Time series count data such as daily cases of Covid-19 requires adequate modelling and forecasting. Traditional time series models do not have limitations in modelling time series count data, also known as unbounded N-valued data. This study involved in-depth analyses of various models in fitting time unbounded N-valued data. Models such as the Zero-Inflated Poisson, zero-inflated Binomial, and ARIMA popularly used to fit time series count were compared with the integer-valued generalized autoregressive conditional heteroscedasticity (INGARCH) models. The investigation involved two critical aspects: simulation and real-life data analysis. First, we simulated the time series count data, modelled and compared the performance of the competing models. The simulation outcomes consistently favoured the Negative Binomial INGARCH models highlighting their suitability for count data modelling. Subsequently, we examined life data on Covid-19 data in Nigeria. The life data also yielded strong support for the NB INGARCH model. This study recommends further exploration of the NB INGARCH model, as it exhibits substantial promise in effectively modelling over-dispersed zero-inflated data. The current study contributes valuable insights into selecting appropriate models for time series count data, addressing the intricate challenges posed by this specialized data type. Also, the overall outcome of the study helps in national planning, and resource allocation for the people needing health intervention.

This paper examines the obstacles to implementing Environmental Management Accounting (EMA) procedures in manufacturing enterprises in emerging economies, highlighting their importance for sustainable development. The study reveals significant barriers, including inadequate institutional support, fragile regulatory frameworks, and resistance stemming from conventional organizational cultures. It emphasizes the necessity of improving environmental awareness among stakeholders to enable the incorporation of EMA practices. This study utilizes a qualitative technique based on an extensive literature analysis to create a conceptual framework that identifies the various obstacles to EMA implementation, including regulatory inconsistencies, insufficient managerial commitment, and operational inefficiencies. The results indicate that effective implementation of EMA can enhance environmental performance and financial results, promoting collaboration among government, companies, and educational institutions to foster a culture of sustainability. The research concludes with recommendations for future studies, emphasizing the need for quantitative methods to further investigate the correlation between EMA implementation challenges and organizational performance metrics, thus enhancing the broader discussion on sustainable manufacturing practices.

Climate change adaptation is a key component of government policy. Because climate change has had a wide-ranging impact in various countries and threatens the community's food security system, health, including the quality of nutrition and child development, is particularly vulnerable to the effects of climate change. Climate change threatens the production system and food availability of the community, worsening access to quality nutrition. In addition, climate change impacts stunting policies by affecting children's nutritional status and creating vulnerabilities in the food system. Therefore, stunting prevention requires not only specific and sensitive nutrition interventions but also adaptation to climate change. This study aims to analyze climate change adaptation in stunting prevention in Pesisir Selatan Regency and to reconnect climate change adaptation policies with stunting prevention. This study uses qualitative methods to analyze the reconnection of climate change adaptation policies and stunting prevention. The main contributors to this study are stakeholders, including representatives of local governments, health practitioners, the environment, and affected local communities. Secondary data were collected from policy documents, official reports, and related literature. The results of the study indicate that stunting prevention policies have not fully included climate change adaptation issues in applicable policies, which can increase the vulnerability of household food security. Pesisir Selatan Regency has a high prevalence of stunting in West Sumatra. The conclusion of this study shows that reconnecting climate change adaptation into stunting prevention policies, including food security, clean water availability, and environmental quality, is essential to achieve more effective and sustainable results. Reconnecting policies through this integration can strengthen the synergy between the two policies and increase the effectiveness of stunting prevention programs in dealing with the impacts of climate change.

The proliferation of Generative AI (GenAI) tools has introduced new dynamics in user behaviour, environmental perception, and digital sustainability. This study, based on a primary questionnaire survey of 1,005 GenAI users aged 18 and above from India, investigates the frequency of GenAI usage and its relationship with climate change awareness, environmental concern, and willingness to adopt energy-efficient digital practices. Using regression-based models, the research reveals a pattern of indirect dependence: lower GenAI usage is related with a greater inclination toward environmentally responsible behaviours, such as transitioning from non- sustainable platforms and adopting energy-efficient digital services. In contrast, frequent GenAI users tend to perceive climate change as temporally distant and of lower immediate importance.

The study also examines how the frequency and nature of social media usage influence users’ attitudes toward sustainable technology choices. These findings provide valuable insights for policymakers, AI educators, digital strategists, and sustainability advocates aiming to foster environmentally conscious technology adoption in emerging economies like India.

Adequate indoor lighting is essential for ensuring visual comfort, energy efficiency, and compliance with architectural standards. This study presents a novel smartphone-based platform for real-time illuminance estimation and visual mapping, that leverages a lightweight machine learning model. The application utilizes the smartphone’s built-in camera to capture images of the scenes and performs illuminance prediction for each patch of the image using a trained regression model, offering a cost-effective alternative to physical lux meter grid. The mobile application generates a color-coded heat maps that visualize the spatial distribution of illuminance and do the assessment of its compliance with an established lighting norm. The advantages of the proposed system include its affordability, portability, and prediction accuracy enabled by the machine learning model trained on image intensity features. Experimental tests in a controlled indoor setting demonstrate high prediction accuracy and low computational requirements, confirming the platform’s suitability for use in real-word applications. The tool enables effective and precise analysis of light and is hence usable in architectural diagnostics, energy audits, and spatial design optimization. In addition, the user-friendly interface benefits both professional and non-professional users, facilitating real-time adjustment and optimization of indoor lighting.

A bridge is a construction that enables traffic to cross a barrier while keeping in touch with roads or railroads. Throughout history, bridges have played a crucial role in human civilisation and remain an essential component of any transportation network. The main purpose of this study is to evaluate the seismic resistance of bridge structure under effect of earthquake action by adopting force-displacement yielding points and performance points methods. The results of force-displacement yielding point and performance points revealed that the transversal yielding points were greater than the longitudinal yielding points and performance points, this indicates that the seismic action on the transversal bents has little effect and that no damage will be done to the bents if they are subjected to this action alone but in longitudinal direction the force-displacement yielding point and performance points were lower, indicating that the seismic resistance performance of bridge bents is small with low elasticity and stiffness and high plasticity. Meaning that bridge bents capacity cannot resist the demand. Therefore, Therefore, this study suggested to improve the structural performance and seismic resistance of bridge bents by increasing the diameter of bridge piers by 1.6m, 1.8m, and 2m. After thickening the piers structure, the results of yielding points and performance points values were increased with increasing the piers diameter. And the seismic displacement decreased with increasing the piers diameter. Indicating that the elastic limit of bridge bents will increase and the bridge piers will resist the earthquake action according to increase in the stiffness and bearing capacity of bridge bents.

Public transportation (PT) plays a vital role in promoting sustainable mobility, particularly in congested urban areas. Effective PT planning requires attention to country-specific objectives and challenges. Evaluating bus services is essential for ensuring that transit systems meet mobility demands while reducing congestion and pollution. However, no globally recognized evaluation indicators currently exist for assessing PT services and stations in developing countries. To address this gap, a systematic literature review was conducted to establish a weighted assessment checklist tailored to the context of developing nations. The study employed expert judgment from 15 professionals and applied the Analytic Hierarchy Process (AHP) to identify best practices. Key indicators for evaluating bus stations included infrastructure, operations, and facilities, while the level of service (LOS) and sustainability emerged as the most critical indicators for bus service evaluation. Among the highest-weighted factors were reliability, safety and security, connectivity and integration, and operational efficiency, underscoring their importance in delivering effective and sustainable PT solutions. This research contributes to the body of knowledge by proposing context-specific indicators that account for the unique challenges faced by low-income countries, such as limited resources, infrastructure constraints, and socio-economic conditions.

The smart-grid-enabled demand-side energy management is used to regulate consumer energy demand. The consumer can adjust their energy consumption in response to the pricing strategy of the grid in the market-based programs. The energy bidding methodology is proposed to predict the electricity rate and optimize the energy demand of the chiller system for energy consumption and cost minimization. The forecasted electricity price and the energy demand schedule generated by the optimization algorithm are used to bid in the electricity market. To forecast the electricity rate, a hybrid model Hilbert Transform-Based Long Short-Term Memory (Hilbert-LSTM) is proposed and the results indicate the improvement in the prediction accuracy in terms of the Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and Mean Absolute Percentage Error (MAPE). The energy consumption is optimized in the dynamic electricity tariff to generate an optimal energy demand schedule. The bid electricity price is calculated for three different cases and the bidding cost and bidding reliability for the optimized energy demand schedule are compared. The results show that the bidding cost is reduced by 37% and bidding reliability is the highest for the proposed electricity forecasting model Hilbert-LSTM.

This research aims to analyze the evolution of water management models in agriculture and determine whether these models align with Integrated Water Resources Management (IWRM). Using the PICO framework question: How have water management models evolved over time and whether their implementation in the agricultural sector has complied with IWRM principles, comparing Latin America with other regions? Articles in English or Spanish were included, while reviews, book chapters, books, and conference papers were excluded. The search was performed in Web of Science (WOS) and SCOPUS in April 2024, with 68 selected for detailed reviews. The methodology of each study was evaluated to identify key trends in IWRM. Results were synthesized, highlighting the influence of water crises in geographic areas, particularly in countries within the tropics. A trend towards basin-level analysis, like the basic management unit, was observed. Additionally, there was an increase in water models incorporating the three approaches of IWRM by the end of the second decade of the 21st century.

The environmental impact of pollution varies based on several conditions, most importantly the number of pollutants released directly into the environment. This research aims to determine the probable levels of pollution and characterize measurements outcomes with GIS techniques, shedding light on spatial correlation and comparing concentrations of heavy metals in different areas of the Haditha refinery in Iraq's Anbar Governorate. Materials and Methods: The concentrations of heavy metals (lead, cadmium, and vanadium) were measured in soil samples collected at a depth of 30 cm from different locations near the new energy refinery in Ramadi city, as well as measuring the accumulation of these elements in the Cynodon dactylon plant in the region. Results: The results pointed out that elemental ratios (V: 0.475, Cd: 2.625, Pb: 0.629) for soil samples and (V: 0.759, Cd: 3.65, Pb: 0.875) for C. dactylon with the record-breaking increase of vanadium concentration in all samples. Conclusion: Spatial analysis revealed that soil in the research area was non-contaminated with the subject elements according to the Iraqi standard as well as the world concentration scale of the heavy elements.

Beyond the conventional five-capital sustainable livelihood framework, this research reveals the significance of philosophical capital—systems of values, local wisdom, and cultural practices—as a crucial dimension in the sustainability of small-scale polyculture fish farmers in Medan City, Indonesia. Through a survey of 89 fish farmers in Medan Labuhan and Medan Belawan Districts, this study develops and validates philosophical capital measurement instruments and analyzes their contribution to livelihood system sustainability through linear and non-linear approaches. Results show that the farmers' livelihood system falls into the "quite sustainable" category (SLCI = 0.571). Social capital emerges as the dominant component (index 0.6067), while philosophical capital (index 0.5290) contributes significantly by integrating traditional values into cultivation practices. Non-linear threshold effects analysis identified significant thresholds for natural capital (0.48), philosophical capital (0.51), and social capital (0.58), revealing how these capitals contribute to sustainability through different critical points. Comparative validation confirms that philosophical capital possesses strong psychometric properties (α = 0.87, AVE = 0.64) with the highest correlation to social capital (r = 0.58, p < 0.01). This research contributes to developing a more holistic sustainable livelihood analysis framework by identifying philosophical capital as a "meta-capital" that influences how other capitals are accessed and maintained. The implications include the importance of an integrative approach to policy development that accommodates local cultural values and dimensions as the foundation for resilience in small-scale polyculture fish farming livelihood systems.

The urgent need for a more responsive built environment in the era of climate change has driven architects, researchers, and construction professionals to adopt integrated design solutions that utilize various technologies to improve buildings' energy performance. This effort aims to reduce dependence on fossil fuels, whose combustion significantly contributes to climate change. Notably, the building sector consumes approximately 41% of electrical energy, with most of this energy used for ventilation, cooling in summer, heating in winter, and artificial lighting. This comparative study employs a descriptive methodology, gathering information about energy sources, their types, and their impact on the construction sector. Additionally, it analyses architectural projects that have adopted smart envelopes as a remedial measure to combat climate change. The research then explores modern treatments for contemporary building envelopes and their transformation into smart envelopes by elucidating the concept of intelligence within these systems. Experiments implementing these methods in the Middle East and North Africa (MENA) region are reviewed, highlighting the benefits and lessons learned. The study emphasizes the impact of renewable energies and their integration with the building envelope, as well as negative treatments at the envelope level that contribute to isolating the building from external environmental conditions. The findings provide a comprehensive description of how different variables affect the energy performance of buildings.

Green areas are considered a major factor in purifying the city’s atmosphere from pollution resulting from the joint use of different lands to improve the city environment. This research aims to study green areas in the center of Al- Samawah city for the period from 2004 to 2022, and to explain the real conditions and developments as far as areal recession and expansion are concerned, due to its importance and its reflection on people’s lives in terms of environmental, neighborhood and overall impact. The study covered different areas and is located partly on the Euphrates River passing through the center of Al-Samawah to identify green spaces and abandoned spaces based on satellite image data of Landsat-5 TM2000, Landsat-7 ETM, and Landsat-8OLV 2015. Images were analyzed using remote sensing programs such as Arc Map and ERDAS. The analysis results showed that the annual rate of vegetation cover deteriorated during the period from 2004 to 2012 by a percentage of around 12.2%. The yearly rate of vegetation cover decrease was 13.8% from 2004 to 2022. While, NDVI value amounted to 50.02% in 2004, to 38% in 2014, then continued to deteriorate until it reached 36.7% in 2022 to all district areas. Finally, the article explains the absence of an important impact in rural regions, highlighting the necessity of the article to assist decision-makers in developing the essential solutions for protecting green spaces around city centers from urban development and reconsidering urban sustainability.

Adaptive governance makes it possible to have a collaborative and flexible approach to resolve intricate environmental problems. This systematic literature review (SLR) was intended to understand some aspects of the framework such as its concepts, methodology, policy dimensions, and its contribution to resilience and sustainability. This SLR utilized the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology to analyze 19 selected high-quality papers from the Scopus database to identify principal trends, challenges, and research deficiencies. The findings suggest that adaptive governance is practiced in diverse settings, such as water resource management, biodiversity conservation, and climate change adaptation, using polycentric and co-management approaches. Institutional rigidity, environmental uncertainty, and deficiencies of long-term evaluation systems still prevail. While cooperation is promoted multilevel governance still requires the involvement of conservative institutions and extensive use of digital technologies. Inclusionary policies and defined evaluation criteria are means through which effectiveness is facilitated, hence strengthening adaptive governance. The practical recommendations in this assessment will help policymakers and practitioners implement an environmental governance strategy that increases the resilience of environmental management.

Multiple-input-multiple-output (MIMO) techniques and effective precoding algorithms are required due to the inherent difficulties of Millimeter-Wave (mmWave) propagation. The challenges include the significant route loss due to high-frequency usage. Further, the sparse channel matrix yields improper channel estimation (CE), resulting in erroneous reception, which limits the implementation of mmWave technology. Therefore, beam formation is required to direct the power in the direction of the user by exploiting the spatial multiplexing of MIMO. The above-stated limitations, along with hardware constraints of using a lesser number of radio frequency (RF) chains, can be mitigated through effective precoding at the transmitter. The efficient utilization of mmWave bandwidth by users is crucial for a spectrally efficient system, as it helps conserve this scarce resource. Hence, this study has examined the spectral efficiency (SE) of mmWave MIMO systems for various precoding strategies, including minimum mean-square estimation (MMSE) precoding, fully digital and hybrid zero-forcing (ZF) precoding, and analog beamforming. The performance in terms of achievable SE has been studied considering variability in the user base and the number of transmit and receive antennas. Simulation results have been presented, showing that the MMSE precoder outperforms the ZF precoder. Furthermore, the dominant fully digital MMSE precoder approaches the SE of single-user MIMO as the number of users increases, compared to the fully digital ZF precoder.