By introducing environmental mediator, for avoiding randomness in selecting indicators and subjectivity in deciding interaction path coefficients to reveal interaction mechanism among forest industry, ecology and environmental capacity, structural Pressure–State–Impact–Response (PSIR) and quantitative Structural Equation Modeling (SEM) are utilized with data on thirty–one provinces of China. We can find that: (i) forest industry has a negative influence on ecology, whereas ecology has a positive influence on industry; (ii) the destructive influence that industry has on ecology is much greater than the positive effect ecology has on industry; and (iii) environmental capacity plays a partial mediating role between industry and ecology. According to these results we can conclude that: (i) forest industry and ecology are not in a symbiotic relationship and have not reached a level of ecological security; (ii) the interaction between industry and ecology is in a period of transition from antagonistic to beneficial; and (iii) industry dominates ecology. However, we prospect the following advice: (i) methods of forest industry should be changed; (ii) new modes of industrial integration and circular economy should be developed; and (iii) the ability of the environment to sustain itself should be enhanced. The level of forest ecological security to discuss the symbiotic mechanism of industry and ecology will propose to be measured in future.

In this study, the decision-making process management of forest tending in the forestry business is decentralized, and forest tending decision-making activities at different points in time are integrated by decision makers at different geographical locations. The decision-making process was analyzed and optimized from a system perspective. Based on the optimized decision-making process, a forest tending business group decision support system (FTGDSS) was established. We first reviewed and discussed the characteristics and development of the forest tending business and forestry decision support system. Business Process Modeling Notation was used to draw a current state flow chart of the forest tending business, to identify and discover important decision points in the process of tending decision-making. We also analyzed the content and attributes of each decision point, and described the system structure, functional framework, knowledge base structure, and reasoning algorithm of FTGDSS in detail. Finally, FTGDSS was evaluated from the two dimensions of the technology adoption model. FTGDSS integrates different levels of time-space decision-making activities, historical tending data, business plans, decision-makers' management tendencies into the decision-making process and automatically extracts decision-making data from the forest business process management enterprise resource planning system (Smartforest) that improves the ease of use of the decision support system (DSS). It also improves the quality of forest tending decisions, and enables the DSS to better support multi-target management strategies.

Linear programming formulations of forest ecosystem management (FEM) problems proposed in the 60s have been adapted and improved upon over the years. Generating management alternatives for forest management planning is a key step in building these models. Global forests are diverse, and a variety of models have been developed to simulate management alternatives. Climate change has made forest management calculations even more complex, requiring flexibility, diverse parameters, models, and methods. Despite this complexity, consistent concepts can be applied in developing management alternatives for forest management planning. This work describes iGen, a flexible forest prescription generator that applies the AI technique Rule-Based System (AI-RBS). iGen projects the state and associated inputs and outputs for a set of management units using rules from its knowledge base. An Inference Engine uses the rules to simulate a set of prescriptions in a tree-like graph structure. Without needing IT specialists, forest managers can describe the potential development of their forest through variables, rules, formulas, functions, and procedures. A key feature of iGen is that it is not limited to, adapted to, or focused on any specific region, landscape, forest condition, projection method, or yield function. Instead, it aims to maximize generality, enabling it to address a broad range of FEM problems. This article introduces iGen, explaining its concepts, structure, and algorithms through two FEM problems: natural regeneration with shelterwood harvests and plantation/coppice. For data and iGen source programs, visit github.com/…/iGen.

Tackling the intricacies of network dismantling in complex systems poses significant challenges. This task has relevance across various practical domains, yet traditional approaches focus primarily on singular metrics, such as the number of nodes in the Giant Connected Component (GCC) or average pairwise connectivity. In contrast, we propose a unique metric that concurrently targets nodes with the highest degree and reduces the GCC size. Given the NP-hard nature of optimizing this metric, we introduce MaxShot, an innovative end-to-end solution that leverages graph representation learning and reinforcement learning. Through comprehensive evaluations on both synthetic and real-world datasets, our method consistently outperforms leading benchmarks in accuracy and efficiency. These results highlight MaxShot’s potential as a superior approach to address the network dismantling problem effectively.

Understory plants are important components of forest ecosystem productivity and diversity. Compared to biomass models of overstory canopy trees, few are available for understory saplings and shrubs and therefore their roles in estimation of forest carbon pools are often ignored. In this study, we harvested 24 understory species including 4 saplings, 9 tree-like shrubs and 11 typical shrubs in coniferous and broadleaved mixed forest in northeastern China and developed the best fit allometric equations of above- and below-ground and total biomass by species-specific or multispecies using morphological measurements of basal diameter, height and crown area as independent variables. The result showed that single basal diameter, height or crown area had good explanatory power for both species-specific and multispecies (p<0.001). The best-fit models included only basal diameter in sapling and tree-like shrubs, and combinations of crown area, height, and basal diameter in typical shrubs. The logarithmic model was most desired among the 4 model forms of linear, quadratic, multiple linear and logarithmic, for species-specific and multispecies. The models we developed should help the estimation of forest ecosystem carbon stocks, especially for belowground component, and provide tools for quantification of individual species biomass of understory plants.

As the primary secondary tree species in Northeast China, Quercus mongolica possesses significant ecological and economic value. This study employed the Biomod2 platform in conjunction with ArcGIS spatial analysis to assess the potential suitable habitat distribution area of Q. mongolica under current climatic conditions. Furthermore, it forecasted the distribution range and niche changes of potentially suitable habitats for Q. mongolica from 2022 to 2090, and pinpointed the key environmental factors influencing its distribution. The findings reveal that the total potential suitable area for Q. mongolica covers 74,994.792 km², predominantly spread across Inner Mongolia, Heilongjiang, Jilin, and other regions. The primary determinants of suitable area distribution were peak temperature of hottest month, lowest temperature of coldest month and altitude. Under future climate scenarios, the potentially suitable habitats of Q. mongolica are anticipated to diminish to varying extents, with the distribution center exhibiting a tendency towards northward migration. Concurrently, the overlap among different climate scenarios is predicted to expand over time. This investigation facilitates a comprehensive understanding of Q. mongolica's adaptation to climate change, enabling informed adjustments and serving as a valuable reference for the preservation and sustainable management of Q. mongolica populations.

Understanding the dynamics of Organic Carbon mineralization is fundamental in forecasting biosphere to atmosphere Net Carbon Ecosystem Exchange (NEE). With this perspective, we developed 3D-CMCC-PSM, a new version of the hybrid Process Based Model 3D‐CMCC FEM where also heterotrophic respiration (R_h) is explicitly simulated. The aim was to quantify NEE as a forward problem, by subtracting Ecosystem Respiration (R_eco) to Gross Primary Productivity (GPP). To do so, we developed a simplification of the Soil Carbon dynamics routine proposed in DNDC [1]. The method calculates decomposition as a function of soil moisture, temperature, state of the organic compartments, and relative abundance of microbial pools. Given the pulse dynamics of soil respiration, we introduced modifications in some of the principal constitutive relations involved in phenology and littering sub-routines. We quantified the model structure related uncertainty in NEE, by running our training simulations over 1000 random parameter-sets extracted from parameters distributions expected from literature. 3D-CMCC-PSM predictability was tested on independent time series for 6 Fluxnet sites. The model resulted in daily and monthly estimations highly consistent with the observed time series. It showed lower predictability in Mediterranean ecosystems, suggesting that it may need further improvements in addressing evapotranspiration and water dynamics.

Linear programming models have been used in forest management planning since the 1960s. These models have been formulated in three basic ways: Models I, II, and III, which are defined by the sequences of management unit states represented by the variables. In Model I, variables represent sequences of states from the beginning of the planning horizon to the end. In Model II, variables represent sequences of states from one intervention to the next. Finally, in Model III, variables represent a single arc in a management unit’s decision tree, i.e., two states. The objectives of this paper are to clarify the definitions of these model variations and evaluate the advantages and disadvantages of each model. This second objective was achieved by formulating a case study problem using each model type. The case study includes three increasingly complex scenarios, each incorporating additional ecosystem services. Results show that despite having more variables and constraints, Model III requires the least time to formulate due to its less dense parameter matrix. Model II has the shortest solution times, followed closely by Model III, while Model I requires the longest times for both formulation and solution. These results are increasingly apparent in more complex scenarios.

This study examines the potential of combining decision support approaches to identify optimal bundles of ecosystem services. A forested landscape, Zona de Intervenção Florestal of Paiva and Entre-Douro and Sousa (Portugal), is used to test and demonstrate this potential. The landscape extends over 14,000 ha, representing 1,976 stands. The property is fragmented into 376 holdings. The overall analysis was performed in three steps. First, we selected six alternative solutions (A to F) in a Pareto frontier generated by a multiple criteria method within a decision support system (SADfLOR) for subsequent analysis. Next, an aspatial strategic multi-criteria decision analysis (MCDA) analysis was performed with the Criterium DecisionPlus (CDP) component of another decision support system (EMDS) to assess the aggregate performance of solutions A to F for the entire forested landscape with respect to their utility for delivery of ecosystem services. For the CDP analysis, SADfLOR data inputs were grouped into two sets of primary criteria: Wood Harvested and Other Ecosystem Services. Finally, a spatial logic-based assessment of solutions A to F for individual stands of the study area was performed with the NetWeaver component of EMDS. The NetWeaver model was structurally and computationally equivalent to the CDP model, but the key NetWeaver metric is a measure of the strength of evidence that solutions for specific land stands were optimal for the unit. Solutions D and B performed best in the aspatial strategic MCDA analysis, and a composite of the maps generated by NetWeaver demonstrated the spatial basis for the performance of solutions D and B in individual land stands. We conclude with a discussion of how the combination of decision support approaches encapsulated in the two systems could be further automated.

In this study, tree selection/plantation DSS (Decision Support Systems) were reviewed and assessed based on essential objectives in the available literature. We reviewed existing DSS using multiple data sources and available online resources such as web interfaces. We compared the existing DSS, focusing primarily on five primary objectives in this study, which DSS may address when it comes to the tree selection, including a) Climate resilience, b) Infrastructure/Space Optimization, c) Agroforestry, d) Ecosystem services, e) Urban sustainability. Climate resilience of tree species and urban sustainability is addressed relatively less in existing systems, which can be holistically internalized in future DSS tools Based on this review, DNN (Deep Neural Networks) is recommended to address achieving trade-offs between complex objectives such as maximizing ecosystem services, climate resilience of tree species, maintaining Agroforestry, and other benefits.

Forest ecosystems provide diverse services and values that contribute to human well-being. Although high proportion of species is still undiscovered, tropical forests alone are thought to host more than 50% of the world’s biodiversity. Beyond supporting via direct use of products like timber, fiber and biomass energy, they play an important role in providing a wide range of ecosystem services such as regulating water flow and quality, water purification, improving infiltration, fresh water, erosion control, carbon sequestration and sedimentation control. They are one of the important parts of terrestrial and the largest carbon pool, occupying an integral position in the global carbon cycle. However, these days, deterioration of nature and forest biodiversity is a severe danger to the global environment. This will be accompanied by increased strains on food supply, causing increased pressure on already delicate ecological systems. Thus, better forest management strategies are necessary for managing forests sustainably. Nevertheless, little is known regarding the existing forest management approaches and their associated benefits and challenges. Multidisciplinary approaches are needed to achieve sustainable management of forests. These can be accomplished if suitable forest management innovations are put in place. Various technologies can also be adopted through applying different forest assessment tools like remote sensing using light detection and ranging technology (LiDAR) in forest management. Harnessing such technologies will definitely result in providing increased socio-economy as well as improved environmental sustainability. However, the speed of transformation depends much on level of commitment of stakeholders including policy makers. As a result, this review is intended to explore existing forest management innovations which are suitable both ecologically as well as socio-economically. This shows that while there is a lot to be improved, there are already developed forest management innovations that are already in use including PFM and MFM, which helps to achieve SFM.

Estimating Forest growing stock volume (GSV) is crucial for forest growth and resource management, as it reflects forest productivity. National measurements are laborious and costly, however integrating satellite data such as optical, Synthetic Aperture Radar (SAR), and Airborne Laser Scanning (ALS) with National Forest Inventory (NFI) data and machine learning (ML) methods has transformed forest management. In this study, Random Forest (RF), Support Vector Regression (SVR), and Extreme Gradient Boosting (XGBoost) were used to predict GSV using Estonian NFI data, Sentinel-2 imagery, and ALS point cloud data. Four variable combinations were tested: CO1 (vegetation indices and LiDAR), CO2 (vegetation indices and individual band reflectance), CO3 (LiDAR and individual band reflectance), and CO4 (a combination of vegetation indices, individual band reflectance, and LiDAR). Across Estonia's geographical regions, RF consistently delivered the best performance. In the northwest (NW), RF achieved an R² of 0.63 and an RMSE of 125.39 m³/plot. In the southwest (SW), it achieved an R² of 0.73 and an RMSE of 128.86 m³/plot. In the northeast (NE), RF achieved an R² of 0.64 and an RMSE of 133.77 m³/plot, and an R² of 0.70 and an RMSE of 120.72 m³/plot was achieved in the southeast (SE). These results underscore RF's precision in predicting GSV across diverse environments, though refining variable selection and improving tree species data could further enhance accuracy.

This paper aims to develop a hierarchical risk assessment model using the newly-developed evidential reasoning (ER) rule, which constitutes a generic conjunctive probabilistic reasoning process. In this paper, we first provide a brief introduction to the basics of the ER rule and emphasize the strengths for representing and aggregating uncertain information from multiple experts and sources. Further, we discuss the key steps of developing the hierarchical risk assessment framework systematically, including (1) formulation of risk assessment hierarchy, (2) representation of both qualitative and quantitative information, (3) elicitation of attribute weights and information reliabilities, (4) aggregation of assessment information using the ER rule and (5) quantification and ranking of risks using utility-based transformation. The proposed hierarchical risk assessment framework can potentially be implemented to various complex and uncertain systems. A case study on the fire/explosion risk assessment of marine vessels demonstrates the applicability of the proposed risk assessment model.

ABSTRACT This study focused for socio-economic benefit of Western shoreline of Lake Ziway ecosystem. The data collected from two woredas they are Adami Tulu Jidu Kombolch (ATJK) and Dugda woreda of fishermen and small scale irrigation users. Lake Ziway great importance food and water for both groups of respondents and additionally sources of raw material, energy, cultivation, organic fertilizers, genetic and medicinal plants. Lake Ziway also has a major economic benefit for both groups of respondents. The sampled fishermen cached mean amount of 2,524Kg per year with minimum and maximum amount of fish 504Kg and 16,800Kg per year respectively and with this fish catching they got average income of 51,398 Birr ($2,570) per year with range of 7,200 Birr ($360) and 288,000 Birr ($14,400) per year. As like of fishermen small scale irrigation also got economic benefit with their production of cereal crops, fruits and vegetables. They produce in average 13.47Quintal of cereal crop and 69.56Quintal of fruits and vegetables per year and they got average income of 7,727 Birr ($386) and with range of 13,714 Birr ($686) per year respectively. this incurred that wetland ecosystem has a lot of socio-economic benefit for the people live near by specially for developing countries like Ethiopia they are more dependent on natural ecosystem like of Lake Ziway. Because of its high importance, we have to protect and conserve and use sustainably of Lake Ziway and similar wetland ecosystems. Key Words: Wetland ecosystem service; Lake Ziway; Socioeconomic benefit; Fishermen; Small scale irrigation users

With increasing electronic medical data and the development of artificial intelligence, Clinical Decision Support Systems (CDSSs) assist clinicians in diagnosis and prescription. Traditional knowledge-based CDSSs follow an accumulated medical knowledgebase and a predefined rule system, which clarifies the decision-making process; however, maintenance cost issues exist in the medical data quality control and standardization process. Non-knowledge-based CDSSs utilize vast amounts of data and algorithms to effectively decide; however, the deep learning black-box problem causes unreliable results. EXplainable Artificial Intelligence (XAI)-based CDSS provides a valid rationale and explainable results. It ensures trustworthiness and transparency by showing the recommendation and prediction results process through explainable techniques. However, existing systems have limitations, such as the scope of data utilization and the lack of explanatory power of AI models. This study proposes a new XAI-based CDSS framework to address these issues; introduce resources, datasets, and models that can be utilized; and provides a foundation model to support decision-making in various disease domains. Finally, we propose future directions for CDSS technology and highlight societal issues that need to be addressed to emphasize the potential of CDSS in the future.

Livestock management is challenging for resource-poor (R-P) farmers due to unavailability of quality feed, limited professional advice and rumor-spreading about animal health condition in a herd. This research seeks to improve animal health in southern Africa by promoting sericea lespedeza (Lespedeza cuneata), a nutraceutical fodder legume. An automated geospatial model for precision agriculture (PA) can identify suitable locations for its cultivation. Additionally, a novel approach of radio frequency identifier (RFID) supported telemetry technology can track animal movement, and the analyses of data using artificial intelligence can determine sickness of small ruminants. This RFID-based system is being connected to a smartphone app (under construction) to alert farmers of potential livestock health issues in real-time so they can take immediate corrective measures. An accompanying Decision Support System (DSS) site is being developed for R-P farmers to obtain all possible support on livestock production, including the designed PA and RFID-based DSS.

Taiwan’s auditors have suffered from processing excessive audit data, including drawing audit evidence. This study advances sampling techniques by integrating machine learning with sampling. This machine learning integration helps avoid sampling bias, keep randomness and variability, and target the risker samples. We first apply a Naive Bayes classifier to classify data into some classes. Next, a user-based, item-based, or hybrid approach is employed to draw audit evidence. The representativeness index is the primary metric for measuring the representativeness of audit evidence. The user-based approach denotes the selection of samples between two percentiles in a class as audit evidence. It may be equivalent to a combination of monetary and variable sampling methods. The item-based approach represents the choice of risky samples as audit evidence. It may be identical to a combination of non-statistical and monetary sampling methods. Auditors can hybridize those user-based and item-based approaches to balance representativeness and riskiness in selecting audit evidence. Three experiments show that sampling using machine learning integration has the benefits of drawing unbiased samples, handling complex patterns, correlations, and unstructured data, and improving efficiency in sampling big data. However, the limitations are the classification accuracy output by machine learning algorithms and the range of prior probabilities.

Strip steel surface defect recognition research has important research significance in industrial production. Aiming at the problems of defect feature extraction difficulty, slow detection speed and insufficient dataset, YOLOv5 is improved on the basis of YOLOv5, and YOLO-LFPD (Lightweight fine particle detection) model is proposed. By introducing the RepVGG (Re-param VGG) module, the robustness of the model to the surface defect features of strip steel is enhanced, and the expressive ability of the model is improved. FasterNet is used to replace the backbone network, which ensures the accuracy and accelerates the inference speed, making the model more suitable for real-time monitoring. The use of pruning, GA genetic algorithm with OTA loss function method further reduces the model size while better learning the strip steel defect feature information, thus improving the generalisation ability and accuracy of the model. The experimental results show that the introduction of the RepVGG module and the use of FasterNet can well improve the model performance, with a reduction of 48% in the number of parameters, a reduction of 13% in the number of GFLOPs, an inference time of 77% of the original, and an optimal accuracy compared with the network models in recent years. The experimental results on the NEU-DET dataset show that the accuracy of YOLO-LFPD is improved by 3% to 81.2%, which is better than other models, and provides new ideas and references for the lightweight strip steel surface defect detection scenarios and application deployment.

The article presents a model for optimizing biomass supply chains using the linear programming framework integrated with a Geographic Information System. Based on the distance from a given type of biomass resource, its calorific value, price, and transportation costs, the model identifies the optimal source of biomass, allowing it to cover the demand for the required total energy value with the lowest possible costs. The case study includes the Połaniec power plant in southeastern Poland and potential sources of forest biomass and agricultural straw within 100 km of the plant. Unit costs of biomass varied depending on biomass availability and energy demands. For energy demand on the level of 1 PJ yearly, the lowest unit costs of biomass (4.08 €/MJ) were when all kinds of biomass were available, and the highest (5.47 €/MJ) was when only stacked wood was available. As energy demand increased, unit costs increased, and the ability to meet this demand with just one type of biomass decreased. The energy biomass sector can utilize the model to benefit both biomass producers and their final buyers.

Global change and human activities are altering biodiversity at an unprecedented rate worldwide, leading to sharp declines in global biodiversity and other ecological issues. Over the past 30 years, ecologists have increasingly focused on the question of whether and how the ongoing loss of biodiversity affects ecosystem functioning. This interest has led to the emergence of the field of biodiversity and ecosystem functioning (BEF) as a major research area in ecology. However, historically, researchers have predominantly focused on individual ecosystem functions, neglecting the capacity of ecosystems to provide multiple ecosystem functions simultaneously, known as ecosystem multifunctionality (EMF). This article reviews advances in EMF research, including the selection of functional indicators in EMF studies, different dimensions of biodiversity, the impacts of microbial diversity and biotic interactions on EMF, and drivers of EMF such as non-biological factors. By comprehensively analyzing the overall effects of aboveground biodiversity, belowground biodiversity, and non-biological factors on EMF, the study emphasizes the need to enhance research and application of ecosystem multiserviceability (EMS) approaches.

The systems ESTE are running in nuclear crisis centers at various levels of emergency preparedness and response in Slovakia, the Czech Republic, Austria, Bulgaria, and Iran (at NPP monitored by International Atomic Energy Agency, IAEA). ESTE is a decision support system, running 24/7, and serves the crisis staff to propose actions to protect inhabitants against radiation in case of a nuclear accident. ESTE is also applicable as decision support system in case of a malicious act with radioactive dispersal device in an urban or industrial environment. Dispersion models implemented in ESTE are Lagrangean particle model (LPM) and Puff trajectory model (PTM). Described are models approaches as implemented in ESTE. PTM is applied in ESTE for the dispersion calculation near the point of release, up to 100 km from the point of nuclear accident. LPM for general atmospheric transport is applied for short-range, meso-scale and large-scale dispersion, up to dispersion on the global scale. Additionally, a specific micro-scale implementation of LPM is applied for urban scale dispersion modelling too. Dispersion models of ESTE are joined with radiological consequences models to calculate a complete spectrum of radiological parameters - effective doses, committed doses and dose rates by various irradiation pathways and by various radionuclides. Finally, radiation protective measures, like sheltering, iodine prophylaxis, or evacuation, evaluated on the base of predicted radiological impacts are proposed. Dispersion and radiological models of the state-of-the-art ESTE systems are described. Results of specific analyses, like number of particles applied, initial spatial distribution of the source, height of the bottom reference layer, are presented and discussed.

Big Data and blockchain technology are coming together to revolutionize how decisions are made in a decentralized way across various industries. This review looks at how these technologies, along with distributed systems, can improve data security, transparency, and real-time processing, making decision-making more efficient and informed. The integration enhances data security with unchangeable records, increases transparency and traceability, and supports real-time data analysis. However, there are challenges to overcome, including scalability, data privacy, interoperability, regulatory compliance, and high costs. By examining case studies such as Estonia's healthcare system, IBM and Walmart's Food Trust, and the Brooklyn Microgrid project, we explore the practical applications and benefits of combining Big Data with blockchain. Despite these hurdles, the review finds that the ongoing advancements and innovative solutions in these technologies offer significant promise. They are set to drive the adoption and effectiveness of decentralized decision-making, ultimately leading to better efficiency and outcomes across multiple sectors.

Early and accurate mapping of winter canola planting areas provides critical support for sustainable cropland management. Although some methods have been proposed to map the winter canola at the flowering or later stage, mapping winter canola planting areas at the early stage is still challenging, due to the insufficient understanding of the multi-source remote sensing features sensitive for winter canola mapping. This study proposed an early-stage winter canola area mapping method by using the combination of optical and synthetic aperture radar (SAR) data. We analyzed the spectral features, backscatter coefficients, and textural features of winter canola based on Sentinel-2 and Sentinel-1 images. Random forest (RF) and Support Vector Machine (SVM) classification models were built to map winter canola based on early-stage images and field samples in 2017 and then apply the best model to corresponding satellite data in 2018-2022. Results showed that: (1) The red edge and near-infrared-related spectral features were most important for the mapping of early-stage winter canola, followed by VV, DVI and GOSAVI, which also showed greater differences than the other features; (2) Based on Sentinel-1 and Sentinel-2 data, winter canola could be earliest mapped around 130 days prior to ripening (i.e., early overwinter stage) with the F-score over 0.85 and the OA over 81%; (3) Adding Sentinel-1 could improve the OA by about 2%-4% and the F-score by about 1% -2% in winter canola mapping. (4) The F-score of winter canola mapping based on the classifier transfer approach in 2018-2022 varied between 0.75 and 0.97, and the OA ranged from 79% to 86%. This study demonstrates the potential of early-stage winter canola mapping using the combination of Sentinel-2 and Sentinel-1 images, which could provide valuable and timely information for stakeholders and decision makers.

Building information modeling (BIM) is the digital representation of physical and functional characteristics (such as geometry, spatial relationship, and geographic information) of a facility to support decisions during its life cycle. BIM has been extended beyond 3D geometrical representations in recent years, and now includes time as a fourth dimension and cost as a fifth dimension, as well as such other applications as virtual reality and augmented reality. The Internet of Things (IoT) has been increasingly applied in various products (smart homes, wearables) to enhance work productivity, living comfort, and entertainment. However, research addressing the integration of these two technologies (BIM and IoT) is still very limited, and has focused exclusively on the automatic transmission of sensor information to BIM models. This paper describes an attempt to represent and visualize sensor data in BIM with multiple perspectives in order to support complex decisions requiring interdisciplinary information. The study uses a university campus as an example and includes several scenarios, such as an auditorium with a dispersed audience and energy saving options for rooms with different functions (mechanical/electric equipment, classrooms, and laboratory). This paper also discusses the design of a common platform allowing communication among sensors with different protocols (Arduino, Raspberry Pi), the use of Dynamo to accept sensor data as input and automatically redraw visualized information in BIM, and how visualization may help in making energy-saving management decisions.

Currently, there is a marked increase in the importance of the environmental and cultural ecosystem services of the forest. Among the social benefits, protection of human health and improvement of human well-being come to the fore. Forests, both reserve and commercial, benefit human health. However, people often have negative attitudes toward forest management activities, especially near urban areas, and prefer forest management that results in a forest that is close to natural. Second-growth forest and clear-cutting are the most visible manifestations of ongoing forest management in forests. The purpose of this study was to compare the benefits derived from exposure to second-growth forest, clear-cutting and mature forest stands. Fifty-five students of the Warsaw University of Life Sciences-SGGW (WULS-SGGW) participated in the study. The study was conducted in October 2023 in a forest within the city of Otwock, within the range of the Warsaw metropolitan area. Participants were exposed for 15 minutes to selected plots: second- growth forest, clear-cutting area and mature forest stand. Before the experiment (PRE-Test control trial) and after each of the exposures four psychological questionnaires were administered: the Positive and Negative Affect Schedule (PANAS), the Restorative Outcome Scale (ROS), the Subjective Vitality Scale (SVS) and Profile of Mood States (POMS). The results indicate that not all analyzed features of the forest landscape have a regenerative effect on people. Exposure to a commercial forest, only in the case of a mature stand, yielded the most benefits in terms of mood improvement. In contrast, exposure to clear-cutting area resulted in a decrease in positive feelings and an increase in negative feelings, leading to a complete disturbance of mood, an increase in tension, depression and a decrease in feelings of vigor. Clear-cutting area has significantly worse restorative properties compared to mature forest stands and second-growth forest. This observation is relevant to forest management within city limits and in the suburban zone of large cities. Not only is clear-cutting area not accepted by people visiting forests for recreational purposes, but it leads to a detraction from the health benefits provided by forests.

Prescribed fire is increasingly utilized for conservation and restoration goals, yet there is limited empirical evidence supporting its effectiveness in reducing wildfire-induced damages to valued resources of significant worth—whether natural, cultural, or economic. This study evaluates the efficacy of prescribed fire in reducing wildfire severity to LANDFIRE-defined vegetation classes and valued resources impacted by the 2017 West Mims event. Wildfire severity, measured using the differenced normalized burn ratio (dNBR) index, was highly heterogeneous both within and between vegetation classes; however, profound differences between treated and untreated areas were evident. The beneficial effects of prescribed fire were most pronounced within ca. two-years post-treatment but remained evident beyond three-years relative to untreated areas. For example, actively managed areas that were treated with prescribed fire just one month prior to the West Mims event exhibited an 88% reduction in mean wildfire severity relative to unmanaged areas. When post-treatment duration reached 38 months, mean wildfire severity remained 17% lower than untreated areas. Findings from this study reveal significant reductions in both average and peak wildfire severities in areas treated with frequent fire, supporting burn rotations of one to two years to balance fuel reduction and ecological conservation.

The wellness economy has developed rapidly recently, particularly in wellness tourism. Wellness tourism transforms sustainable strategy through the availability of extended multi-actor engagement in service innovation to further the tourism experience. Therefore, when operating a wellness tourism destination, it is necessary to encourage multi-actor engagement in service innovation to achieve sustainable development. This paper introduces a mixed research method with the SWOT-AHP-QSPM, as it qualitatively analyzes the advantages, disadvantages, opportunities, and threats, constructs an evaluation index system, conducts quantitative analysis using the analytic hierarchy process, determines the index weight and weighted score, and calculate the sustainable development strategy, which is appropriate for wellness development of Chongqing Tea Mountain and Bamboo Forest National Forest Park. Purposive field surveys and personal interviews were conducted in Chongqing forest tourism destinations, involving 356 effective respondents. The research reveals that multi-actor engagement significantly enhances sustainable development. These findings bridge service innovation knowledge gaps and provide valuable insights for forest tourism destination management and sustainable policy strategies to elevate multi-actor engagement in forest sites.

Chronic stress, culminating in burnout, evolves gradually and is notoriously difficult to detect due to vague symptoms and individuals' hesitance to acknowledge their struggles. To expedite the identification and recognition of this condition, enabling timely psychological intervention, the integration of Artificial Intelligence (AI) is increasingly considered. This research does not assert the feasibility of an AI system managing all aspects of chronic stress's impact. However, it highlights the capability of current technology to detect stress indicators based on quantifiable data like Heart Rate (HR) and organizational climate dimensions. Although other physiological markers such as oximetry, skin galvanic response, and EKG have been explored, they have not shown reliable differentiation between stress and joy. Focused on the unique context of school principals in the Bedouin regions of Israel, this study investigates the interplay between HR, organizational climate, and stress levels. It introduces a novel application of a fuzzy logic tool that combines HR and organizational climate metrics to aid in stress diagnosis. This tool incorporates the psychologist's expertise to provide real-time data crucial for developing effective coping strategies. While the AI-supported fuzzy system does not replace professional psychological intervention, it significantly enhances the speed of condition identification and intervention planning, thus shortening the response time to stress-related issues in educational leadership within culturally specific settings. The application of such AI-driven tools is pivotal for sustaining the well-being and effectiveness of educational leaders, thereby supporting the broader goal of educational sustainability.

We study a two-echelon supply chain made up of a supplier and a manufacturer, both of which can reduce their component/product carbon emissions. With the vertical technology spillovers, we explore the optimal decisions of centralized and decentralized supply chains with price dependent demand and propose coordination strategy for the decentralized supply chain. Considering the cost contraction effectiveness of the technology spillovers, the centralized and decentralized game theoretic models of a two-echelon supply chain are developed to investigate optimal decisions of pricing and carbon emissions reduction. Through a systematic comparison and numerical analysis, we show that the profits of both players and the entire supply chain improve with the effect of technology spillovers increasing. Carbon emissions reduction will be taken by various protective measures so that the supplier and the manufacturer who do not innovate can hardly share the results of innovating via the “free-riding” methods when the technology spillover is relatively small. We also propose a revenue-cost sharing contract through bargaining to enhance the performance of the decentralized supply chain.

Reducing fertilizer-N rate, applying nitrification inhibitor (NI), and incorporating straw are widely recommended to improve N use efficiency of crops and decrease N losses. However, how these practices affect crop N uptake by regulating the fertilizer-N fate during the growing season is not well understood. We conducted a field 15N tracer study to explore the effectiveness of reduced N application, NI, and straw incorporation on fertilizer-N fates for maize cultivation in Northeast China. The following six treatments were used: 1) no N fertilization (control); 2) 200 kg urea-N ha−1 (100%N); 3) 200 kg urea-N ha−1 and 2400 kg dry-straw ha−1 (100%N+S); 4) 160 kg urea-N ha−1 (80%N, i.e., 20% reduction of N fertilization); 5) 160 kg urea-N ha−1 and nitrification inhibitor (Nitrapyrin) (80%N+NI); 6) 160 kg urea-N ha−1, nitrification inhibitor and 2400 kg dry-straw ha−1 (80%N+NI+S). Our results showed that the five N fertilization treatments yielded 16%–25% more grain and 39%–60% more crop N uptake than the control receiving no N fertilizer, but the differences among the five treatments were not statistically significant. In contrast to plant growth, changes in fertilizer-N fates in soil and plant pools were more pronounced. Compared with the 100%N treatment, 20% fertilizer-N reduction (80%N) decreased 15N concentration in topsoil and plant pools but increased the proportion of plant 15N recovery at harvesting (N use efficiency, NUE15N, 60% vs. 50%) (P &lt; 0.05). Nitrapyrin co-application (80%N+NI) delayed soil nitrification and increased soil 15N retention at harvesting (52% vs. 36%), thereby decreasing NUE15N significantly (P &lt; 0.05). Straw incorporation (100%N+S) decreased fertilizer-N retention compared with NI co-application because it promoted NUE15N significantly (P &lt; 0.05). The combined application of NI and straw (80%N+NI+S) was more conducive to fertilizer-N retention in soil. Our findings demonstrate that NI and straw additions are efficient strategies for stabilizing fertilizer-N in soils and potentially minimizing N loss; however, their effects on NUE15N vary and the related mechanism must be further clarified in long-term trials.

Ambrosia trifida is a kind of malignant invasive plant, which has very high reproductive and en-vironmental adaptability. Through strong resource acquisition ability and allelopathy, it could inhibit the growth and reproduction of surrounding plants, destroy the stability of an invasive ecosystem. It is very important to predict the change of suitable distribution area of Ambrosia trifida with climate change before implementing scientific control measures. Based on 106 Ambrosia trifida distribution data and 14 environmental data, the optimal parameter combination (RM = 0.1, FC = LQ) was obtained by using the MaxEnt model optimized by Kuenm package, the potential suit-able areas of Ambrosia trifida in Northeast China under three different climate scenarios (RCP2.6, RCP4.5, RCP8.5) with different emission intensities in the future (2050,2070) were predicted. The changes of Ambrosia trifida suitable area in Northeast China under three climate scenarios were compared, and the relationship between the change of suitable area and emission intensity was analyzed. In general, the suitable area of Ambrosia trifida in Northeast China will expand gradually in the future, and the area of its highly suitable area will also increase with the increasing emission intensity, which is unfavorable to the control of Ambrosia trifida.

The emerging working mode of coordinated trucks and drones has demonstrated significant practical potential in various fields, including logistics and delivery, intelligence surveillance reconnaissance, area monitoring, and patrol. The seamless collaboration between trucks and drones is garnering widespread attention in academia and has emerged as a key technology for achieving efficient and secure transportation. This paper provides a comprehensive and in-depth review of the research status on the routing problem for coordinated trucks and drones, covering aspects such as application background, cooperative modes, configurations, issues have been taken into consideration, and solution methodologies.

The increasing demand for large-scale, high-frequency environmental monitoring has driven the adoption of satellite-based technologies for effective forest management, especially in the context of climate change. This study explores the potential of SAR for estimating the mass of harvesting residues, a significant component of forest ecosystems that impacts nutrient cycling, fire risk, and bioenergy production. The research hypothesizes that while the spatial distribution of residues remains stable, changes in moisture content—reflected in variations in the dielectric properties of the woody material—can be detected through SAR techniques. Two models, generalized linear model (GLM) and random forest (RF), were used to predict the mass of residues using interfer-ometric variables (phase, amplitude and coherence) as well as the backscatter signal from several acquisition pairs. The models provided encouraging results (R2 of 0.48 for GLM and 0.13 for RF), with acceptable bias and RMSE. We concluded that SAR data are feasible to predict the mass of harvesting residues and the findings could lead to improved monitoring and management of forest residues, contributing to sustainable forestry practices and enhanced bioenergy resource utilization.

(1) Background: Artificial intelligence (AI) has existed in some form for decades, but recent rapid advances in a subset called machine learning (ML) — and more specifically deep learning (DL), a neural network-based approach — have made headlines for the potential to revolutionize and automate multiple large sectors of society, including scientific research and the healthcare field. Furthermore, large language models (LLMs) — which are built on DL — could lead to a more seamless, natural interaction between humans and computers. (2) Methods: We reviewed numerous publications on this subject from recent years. (3) Results: We found these studies collectively show that AI is positively disrupting both biomedical research and medical practice, such as optical imaging in surgery guidance. (4) Conclusions: However, we recommend caution in over-reliance on AI in the laboratory or the clinic due to anticipated risks and current limitations.

It’s very well known that the world's attention is currently focused on the energy transition. The energy security, in addition to the drive to reduce greenhouse gas emissions in order to limit global warming, and the generalization of the access to energy, have contributed to adoption of the Moroccan Energy Strategy with a strong focus on Renewable Energies (RE). Morocco is notoriously poor in conventional fossil primary energy resources with an energy dependence on the order of 90%. The energy crisis and that resulted from Covid-19 pandemic and the geopolitical conflicts, combined compounded with steady increase in demand have heavily affected the security and the stability of the country's energy situation. The transition to RE, by strongly engaging in the implementation of several solar, wind and hydro energy projects benefiting from the country's excellent solar and wind energy potential, has made the country the leader in RE in Africa. By 2030, the share of RE in the installed capacity is expected to reach 52%. In this work, an overview of the current situation of RE (particularly solar energy) in Morocco is provided, including, the potentials, obstacles and challenges, and future perspectives. Thanks to its high solar potential location, it’s predictable that Morocco effort will be focused on this field: the Erasmus Plus Innomed Project is a virtuous example of international cooperation, aiming at create a Solar Energy Network, in synergy with EU Partners.

Forest road pavement needs an evaluation methodology based on a comprehensive assessment of road conditions. This research was conducted to evaluate the performance of a method for rating the surface condition of forest roads and eventually to adapt the method to the situation prevailing in a forest road network. The rating method selected as the basis for this experiment was the pavement condition index (PCI) developed by the U.S. Army Corps of Engineers. A 53 km of forest roads were selected contained the most influential factors and conditions variability. Eventually, 201 road segments were delineated between 150-300 m in length. Within the given segments, sample plots were set 20 m in length consecutively. It was concluded that the panel scores for distress and surface condition of sample unit and section differed from forest road pavement condition index (FRPCI) and PCI. Linear regression was used to derive equations between distress and PCI score to determine effective FRPCI parameters that provide a numerical rating for the condition of road segments within the road network, where 0 worlds are the worst possible condition, and 100 is the best possible condition best. Also, regression analysis showed the FRPCI model with a 0.87 correlation for the total of the road is a performance index used for the first time in forest roads. This study showed a range of FRPCI from 7.8 to 96.3, different from PCI and URCI ratings (0.85-45 and 1.2-53). The FRPCI index helps forest managers in road maintenance, harvesting, and planning to use road information.

The Himalaya is well known for high diversity and ethnobotanical uses of medicinal plants. However, not all areas of the Himalayas are well studied. In particular, studies on ethnobotanical uses of plants from the Eastern Himalayas are rare and lacking for many tribes. Past studies primarily focused on listing plants name and their traditional medicinal uses. However, studies on traditional ethnopharmacological practices on medicine preparation had not yet been reported in published literature from the Eastern Himalaya. In this study, we are reporting the first time ethnopharmacological used 24 medicines, their procedures of preparation and listed 53 plant species used for those medicines for Monpa tribe. Such documentations had not yet been done for other tribes in India. Our research demonstrates the urgent need to documents traditional medicine preparation procedures from the local healers before rapid cultural modernization forgets them in transforming country like India. This study should motivate national and international researchers to do more works on ethnopharmacology and bioprospecting.

In recent years, Ethiopia's number of returnee entrepreneurs has increased dramatically, significantly contributing to personal financial achievement and the nation's economic development and transformation. However, their businesses are typically smaller and less profitable than non-migrant entrepreneurs. This study explores factors affecting the success of these returnee migrant entrepreneurs, including individual characteristics, entrepreneurial skills, and experience, financial resources, as well as the mediating role of government policies and institutional support. Data was collected from 390 returnee migrants who started their businesses and analyzed using a robust theoretical foundation and PLS-SEM to understand the impact of success factors. The results show that entrepreneurial skills, experience, and financial resources positively and significantly influence the success of returnee migrant entrepreneurs. Furthermore, social networks, while not directly significantly influencing the entrepreneurial success of returnee migrants are mediated by government policies and institutional support. Professional guidance and counseling provided by experts and mentors of the accelerator program were found to be essential for entrepreneurial success but did not improve the effectiveness of the policy. Financial resources, although crucial, did not significantly impact policy efficacy. These findings underscore the need for tailored policy interventions addressing the challenges faced by returnee migrants, including economic self-sufficiency, socio-psychological support, and effective social networks to boost the success of returnee migrants. Experienced returnee entrepreneurs play a key role in developing a vigorous and supportive entrepreneurial ecosystem through community engagement and policy advocacy. Additionally, personal traits are crucial for entrepreneurial success, while practical experience influences government policies for substantiated policy development in returnee migrant entrepreneurship and its benefits.

With the development of higher voltage power grid, the high and low voltage parallel loops are emerging, which lead to energy losses, even threaten the security and stability of power system. The multi-infeed HVDC configurations widely appearing in AC/DC interconnected power system make this situation even worse. Aimed at energy saving and system security, a decision optimization method for power grid operating condition with high and low voltage parallel loops is proposed in this paper. Firstly, considering hub substation distribution and power grid structure, parallel loop opening schemes are generated with GN algorithm. Then, candidate opening schemes are preliminarily selected from all these generated schemes based on a filtering index. Finally, with the influence on power system security, stability and operation economy in consideration, an evaluation model for candidate opening schemes is founded based on analytic hierarchy process (AHP). And a fuzzy evaluation algorithm is used to find the optimal scheme. Simulation results of New England 39-bus system and an actual power system validate the effectiveness and superiority of this proposed method.

Background/Objectives: Adolescence represents a period of heightened vulnerability to risky sexual behaviours. In Portugal, adolescents are initiating sexual activity at increasingly younger ages. Yet, there are no studies evaluating their knowledge about sexually transmitted diseases and prevention methods at an earlier stage of their development. Study how middle school ad-olescents define their sexuality and assess their level of information regarding sexuality, STI, and prevention methods. Additionally, it explores adolescents' sources of information on sexuali-ty-related issues. Methods: A cross-sectional observational study was conducted involving the application of a questionnaire to 750 9th-grade students of Vila Nova de Gaia, Porto, Portugal. The questionnaire covered topics such as perceptions of sexuality, knowledge of sexuality and STI, methods of transmission prevention, and sources of information. Results: The mean age was 14.4 years, with 19.9% reporting having already initiated sexual ac-tivity. Overall, the knowledge about sexually transmitted infections is low, pointing to 7.08 out of a maximum score of 18. Condoms and immunisation are well-recognized preventive measures, but many misconceptions persist. Better knowledge is associated with better attitudes in the likelihood of having sexual intercourse. Conclusions: This study reveals adolescents' limited sexually transmitted infections knowledge, misconceptions about prevention, and reliance on peers and the internet over trusted sources, highlighting the need for comprehensive sexual education in middle school.

Aiming at the limitations of single-functionality, limited applicability, and complex designs prevalent in current metasurfaces, we propose a terahertz multifunctional and multiband tunable metasurface utilizing a VO2-metal hybrid structure. This metasurface structure comprises a top VO2-metal resonance layer, a middle polyimide dielectric layer, and a gold film reflective layer at the bottom. This metasurface exhibits multifunctionality, operating independently of polarization and incident angle. The varying conductivity states of the VO2 layers, enable the metasurface to achieve various terahertz functionalities, including single-band absorption, broadband THz absorption, and multiband perfect polarization conversion for linear (LP) and circularly polarized (CP) incident waves. Finally, we believe that the functional adaptability of the proposed metasurface expands the repertoire of options available for future terahertz device designs.

The scientific community has shown considerable interest in Industry 4.0 due to its capacity to revolutionise the manufacturing sector through digitalisation and data-driven decision-making. However, the actual implementation of Industry 4.0 within complex industrial settings presents obstacles that are typically beyond the scope of mainstream research articles. In this paper, a comprehensive case-study detailing our collaborative partnership with a leading medical device manufacturer is presented. The study traces their evolution from a state of limited digitalisation to the development of a digital intelligence platform that leverages data and machine learning models to enhance operations across a wide range of critical machines and assets. The main business objective was to enhance the energy efficiency of the manufacturing process, thereby improving its sustainability measures while also saving costs. The project encompasses energy modelling and analytics, Fault Detection and Diagnostics (FDD), renewable energy integration and advanced visualisation tools. Together, these components enable informed decision making in the context of energy efficiency.

Tropical cyclones devastate large areas, take numerous lives and damage extensive property in Bangladesh. Research on landfalling tropical cyclones affecting Bangladesh has primarily focused on events occurring since AD1960 with limited work examining earlier historical records. We rectify this gap by developing a new tornado catalogue that include present and past records of tornados across Bangladesh maximizing use of available sources. Within this new tornado database, 119 records were captured starting from 1838 till 2020 causing 8,735 deaths and 97,868 injuries leaving more than 1,02,776 people affected in total. Moreover, using this new tornado data, we developed an end-to-end system that allows a user to explore and analyze the full range of tornado data on multiple scenarios. The user of this new system can select a date range or search a particular location, and then, all the tornado information along with Artificial Intelligence (AI) based insights within that selected scope would be dynamically presented in a range of devices including iOS, Android, and Windows. Using a set of interactive maps, charts, graphs, and visualizations the user would have a comprehensive understanding of the historical records of Tornados, Cyclones and associated landfalls with detailed data distributions and statistics.

The paper presents a new vision on the energy consumption management in the case of the Small and Medium Enterprises (SMEs), integrated into an advanced decision support platform, with technical and economic benefits on increasing the energy efficiency, which contains modules for database management, profiling, forecasting, and production scheduling. Inside each module, Artificial Intelligence and Data Mining techniques were proposed to remove the uncertainties regarding the dynamic of technological flows. Thus, the data management module includes the Data Mining techniques, that extract the technical details on the energy consumption needed in the development of production scheduling strategies, the profiling module uses an original approach based on clustering techniques to determine the typical energy consumption profiles required in the optimal planning of the activities, the forecasting module contains a new approach based on an expert system to forecast the total energy consumption of the SMEs, and production scheduling module integrates a heuristic optimization method to obtain the optimal solutions in flattening the energy consumption profile. The testing was done for a small enterprise from Romania, belonging to the domain of trade and repair of vehicles. The obtained results highlighted the advantages of the proposed decision support platform on the decrease in the intensity of energy consumption per unit of product, reduction of the purchase costs, and modification of the impact whom the energy bills have on the operational costs.

Cultural ecosystem services has been increasingly influential in both environmental research and policy decision-making, such as for urban green spaces However, its popular definition conflates the concepts of ‘services’ and ‘benefits’ which made it challenging for planners to employ it directly for urban green space management. One the most widely used definition of this non-tangible ecosystem services are “functions of environmental spaces and cultural activities which may then result in the enjoyment of cultural ecosystem benefits”; yet the latter itself have never found its way into official laws and regulations. In this study, via a case study in Singapore, we propose new evidence to re-evaluate and re-position the two of the most important emerging concepts in managing the green spaces in urban areas. Using the transdisciplinary mixed methods of public participation GIS and social media text mining analysis, a wealth of cultural ecosystem services and their associated benefits were reported. This was especially so with regards to recreational and aesthetic services and experiential benefits. Recommendations to improve the park were also suggested, alongside sharing of methodological considerations for future research. Overall, this paper recommends the employment of the redefined cultural ecosystem services conceptual framework to generate relational, data-driven and actionable insights to better support urban green space management, which is not only useful to Singapore governments but also world-wide relevant.

Accurate forecasting of icing thickness has a great significance for ensuring the security and stability of power grid. In order to improve the forecasting accuracy, this paper proposes an icing forecasting system based on fireworks algorithm and weighted least square support vector machine (W-LSSVM). The method of fireworks algorithm is employed to select the proper input features with the purpose of eliminating the redundant influence. In addition, the aim of W-LSSVM model is to train and test the historical data-set with the selected features. The capability of this proposed icing forecasting model and framework is tested through the simulation experiments using real-world icing data from monitoring center of key laboratory of anti-ice disaster, Hunan, South China. The results show that the proposed W-LSSVM-FA method has a higher prediction accuracy and it may be a promising alternative for icing thickness forecasting.

Since the 1960s, forest planners have used linear programming models to develop management plans for large, forested areas. Hundreds of academic papers have presented such models, incorporating multiple objectives, a growing diversity of management interventions, and uncertainty, among other things. Three basic ways to formulate these models have been used: Model I, III, and III. We define these models based on the sequence of management unit states represented by the variables. In Model I, variables represent a sequence of states from the beginning of the planning horizon to the end. In Model II, variables represent a sequence of states from one intervention to the next. Finally, in Model III, variables represent a single arc in a management unit’s decision tree, including only a beginning and an ending state. We formulate each type of model for a case study with three increasingly complex scenarios incorporating additional ecosystem services. Our results indicate that, despite requiring more variables and constraints, Model III requires the least time to formulate, largely because it has the least dense parameter matrix. Model II has the shortest solution times, with Model III close behind. Model I requires both the longest formulation and solution times.

The giant octopus caught off the east coast of Korea is an important fishery resource with the largest haul and is mostly caught using net pots. While giant octopus pots have different shapes, drum, rectangular, and cylinder pots are mostly used. However, these pots have not been thoroughly evaluated. Here, we evaluated the fishing performance of three giant octopus pots, their fluid resistance, and underwater movement. Six sea trials were conducted near the Hupo region in Gyeongsangbuk-do. In total, 163 individuals (62,724 g), 190 individuals (49,913 g), and 154 individuals (52,364 g) were caught using drum, rectangular, and cylinder pots, respectively. Drum pots showed significant differences in the amount of catch by weight (ANOVA test, p&lt;0.05). Moreover, the percentage of minimum conservation size was the lowest in cylinder pots (6.5%). Drum pots showed the highest stability in the underwater stability test, while underwater fluid resistance at 2.5 kt fluid speed was in the following order: drum pots (7.66 kgf) &gt; rectangular pots (7.16 kgf) &gt; cylinder pots (7.04 kgf). Therefore, drum pots showed excellent fishing performance. This study provides foundational data for developing managing systems for giant octopus resources and establishing the extent of fishing gear usage.

Recent advances in satellite technology have brought enormous potential to ecosystem mapping, which is one of the fundamental components of environmental studies. In this paper, a Random Forest classifier is applied for the strict assessment of the efficiency of ecosystem mapping through a detailed comparative analysis between combined Sentinel-1 and Sentinel-2 data and stand-alone Sentinel-2 imagery over three priority ecosystems, including wetlands, riverine areas, and mangroves in Bangladesh. The collocated images, based on the integration of Sentinel-1 data with Sentinel-2 data, would do better than Sentinel-2 imagery alone over various ecosystems. Particularly, in this study, attention focused on the Hakaluki Haor area for the wetlands, the Padma-Jamuna River confluence for the riverine ecosystem, and the Sundarban forest for mangroves. By leveraging Synthetic Aperture Radar (SAR) data in C-band dual-polarization from Sentinel-1 and four spectral bands (blue, green, red, and near-infrared) from Sentinel-2, the study analyzes imagery from December 2022 to February 2023. A 5% cloud masking filter is applied to optical data to enhance accuracy. In this methodology, 70% of the total signature values are used for training the classification model and the remaining 30% for testing. It can be noticed from the results that with the use of fused data, remarkably high accuracy in classification has been improved, such as overall accuracies of 94.17% for mangroves, 87.30% for riverine, and 85.96% for wetland ecosystems. In contrast, the use of singular Sentinel-2 imagery yields lower accuracies of 91.56%, 85.21%, and 82.51% for the respective ecosystems. The integration of radar data is shown to provide critical information, especially in environments with dense vegetation or cloud cover, where optical data alone may be insufficient. The findings of this study underline the limitations of relying on Sentinel-2 imagery to capture complex details of diverse ecosystems and highlight the need to include Sentinel-1 data for a more holistic analysis. This fusion allows improved accuracy to be achieved, which not only brings in more depth of ecological knowledge but also underpins more effective conservation strategies.

This study focuses on predicting PM2.5 levels at the University of Petroșani by employing advanced machine learning techniques to analyze a dataset that encapsulates a wide array of air pollutants and meteorological factors. Utilizing data from IoT sensors and established environmental monitoring stations, the research leverages Random Forest, Gradient Boosting Machines, and Support Vector Regression models to forecast air quality, emphasizing the complex interplay between various pollutants. The models demonstrate varying degrees of accuracy, with the Random Forest model achieving the highest predictive power, indicated by an R2 score of 0.82764. Our findings highlight the significant impact of specific pollutants such as NO, NO2, and CO on PM2.5 levels, suggesting targeted mitigation strategies could enhance local air quality. Additionally, the study explores the role of temporal dynamics in pollution trends, employing time-series analysis to further refine the predictive accuracy. This research contributes to the field of environmental science by providing a nuanced understanding of air quality fluctuations in a university setting and offering a replicable model for similar environments seeking to reduce airborne pollutants and protect public health.

Protected areas serve as an effective mechanism usually supported by a statute to conserve and protect wildlife-rich areas worldwide. Such extensive external management needs elaborate planning, regular monitoring, and periodic assessment of results. Thus, management effective evaluation (MEE) has emerged as one of the major tools for quantifying the performance of any protected area, globally since 2006. India has successfully conducted a complete round of management effectiveness evaluation of all its terrestrial protected areas (except conservation reserves and community reserves), with 25 such areas already undergoing a repeat assessment. Simultaneously, India biennially carries out a country-wide status of forest cover in all its states and union territories. This study has correlated the trend in change of forest cover for the states that contain those 25 repeat evaluated protected areas with their change in MEE scores over the same period. Our study found a positive correlation between the change in forest cover and the change in management effectiveness score from 2005-06 to 2018-19. Owing to increased protection regimes in the PAs including the degraded areas if they make part of such areas, the habitat often shows recovery. Effective habitat conservation is thus intertwined with the monitoring and preserving the wildlife populations in the protected areas, thereby, reduction in forest cover equally impacts the management scores. However, in states where the loss in forest cover has been reported, more protected areas need to be assessed to plan complementary strategies for both habitat and species conservation.

Citizens face online privacy threats from social media, organisations and governments. Privacy-enhancing tools (PETs) can help people to preserve their privacy, but the uptake of these is limited. We developed a conceptual framework for privacy self-protection, using a classification framework of four distinct privacy threats and our own novel staged model of PET adoption requisites. Through an expert survey (N = 12) and a lay user survey (N = 500), we identified suitable PETs for non-expert users and identified potential barriers to PET adoption. Based on the studies and our theoretical framework, we then developed and implemented a PET decision support tool called PEDRO, and conducted expert evaluations (N = 10).

In the field of visualization, understanding users’ analytical process is important to determine the effectiveness of a designed visualization application. To understand the users’ analytical process, numerous studies have been performed to capture and analyze user interactions. Although many studies have emphasized the importance of analyzing user interactions to understand users’ analytical reasoning processes, few have successfully linked these interactions to users’ reasoning processes. This paper introduces an approach that bridges this gap by correlating semantic user interactions with analysis decisions through an interactive wire transaction analysis system and a visual state transition matrix. With the designed analysis system, interactive analysis can be performed to evaluate financial fraud in wire transactions. It also allows the mapping of captured user interactions and analytical decisions back onto the visualization, revealing users’ distinct results. The visual state transition matrix further aids to help understanding users’ analytical flows, revealing their decision-making processes. Classification machine learning algorithms are applied to assess the effectiveness of our approach in understanding analysts’ strategies by connecting them to their decisions. From the study, we observed an average of 72% accuracy in clearly classifying the semantic user interactions. For classifying individual decisions, we observed an average of 70% classification accuracy. This emphasizes the importance of capturing semantic user interactions to understand users’ analytical processes. Overall, it is determined that the proposed approach not only enhances the understanding of analytical behaviors but also offers a robust approach for evaluating user interactions in visualization tools.

Intellecta dataset emerges as an innovative synthetic dataset, engineered to enhance the cognitive processing capabilities of contemporary language models. With a composition of 11.53 billion tokens, integrating 8.01 billion tokens of synthetic data with 3.52 billion tokens of rich textbook data, Intellecta is crafted to foster advanced reasoning and comprehensive educational narrative generation. Leveraging the Mixtral-8x7B-Instruct-v0.1 model, the dataset facilitates the generation of complex thought processes and detailed, textbook-style explanations, thus enabling language models to engage in both critical thinking and profound educational discourse. This hybrid dataset stands as a testament to the potential of synthetic data in pushing the boundaries of AI, offering a repository that is not only vast and varied but also refined to align with ethical standards and intellectual rigor.

Objective: The goal of this review is to provide a succinct, yet comprehensive, guide to the an-tepartum management of multiple gestations. Methods: This narrative review on twins and multiple gestation provides a concise guide for providers with this ever-growing patient population. PubMED was utilized for literature search. Each reference was evaluated and graded for quality of evidence by US Preventative Services Task Force and Cochrane Review guidelines. Screening, surveillance, and management strategies are delineated succinctly and organized by type of multiple gestation being managed from the lowest risk dichorionic twin pair to the increasingly escalating risks of twins and higher order multiples sharing a placenta and/or amniotic sac. Results: The incidence of twin pregnancies has grown over the past several years, due in large part to delayed reproductive timing and surge in assisted reproductive technologies. Twin pregnancies are associated with higher morbidity for both gravidas and offspring, warranting evidence-based approach in counseling and management. Conclusions: We note the important differences among multiples in chorionicity and amnionicity, in the importance of close surveillance and early delivery. Graded clinical pearls and evi-dence-based recommendations are presented to guide obstetric providers.

With the rapid developments that have been made in computer vision and computer technology, semantic segmentation of high-resolution images has emerged as a mainstream and challenging task. The, segmentation of water areas remains a complex task due to the similar features between water areas or between water areas and other land objects. Meanwhile, since coastlines and riverbanks are typically presented in irregular shapes, it is difficult to obtain the categories of pixels at boundaries. This paper proposed the use of a Water-Land Boundary Attention Network (WLBANet) to improve the accuracy of pixels at boundaries between water and land. This network also reinforced the performance of contextual information extraction into distinguishing between water areas and other land objects. To prove the validity of this proposed network, WLBANet was performed on the Water Land Dataset (WLD), which is also by this paper, and which contains water areas and other land objects categories. The results demonstrates that the proposed method has significant effectiveness.

The rapid increase in the number of mobile phone base stations (MPBS) has raised global concerns about the potential adverse health effects of exposure to radiofrequency electromagnetic fields (RF-EMF). The application of machine learning techniques can enable healthcare professionals and policymakers to proactively address concerns surrounding RF-EMF exposure near MPBS. In this study, we investigated the potential of machine learning models to predict health symptoms associated with RF-EMF exposure in individuals residing near MPBS.We utilized support vector machine (SVM) and random forest (RF) algorithms, incorporating 11 predictors related to participants' living conditions. A total of 699 adults participated in the study, and model performance was assessed using sensitivity, specificity, accuracy, and the area under the receiver operating characteristic curve (AUC). The SVM-based model demonstrated strong performance, achieving accuracies of 85.3%, 82%, 84%, 82.4%, and 65.1% for headache, sleep disturbance, dizziness, vertigo, and fatigue, respectively. The corresponding AUC values were 0.99, 0.98, 0.920, 0.89, and 0.81. Compared to the RF model and a previously developed model, the SVM-based model exhibited higher sensitivity, particularly for fatigue, with sensitivities of 70.0%, 83.4%, 85.3%, 73.0%, and 69.0% for these five health symptoms. Particularly for predicting fatigue, sensitivity and AUC were significantly improved (70% vs 8% and 11.1% for SVM, MLPNN, and RF, respectively, and 0.81 vs 0.62 and 0.64, for SVM, MLPNN, and RF, respectively). These findings suggest that machine learning methods, specifically SVM, hold promise in effectively managing health symptoms in individuals residing near or planning to settle in the vicinity of MPBS.

Caries is a prevalent oral disease that primarily affects children and teenagers. Advances in ma-chine learning have caught the attention of scientists working with decision support systems to predict early tooth decay. Current research has developed machine learning algorithm for caries classification and reached high accuracy especially in ML for image data. Unfortunately, most studies on dental caries only focus on classification and prediction tasks, meanwhile dental carries prevention is more important. Therefore, this study aims to design an efficient feature for decision support system machine learning based that can identify various risk factors that cause dental caries and its prevention. The data used in the research work was obtained from the 2018 Korean Children's Oral Health Survey, which totaled nine datasets. The experimental results show that combining the mRMR and GINI Feature Importance methods when training with the GBDT model achieved the optimum performance of 95%, 93%, 99%, and 88% for accuracy, F1 score, precision, and recall, respectively. So, the proposed method has provided effective predictive model for dental caries prediction.

This study examines the capacity of a Site-Specific Fodder Management Decision Support System (SSFM-DSS) model to assist in successful cultivation of sericea lespedeza [SL; Lespedeza cuneata (Dum.-Cours.) G. Don.], with a specific focus on small-scale agricultural systems in the southeastern United States (U.S.). The study emphasizes incorporating advanced geospatial technologies, such as Geographic Information Systems (GIS), remote sensing, and Global Navigation Satellite Systems (GNSS), to improve fodder production. The research showcases the versatility of SL under varying environmental conditions, underscoring its significance in promoting sustainable livestock production. The methodology integrates empirical field data, geospatial analysis, and predictive modeling to formulate an SSFM strategy customized to address the distinct difficulties climate change presents, such as sudden and severe drought conditions. The study introduces an automated geospatial model for assessing the appropriateness of SL production throughout Alabama, Georgia, and South Carolina. The model uses many environmental criteria, including soil properties, climate fluctuations, and terrain. Furthermore, the research was instrumental in development of a WebGIS Dashboard, which offers farmers a decision assistance system to enhance the sustainable production of SL. The results highlight the significant impact that a SSFM-DSS can have on promoting sustainable agricultural practices, providing a solution to address the issues of food security and environmental concerns in agriculture.

There is increasing demand from stakeholders for tools to support outcome-based approaches in environmental management. For such tools to be useful, understanding user requirements is key. In Scotland, UK, stakeholders were engaged in the development of an Environmental Decision Support System (EDSS) to support the management of land and freshwater resources for multiple policy outcomes. A structured participatory engagement process was employed to determine stakeholder requirements, establish development principles to fulfil these requirements and road-test prototypes. The EDSS specification that emerged from this bottom-up process was spatially-explicit, free at the point of use, and touch and mobile device compatible. This application, which is under development, does not closely resemble most existing published EDSS. We suggest that there is a mismatch between the way scientists typically conceptualise EDSS and the kinds of applications that are likely to be useful to decision-makers on the ground. Interactive mobile and web-based geospatial information services have become ubiquitous in our daily lives, but their importance is not reflected in current literature on EDSS. The current focus in environmental management on adaptive, stakeholder-centred strategies based on outcomes offers an opportunity to make better use of these new technologies to aid decision-making processes.

This paper explores the capability of various machine learning algorithms, including Random Forests and advanced gradient boosting techniques such as XGBoost, LightGBM, and CatBoost, to predict customer churn in the telecommunications sector. For this analysis, a dataset available to the public was employed. The performance of these algorithms was assessed using recognized metrics, including Accuracy, Precision, Recall, F1-score, and the Receiver Operating Characteristic Area Under Curve (ROC AUC). These metrics were evaluated at different phases: subsequent to data preprocessing and feature selection; following the application of SMOTE and ADASYN sampling methods; and after conducting hyperparameter tuning on the data that had been adjusted by SMOTE and ADASYN. The outcomes underscore the notable efficiency of upsampling techniques such as SMOTE and ADASYN in addressing the imbalance inherent in customer churn prediction. Notably, the application of random grid search for hyperparameter optimization did not significantly alter the results, which remained comparatively unchanged. The algorithms' performance post- ADASYN application marginally surpassed that observed after SMOTE application. Remarkably, LightGBM achieved an exceptional F1-score of 89% and an ROC AUC of 95% subsequent to the ADASYN sampling technique. This underlines the effectiveness of advanced boosting algorithms and upsampling methods like SMOTE and ADASYN in navigating the complexities of imbalanced datasets and intricate feature interdependencies.

Dhaka one of the fastest-growing megacities in the world is increasingly vulnerable to urban flooding due to rapid urbanization, inadequate drainage infrastructure, and climate change-induced extreme weather events. This study presents the development of an urban flood forecasting system for Dhaka City using the MIKE+ hydrodynamic modeling software. The system integrates detailed hydrological and hydraulic models to simulate urban flooding scenarios, accounting for complex drainage networks and catchment characteristics. Real-time data from river gauges, rainfall stations, and drainage systems are incorporated to enhance the accuracy and reliability of the forecasts. The model's predictive capability is validated against historical flood events, demonstrating its effectiveness in forecasting flood extents, depths, and durations in critical areas of the city. This system serves as a valuable tool for city planners, emergency response teams, and decision-makers, enabling proactive flood management and risk mitigation. The study underscores the importance of such advanced modeling tools in safeguarding urban populations and infrastructures from the increasing threat of urban floods in Dhaka and similar rapidly urbanizing cities.

"Politography" refers to the study or portrayal of political events, figures, and systems. It is used to analyze political trends, to visualize data, and create a data driven narrative. Decision support tools (DST) for policy makers tend to be narrowly defined in terms of specific domain applications. In this work, we describe the design and construction of a DST related to intergroup conflict management and resolution, focused on the Israeli-Palestinian conflict. It provides a case study that can be generalized to other applications. The DST, labeled T-Politography, integrates over 9000 indicators pertaining to the Israeli-Palestinian conflict, in a hierarchical structure that begins with the specific geographical areas influenced by the Israeli-Palestinian conflict. These include geographical areas: A, B, and C in the West Bank and East Jerusalem. The designated areas have distinct legal status specified in the 1993 Oslo Accords between Israel and the Palestinians Authority (PA). The second level of the DST hierarchy consists of three domains that characterize Israeli and Palestinian levels of control, in each one of the geographic areas: security, geo-spatial, and economic. In addition, the T-Politography DST contains contextual data relevant to the Israeli and Palestinian control such as political, diplomatic, social, and legal domains. Each domain is designated as influencing either Israeli or Palestinian Authority (PA) control and is further subdivided into categories. For example, the geo-spatial domain is one of the categories covering Israeli demographics in Area C. Each category is represented by a set of specific indicators, e.g., Israeli birth rates in Area C.

The advancement in science and technology has led to luffa sponge (LS) being widely used as a natural material in industrial application as its polyporous structure and light texture. In order to enhance the utility of LS fibers as the reinforcement of lightweight composite materials, this study investigate its water absorption, mechanical properties, anatomical characteristic and thermal performance. Hence, moisture regain, tensile properties of LS fiber bundles were measured in accordance with standards and the structural characteristics were investigated via microscopic observation. Scanning electron microscopy (SEM) was used to observe the surface morphology and fracture surface of fiber bundles. Test results shows that the special structure where the phloem tissues degenerate to cavities had a significant influence on the mechanical properties of LS fiber bundles. Additionally, the transverse sectional area occupied by fibers in a fiber bundle (SF), wall thickness and ratio of wall to lumen of fiber cell, and crystallinity of cellulose had an impact on the mechanical properties of LS fiber bundles. Furthermore, the fiber bundles density of LS varies range of 385.46-468.70 kg/m³, much less than that of jute (1360.40 kg/m³) and Arenga engleri (950.20 kg/m³) while LS fiber bundles has superior specific modulus.

As global concerns about climate change intensify, the need for effective strategies to reduce carbon emissions has never been more urgent. This paper explores the crucial role of data automation (DA) and decision support systems (DSS) in enhancing decarbonation efforts within the realms of solid waste management (SWM), wastewater treatment (WWT), and contaminated soil remediation (CSR). Specifically, the paper provides: (i) an overview of the carbon footprint (CFP) in relation to environmental management (EM) and the role of DA and DSS in decarbonization; (ii) DA case studies in areas of SWM, WWT, and CSR; (iii) 3. life cycle assessment (LCA) based DSS case studies in areas of SWM, WWT, and CSR; (iv) multi-criteria decision analysis (MCDA) based DSS case studies in areas of SWM, WWT, and CSR; and (v) optimal contractual delivery methods based DSS case studies in EM practices. The analysis disclosed that adoption of DA and DSS in SWM, WWT, and CSR holds significant potential for decarbonizing these EM processes. By optimizing operations, enhancing resource efficiency, and integrating renewable energy sources, these technologies contribute to the reduction of GHG emissions and promote sustainable environmental practices. As the demand for more effective and eco-friendly solutions grows, the role of DA and DSS will become increasingly pivotal in achieving global decarbonization goals.

Background: Low back pain (LBP) is a major cause of disability globally, and the diagnosis of LBP is challenging for clinicians. Objective: This study aimed to assess the accuracy level of arti-ficial intelligence as a Clinical Decision Support System (CDSS) called Therapa compared to MRI in predicting lumbar disc herniated patients. Methods: One hundred low back pain patients aged ≥18 years old were included in the study. The study was conducted in 3 stages, at first, a case se-ries was conducted by matching MRI and Therapha diagnosis for 10 patients. Subsequently, Delphi methodology was employed to establish a clinical consensus. Lastly, to determine the ac-curacy of the newly developed software, a cross-sectional study was undertaken, involving 100 patients. Results: The software showed a significant diagnostic accuracy with the area under the curve in the ROC analysis determined as 0.84 with a sensitivity of 88% and a specificity of 80%. Conclusion: The study’s findings revealed that CDSS using Therapha has a reasonable level of ef-ficacy, and this can be utilized clinically to acquire faster and more accurate screening of patients with lumbar disc herniation.

The sustainable development of resource-based cities has long been a focal point for both governmental policies and academic research. A wealth of academic literature identifies the 'resource curse' as a critical barrier to sustainable development in these urban areas. Despite extensive studies, a consensus on effective strategies to circumvent or resolve this challenge remains elusive. Recent empirical data reveals a noticeable pattern of economic growth differentiation in China's resource-based cities. Why do some resource-based cities experience economic decline while others achieve sustainable economic growth? Therefore, this paper analyzes the reasons for the divergence of economic growth in resource cities from the perspective of heterogeneity. The results of the study show that the phenomenon of the resource curse is conditional, and the heterogeneity of demo-graphic changes, geographic location and institutional environment has a significant impact on the economic growth of resource cities. These factors are investigated to elucidate the observed diversification and heterogeneity in the economic growth of resource-based cities. Building upon this analysis, the paper proposes a framework for differentiated sustainable development pathways tailored to the unique contexts of these cities. This approach not only contributes to the current understanding of the 'resource curse' but also offers novel insights for future research aiming to adopt a more nuanced and context-specific perspective in examining sustainable urban development.

Integrating ecosystem services (ESs) to delineate ecological functional zones (EFZs) is a fundamental work in terrestrial spatial planning and ecosystem management. However, existing studies have largely overlooked the refinement of EFZs at local scales, which hinders targeted and multi-scale ecosystem management. This study introduced a "two-step refinement zoning method" to address this gap, first using the self-organizing feature mapping method to delineate EFZs based on ESs at a township scale, and then applying hotspot overlay analysis to identify hotspots of dominant ESs within EFZs at a village scale. By overlaying the outcomes of these two steps, EFZs can be further graded by designating villages with hotspots of dominant ESs as level I and those without as level II. The proposed method was applied in Wuhan City, dividing it into five types of EFZs with different ESs combinations and land use compositions. Furthermore, 5.23% of villages were identified as Level I of EFZs, serving as advantageous areas of dominant ESs in the study area. On this basis, diversified management strategies and conservation priorities were proposed. This study provides a theoretical and methodological reference for terrestrial spatial planning and sustainable ecosystem management.

Durum wheat (DW) is one of the major crops grown in the Mediterranean area, a climate-vulnerable region where the increase in day/night (d/n) temperature are severely threatening DW yield stability. In order to improve DW heat tolerance, the introgression of chromosomal segments derived from the wild gene pool is a promising strategy. Here, four DW-Thinopyrum spp. near-isogenic recombinant lines (NIRLs) were assessed for their physiological response and productive performance after an intense heat stress (IH, 37/27°C d/n) applied for 3 days at anthesis. The NIRLs included two primary types (R5, R112), carriers (+) of a differently sized Th. ponticum 7el1L segment on DW 7AL arm, and two corresponding secondary types (R69-9/R5, R69-9/R112), possessing a Th. elongatum 7EL segment distally inserted into the 7el1L ones. Their response to the IH stress was compared to that of corresponding non-carrier sib lines (−) and of the heat tolerant cv. Margherita. Overall, the R112+, R69-9/R5+ and R69-9/R112+ NIRLs exhibited a tolerant behaviour towards the applied stress, standing out for maintenance of leaf relative water content, but also for accumulation of proline and soluble sugars in flag leaf and for preservation of photosynthetic efficiency. As a result, all the above three NIRLs (R112+ > R69-9/R5+ > R69-9/R112+) displayed good yield stability under the IH, also in comparison with cv. Margherita, R112+ particularly relying on strength of spike fertility/grain number traits, R69-9/R5+ on efficient compensation by grain weight increase. This work largely confirmed and further substantiated the value of exploiting the wild germplasm of Thinopyrum species as useful source for the improvement of DW tolerance to even extreme abiotic stress conditions, such as the severe heat treatment throughout day- and night-time applied here.

Despite being stored at 113 K and atmospheric pressure, LNG cold potential is not exploited to reduce green ships’ energy needs. An innovative system based on three Organic Ranking Cycles integrated into the regasification equipment is proposed to produce additional power and recover cooling energy from condensers. A first-law analysis identified ethylene and ethane as suitable working fluids for the first and the second ORC making available freshwater and ice. Propane, ammonia and propylene could be arbitrarily employed in the third ORC for air-conditioning. An environmental analysis that combines exergy efficiency, ecological indices and hazard aspects for marine environment and ship’s passengers, indicated propylene as safer and more environmentally friendly. The exergy analysis confirms more than 20% of the LNG potential can be recovered from every cycle to produce a net clean power of 76 kW, whereas 270 kW can be saved by recovering condensers' cooling power to satisfy some ship needs. Assuming the sailing mode, a limitation of 162 kg in LNG consumptions was determined, avoiding the emission of 1584 kg of CO2 per day. Marine thermal pollution is reduced by 3.5 times by recovering the working fluids' condensation heat for the LNG pre-heating.

Large language models (LLMs) are known for their exceptional performance across a range of natural language processing tasks, but their deployment comes at a high computational and financial cost. On the other hand, smaller language models (SLMs), which can be deployed on lower-cost edge devices, struggle to match the performance of their larger counterparts. This paper presents a novel hybrid inference approach that leverages the strengths of both model types while minimizing reliance on costly cloud-based LLMs. Unlike existing methods that route entire queries to either an SLM or a cloud LLM, our approach introduces a reward-based mechanism to dynamically determine the involvement of the cloud LLM during token generation. Specifically, each token predicted by the SLM is evaluated against a reward score, and only when this score falls below a certain threshold is the cloud LLM consulted for assistance in the next token prediction. This method not only reduces the traffic to the cloud LLM, thereby lowering costs, but also allows for flexible control over response quality depending on the reward score threshold. Experimental results demonstrate that our approach significantly reduces cloud LLM usage with minimal impact on overall response quality, offering a cost-effective solution for deploying high-performance language models.

This paper proposes a Case-Based Reasoning (CBR) system to contribute to reinforce the sustainable performance of an environmental management system. The CBR system aims to support the decision-making process to select environmental management actions aimed at reducing risky trends of the environmental state of a region. The CBR system takes advantage of a set of situation-solution pairs called cases, which are stored in a memory and then retrieved as candidates to solve new problems. Situations in this work are represented by a set of risky trends of the following key environmental variables: CO2 emissions, Air-Quality, Loss of Vegetation Cover, Water Availability, and Solid Waste, whose combination damage the environmental state quality of a region. Meanwhile, solutions are represented by a set of environmental management actions. Similar situations to a given current situation are retrieved from the memory of cases and then their solutions are combined, through an adaptation mechanism, until the solution of the current problem is obtained. We used risky trends derived from real data related to the environmental states of a Mexican region to test the proposed CBR system. The results obtained provided insights into the potential of CBR systems to support the decision-making process to select environmental management actions aimed at reducing risky trends of current environmental states.

Analysing and understanding donor behaviour in Non-profit Organisations (NPOs) is challenging due to the lack of human and technical resources. Machine learning (ML) techniques can analyse and understand donor behaviour at a certain level; however, it remains to be seen how to build and design an Artificial Intelligence enabled Decision Support System (AI-enabled DSS) to analyse donor behaviour. Thus, this paper proposes an AI-enabled DSS conceptual design to analyse donor behaviour in NPOs. A conceptual design is created following a Design Science Research approach to evaluate an AI-enabled DSS's initial DPs and features to analyse donor behaviour in NPOs. The evaluation process of the conceptual design applied formative assessment through conducting interviews with stakeholders from NPOs. The interviews were conducted using the Appreciative Inquiry framework to facilitate the process of interviews. The results of analysis based on the interviews provide insightful information not only about the proposed conceptual design for an AI-enabled DSS, but also about what is required to analyse donor behaviour NPOs using DSS. Evaluating the conceptual design results recommend efficiency, effectiveness, flexibility, and useability in the requirements of the AI-enabled DSS. This research contributes to the design knowledge base of AI-enabled DSS for analysing donor behaviour in NPOs. Future research will combine theoretical components to introduce a practical AI-enabled DSS for analysing donor behaviour in NPOs. This research is limited to such analysis on donors who donate money or volunteer time for NPOs.

Finding a suitable support is a key process in the life history of climbing plants. Climbers that find a suitable support have greater performance and fitness than those that remain prostrate. Numerous studies on climbing plant behavior have elucidated the mechanistic details of support searching and attachment. Far fewer studies have addressed the ecological significance of support-searching behavior and the factors that affect it. Among these, the diameter of supports influences their suitability for twining plants. When support diameter increases beyond some point climbing plants are unable to maintain tensional forces and therefore lose attachment to the trellis. Here we further investigate this issue by putting pea plants in the situation to choose between supports of different diameters while their movement was recorded by means of a three-dimensional motion analysis system. The results indicate that the way climbing plants move can vary depending on whether they are presented with one or two potential supports. Furthermore, when presented with a choice between a thin and a thick support, the plants showed a distinct preference for the former than the latter. The present findings shed further light on how climbers make decisions as far as support search is concerned, and provide evidence that plants adopt one of several alternative plastic responses in a way that optimally corresponds to environmental scenarios.

The problem of scheduling is an area that has attracted a lot of attention from researchers for many years. Its goal is to optimize resources in the system. The assigning task to the lecturer is an example of the timetabling problem, a class of scheduling. This study introduces a mathematical model to assign fixed tasks (the time and required skills to be fixed) to university lecturers. Our model is capable of generating a calendar that maximizes faculty expectations. The formulated problem is in the form of a multi-objective problem that optimal makes decisions require the trade-off presence of trade-offs between two or more conflicting objectives. To solve this, we use the Compromise Programming approach to multi-objective programming. We then proposed the new version of the Genetic Algorithm to solve the introduced model. Finally, the model and algorithm tested with real scheduling data collected at the Computing Fundamental Department, FPT University, Hanoi, Vietnam.

Crawling robots are the focus of intelligent inspection research, and the main feature of this type of robot is the flexibility of in-plane attitude adjustment. The crawling robot HIT_Spibot is a new type of steam generator heat transfer tube inspection robot with a unique mobility capability different from traditional quadrupedal robots. This paper introduces a hierachical motion planning approach for HIT_Spibot, aiming to achieve efficient and agile maneuverability. The proposed method integrates three distinct planners to handle complex motion tasks: a nonlinear optimization-based base motion planner, a TOPSIS-based base orientation planner, and a Mask-D3QN (MD3QN) algorithm-based gait motion planner. Initially, the robot's base and foot workspace are delineated through envelope analysis, followed by trajectory computation using Larangian methods. Subsequently, the TOPSIS algorithm is employed to establish an evaluation framework conducive to foundational turning planning. Finally, the MD3QN algorithm trains foot points to facilitate robot movement along predefined paths. Experimental results demonstrate the method's adaptability across diverse tube structures, showcasing robust performance even in environments with random obstacles. Compared to the D3QN algorithm, MD3QN achieves a 100% success rate, enhances average overall scores by 6.27%, reduces average stride lengths by 39.04%, and attains a stability rate of 58.02%. These results not only validate the effectiveness and practicality of the method but also showcase the significant potential of HIT_Spibot in the field of industrial inspection.

To better understand farmers’ refusal and corresponding negative emotions to pay agricultural water fee under current policy in rural China, this paper applies mental accounting, a behavioral economics framework, to explore how the governmental policies of reform of rural taxes and fees, direct agricultural subsidy programs and agricultural water fee individually influence farmers’ decisions in paying agricultural water fee. Using fieldwork data from 577 farmers and 20 water managers in Sichuan, we explore farmers’ information processing regarding paying agricultural water fee via three sequential mental accounting processes, with the associated underlying principles and measures behind each process. We find that the information processing in three mental accounting scenarios related to the agricultural water fee elucidates farmers’ observed behaviors in rural China. Generally, in the three mental accounting scenarios, two conditional intuitive expectations and nine conditional intuitive preferences are formed, however, the conditions of those expectations or preferences cannot be matched with the facts due to the reform of rural taxes and fees, the direct agricultural subsidy programs and the internal attributes of agricultural water fee, which interpret those negative behaviors in rural China. Additionally, this paper offers a view into how previous policies create negative psychological externalities (such as farmers’ psychological dependence on the government) through mental accounting to negatively influence agents’ subsequent decision making; it highlights the significance of underlying mental factors and information processing of negative behaviors in policymaking for managing or conserving common pool resources.

The concept of transformative resilience emerges from complex recent literature and represents a way to interpret the potential opportunities to change in vulnerable territories, where a socio-economic change is required. This article extends the perspective of transformative resilience to assessing of the landscape multi-functionality of inland areas, exploring the potentials to identify a network of synergies among the different municipalities able to trigger a process of territorial resilience. A Spatial Decision Support System (SDSS) for multi-functionality landscape assessment aims to support the local actors to understand local resources and multi-functional values of the Partenio Regional Park (PRP) and surrounding municipalities, in the South of Italy, stimulating their cooperation to the management of environmental and cultural sites and the co-design of new strategies of enhancement.

Nowadays the dynamism induced by the constant change of strategic decisions on the markets produces an extra difficulty in the management of an organization. The strategic decisions made by managers can become obsolete in a short period of time. One of the major difficulties in managing a commercial organization is to predict, with some precision degree, the impact some strategic decisions have on the financial results. Business Intelligence (BI) is an area widely used to help managers to make strategic decisions. But the methods used, behind the scenes, to achieve the conclusions are kept secret by BI company-based services. Modelling the environment can be a good option to study the impact of an action, provided by a strategic decision, in a real environment. A good model should give an approximate result of an action applied to a previous state of the environment. Artificial Neural Networks (ANNs) are proven to be excellent in modeling environments with some level of data noise. The same strategic action can have different results in different organizations. A tool that allows an evaluation of a strategic action applied to an environment should have a major importance in the management scope. Modeling the environment should save time and money for the organization to improve the performance of the strategic plan. If one evaluates the state of the environment after a strategic action being applied, it can be possible to mitigate the risk of failure of the strategic plan. As we will be able to verify, it is possible to use ANNs to model strategic environments that should allow the prediction of sales and operating results by the used strategies with some precision, although the level of success will be directly related with the user’s necessity.

This paper addresses the evolving landscape of electricity markets in Europe, with a focus on the integration of Renewable Energy Communities as introduced by the Renewable Energy Directive 2018/2001. Residents within a postal code area are highly incentivized to join a community, which enables them to exchange energy among themselves at lower procurement costs. Thereby, energy management systems optimize the operation of respective energy systems, with electrical load forecasting playing a key role. Given that prosumers may switch between communities on a daily basis, electricity demands within these groups will vary, leading to data that is non-stationary, discontinuous as well as non-identical and independently distributed. To encounter this issue, we propose a sophisticated forecasting model that applies federated learning, using informations from various distributed communities to learn domain-invariant features. To achieve this, we initially utilize synthetic electrical load time series at district level and aggregate them to profiles of Renewable Energy Communities with dynamic portfolios. Subsequently, we develop a forecasting model that accounts for the composition of residents of a Renewable Energy Community, adapt data pre-processing in accordance with the time series process, and detail a federated learning algorithm that incorporates weight averaging and data sharing. Following the training of various experimental setups, we ultimately evaluate their effectiveness. The findings suggest that our proposed framework is capable of effectively forecast non-stationary and discontinuous time series and that it can be applied to new, unseen data through the integration of knowledge from multiple sources.

In this paper, we study the asymptotic behavior of the sequential empirical process and the sequential empirical copula process, both constructed from residuals of multivariate stochastic volatility models. Applications for the detection of structural changes and specification tests of the distribution of innovations are discussed. It is also shown that if the stochastic volatility matrices are diagonal, which is the case if the univariate time series are estimated separately instead of being jointly estimated, then the empirical copula process behaves as if the innovations were observed; a remarkable property. As a by-product, one also obtains the asymptotic behavior of rank-based measures of dependence applied to residuals of these time series models.

Despite of being an exceptionally biodiversity rich country, the forest coverage of Bangladesh is declining at an alarming rate. Declaration and management of protected areas in this regard is one of the efforts from government side to tackle the loss of biodiversity. The limited numbers of forest-protected areas (FPA), established to conserve the dwindling forest biodiversity of the country with high pressure on them for timber, non-timber forest products, and fuelwood - makes their management challenging. Moreover, most of the FPAs of the country declared only in the recent decades with very limited infrastructure, manpower and policy support for monitoring and governance. Some people-centred approaches for the management of FPAs and alternative livelihood and income generation subsidies although made available through a few project interventions, their number are still inadequate and performance remains less than satisfactory. This chapter provides a critical review of the FPAs of Bangladesh looking at their role in biodiversity conservation, management challenges, and key lessons from previous management interventions with recommendations for the future. It has been revealed that the FPA system of Bangladesh still poorly represents the diverse forest ecosystems with relatively small forest size and lack of corridors for the movement of wildlife. There are ample opportunities to render co-management of FPAs an effective strategy to minimize the conflicts in FPAs management in the country. It is, however, important to ensure the access of local forest-dependent people to different alternative income generating options that may adequately support their livelihoods.

This study evaluated batch fermentation modes, namely, separate hydrolysis and fermentation (SHF), Quasi-simultaneous saccharification and fermentation (Q-SSF), and simultaneous saccharification and fermentation (SSF), and fermentation conditions, i.e., enzyme and yeast loadings, nutrient supplementation and sterilization, on high titer bioethanol production from SPORL-pretreated Douglas-fir forest residue without detoxification. The result indicated Q-SSF and SSF were obviously superior to SHF operation in terms of ethanol yield. The enzyme loading showed a strong positive correlation between enzyme loading and the ethanol yield. The nutrient supplementation and sterility was not necessary for ethanol production from SPORL-pretreated Douglas-fir. The yeast loading showed no significant influence on the ethanol yield for typical SSF conditions. The terminal ethanol titer of 43.2 g/L, or 75.1% theoretical based on glucose, mannose, and xylose theoretical was achieved when SSF was conducted at the condition of following: whole slurry solids loading of 15%, enzyme loading of 20 FPU/g glucan, 1.8 g/kg (wet) yeast loading, without nutrition supplementation and sterilization, at 38°C, on shake flask at 150 rpm for 96h. It is believed that with mechanical mixing, enzyme loading can be substantially reduced with affect ethanol yield by using a long fermentation time.

Biodiversity loss and nature degradation are pressing issues with huge impacts on our society and economy. Businesses, investors, and regulators focus on corporate efforts to support biodiversity and nature positive actions. This review provides a comprehensive overview about the importance of biodiversity in businesses, the materiality of biodiversity and the role of mandatory and non-mandatory regulations on corporate environmental reporting and sustainability disclosure frameworks. The review also discusses descriptive information on the evolution of sustainability frameworks with a comparison of most prominent sustainability frameworks with a key focus on the materiality approach and biodiversity-related disclosure recommendations. Further, we provide suggestions for more holistic approaches to improve the future sustainability frameworks focusing on the biodiversity impact while showing the necessity of more focus on the decision-making paradigm. Further research on measuring biodiversity impact and innovative trends in sustainability reporting are required to better reflect nature-positive outcomes in corporate sector businesses.

The study investigates the influence of power dynamics on supply chain decision-making processes through a qualitative lens, aiming to understand how various forms of power impact interactions, relationships, and strategic outcomes within supply chains. Using semi-structured interviews with supply chain managers and executives from diverse industries, the research explores the multifaceted nature of power, identifying key sources such as economic leverage, expertise, resource control, and network centrality. Findings reveal that power dynamics significantly shape decision-making by determining negotiation outcomes, governance structures, and operational efficiencies. Economic power, often exercised by larger firms, enables them to dominate negotiations and impose favorable terms, creating pressures on smaller partners and potentially leading to conflicts. Expertise and resource control allow firms with specialized knowledge or unique inputs to influence product development and process innovation, further dictating supply chain configurations. The study differentiates between coercive and non-coercive power strategies, showing that while coercive power enforces compliance, it can erode trust and collaboration. Non-coercive power, on the other hand, promotes positive relationships through incentives and collaborative approaches, fostering trust and alignment of supply chain objectives. Governance structures imposed by dominant firms often reflect their strategic priorities but can burden less powerful partners, highlighting the need for balanced oversight. Power also plays a crucial role in risk management, resilience, sustainability, and innovation within supply chains, with digitalization introducing new dimensions of influence. The research underscores the importance of balanced and equitable power dynamics to enhance cooperation, resilience, and innovation in supply chains. These insights provide valuable implications for practitioners and scholars in developing more effective and adaptive supply chain strategies.

Real-Time Kinematic (RTK) is currently the most widely used method for positioning using Global Navigation Satellite Systems (GNSS) due to its accuracy, efficiency and ease of use. In forestry, position is a critical factor for numerous applications, with GNSS nowadays the preferred solution for obtaining such data. However, the decreased performance of GNSS observations in challenging environments, such as under the forest canopy, must be considered. This paper analyzes the performance of a survey-grade GNSS receiver under coniferous/deciduous tree cover, with short observation times of 30 seconds and corrections received in the field from a Continuously Operating Reference Station (CORS) of the national RTK network in Romania. 84 test points were determined, randomly distributed under the canopy, with reference data collected by topographical surveys using total station equipment. Relatively good accuracies were obtained, especially for horizontal position (RMSEs of 1.47 – 2.07 meters). The variation of GNSS performance under the different forest compositions was found to be statistically significant, while GNSS-specific factors such as DOP values only influenced the precision and not the accuracy of observations. We established that this methodology offers sufficient accuracy, application-dependent, even if the majority of GNSS solutions were code-based, rather than carrier phase-based, due to strong interference from the vegetation.

Wood is poised to become a material of choice for future construction. When appropriately managed, it is a renewable material with unique mechanical properties. Beyond its inherent sustainability, the use of wood has a crucial role in addressing climate change concerns due a significantly lower energy consumption and emissions during manufacturing and transportation, while simultaneously acting as a carbon sink. Thus, there has been a growing demand for hardwoods for structural applications, including Castanea sativa Mill., the focal point of this investigation. Albeit in a limited capacity, Eurocode 5-2 offers friction coefficients for softwoods, but it falls short for hardwoods which it is a gap to bridge with this research. These coefficients play a critical role in numerical simulations involving friction, enabling the optimization of joints and, by extension, the overall structural integrity. Test samples were evaluated at 18% moisture content, which is typical of Service Class 2, for various orientations of timber-to-timber and timber-to-steel friction. Considering previous friction coefficients at 12% moisture content, the linear variation was evaluated at an intermediate value of 15%. The results provide an experimental database for numerical simulations and highlight the influence of moisture content on the coefficients, which increase linearly along moisture percentages.

In recent years, large language models have demonstrated remarkable performance across various natural language processing (NLP) tasks. However, deploying these models for real-world applications often requires efficient inference solutions to handle the computational demands. In this paper, we explore the utilization of CPUs for accelerating the inference of large language models. Specifically, we introduce a parallelized approach to enhance throughput by 1) Exploiting the parallel processing capabilities of modern CPU architectures, 2) Batching the inference request. Our evaluation shows the accelerated inference engine gives an 18-22x improvement in the generated token per sec. The improvement is more with longer sequence and larger models. In addition to this, we can also run multiple workers in the same machine with NUMA node isolation to further improvement in tokens/s. Table 2, we have received 4x additional improvement with 4 workers. This would also make Gen-AI based products and companies’ environment friendly, our estimates shows that CPU usage for Inference could reduce the power consumption of LLMs by 48.9% (1252 W for A100 with AMD EPYC 7V13 vs 613 W for Intel® Xeon® Gold 6538N) while providing production ready throughput & latency.

Thromboprophylaxis of hospitalized patients at risk of venous thromboembolism (VTE) presents challenges owing to patient heterogeneity and lack of adoption of evidence-based methods. Intuitive practices for thromboprophylaxis have resulted in many patients being inappropriately prophylaxed. Multiple interventions for thromboprophylaxis have been tested, including use of multifaceted approaches such as national VTE prevention programs with audits, use of passive alerts (either human or electronic), and more recently use of active clinical decision support (CDS) tools incorporated into electronic health records (EHRs). Multifaceted health-system interventions have shown mixed results in their ability to increase appropriate thromboprophylaxis and reduce VTE, unless mandated through a national VTE prevention program, though the latter approach is potentially costly, effort- and time-dependent. Studies utilizing passive human or electronic alerts have also shown mixed results in increasing appropriate thromboprophylaxis and reducing VTE. The use of active CDS tools holds the greatest promise, especially with further refinement and widespread implementation within various EHRs and clinical workflows. Recent-ly a universal cloud-based and EHR-agnostic CDS VTE tool revealed high adoption and effectiveness in increasing appropriate thromboprophylaxis and reducing major thromboembolism. This narrative review summarizes the literature on system-wide thromboprophylaxis interventions in hospitalized patients, with a focus on CDS tools.

Agroforestry practices evolve with the development of basic and advanced facilities, changes in natural and artificial factors of land use change. The study aims to examine the socioeconomic characteristics and compare satisfaction levels across forest products and vegetable farming of the agroforestry community in Khost Province. Data were collected from 662 farmers and was analyzed using univariate Analysis of Variances (ANOVA) and Multivariate Analysis of Variances (MANOVA). The results found that forest and vegetable products, including fruits, berries, herbs, mushrooms, wild animals, oils, wood, honey, okra, eggplant, carrot, cucumber, pine nuts, pepper, and timber has different impact in terms of satisfaction with basic and advanced facilities, knowledge about land use and impacts, natural and artificial factors of land use change, and positive and negative impact of land use change. The limitation of the study included an absence of exogenous factors in the model such as climate change, financial conditions, market fluctuations, regulatory system, an area in which the study is selected, research design, and current condition of endogenous factors. Overall, agroforestry practices can improve the socioeconomic development of agroforestry-dependent communities in Khost Province, Afghanistan. This study provided useful insights for policymakers and development practitioners to promote agroforestry practice adoption and improve the socioeconomic development of agroforestry-dependent communities. Future works could explore the implications of agroforestry practices on the socioeconomic development of other dependent communities in Afghanistan.

The conversion of LULC classes into other types is occurring at a faster rate globally including United States. Many studies have examined the drivers and causes of LULC changes, particularly, in northeast Georgia and Atlanta in the United States, while the case of southeast Georgia remains unstudied. Thus, this study filled the gap by quantifying and predicting the impact of LULC change on forestlands and how these LULC changes have affected forest health. The study used Landsat images, including 2005 and 2023 together with the random forest algorithm to perform LULC classification on the Google Earth Engine. Also, the study predicted how LULC will impact forest cover by 2050. The results revealed that forest hectares have generally changed approximately from 65% to 62% between 2005 and 2023, and this is projected to likely further decline to about 53% by 2050. This change in forest acreage has resulted decline in the health of the forest, that is the NDVI values changed from 0.992 to 0.866 between 2005 and 2023. The study concludes that the forest cover in southeast Georgia has changed to urban features over the years. Agriculture land is projected to gain most of the forest cover by 2050.

Phenolic compounds, present in plants, provide substantial health advantages, such as antioxidant and anti-inflammatory properties, which enhance cardiovascular and cognitive well-being. Australia is enriched with a wide range of plants with phytopharmacological potential, which needs to be fully elucidated. In this context, we analyzed leaves of aniseed myrtle (Syzygium anisatum), lemon myrtle (Backhousia citriodora), and cinnamon myrtle (Backhousia myrtifolia) for their complex phytochemical profile and antioxidant potential. LC-ESI-QTOF-MS/MS was applied for screening and characterizing these Australian myrtles’ phenolic compounds and the structure-function relation of phenolic compounds. This study identified 145 and quantified/semi-quantified 27 phenolic compounds in these Australian myrtles. Furthermore, phenolic contents (total phenolic content (TPC), total condensed tannins (TCT), and total flavonoids (TFC)) and antioxidant potential of phenolic extracts from the leaves of Australian myrtles were quantified. Aniseed myrtle was quantified with the highest TPC (52.49 ± 3.55 mg GAE/g) and total antioxidant potential than other selected myrtles. Catechin, epicatechin, isovitexin, cinnamic acid, and quercetin were quantified as Australian myrtles' most abundant phenolic compounds. Moreover, chemometric analysis further validated the results. This study provides a new insight into the novel potent bioactive phenolic compounds from Australian myrtles that could be potentially useful for functional, nutraceutical, and therapeutic applications.

This study investigates the emerging applications of Artificial Intelligence (AI) in strategic management and its impact on organizational decision-making processes. As AI technology rapidly advances, it has become a crucial component in redefining organizational strategies. According to a global survey by the Boston Consulting Group, over 60% of executives believe that AI can significantly improve key performance indicators and enhance opportunities for strategic differentiation and sustainable competitive advantages. Using a descriptive-survey methodology and questionnaire-based data collection, this research examines the influence of AI on strategic decision-making in large organizations. The statistical population comprises senior managers across various industries, with sampling conducted through convenience sampling. Data analysis was performed using SMART-PLS software, revealing significant improvements in strategic decision-making processes through AI implementation. The study demonstrates that AI, by analyzing complex data sets and identifying hidden patterns, assists managers in making more optimal decisions. Furthermore, AI's predictive analytics capabilities contribute to enhanced strategic planning and risk mitigation in decision-making processes. The research findings indicate a statistically significant positive correlation (r = 0.72, p < 0.001) between AI adoption and the quality of strategic decisions. This paper concludes by proposing that organizations should leverage AI as a tool to strengthen their organizational strategies, while emphasizing the importance of ethical considerations and human oversight. The implications of this research extend to both academic and practical domains, offering insights into the integration of AI in strategic management processes and its potential to revolutionize organizational decision-making paradigms.

Herbal Coffee is getting more popular due to its unique flavor and higher concentration of bioactive compounds, mainly polyphenols. Rosemary is a unique herb with potent antioxidant properties. A green technique called ultrasound aids in releasing bioactive substances from the plant cell wall. In this context, ultrasound-assisted herbal coffee prepared and their anti-diabetic, anti-Alzheimer, and antioxidant activities were measured while the LC/MS was used to determine the bioactive components from the herbal coffee infusion. A total of 95 secondary metabolites were putatively reported, including alkaloids, phenolic acids, and flavonoids, which we identified in the herbal coffee infusion. The total phenolic content (61.5 ± 5.1 mg GAE/g), total flavonoid content (19.3 ± 0.9 mg QE/g), and total condensed tannin (7.6 ± 0.7 mg CE/g) of herbal coffee infusion were observed to be highest of the two remaining samples. The herbal coffee extracts were used to determine all samples' antioxidant, anti-diabetic, and anti-Alzheimer activities. Using LC-MS/MS, we semi-quantified 19 phenolic metabolites. The results show numerous bioactive components in herbal coffee infusion with more potent antioxidant, antidiabetic, and anti-Alzheimer potential. Moreover, more studies must be conducted to confirm how the metabolomic profile of herbal coffee affects its flavor profile.

Background and Objectives: In this current state, Type 2 Diabetes Mellitus (T2DM) is more prevalent in the population, and metformin is used as a first-line medication for treating it. There was up surge in the utilization of alternative therapies in the management of diabetes. Especially in diabetes, Herbal medicines are considered safe and reliable by majority of the population. Aim: The purpose of this research is aimed to estimate the safety and efficacy of Polyherbal Metabolite Compounds (PHMC) capsules in adults with T2DM. Materials and Methods: In this study, 60 T2DM patients aged 18-60 years were randomly assigned to Groups A (30) receiving Metformin 500 mg twice a day after food and Group B (30) receiving PHMC capsule 500 mg twice a day after food in a prospective, randomized and open-label clinical study. The in-silico simulation study was performed to selected plants major compounds on target protein Insulin-like Growth Factor-1 (1K3A). Result: The PHMC is found to be safe and effective in the management of T2DM. The FBS, PPBS and HbA1c were significantly lowered (p

This paper formulates a new strategy for participatory forest management consisting of encouraging public participation as long as it increases empathy among participants. The strategy requires the homogeneous representation of the opinion of a participant (i.e. to determine how they assess a forest plan and identify the best one). Utility assessments are prepared for participants through pair-comparisons between meaningful points in the territory and from value functions based on forest indicators. The best plan is designed by applying combinatorial optimization algorithms to the utility of a participant. The calculating of empathy -of one participant relative to another - is based on the equivalence of their respective utilities when the current forest plan is modified. This involves calculating the opinions that are due to systematic changes in the collective plan for those participants that each participant supposes will affect the utility of the other participants. Calculating empathy also requires knowing the interactions among participants, which have been incorporated through agent-based simulation models. Application of the above methodology has confirmed the association between increases in empathy and convergence of opinions in different scenarios: well and medium-informed participants and with and without interaction among them, which verifies the proposed strategy. In addition, this strategy is easily integrated into available information systems and its outcomes show advantages over current participatory applications.

Human activities (e.g., exploitation, utilization, and conservation) exert substantial impact on forest ecological functions (FEF). The human influence on FEF significantly varies across developmental stages, which are attributed to the temporal and spatial in nature. This study evaluates some notable FEF indices (FEFI) in the 448 counties within the Yellow River basin (YRB) using panel data from the Seventh to Ninth National Forest Inventory. Data analysis involves employing the residual trends, geographical and temporal weighted regression. Results indicate that, firstly, the overall forest ecological function in the YRB is moderate to inferior, with superior FEF in counties endowed with more natural forest resources. Afforestation demonstrates a short-term improvement, but its effectiveness diminishes over time. Secondly, the positive spatial correlation is stronger among counties, characterized by both high-high and low-low agglomeration effects. Scattered planting, simple stand structure, and concentrated harvesting of forests at the identical stand ages hinder the formation of complex and large-scale FEF agglomerations. Thirdly, significant spatial-temporal differences exist in the impact of human activities on FEF. In the upper reaches of the basin, increased vegetation coverage through agricultural and forestry production benefited FEF, however, some over-exploitation of forests and grasslands is observed there. In the middle reaches, the economic development, expanding population and the greening activities help to improve FEF effectively, but excessive water using in agricultural production brought more difficulties to FEF improvement. In the lower reaches of the basin, improved wastelands and forest protection positively influence FEF. This study recommends that county governments should prioritize forest management with multi-species and multi-layered complex forests. Each county should define its position in regional development and ecological protection, considering the potential impacts on neighboring regions. This holistic approach promotes more effective integrated regional management.

This paper proposes a model that combines multi-criteria and multi-faceted risk assessment. Using two data sources, a fuzzy Technique for Order of Preference by Similarity to Ideal Solution FTOPSIS method combined with FMEA assessment. The FMEA method presented in this paper combines the technique of prioritising preferences according to FTOPSIS similarity to the ideal solution and conviction structure to overcome the defects of traditional FMEA indicators. The paper describes the exact process and tool selection, and the results obtained in the study verified accurate data. Finally, a numerical case study was presented on hospital reorganization services and case adaptation to prioritize surgical abandonment in Poland. The problem considered in the publication is the priority of patients’ operations in hospitals. The selection of relevant criteria, their importance and patient preferences are presented. The results obtained from the method provide a viable action plan for the proposed research problem. The proposed method is multi-faceted and can be part of an information system supporting reorganization, restructuring, and modification of the operational process.

The necessity for precise and current data concerning the dynamics of land cover change in Indonesia is crucial for efforts to reduce natural vegetation cover due to agricultural expansion. The functionality of monitoring systems that incorporate Terra-MODIS is currently compromised by the limited availability of data for the immediate future. This study seeks to assess the potential of VIIRS satellite imagery in developing an early warning system for monitoring vegetation cover change in Indonesia. The Normalized Differential Open Area Index (NDOAI) computed from the 8-day’ VIIRS data was employed to detect changes in vegetation cover based on pixel-by-pixel subtraction in the NDOAI data time series. Evaluating the pixel-level accuracy of change detection is complicated due to the fact that we evaluate a change map at a coarser resolution than the Landsat-based reference map. The results revealed that increasing the threshold percentage is associated with improved accuracy. In change detection, there is often a trade-off between accuracy and sensitivity. A threshold that is too low may result in false positives, while a threshold that is too high may lead to missed changes. This study demonstrates that when a threshold value of less than 20% is applied, Landsat can identify vegetation cover changes at an earlier stage. Conversely, when a threshold value greater than 20% is employed, VIIRS will detect the change 4.5 days earlier than Landsat. Additionally, VIIRS is capable of detecting changes 25.4 days and 54.8 days faster than Landsat, respectively, when using thresholds of 40% and 75%.

Cloud computing refers to applications delivered as services over the Internet. Cloud systems employ policies that are inherently dynamic in nature and that depend on temporal conditions defined in terms of external events, such as the measurement of bandwidth, use of hosts, intrusion detection or specific time events. In this paper, we investigate an optimized resource management scheme named v-Mapper. The basic premise of v-Mapper is to exploit application-awareness concepts using software-defined networking (SDN) features. This paper makes three key contributions to the field: (1) We propose a virtual machine (VM) placement scheme that can effectively mitigate the VM placement issues for data-intensive applications; (2) We propose a validation scheme that will ensure that a service is entertained only if there are sufficient resources available for its execution and (3) We present a scheduling policy that aims to eliminate network load constraints. An evaluation was carried out with various benchmarks and demonstrated that v-Mapper shows improved performance over other state-of-the-art approaches in terms of average task completion time, service delay time and bandwidth utilization. Given the growing importance of supporting large-scale data processing and analysis in datacentres, the v-Mapper system has the potential to make a positive impact in improving datacentre performance in the future.