Operator Expansion Research Articles

Assessing the impact of sewage and wastewater on antimicrobial resistance in nearshore Antarctic biofilms and sediments

BackgroundDespite being recognised as a global problem, our understanding of human-mediated antimicrobial resistance (AMR) spread to remote regions of the world is limited. Antarctica, often referred to as “the last great wilderness”, is experiencing increasing levels of human visitation through tourism and expansion of national scientific operations. Therefore, it is critical to assess the impact that these itinerant visitors have on the natural environment. This includes monitoring human-mediated AMR, particularly around population concentrations such as visitor sites and Antarctic research stations. This study takes a sequencing discovery-led approach to investigate levels and extent of AMR around the Rothera Research Station (operated by the UK) on the Antarctic Peninsula.ResultsAmplicon sequencing of biofilms and sediments from the vicinity of Rothera Research Station revealed highly variable and diverse microbial communities. Analysis of AMR genes generated from long-reads Nanopore MinION sequencing showed similar site variability in both drug class and resistance mechanism. Thus, no site sampled was more or less diverse than the other, either in the biofilm or sediment samples. Levels of enteric bacteria in biofilm and sediment samples were low at all sites, even in biofilm samples taken from the station sewage treatment plant (STP). It would appear that incorporation of released enteric bacteria in wastewater into more established biofilms or associations with sediment was poor. This was likely due to the inactivation and vulnerability of these bacteria to the extreme environmental conditions in Antarctica.ConclusionsOur results suggest minimal effect of a strong feeder source (i.e. sewage effluent) on biofilm and sediment microbial community composition, with each site developing its unique niche community. The factors producing these niche communities need elucidation, alongside studies evaluating Antarctic microbial physiologies. Our data from cultivated bacteria show that they are highly resilient to different environmental conditions and are likely to thrive in a warmer world. Our data show that AMR in the Antarctic marine environment is far more complex than previously thought. Thus, more work is required to understand the true extent of the Antarctic microbiota biodiversity, their associated resistomes and the impact that human activities have on the Antarctic environment.

Issues in the Expansion and Contraction of Operators

This paper explores fundamental issues in the correct contraction and expansion of operators, with a primary focus on the concept of symmetry within operator theory. Special attention is given to how symmetry influences the behavior of operators, particularly regarding their approximation and convergence properties. In the domains of quantum mechanics and condensed matter physics, such operators are essential for modeling phenomena like superconductivity, excitons, and surface states. The symmetric properties of operators have a profound impact on the physical interpretations and predictions these models generate. A rigorous analysis is provided regarding the existence of correct contractions and expansions for a specific class of nonlinear operators, demonstrating how symmetry affects the structural integrity of operators under natural conditions. The study presents a comprehensive description of the set of all correct contractions, expansions, and regular expansions, with an application to a third-order nonlinear differential expression. Additionally, a condition for the unique solvability of a Bitsadze–Samarskii-type problem is derived, showcasing how symmetry plays a crucial role in guiding the solution of complex physical models. Furthermore, the paper emphasizes the importance of preserving symmetry in the construction of operators, ensuring the consistency and accuracy of mathematical models. This has significant implications for both theoretical research and practical applications in various fields, including nuclear physics and quantum theory.

R-matrices and Miura operators in 5d Chern-Simons theory

We derive Miura operators for WW- and YY-algebras from first principles as the expectation value of the intersection between a topological line defect and a holomorphic surface defect in 5-dimensional non-commutative \mathfrak{gl}(1)𝔤𝔩(1) Chern-Simons theory. The expectation value, viewed as the transition amplitude for states in the defect theories forming representations of the affine Yangian of \mathfrak{gl}(1)𝔤𝔩(1), satisfies the Yang-Baxter equation and is thus interpreted as an R-matrix. To achieve this, we identify the representations associated with the line and surface defects by calculating the operator product expansions (OPEs) of local operators on the defects, as conditions that anomalous Feynman diagrams cancel each other. We then evaluate the expectation value of the defect intersection using Feynman diagrams. When the line and surface defects are specified, we demonstrate that the expectation value precisely matches the Miura operators and their products.

Machine learning-based identification of animal feeding operations in the United States on a parcel-scale.

The increasing global demand for meat and dairy products, fueled by rapid industrialization, has led to the expansion of Animal Feeding Operations (AFOs) in the United States (US). These operations, often found in clusters, generate large amounts of manure, posing a considerable risk to water quality due to the concentrated waste streams they produce. Accurately mapping AFOs is essential for effective environmental and disease management, yet many facilities remain undocumented due to variations in federal and state regulations. Current techniques for mapping AFOs in the US rely on a mix of manual digitization, aerial imaging, and image processing. By applying a machine learning-based random forest (RF) classification method to a socio-environmental dataset that excluded aerial images in this work, we overcame some of the limitations associated with aerial image-based approaches, enhancing mapping accuracy to 87%. We used publicly available environmental, nutrient-focused, and socioeconomic data downscaled to the parcel level, which more accurately reflects farm boundaries and operations than previous methods. Our study incorporates 58 variables, with canopy cover, surrounding vegetation, day and nighttime land surface temperatures, and phosphorus from animals identified as key predictors of AFO presence. The relevance of these variables varies across states, influenced by whether the dominant land covers are human-induced, like croplands, or natural, such as savannas and grasslands. Thus, our public-data based approach, easily replicable, not only improves the precision of AFO detection, but also facilitates the monitoring of nutrient flows at the parcel level-critical for nutrient budgeting and recovery, water quality management, and disease risk assessment and tracing.

Multi-Factor analysis of profitability drivers of local rabbit production in Zamboanga Peninsula Region, Mindanao , Philippines

Literature supporting empirical evidence on the financial viability of rabbit production in the Philippines is limited. This study was conducted to examine the profitability drivers of rabbit production in the Zamboanga Peninsula, focusing on its potential as a sustainable alternative to traditional livestock production, protein source and its capacity to augment household income. The research employed MRL (multiple linear regression) analysis to identify key factors influencing profitability, utilising a sample of 123 rabbit raisers randomly selected from the list in the Provincial Agriculture Offices. Data were collected during the first half of 2024 through personal interviews with the aid of an interview schedule validated by experts. Model specification was done to ascertain heteroscedasticity and multicollinearity issues in the data. The loglog model for expense categories and log-linear for profitability drivers having the lowest Akaike information criteria (AIC) were chosen for its better fit, providing a more accurate representation of the data. Results indicate that larger farms achieve significantly higher gross and net incomes, benefiting from economies of scale. Expense categories such as feed cost, veterinary expenses and labour costs positively and significantly influenced profitability, while outlay on utilities and cost of stocks negatively affected financial performance. The study also revealed that farming experience, feeds used, training attendance, breed type and marketing practices do not significantly influence profitability, implying that management practices and operational scale are more important considerations for rabbit profitability. Knowing the relationship and influence of these profitability drivers of rabbit production can help identify strategies to optimise returns, support the adoption of rabbit farming and improve the livelihoods of rural households in Philippines. The findings suggest the expansion of rabbit farming operations of up to more than 100 head per farm to optimise profitability.

Series expansion of a scalable Hermitian excitonic renormalization method.

Utilizing the sparsity of the electronic structure problem, fragmentation methods have been researched for decades with great success, pushing the limits of abinitio quantum chemistry ever further. Recently, this set of methods has been expanded to include a fundamentally different approach called excitonic renormalization, providing promising initial results. It builds a supersystem Hamiltonian in a second-quantized-like representation from transition-density tensors of isolated fragments, contracted with biorthogonalized molecular integrals. This makes the method fully modular in terms of the quantum chemical methods applied to each fragment and enables massive truncation of the state-space required. Proof-of-principle tests have previously shown that an excitonically renormalized Hamiltonian can efficiently scale to hundreds of fragments, but the ad hoc approach to building the Hamiltonian was not scalable to larger fragments. On the other hand, initial tests of the originally proposed modular Hamiltonian build, presented here, show the accuracy to be poor on account of its non-Hermitian character. In this study, we bridge the gap between these with an operator expansion that is shown to converge rapidly, tending toward a Hermitian Hamiltonian while retaining the modularity, yielding an accurate, scalable method. The accuracy is tested here for a beryllium dimer. At distances near equilibrium and longer, the zeroth-order method is comparable to coupled-cluster singles, doubles, and perturbative triples and the first-order method is comparable to full configuration interaction (FCI). The second-order method agrees with FCI for distances well up the inner repulsive wall of the potential. Deviations occurring at shorter bond distances are discussed along with approaches to scaling to larger fragments.

Optimizing ICMS Strategies for E-Commerce in Brazil: Tax Benefits and Profitability Insight

This study aimed to analyze the ICMS in the interstate operations of e-commerce companies, targeting non-taxpayers. The objective was to identify more economically advantageous strategies that would remain compliant with the tax legislation. The economic implications are evident in the use of tax benefits by various states, seeking to attract large companies to their jurisdictions through these incentives. A case study was conducted at a medium-sized company that has experienced significant growth in recent years, driven by both operational expansion and the rise of e-commerce on digital platforms. To carry out this case study, information from the company's tax documents was used, including verification of files and tax ledgers, and calculation of taxes and other reports relevant to the study. The analysis revealed that ICMS has a significant effect on the company's operations, necessitating the implementation of strategies to efficiently and legally reduce the tax burden. To implement these strategies, this study analyzed the tax benefits offered by specific states within the federation to this particular industry segment. By comparing these benefits to the company's operational values, opportunities to reduce the tax burden and increase profitability can be identified. The study demonstrated the potential for substantial cost savings by considering the company's location as the primary focus. These savings could range from 41.9% to 72.5%.

DAO Token Transferability: Property, Contract, and Technology

Abstract A Decentralised Autonomous Organisation (DAO) is a new form of digital enterprise that operates on blockchain networks. It enables a new model of collaboration through diverse capital contributions and equitable sharing of benefits and risks. This paper explores the legal dimensions of DAO token transferability, a vital aspect for the expansion of DAO operations. First, it evaluates how property law (including the proposal by the Law Commission of England and Wales for a third category of digital asset ownership) might apply to DAO tokens so as to mitigate legal risks and ensure smooth transferability. Secondly, it investigates the potential for DAO software protocols to implement contractual transferability restrictions and examines their technological design. Finally, it looks at the legal enforceability of such restrictions and the policies needed to support their legal recognition.

ТЕОРЕТИЧНІ ВИКЛИКИ ТА ПРАКТИЧНІ МОЖЛИВОСТІ ОЦІНЮВАННЯ ЕФЕКТИВНОСТІ ЗАХОДІВ ПСИХОЛОГІЧНИХ ОПЕРАЦІЙ

The article discusses theoretical challenges and practical possibilities of assessing the effectiveness of psychological operations in modern armed conflicts. The author analyses the role of psychological operations, which are an important component of hybrid warfare aimed at manipulating the information space and the psychological state of the enemy. The main attention is paid to the use of modern technologies, such as electronic means, unmanned aerial vehicles, cyber tools and social networks for information and psychological influence. Modern armed conflicts are characterised by the intensive use of technology, which allows for a significant expansion of psychological operations. Thanks to innovative communication tools such as automated data analysis systems, artificial intelligence algorithms and big data technologies, new opportunities are emerging to assess enemy reactions in real time. This approach allows for a more accurate and timely correction of psychological influence measures depending on the results. The article examines various methods of psychological influence: demoralisation of the enemy, spreading disinformation, manipulation of public opinion, creating a negative information background among the enemy's civilian population, and maintaining the morale of own troops. Particular importance is attached to information and analytical operations, which can influence not only individual decisions of the enemy command, but also strategic management processes. In addition, the importance of evaluating the effectiveness of such operations is emphasised, in particular through the use of quantitative and qualitative metrics that allow to determine the impact on the enemy, changes in his behaviour and morale. In particular, quantitative indicators include the level of information coverage of the target audience, the number of changes in the behaviour or decision-making of the enemy command. Qualitative indicators analyse deeper aspects, such as the emotional stability of personnel, changes in confidence in the command, and demoralisation of the population in the combat zone. Particular attention is paid to methods of assessing the effectiveness of psychological operations, including analysing the enemy's reactions, monitoring changes in his behaviour, and measuring the impact on the enemy's information and communication systems. An important component is the analysis of the results of disinformation campaigns, in particular, the assessment of their long-term impact on public opinion. The article emphasises the importance of a comprehensive approach to determining the results of psychological operations, which combines different types of analysis to achieve strategic goals in modern military conflicts.

Amplified threat of tropical cyclones to US offshore wind energy in a changing climate

The vulnerability of US offshore wind energy to tropical cyclones is a pressing concern, particularly along the Atlantic and Gulf Coasts, key areas for offshore wind energy development. Assessing the impact of projected climate change on tropical cyclones, and consequently on offshore wind resources, is thus critical for effective risk management. Herein, we investigate the evolving risk to offshore wind turbines posed by Atlantic tropical cyclones in a non-stationary climate using a synthetic tropical cyclone model. Integrated with climate model simulations, projections show widespread increases in tropical cyclone exposure, with historical 20-year storms occurring every ~12.7 years on average, increasing in intensity by about 9.3 ms−1. Subsequent fragility analysis reveals that the probabilities of turbine yielding and buckling from a 20-year tropical cyclone could increase by about 37% and 13%, respectively, with regional increases reaching up to 51%. These findings carry substantial implications for the operation and future expansion of offshore wind farms.

Wings of progress: The economic and social impacts of low-cost carriers in the Philippines

Low-cost carriers (LCCs) have become a significant force in the Philippine aviation industry, providing affordable air travel and contributing to regional economic and social development. However, the full extent of their economic, social, environmental, and infrastructural impacts has not been comprehensively studied. This study aims to evaluate the economic and social implications of LCCs in the Philippines, focusing on tourism growth, job creation, business development, and the accessibility and mobility of the population. The study also explores environmental challenges and infrastructural strains caused by the expansion of LCC operations. A mixed-methods approach was employed, incorporating quantitative data analysis of tourism statistics, employment rates, business growth, and regional economic output. Qualitative data were collected through interviews and surveys with stakeholders, including passengers, local businesses, government officials, and environmental agencies. Statistical tools such as regression analysis and chi-square tests were used to determine significance levels in the findings. The results demonstrate that LCCs have contributed significantly to regional economic growth, with a 25% increase in tourist arrivals and a 30% rise in tourism revenue. Social impacts include enhanced travel accessibility, increased cultural exchange, and improved quality of life. However, environmental challenges, such as a 15% increase in carbon emissions, highlight the need for sustainable practices. The study recommends further investment in airport infrastructure, implementation of sustainable aviation practices, and ongoing government support for regional tourism. Future studies should focus on long-term environmental impacts and strategies for reducing carbon footprints.

Off-farm employment, farmland transfer and agricultural investment behavior: A study of joint decision-making among North China Plain farmers

Improving farmers’ ability to make joint decisions on multi-factors is crucial for efficient resource allocation in agriculture, promoting sustainable land use, and fostering rural revitalization. Using survey data from 1780 households in the North China Plain, this study used the Conditional Mixed Process (CMP) joint estimation to address potential endogeneity issues and investigate the interaction of off-farm employment, farmland transfer, and agricultural capital investment. First, the results reveal a significant relationship between the three factors in farmers’ decision-making. Particular, there is a positive relationship between off-farm employment and farmland outflow (ρ = 0.163, P < 0.05), while a negative correlation is observed between farmland outflow and capital investment (ρ = − 0.476, P < 0.01). Secondly, there is heterogeneity in the correlation of joint decision-making regarding off-farm employment types and agricultural capital input types. For risk-averse farmers and farmers with low capital liquidity, off-farm employment is not significantly correlated with farmland outflow, while farmers with land-poor farmers have the strongest positive correlation between farmland inflow and capital input. Third, even if increasing farmland outflow can facilitate the expansion of large-scale land operations, the investment effect of off-farm employment remains unrealized. Finally, the lag of the farmland transfer market relative to the labor transfer market and the rural capital market hinders the growth of optimal-scale agricultural management.

Commutator technique for the heat kernel of minimal higher derivative operators

We suggest a new technique of the asymptotic heat kernel expansion for minimal higher derivative operators of a generic 2Mth order, F(∇)=(−□)M+⋯, in the background field formalism of gauge theories and quantum gravity. This technique represents the conversion of the recently suggested Fourier integral method of generalized exponential functions [A. O. Barvinsky and W. Wachowski, Heat kernel expansion for higher order minimal and nonminimal operators, ] into the commutator algebra of special differential operators, which allows one to express expansion coefficients for F(∇) in terms of the Schwinger-DeWitt coefficients of a minimal second-order operator H(∇). This procedure is based on special functorial properties of the formalism including the Mellin-Barnes representation of the complex operator power HM(∇) and naturally leads to the origin of generalized exponential functions without directly appealing to the Fourier integral method. The algorithm is essentially more straightforward than the Fourier method and consists of three steps ready for a computer codification by symbolic manipulation programs. They begin with the decomposition of the operator into a power of some minimal second-order operator H(∇) and its lower derivative perturbation part W(∇), F(∇)=HM(∇)+W(∇), followed by considering their multiple nested commutators. The second step is the construction of special local differential operators—the perturbation theory in powers of the lower derivative part W(∇). The final step is the so-called procedure of their “Syngification,” consisting of a special modification of the covariant derivative monomials in these operators by the Synge world function σ(x,x′) with their subsequent action on the Hadamard-Minakshisundaram-DeWitt coefficients of H(∇). Published by the American Physical Society 2024

Review on Automated Storage and Retrieval System for Warehouse

The swift expansion of e-commerce and supply chain operations has significantly enhanced the efficiency of warehouse management systems, establishing them as vital components in augmenting organizations' competitiveness. This paper delves into warehouse sorting systems to enhance the sorting process, reduce error rates, and simplify internal warehouse procedures. It aims to develop a scalable, adaptable, and efficient warehouse sorting system that can maximize sorting efficiency while effectively responding to changing market demands through the use of advanced automation technologies. The study provides an in-depth review of the literature that has explored the Automated Storage and Retrieval System (ASRS) within the context of warehouse operations. It offers a comprehensive introduction to the operational systems of warehouses, detailing each type of ASRS along with the technologies that can improve the efficiency and accuracy of these systems. Moreover, the paper thoroughly investigates and classifies the ASRS design decision problem and compares multiple types of ASRS. The analysis aims to delineate the distinctions among various ASRS configurations, assessing their scalability, adaptability, and their impact on operational efficacy in warehouse environments Through this comparative review, the paper emphasizes the potential enhancements in sorting processes that modern ASRS can provide, ensuring that warehouse operations can rapidly adapt to market changes and demands. The goal is to highlight best practices and technological innovations that can lead to more responsive and efficient warehouse management systems. This exploration contributes to a better understanding of how cutting-edge automation and adaptable system designs can significantly influence the efficiency of warehouse sorting processes.

Erratum: Heat kernel expansion for higher order minimal and nonminimal operators [Phys. Rev. D 105 , 065013 (2022)

Erratum: Heat kernel expansion for higher order minimal and nonminimal operators [Phys. Rev. D <b>105</b> , 065013 (2022)

Bootstrapping Lagrangian perturbation theory for the large scale structure

We develop a model-independent approach to Lagrangian perturbation theory for the large scale structure of the universe. We focus on the displacement field for dark matter particles, and derive its most general structure without assuming a specific form for the equations of motion, but implementing a set of general requirements based on symmetry principles and consistency with the perturbative approach. We present explicit results up to sixth order, and provide an algorithmic procedure for arbitrarily higher orders. The resulting displacement field is expressed as an expansion in operators built up from the linear density field, with time-dependent coefficients that can be obtained, in a specific model, by solving ordinary differential equations. The derived structure is general enough to cover a wide spectrum of models beyond ΛCDM, including modified gravity scenarios of the Horndeski type and models with multiple dark matter species. This work is a first step towards a complete model-independent Lagrangian forward model, to be employed in cosmological analyses with power spectrum and bispectrum, other summary statistics, and field-level inference.

Managing the Twin Faces of AI: A Commentary on “Is AI Changing the World for Better or Worse?”

This commentary explores the transformative potential and inherent risks associated with artificial intelligence (AI), particularly generative AI. It focuses on the dual nature of AI, where it offers substantial benefits in industries like retailing, and social media, while also presenting challenges related to ethics, privacy, and job displacement. The article introduces a framework to analyze AI's organizational impact, categorizing outcomes based on how AI supports or disrupts the status quo. It discusses AI's contribution to operational efficiency, demand expansion, and business transformation, acknowledging both the promise of enhanced customer experiences and the peril of ethical dilemmas such as algorithmic bias and data privacy concerns. It explores real-world applications of AI, such as personalized customer engagement and healthcare diagnostics. At the same time, the article critically examines negative implications, including job displacement, wage polarization, and the erosion of human connection in customer service. The commentary offers a balanced perspective, urging businesses and policymakers to embrace Responsible AI principles like fairness, reliability, and accountability. The article projects the future of AI, particularly advancements in agentic AI, artificial general intelligence (AGI), and artificial superintelligence (ASI), as holding both enormous potential and severe risks. It concludes by calling for a thoughtful, regulated approach to AI development to ensure it serves the greater good while mitigating its risks.

Pembuatan Barcode Kemasan Produk Pada CV. Kuliner Makmur Sejahtera Kota Padang

Rendang, a traditional Minangkabau dish from West Sumatra, Indonesia, has gained international recognition and has inspired many entrepreneurs to promote this cuisine globally. One such entrepreneur is CV. Kuliner Makmur Sejahtera (KMS), which produces rendang under the brand Katuju. To enhance operational efficiency and support global market expansion, KMS requires an improved inventory management system. The primary challenge is the difficulty in monitoring inventory and tracking products without a barcode system. This community service program aims to address this issue by implementing barcode technology on Katuju's product packaging. The activities include designing, creating, and printing barcodes, as well as training KMS staff on how to use them. The process starts with observation and interviews to identify needs, followed by barcode design and production using an online barcode generator, and testing the barcode functionality with a scanning system. The results indicate that the community service team successfully trained KMS in barcode creation and printing, and integrated barcodes into product packaging. Evaluation through questionnaires showed participant satisfaction with the program and readiness to apply the barcode system to their products. This program is expected to improve inventory management, data accuracy, and support CV. KMS's global market expansion by providing tools necessary for efficient product tracking and valuable consumer data collection.

Debt is Not Always Bad Even in Difficult Economic Conditions

The problem faced by the company is the use of debt that is not according to plan. The purpose of this study is to determine whether debt policy affects company performance. This study was conducted on manufacturing sector companies listed on the Indonesia Stock Exchange in 2018–2023 with a population of 296 companies, the sample used was 73 companies for six years. Thus, the number of samples used in this study was 438. The company performance variable is proxied by (ROE) while the independent variables in this study are debt policy proxied by (DER and DER*KE), investment decisions (KI), managerial ownership (MNJR), and economic conditions (KE). The data analysis method uses multiple linear regression methods with SPSS software version 26. The results of the study show that the debt policy variable (DER) has a negative and significant effect on company performance (ROE) and the debt policy variable*economic conditions (DER*KE) has a positive and significant effect on company performance (ROE). This means that when economic conditions are supportive, companies that use debt to finance operational activities or business expansion tend to experience increased performance. Funds obtained from debt can be used for investment, new product development, or market expansion. Proper use of debt can increase a company's return on equity (ROE), as long as the rate of return on the investment financed by debt is higher than the cost of debt. Timing the debt decision is crucial. In improving economic conditions, companies can take advantage of low interest rates and increase production capacity.

Strategic insights for sustainable growth of mushroom farming industry in Bangladesh: A comprehensive evaluation using SWOT-AHP and TOPSIS frameworks

Mushroom farming using agri-waste as substrates can offer a sustainable solution to the food security challenges of inadequate and imbalanced diets. Developing strategies to exploit the potential of the mushroom industry fully is yet to be explored in Bangladesh. We, thus, conducted this study to investigate the challenges and opportunities associated with mushroom farming, as well as the characteristics of farms and employees engaged in this industry. A directional stepwise multiple regression analysis showed self-motivation, spawn cost, farm size, and training are the key influencing factors driving profitability. Farm surveys identified SWOT factors with 24 sub-factors cross-validated with expert consultations. The sub-factors were categorized as beneficial (strength and opportunities) and cost (weakness and threats) to formulate the SWOT strategies using the Grey-TOPSIS method. Results indicate a favorable scenario exists for the industry in Bangladesh, with significant opportunities (group weight 0.53) and minimal threats (group weight 0.09). We proposed 12 strategic alternatives for the sustainable growth of this industry. This top-ranking strategy is not only to secure funding (relative closeness, C+ = 0.87) but also to provide more accessible loan options envisioning a strategic expansion of business operations in Bangladesh. The evaluation highlighted the significance of collaboration with other mushroom farmers to maximize the gain in marketing that will substantially expand the local demand (C+ = 0.697). Besides, product diversification is also underscored as an important strategy for the growth of the industry. These prioritized SWOT strategies lay the groundwork for policy development, aiding decision-makers in steering the mushroom industry towards sustainable growth for developing countries like Bangladesh. Promoting such an eco-friendly industry will generate ample opportunities for women's employment and appreciable profit while contributing to environmental improvement through recycling agri-waste.