pesticides, raising concerns about environmental degradation, pest resistance, and human health. In response, this study explores the efficacy and integration of two biological and agroecological pest control methods-Metarhizium anisopliae, an entomopathogenic fungus, and the augmentorium, a physical containment structure designed to recycle natural enemies while preventing pest escape. Through a mixed-methods approach involving field trials, laboratory assays, and farmer interviews across three agroecological zones in Southeast Asia, we assess the viability of these interventions as components of sustainable pest management strategies. Results indicate that M. anisopliae achieved a 60-75% reduction in target pest populations (notably Spodoptera frugiperda and Bactrocera dorsalis), while augmentoria retained over 80% of parasitoids and predators introduced to control fruit fly populations. Farmers reported increased yields and reduced pesticide use, though challenges related to initial setup costs and limited technical knowledge were noted. The findings support a synergistic approach that combines biological agents with agroecological infrastructure, offering a resilient alternative to chemical-intensive pest control. Policy implications highlight the need for training, extension services, and support for locally adapted biocontrol innovation. Methodology Research Design This study employed a mixed-methods research design, combining quantitative field trials and laboratory experiments with qualitative interviews and participatory observation. This approach enabled both the empirical measurement of pest suppression outcomes and an understanding of

Agroecological innovation has emerged as a transformative approach to achieving sustainability in agricultural systems, particularly in the face of environmental degradation, climate change, and food security challenges. This article explores the role of agroecological practices in fostering sustainable agriculture by integrating ecological principles with innovative farming techniques. By emphasizing biodiversity, soil health, water conservation, and local knowledge, agroecology offers a holistic framework for addressing the complex interconnections between agriculture, ecology, and society. The paper reviews key agroecological innovations, including agroforestry, crop diversification, integrated pest management, and the use of organic fertilizers, highlighting their potential to enhance resilience and reduce the environmental footprint of food production. Through case studies and empirical evidence, this article also examines the socioeconomic impacts of agroecological practices on rural communities, including improvements in livelihoods, food security, and community empowerment. Ultimately, it argues that agroecological innovation is critical for advancing long-term sustainability in agriculture, urging policymakers, farmers, and stakeholders to embrace these practices as a pathway toward a more sustainable and equitable food system.

\begin{abstract} In this paper, we study the existence of normalized solutions to the following curl-curl problem \begin{equation*} \left\{ \begin{array}{ll} abla imes abla imes U+V(x)U+\lambda U=|U|^{p-2}U\quad ext{in}\ \mathbb{R}^{N}, \\ \int_{\mathbb{R}^{N}}|U|^2\,\mathrm {d}x=a>0, \end{array} ight. \end{equation*} which originates from the system of Maxwell equations, where $N\geq3$, $2

Plant responses to climate are shaped by their climatic niche, defined by the range of climatic conditions under which they can persist. A mismatch between this niche and current climate can lead to climatic disequilibrium, impacting plant performance and plant--soil interactions. We investigated how such disequilibrium influences soil microbial functioning by quantifying eight enzyme activities involved in carbon, nitrogen, and phosphorus cycling. We sampled 300 soils beneath plant canopies and adjacent open areas along a gradient of climatic disequilibrium in ten Mediterranean shrublands in the Iberian Peninsula. Plant communities near their climatic optimum exhibited similar enzymatic activity under canopies and in open ground. However, as plant communities deviated from their optimum, soils beneath canopies hosted higher enzyme activity compared to open grounds. These results suggest that plant canopies buffer microbial functioning under climatic stress, shedding light on how climate-driven disequilibria alter plant--soil interactions and ecosystem resilience.

The brushless dc motor (BLDCM) drive is suffering from current/torque ripples. The DC-DC converter based BLDCM drive topology is widely preferred to suppress ripples. The improved characteristics such as low mechanical oscillation and stiff-speed regulation of BLDCM further introduces undamped AC signal which results destabilization of DC-link power (DCLP) supply. The different breeds of filter techniques have been recommended to stabilize the DCLP supply. However, the recommended filters are composed on the basic of concept of low pass filter (LPF) or band pass filter (BPF). In addition, these filters requires a precise association with active atonement, current reference and nonlinear interaction. In this context, a notch-filter (NF) has applied to stabilize the DCLP supply. The NF is investigated for a super-lift-Luo (SLL) converter assist BLDCM drive. The SLL converter is employed to suppress commutation current ripple (CCR) whereas the NF is applied to draw stabilized DCLP supply. A hardware prototype is developed and results are given to confirm the effectiveness of the proposed stabilization strategy and design. Thus, the suggested compensation technique effectively stabilized the DCLP supply of SLL converter employed to CCR mitigation in the BLDCM drive.

Motivation: Studies of biological invasions are limited by availability of comprehensive lists of species that have been introduced. Most lists of introduced species are limited to "successful" introduced species that have established at least one population; data about introduced species that fail to establish is very rare yet is highly informative for understanding factors affecting invasion success. This data paper describes the content, compilation methods and planned updates of the IntFloraAus dataset. IntFloraAus contains a comprehensive, taxonomically standardised list of all known alien vascular plant species that have been introduced to Australia, along with selected species characteristics. Uniquely, the dataset includes information about introduced species that have failed to naturalise, enabling comparisons across different statuses of biological invasion (i.e. introduced, naturalised and invasive). Such lists can also identify changes in patterns and processes in biological invasions overtime if they are maintained and updated. For instance, more species may be introduced and previously introduced species may go on to naturalise or become invasive given time and with synergistic processes such as climate change. Data on introduced species can also be used to assess effectiveness of biosecurity measures and to gain understandings of ecological processes such as changes to native floras. Main types of variables included: The dataset includes a comprehensive list of 34,650 plant species that are known to have been introduced to Australia since 1770, 4,081 of which are classified as naturalised and 428 as invasive. Data on plant characteristics includes: invasion status in Australia, growth forms, and three factors related to species introduction histories (native regions, introduction pathway, and minimum residence times in Australia). Spatial location and grain: The dataset applies to the six states and three mainland territories of Australia (mainland plus Tasmania) as a single location and grain. Islands or territories away from mainland Australia are not included. The dataset is global when considering species' native range; this grain size ranges from countries to continents. Time period and grain: Records in the dataset span from 1770 to 2023.

Salt stress (SS) is a major environmental challenge that disrupts plant growth by affecting water balance, ion homeostasis, and cellular functions. To cope with SS, plants use sensory mechanisms and molecular pathways like the SOS (Salt Overly Sensitive) pathway, which regulates sodium ion homeostasis through Na + exclusion and sequestration. Recent breakthroughs have identified several novel components within the SOS pathway, including GSO1 receptor-like kinase, which activates the SOS2-SOS1 module for Na + detoxification, the FREE1 protein, which regulates vacuolar dynamics and sodium sequestration, and phosphatidic acid (PA), which enhances SOS2 activity to maintain Na +/K + homeostasis. The SbiSOS1 protein helps pump Na + into vacuoles, while SbiSALTY stabilizes cellular machinery under SS. In addition to the SOS pathway, the GABA shunt pathway plays a critical role in osmotic regulation under SS. Phytohormones, including abscisic acid (ABA), brassinosteroids (BRs), ethylene (ETH), and jasmonic acid (JA), orchestrate the plant’s stress response through coordinated signaling. Integrating hyperspectral sensing, genomics, precision irrigation, and digital twin modeling into a unified AI framework enables a shift from reactive to proactive SS management. This integrated approach empowers growers to sustain high yields on marginal lands, curb salinization spread, and conserve both freshwater and soil health. These adaptive mechanisms, including ion transporter regulation, phytohormone signaling, and emerging AI models, play a crucial role in plant resilience against SS. Future research in epigenome editing, microbial priming, spatial transcriptomics, and epitranscriptomic remodeling will drive the development of salt-tolerant crops, improving productivity in saline environments and enhancing resilience in the face of climate change.

This work presents a machine learning (ML) enhanced design methodology for Operational Transconductance Amplifiers (OTA) for low-frequency applications. The method integrates the g m/I d with Random Forest Regression (RFR) to enable prediction of optimal transistor geometries. Unlike the g m/I d look-up-table based methods, which require iterative manual tuning, the ML integrated process uses the data from extensive SPICE simulations to train a predictive model. The model achieves an R 2 score of 0.955 and a Mean Absolute Percentage Error (MAPE) of 5.5%. The RFR-optimized OTA is subsequently used in a Capacitive-Coupled Instrumentation Amplifier (CCIA) architecture which utilizes the switched-capacitor configuration to replace large feedback resistors. The ML-assisted approach in the analog design addresses key challenges in low-frequency signal acquisition, including power efficiency, area minimization, and signal fidelity. The simulation results indicate that the OTA achieves an open-loop gain of 65 dB and 90 µW power consumption, while the switch-capacitor-based CCIA delivers a closed loop gain of 53 dB while consuming 150 µW of power. The proposed CCIA achieves a third-harmonic distortion of 14.33 dB and an input-referred noise level of 9.7 mV/√Hz. The overall figure of merit is calculated at 104.9 × 10 9, validating the effectiveness of the ML-assisted methodology.

Accurate quantification of uncertainty and error in flood prediction is crucial for informed decision-making process. This study investigates the application of various uncertainty quantification approaches for flood prediction using state-of-the-art neural network models. By incorporating techniques such as Bayesian inference, Monte Carlo approach, and quantile regression, we aim to provide a comprehensive assessment of predictive uncertainty for flood prediction across multiple timescales. Our results highlight the comparative effectiveness of these models under different flood magnitudes, emphasizing their strengths and limitations across scales. This research contributes to improving the reliability of flood prediction models, thereby enhancing decision-making processes for water resource management and disaster preparedness. We expect that the findings will offer valuable insights into the integration of advanced deep learning techniques with uncertainty quantification to reduce error in short-long-term flood predictions.

AI systems have fast-tracked industry transformation by bringing automation, personalization, and data-backed decision-making. However, the integration of immense amounts of personal and sensitive data into the AI model raises huge issues from the standpoint of privacy and security. This paper captures these multifaceted challenges ranging from data breaches, model inversion attacks, and data poisoning, to misuse of surveillance technologies. In particular, the lumping of AI models, which operate on the principle of learning from large-scale datasets, raises the issues that they may accidentally leak private information or enhance existing biases. The abstract also looks into existing countermeasures like differential privacy, federated learning, and secure multi-party computation. Despite recent efforts, developments in AI also greatly improve the sophistication of threats, warranting continuous research and strong regulatory mechanisms that would allow for responsible AI development and deployment. This piece speaks to the urgent need for addressing ethical concerns concerning the likely infringement of individual liberties by the intelligent system-based innovations.

Background: Physical restraint of horses for veterinary procedures is necessary to allow completion of tasks effectively and without injury to patient or personnel. Objectives: Comparison of physiological effects and behavioural responses to four commonly used restraint techniques for upper respiratory tract (URT) endoscopy in unsedated horses. Study design: Blocked and randomised interventional study. Methods: Twelve university owned teaching horses were blocked into groups of four and randomly allocated to one of four restraint methods (nose twitch, ear hold, Stableizer® and nil restraint) for URT endoscopy. Horse response to restraint and endoscopy was evaluated subjectively and by objective measures of procedural efficacy (duration and head movement), time domain indices of heart rate variability (HRV), saliva cortisol and plasma β-endorphin concentrations. Results: Horses demonstrated strong individual differences for procedure tolerance and preferred method of restraint, but there were no differences observed in outcome measures related to procedural efficacy or neuroendocrine response associated with restraint type. Repetition of experimental procedures was associated with progressive decrease in mean (95% CI) minimum heart rate from 34.2 (31.7-36.7) to 30.1 (28.0-32.2) bpm (P<0.001), and increased HRV measures related to parasympathetic dominance. Cortisol was greatest on Day 2 (1.5, 1.1-2.5 nmol/L; median, 95% CI), compared with Day 1 (0.8, 0.5-1.6 nmol/L; P=0.016), associated with interactions with novel personnel. Main limitations: Convenience sample of horses familiar with most study interventions; intervention was minimally invasive and of short duration. Conclusions: Study findings did not support current recommendations to prioritise one type of restraint over other available techniques.

Urbanization is transforming ecosystems and food webs, yet the role of nutritional needs in species persistence in cities remains poorly understood. For bees, sterols are essential lipids they cannot synthesize de novo and must obtain from pollen. Pollen sterol composition varies widely across plant species, potentially constraining bee--plant interactions. However, sterol composition in wild bees and their pollen sources is understudied. To fill the gap, we analyzed 22 sterols in 274 nest pollen samples and 1,267 individuals from four wild bee species, ranging from specialists to generalists, in five European cities. The most abundant sterols in bee bodies were shared across species and overlapped with those in their nest pollen, yet overall sterol profiles varied by species and city. The specialist bee showed more conserved sterol patterns and was less common in highly urbanized areas. Our findings highlight species-specific nutritional strategies and the importance of integrating nutritional traits into urban bee conservation.

Current global changes are driving many species towards extinction, making the early detection of threatened species a priority for efficient conservation actions. However, the threat status of many species remains unknown due to insufficient data on updated distributions, population sizes and population trends, and using ecological indicator traits, such as range size, is not always straightforward. Recent advances suggest that macroevolutionary indicators (rates of extinction, net diversification or niche evolution) could provide novel insights into extinction risk based on the assumption that macroevolutionary rates can serve as proxies for extinction-promoting traits (small range size, narrow niche breadth or low evolutionary potential). However, this assumption has not yet been sufficiently investigated to use this approach. Here, we assess current understanding of the assumptions underlying the relationship between macroevolutionary indices and contemporary extinction risk. We find that only past extinction rates can be reliable predictors of current extinction risk due to their correlation with inherited extinction-promoting traits. Assumptions underlying relationships between current extinction risk and diversification and niche evolution rates vary by taxon or ecological conditions, and require further investigation through targeted studies. When underlying assumptions are validated, macroevolutionary indicators could be promising tools complementing trait-based approaches in identifying inherent extinction risk.

This paper proposes a transformer-based neural network model for predicting credit card default, a crucial task for financial institutions to minimize losses and improve lending practices. Existing state-of-the-art (SOTA) methods often rely on either tree-based models which struggle to capture the temporal latent features in the financial data or complex ensemble approaches which are computationally intensive. The proposed transformer-based model addresses these limitations using self-attention mechanisms to uncover hidden patterns in the temporal dimension. The model is evaluated on a credit card default dataset from American Express and a dataset of credit card clients from a Taiwan bank. The experimental results demonstrate the efficacy and generalizability of our proposed model. The proposed transformer based model outperforms the SOTA lightGBM model and achieves comparable performance to SOTA ensemble methods. An ensemble approach, incorporating the transformer based model and lightGBM model has also been proposed. It outperforms the SOTA ensemble approaches. This paper highlights the potential of transformer based models in financial risk assessment, suggesting that they can lead to more accurate and reliable predictions. It offers a foundation for future research in enhancing credit risk models and their applications in related financial domains.

The weaponisation of the womb—where reproductive capacity is treated as the defining feature of womanhood—harms both transgender women and cisgender women who cannot or choose not to bear children, undermining their autonomy and reinforcing rigid reproductive norms within public health discourse. Simultaneously, transgender men, who face distinct reproductive and healthcare challenges, are often excluded from such conversations, underscoring the restrictive nature of dominant reproductive ideals and their failure to accommodate gender diversity. This perspective paper introduces the Empathy-Based Health Equity Framework (EBHEF) as a policy-relevant model for transforming public health systems towards trans-inclusivity. Grounded in the Ethics of Care, the EBHEF critiques exclusionary policies, institutionalised biases, and clinical pathologisation that together enact structural violence against transgender communities. Rather than treating empathy as an individual disposition, this framework embeds it as a structural principle within health policy-making, service planning, and management. To operationalise the EBHEF, the paper applies the CARR model—competence, attentiveness, responsibility, and responsiveness—as the foundational pillars for inclusive public health systems. This paper argues that advancing transgender health equity is not merely a matter of service provision but a systemic imperative. Public health systems at national, regional, and local levels needs to reimagine governance structures and planning processes to ensure that transgender people are not just included but recognised as co-architects of a just and flourishing health system.