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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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%.

Good governance in natural resource management (NRM) is one of the most challenging issues in developing countries that often inappropriately embedded in national policies and political agendas. It is, in fact, even more important for countries like Bangladesh with exceptionally high pressure and dependence on its natural resources for sustaining rural livelihoods. Globally, nowadays, good governance is considered as one of the key factor for achieving the goal of sustainable development and biodiversity conservation. Bangladesh, of late has responded to that global zeal by involving local communities in the management of country’s declining forest and other natural resources. The colonial legacy of the forestry sector of Bangladesh was planned and, managed as interim projects through donors’ prescriptions. Thus, institutions, management processes and conservation outcomes were problematic. The conventional approach adopted by colonial and post-colonial regimes for forest management also proved to be inefficient due to its top-down management system. The absolute dependency on donor support, and their prescription sometimes worsened the situation both ecologically and socially. Global, regional and local trends supported the need for a different dimension in the governance paradigms. The introduction of a pluralistic approach, known as co-management in protected areas (PAs) is an example of an attempt whereby shared governance mechanism are implemented to attain the desired goals of conservation that will also address the livelihoods and aspirations of communities living in and around PAs of the country. However, in designing future forest and PA regimes the concern of the external aid support and attached conditions remain a reality that needs to be addressed. Adequate attention should be given to our vanishing biodiversity, culture and community livelihoods through devising an appropriate governance mechanism recognizing and supporting local rights, access and participation in the environmental management. It is now time to mainstream the adhoc nature of governance according to our national conservation strategy and policy frameworks in order to achieve the goals and objectives of the Bangladesh NRM sector addressing the human and community right of people in the specific context of forest protected areas management.

The decline in global pollinator populations has raised serious concerns about the sustainability of ecosystems and agricultural productivity. Urban environments, often characterized by habitat loss and environmental stressors, are considered challenging habitats for pollinators. However, urban green spaces, including street and ornamental trees, can play a crucial role in supporting pollinators by providing essential food resources. This study aimed to evaluate the sugar and amino acid production of eight urban tree species to determine their potential contributions to pollinator nutrition. Our findings revealed significant variability among species regarding nectar volume, sugar content, and amino acid composition, emphasizing the importance of considering multiple factors when assessing the utility of floral resources. Consequently, Tilia amurensis, Heptacodium miconioides, Aesculus turbinata, and Wisteria floribunda demonstrated superior potential in supporting urban pollinator communities. Contrastingly, Sorbus commixta, Styrax japonicus, and Cornus kousa, often favored for their aesthetic appeal, showed less potential. This study underscores the need for urban tree selection that prioritizes species that provide diverse and abundant nutritional resources for pollinators while balancing aesthetic and ecological values. Our results offer a foundation for better-informed urban biodiversity management and contribute to creating pollinator-friendly urban environments.

The construction industry has a notable negative impact on the environment; thus, the promotion of the use of timber structures is an alternative to mitigate its effects. The research develops an artificial intelligence-based decision approach in the calculation of timber structures focused on the enhancement of the sustainability of roof structures. Based on the optimization carried out through genetic algorithms and the framework established in Eurocode 5, a general set of equations has been proposed for a laminated timber roof structure. The tool, which determines the most suitable roof structure for each strength class of laminated timber, allows for the determination of the dimensions of beams and purlins and their respective separations in order to minimize wood consumption. The ultimate goal is to offer multiple solutions regarding strength classes and structural designs in order to foster informed sustainable choices that promote efficient use of resources in construction.

The silver fir (Abies alba Mill.) is among the most valuable conifers in Europe for ecological and economic reasons. In the course of history, primarily in the 20th century, its share in stands has been declining due to ill-suited management practices, especially clear-cut management, air pollution, and wildlife-induced damage. Based on recent knowledge of fir ecology and population dynamics, small-scale shelterwood and selection management have been introduced in fir stands, which have also stabilized them. Fir is an essential species for maintaining high stability and biodiversity, especially on planosols and in waterlogged habitats. Due to its shade tolerance and environmental flexibility, particularly at higher altitudes, it can coexist very well with many tree species in mixtures, which can increase the productive potential of stands. It can form stands of heterogeneous structure, ranging from single to multi-layer to selection. For its successful natural regeneration, it is essential to reduce cloven-hoofed game and thus prevent bud browsing damage. On the other hand, fir is a species relatively resistant to bark stripping and the spread of secondary rot compared to Norway spruce (Picea abies [L.] Karst.). During global climate change, fir is expected to shift to higher elevations with sufficient precipitation, while in the southern part of its natural range or at lower elevations, outside water-influenced habitats, it is likely to decline. This paper essentially reviews the description and distribution of the species, its ecological requirements, threats and diseases, habitat and stand conditions, and close-to-nature forest management practices with emphasis on ongoing climate change.

Forests have multifunctional roles. They not only increase forest carbon sinks through afforestation and management, but also indirectly realize multiple benefits of "economy + ecology + society". Therefore, we construct a multiple output-benefit system of forest carbon sinks based on the development of forest carbon sinks in provinces of China. This system is also able to distinguish the importance of forest carbon sinks from other outputs. For the efficiency evaluation of multi-level output systems, this paper constructs a cross-evaluation model based on evidential reasoning method, the original efficiency information by conducting efficiency evaluation before information fusion. Further, the spillover effects and influencing factors of forest carbon sink efficiency are analyzed through a spatial regression model to explore the spatial characteristics of forest carbon sink efficiency in China. The results show that: (1) there is a large gap between the cross-efficiency levels of forest carbon sinks in various regions of China, with significant spatial correlation. For the perspective of direct output benefits, the cross-efficiency of the eastern region is higher, while the cross-efficiency of the northwestern region is lower; for the perspective of indirect output benefits, the cross-efficiency of the southeastern region is higher. (2) China's forest carbon sink has a significant positive spillover effect, and the indirect output cross-efficiency has a significant negative impact on the forest carbon sink in its own region. Further analyzing the impact of indirect output cross-efficiency, it is found that the cross-efficiency of the ecological dimension and the cross-efficiency of the social dimension have a significant negative impact on the forest carbon sink in its own region, and the economic dimension has a significant positive impact on the forest carbon sink in its own region. In conclusion, corresponding policy recommendations are proposed for the development of forest carbon sinks to promote the realization of the dual-carbon goal.

Understanding morphological and genetic diversity of forest trees is essential to facilitate their conservation, sustainable management and genetic improvement for addressing the economic and environmental demands and challenges. We have examined variation in morphometric seed and pod traits and seed germination of multipurpose and widely distributed forest tree Albizia lebbek (Indian siris) from 12 provenances covering the species’ range in northern India and its relationships with bioclimatic factors. Highly significant inter-provenance variation was observed in seed size (seed length (SL), seed width (SW), SW/SL, SL×SW), seed mass (1000 seed weight), pod size (pod length (PL), pod width (PW), PW/PL), seed germination, number of seeds per pod, and insect infected seeds (IIS). The provenance effect contributed a major portion to the total phenotypic variance. Morphometric variation in all traits except for IIS was not clinal related to longitude, latitude, altitude or rainfall. There was an indication of the presence of ecotypic variation. IIS showed clinal variation with significant negative correlation with latitude. Most seed size and pod size traits showed positive correlations with several temperature bioclimatic factors and negative correlations with several precipitation bioclimatic factors. Our results have significance for genetic improvement, population genetics and genomics studies and conservation and sustainable management of A. lebbek genetic resources.

The toxic cadmium (Cd) poses a serious threat to plant growth and human health. Populus euphratica CPK21 has previously been shown to attenuate Cd toxicity by reducing Cd accumulation, enhancing antioxidant defense and improving water balance in transgenic Arabidopsis. Here, we confirmed a protein-protein interaction between PeCPK21 and AtNF-YC3 by yeast two-hybrid, HaloTag pull-down and bimolecular fluorescence complementation assays. AtNF-YC3 was induced by Cd and strongly expressed in PeCPK21-overexpressed plants. Overexpression of AtNF-YC3 in Arabidopsis reduced salt inhibition of root length, fresh weight and membrane stability under Cd stress conditions (100 µM, 7 d), suggesting that AtNF-YC3 appears to contribute to the improvement of Cd stress tolerance. AtNF-YC3 improved Cd tolerance by limiting Cd uptake and accumulation, activating antioxidant enzymes, and reducing H2O2 production under Cd stress. We conclude that PeCPK21 interacts with AtNF-YC3 to limit Cd accumulation, enhance the ROS scavenging system, and thereby positively regulate plant adaptation to Cd environments. This study highlights the interaction between PeCPK21 and AtNF-YC3 under Cd stress conditions, which can be utilized to improve Cd tolerance in higher plants.

Forest quality has a significant impact on carbon emissions of the economic system. This paper, based on an analysis of the Environmental Kuznets Curve (EKC) of Forest quality and carbon emission of economic systems, explores effective carbon emission reduction paths from the perspective of symbiosis between Forest quality and economic growth system. This paper carries out empirical analysis based on the provincial-level regional data of China and EKC panel data analysis for several major geographic regions in China. The correlation statistical test shows that the model considering Forest quality is more consistent with the test requirements. It is found that the overall EKC curve of China as a whole shows an inverted U-shape. In light of geographic differences, the EKC curve in the northwest also shows an inverted U-shape; it shows a U-shaped curve in North, Northeast, and East China. Meanwhile, in South Central and Southwest China, there is an approximately linear increasing curve. The data simulation of the symbiotic system of Forest quality and economic growth suggests that simultaneously increasing the proportion of forestry output value and the systematic carbon inhibition coefficient is the most effective pathway for carbon emission reduction. Choosing an appropriate carbon reduction pathway can contribute to the sustainable development of Forest quality and economic growth. The main research highlight of this paper is the application of the symbiotic system model to the EKC analysis of carbon emissions from the economic system with Forest quality.

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.

Co-management is increasingly recognized as an effective approach of forest governance while recognizing local people's forest dependency and traditional rights. Moreover, co-management is expected to positively impact forest cover and ecosystem health. Bangladesh, facing a rapid decline in forest cover, has one of the lowest per capita forest areas worldwide. For quite a while, co-management practices have been used to improve forest cover and the livelihoods of forest-dependent people in Bangladesh. Several studies have assessed the efficacy of co-management in different protected areas in Bangladesh from livelihood, social, and cultural aspects. However, the overall changes in forest cover due to co-management from the very beginning of co-management initiatives in the country have been overlooked. We used remotely sensed Landsat images to assess the changes in forest cover and overall land use in five forest protected areas where co-management was piloted. Three major vegetation indices (NDVI, EVI, and MSAVI) were examined to ensure the study's robustness. This study aims to assess the scope and weaknesses of the current policy that incorporates co-management in the country's first five protected areas (SNP, LNP, RKWS, TWS, and CWS), which were brought under co-management in 2003, and compare the efficacy of the co-management relative to non-comanaged protected areas (RRF). The land cover analysis based on NDVI, MSAVI, and EVI values indicated dominant dense forest coverage (41–71%) across five protected areas. During the co-management period (2004–2015), there was a significant decrease in the dense forest proportion, with slopes ranging from -3.7 to -0.96. Similarly, the RRF showed a decreasing pattern, with the slope of the decreasing trendline ranging from -0.48 to -0.62 across the three indices. Agriculture and forest-agriculture mosaics showed a significant increase, with varying slopes among protected areas in both co-managed and non-comanaged sites. Pixel-to-pixel changes revealed dynamic shifts in vegetation indices, and Global Forest Watch data highlighted forest cover losses, notably in CWS (158.77 ha) and SNP (0.49 ha).

Expansins are cell wall (CW) proteins that mediate the CW loosening and regulate salt tolerance in a positive or negative way. The transcription of Populus trichocarpa expansin A6 (PtEXPA6) was downregulated upon prolonged duration of salt stress (48 h) after a transient increase induced by NaCl (100 mM). The role of PtEXPA6 in salt tolerance was determined by transferring the PtEXPA6 gene into the hybrid species Populus alba × P. tremula var. glandulosa clone 84K (84K) and Populus tremula × P. alba INRA '717-1B4' (717-1B4). The PtEXPA6-transgenic poplars of 84K and 717-1B4 showed a greater reduction in photosynthesis, stem height and diameter growth under salt stress and a reduced capacity for ROS scavenging by antioxidant enzymes, which is associated with excessive Na+ accumulation in roots and shoots. NMT flux recordings showed that the radial translocation of Na+ salt into the root xylem and real-time Na+ translocation from roots to leaves were greater in PtEXPA6-transgenic poplars than in wild-type poplars. Analysis of comparative contractility and comparative extensibility of intact root tips indicated that PtEXPA6 increased the CW loosening in the transgenic poplars of 84K and 717-1B4. Noteworthy, the PtEXPA6-promoted CW loosening was shown to facilitate radial and longitudinal transport of Na+ in transgenic poplars. We conclude that overexpression of PtEXPA6 leads to CW loosening that facilitates radial translocation of Na+ into the root xylem and subsequent Na+ translocation from roots to leaves, resulting in excessive Na+ accumulation and consequently reduced salt tolerance in transgenic poplars.

Mangrove plant seedling cultivation is crucial for the protection, management and restoration of mangrove ecosystem. In this study, we focused on Kandelia obovata, a typical mangrove, and evaluated nursery cultivation with different combinations of three salinity levels (S1: 0 ppt, S2: 10 ppt, and S3: 20 ppt), three genealogies (EZD, JX, and YZ), and five growth media (M1: 100% loess, M2: 100% sandy, M3: 50% loess + 50% sandy, M4: 40% loess + 40% sandy + 20% peat, and M5: 40% loess + 40% sandy + 20% coir), by measuring the growth parameters such as mortality rate, seedling height, diameter, and biomass partition. These growth indexes were significantly affected by salinity and medium, and genealogies also had significant effects on mortality rate and biomass accumulation. S2 or S3 both had lower mortality and higher growth indexes than S1. M1 was the medium that increased seedlings height, diameter and biomass the most and had the lowest death rate. EZD and JX were also higher levels than YZ in these indicators, but the difference between them was not obvious. S3, M1, and EZD consistently performed well in fuzzy evaluation and quality assessment. Furthermore, combinations involving these treatments also produced highly favorable results. These results furnish both a theoretical and practical foundation for advancing nursery cultivation techniques and germplasm breeding of K. obovata in mangroves.

In 2004, a significant portion of the spruce-dominated forests in the Tatra National Park (northern Slovakia) was destroyed by an exceptionally strong windstorm. Our study focused on the status of deadwood and its influence on post-disturbance forest regeneration 18 years after the destruction. Since some disturbed areas were salvaged and others were not, we could compare the situations between these two management approaches. In total, 40 research sites (20 salvaged and 20 unsalvaged) were measured, each represented by four satellite-like plots with a radius of 4 m. We measured the diameters of deadwood and its decay status (classes 1 – 5). At the same time, we recorded the sizes (stem base diameter and height) and species of young trees. The results showed that while salvage conditions and contact with soil stimulated deadwood decomposition, the diameter of logs was not a significant factor. The highest decay class (3.83) was found in deadwood on salvaged areas and attached to the soil, while the lowest decay class (3.10) was found in deadwood on unsalvaged areas and in the case of logs unattached to the soil. While carbon content (based on percentage of wood mass) did not change with decay class, wood density decreased sharply with decay class. Water moisture showed an increasing tendency with decay class. Moreover, our results showed that post-disturbance management influenced forest regeneration. Significantly higher tree diversity was observed in salvaged areas (5.40 species per plot) compared to unsalvaged areas (3.85 species per plot). While areas with logging were predominantly covered by broadleaved species, those without logging were typically dominated by Norway spruce (Picea abies L. Karst). We concluded that although salvage logging decreased the total carbon and water content in deadwood, it can stimulate tree species diversity. Thus, the currently prevailing opinion about the exclusively negative ecological effects of salvage logging should be accepted with caution, and local conditions must be considered before making broad judgments.

Bone/fracture healing is a complex process with different steps and four basic tissue layers being affected: cortical bone, periosteum, fascial tissue surrounding the fracture, and bone marrow. Stem cells and their derivatives, including embryonic stem cells, induced pluripotent stem cells, mesenchymal stem cells, hematopoietic stem cells, skeletal stem cells, and multipotent stem cells, can function to artificially introduce highly regenerative cells into decrepit biological tissues and augment the healing process at the tissue level. Stem cells are molecularly and functionally indistinguishable from standard human tissues. The widespread appeal of stem cell therapy lies in its potential benefits as a therapeutic technology that, if harnessed, can be applied in clinical settings. This review aims to establish the molecular pathophysiology of bone healing, the current stem cell interventions that disrupt or augment the bone healing process, and finally, consider the future direction/therapeutic options related to stem cells and bone healing.

Following the adoption of the Kunming-Montreal Global Biodiversity Framework (KM-GBF), with the Convention on Biological Diversity serving as its guiding treaty, the European Union (EU) has just reached an agreement on an EU Nature Restoration Regulation. This study carries out a systematic cross-walk between the restoration regulation and the KM-GBF, focusing on their implications for forest ecosystems. This paper identifies areas of alignment, divergence, and potential gaps related to habitat restoration, species protection, climate resilience, and the sustainable use of natural resources. The methodology adopts a grounded approach, starting with the 23 targets outlined in the KM-GBF and proceeding to the 28 articles set out in the restoration regulation. The results highlight the need for better alignment between the KM-GBF, the restoration regulation, and other forest-relevant EU policy instruments. The study stresses the need for a coherent and integrated EU policy approach that can address the diverse challenges and policy objectives facing forests. It concludes that amendments to the restoration regulation have significantly diluted its potential impact, limiting the EU Members States accountability and ability to meet KM-GBF goals and targets. It further stresses the need for strategies that can reconcile divergent EU policy pathways, support forest management and restoration efforts, while aligning with global biodiversity objectives.

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.

The forest is a complex and dynamic ecosystem that requires integrated planning and management, considering economic, social, environmental, and ecological dimensions. Different stakeholders often have conflicting goals, making optimal management decisions challenging. This study aims to balance increasing economic benefits for forest users with reducing negative environmental impacts, aligning the interests of managers, government institutions, and policymakers. To achieve this, multi-objective optimization methods and game theory were used. The objectives were to maximize the net present value of wood harvesting and the amount of carbon sequestration. The study was conducted in the Shafarood forest in northern Iran. Data collected included stumpage prices, variable harvesting costs, tree density per ha, volume per ha, growth rates, interest rates, carbon sequestration, and labor costs. Using this data, models were developed for the expected mean price of various species, stored carbon, logistics growth, and labor. After adjusting the objective functions and model constraints, the range of Pareto optimal solutions for the multi-objective model and the Nash equilibrium for the game theory model were determined using the epsilon-constrain method. The results indicate that both modeling approaches can identify optimal points for balancing economic and environmental goals. The Pareto optimal range and Nash equilibrium allow for informed decision-making in managing the Hyrcanian forests, accommodating different user goals. Therefore, both models are effective tools for forest management planning and provide a framework for achieving sustainable management practices.

With the burgeoning growth of online retail, the issue of counterfeit products has become a significant threat to both consumer trust and brand integrity, posing challenges to sustainable market practices. Online retail platforms are promoted to redesign their channel structures and enhance brand traceability to tackle counterfeits using blockchain technology. To investigate the interactions between blockchain technology and the channel structures of online retail platforms, we design a game theory model incorporating a manufacturer with(out) blockchain participation selling products via an online retail platform that operates an agency channel and/or a reselling channel. We show that the critical trade-off of blockchain participation comprises the brand assurance effect of the blockchain, the channel competition intensification effect, and the blockchain adoption cost. The platform is capable of strategically designing the channel structure to encourage (while discouraging) blockchain participation by the manufacturer. Our analysis highlights the complementarity between blockchain participation and the channel structure. Furthermore, the effect of the consumer’s belief in the reselling channel, the commission fee in the agency channel, and the substitutability between channels are investigated in depth. The research provides valuable insights for online retail platforms and policymakers seeking to foster a sustainable and trustworthy e-commerce ecosystem.

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.

The goods and services produced by forest vegetation ecosystem are the driving force for the rapid, high-quality and sustainable development of human society. In history, with the increase of population and economic development, there is an increasing demand for the quantity and variety of forest vegetation ecosystem products and services in a country or a region. To meet the demands, most of the original forest has become farmland, plantation and grass and crops land. As a result, the plant water relationship changed from equilibrium to non-equilibrium, which easily led to soil drought, soil degradation and vegetation decline in dry years or waste of soil water in rainy years in most of water-limited regions. In order to solve the question and realize the sustainable utilization of soil water resources and the high-quality sustainable development of forest, it is necessary to apply the limit theory of plant utilization of soil water resources and the vegetation carrying capacity theory in the critical period of plant-water relationship regulation to adjust the relationship between plant growth and soil moisture to obtain the maximum yield and benefit and realize high-quality and sustainable development.

This study examines the differential impact of proactive and reactive job crafting behaviors on job well-being, drawing from the perspective of boundary theory. The increasing popularity of work connectivity behavior after-hours (WCBA) after work has attracted widespread attention from scholars on the relationship between WCBA and employee well-being. One view suggests that the impact of WCBA is negative, while another view suggests it is positive. Obviously, the impact of (WCBA) on well-being at work is still contradictory. To clarify the complexity of the impact of WCBA on well-being at work, based on boundary theory, we divided WCBA into proactive WCBA (PC) and reactive WCBA (RC), and examined the double-edged sword effect of WCBA on well-being at work, as well as the mediating mechanisms of job control and work-to-home conflict, and the moderated effects of boundary segmentation preferences. This study uses empirical sampling method to collect data from 125 employees for a period of five days for quantitative research. The results show that, first, PC has an inverted U-shaped effect on job control, and job control is negatively related to well-being at work. Thus the mediating effect of job control is significant. Second, RC has a negative impact on job control, and there is also a negative relationship between job control and well-being at work. Therefore, the mediating effect of job control is significant. Third, PC and RC are positively correlated with work-to-home conflict, and work-to-home conflict has a significant positive impact on well-being at work. Therefore, the mediating effect of work-family conflict is significant. Fourth, the study also found that integration preference moderates the relationship between work-to-home conflict and PC on well-being at work, that is, the mediating effect of work-to-home conflict is stronger for employees with low integration preference. This study enriches our understanding of WCBA, PC and RC from the perspective of proactive and passive employee behaviors. The study also provides a new interpretation of the impact of WCBA on well-being at work and offers valuable insights for the sustainable development in digital social transformation and the application of boundary theory and the theory of empowerment-subjugation in achieving the United Nations Sustainable Development Goals. In addition, the study deepens our understanding of the heterogeneous regulatory role played by work-family integration preferences in influencing well-being at work under different types of WCBA.

Accurate detection of forest restoration sites using deep learning is crucial for effective ecosystem management, optimizing reforestation efforts, and ensuring environmental sustainability. While various deep learning-based systems have been developed for this purpose, the influence of different neural network architectures on model performance during the identification of forest restoration sites remains underexplored. This study aimed to address this gap by exploring the optimal methodology for extracting and classifying candidate sites for forest restoration based on Convolutional Neural Networks (CNNs), specialized for image recognition. Four categories—arable land, road and barren, quarry, and forest—were defined as candidate sites for forest restoration. A dataset comprising 17,043 samples was divided into training (11,929) and validation (5,114) sets at a ratio of 7:3 for model learning. Model accuracy was evaluated using pixel accuracy (PA) and mean intersection over union (mean IoU). The Resnet152-U-Net model demonstrated a pixel accuracy of 95.2% and a mean IoU of 61.3%, indicating excellent performance in extracting candidate sites for forest restoration. This approach offers spatial and temporal advantages over conventional field surveys or aerial image-based assessments, potentially serving as valuable data for selecting future forest restoration sites.

This study aimed to develop a carbon yield model for a Makino bamboo plantation and addressed a monoculture Makino bamboo plantation in central Taiwan. We established a long-term trial to monitor the stand dynamic following thinning. A total of 16 plots were installed based on four thinning treatments, each sharing four plots. After thinning, data were collected from two investigations, resulting in 32 records. We calculated the number of culms (N), mean diameter at breast height (MDBH), and basal area (BA) for each record. An allometric function developed by previous research was used to predict aboveground biomass and to obtain aboveground carbon storage (AGCS). The model used the N, MDBH, and BA with various types (reciprocal and natural logarithm types) as independent variables to predict ln(AGCS) by the stepwise method. According to the Radj2 value, the best predictive models were: ln(ABCS) = -0.004 + 0.820 ln(MDBH) + 1.008 ln(BA), and ln(ABCS) = 7.550 - 0.820 ln(N) + 1.828 ln(BA). From these two models, we found the factors, either BA and MDBH or BA and N, can effectively predict AGCS. It indicated that stand density was critical in affecting AGCS, especially the variable BA.

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.

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.

Brazil has ideal conditions for the large-scale use of cabotage cargo transportation: a vast coastline with almost 7,400 km, approximately 70% of the population resident in a coastal range of up to 200 km from the coast and a strong concentration of activities economic along the coast. However, there is an absolute domain of road modal for the transportation of goods, and cabotage is underused. The purpose of the study was to raise the main barriers that negatively impact the performance of Brazilian cabotage. To this end, it was used to bibliographic and documentary research, based on studies already published in journals, doctoral theses, master's dissertations, congress annals, technical reports and research on government-related government agencies sites, navigation companies and entities related to cabotage. The main barriers that contribute to the low use of cabotage in Brazil were identified and analyzed, as well as possible alternatives of solution for sustainable development of this mode of transport. Such a study will certainly serve as subsidies for the definition of public policies aimed at better use of Brazilian cabotage, as well as to encourage navigation companies to invest more in the transport of goods by cabotage, in order to contribute to the reduction of trucks on highways Brazilian and for environmental preservation.

This study investigates the impact of circular economy (CE) strategies on organizational resilience (OR) within the metal industry, focusing on a case study of a forging department in a large man-ufacturing company. The idea of the paper is to link the macroeconomic topic of circular economy (CE) with microeconomic and practical activities in the organization, exploring how CE principles can improve resource efficiency, sustainability, and organizational resilience. A systematic literature review and case study methodology were used to assess the relationship between CE and OR. The findings reveal that companies with higher circularity demonstrate better resilience, both in normal operations and during crises. Specifically, in the case study, the closure of the forging department, following a detailed analysis using the Inputs-Mediators-Outputs (IMO) model, emerged as the most sustainable option, yielding financial, environmental, and operational benefits. This decision aligned with the company’s long-term goals of energy efficiency and carbon neutrality. The research highlights the significance of integrating CE strategies with business processes to not only achieve sustainability but also to build a more adaptable and resilient organization. Future research should explore broader applications of this framework across different industries to strengthen the links between circular economy practices and organizational resilience.

At present, farmland abandonment（FA）is a severe phenomenon in China, imposing a serious restriction on agricultural production. In this context, it is of great significance to explore the logical relationship between high-standard farmland construction (HSFC) and FA to optimize land resource allocation and guarantee national food security. Based on 838 samples of farmers in the main rice production area of the Yangtze River Basin in China, this paper employs the Tobit model, the mediating effect model, and the moderated mediating effect model to analyze the impact of HSFC on FA at the micro level. The results show that (1) HSFC inhibits FA, and FA proportion decreases by 1.14% for every 1% increase in HSFC proportion, and the robustness test and endogeneity treatment also yield consistent conclusions. (2) Agricultural socialization services (ASS) play a positive mediating role in the influence path. HSFC promotes farmers' purchase of ASS, which in turn inhibits FA. (3) The agricultural labor transfer distance (ALTD) plays a positive moderating role in the relationship between HSFC and FA. The farther the distance, the more likely HSFC can promote farmers' purchase of ASS and inhibit FA. The results provide enlightenment on how to precisely implement HSFC policy, that is, to inhibit FA by improving the construction of high-standard farmland and the post-construction management as well as protection system. Moreover, this study helps guiding the development of ASS and optimizing the institutional environment for agricultural labor transfer, creating conditions for agricultural scale management.

To decrease privacy risks, consumers may choose to misrepresent when they are asked to offer personal information. This paper examines the impact of consumer misrepresentation on a monopolistic firm and consumers with an economic model. The results show that consumer misrepresentation may benefit the firm but hurt consumers. In addition, consumers misrepresentation may encourage the firm to provide higher personalized service level in certain scenarios, such as, when the unit cost of personalized service is low. Thirdly, when consumers misrepresent, and the firm only covers part of the market, a greater unit value of consumer’s privacy information will reduce the firm’s profit, but a greater unit cost of personalized service increases the firm’s profit.

Under the new pattern of “double carbon” development, good ESG performance is the best way to promote the sustainable development of enterprises, and the ESG investment strategies are directly affected by the strategic vision of managers. Based on the upper echelons theory and stakeholder theory, this paper takes Chinese A-share listed companies from 2011 to 2022 as samples to empirically analyze the impact of managerial myopia on corporate ESG performance. The results show that managerial myopia significantly inhibits corporate ESG performance, mainly by inhibiting corporate green investment and green innovation sustainability. Furthermore, for state-owned enterprises (SOE), heavy polluting enterprises (HPE) and non-high-tech enterprises, the inhibitory effect of manager myopia on ESG performance is stronger. When the enterprise is in the growth stage, the above inhibition effect is more severe. For external governance, the greater the analyst attention and public environmental attention are, the more conducive they are to alleviating the restraining effect of managerial myopia on enterprise ESG performance. Therefore, effectively improving the time cognition level of managers and strengthening external supervision have become important measures for comprehensively optimizing the ESG performance.

The collaboration of multimodal transport documents is the foundation for conducting multimodal transport operations. Blockchain technology can effectively address trust issues and information sharing difficulties in current multimodal transport document collaboration. However, in current blockchain-based electronic document research, the bottleneck problem lies in the collaboration of multiparty in multimodal transport under the "one-bill coverage system". We design a blockchain platform architecture for multimodal transport, reshaping collaboration processes for different modes of transport based on the blockchain platform. It proposes a collaboration model for multimodal transport under the "one-bill coverage system" and performs model solving and case analysis. The obtained collaboration strategies are embedded into the platform's application layer. Finally, using Solidity language, it writes smart contracts related to multimodal transport under the "one-bill coverage system " and deploys and executes them on the Remix platform.

Membranes are a selective barrier that allows certain species (molecules and ions) to pass through while blocking others. Some rely on size exclusion, where larger molecules get stuck while smaller ones permeate through. Others use differences in charge or polarity to attract and repel specific species. Membranes can be used to purify air and water by allowing only air and water molecules to pass through but preventing contaminants such as microorganisms and particles or to separate target gas or vapor, such as H2, CO₂, from other gases. The higher the flux and selectivity, the better a material is for membranes. The desirable performance can be tuned through material type (polymers, ceramics, and biobased materials), microstructure (porosity and tortuosity), and surface chemistry. Most membranes are made from plastic from petroleum-based resources, which contribute to global climate change and plastic pollution. Cellulose can be an alternative sustainable resource to make renewable membranes. Cellulose exists in plant cell walls as natural fibers, which can be broken down into smaller components such as cellulose fibrils, nanofibrils, nanocrystals, and cellulose macromolecules through mechanical and chemical processing. Membranes made from reassembling these particles and molecules have variable pore architecture, porosity, and separation properties and therefore have a wide range of applications in nano-, micro-, and ultrafiltration or forward osmosis. Despite their advantages, cellulose membranes face some challenges. Improving the selectivity of membranes for specific molecules often comes at the expense of permeability. The stability of cellulose membranes in harsh environments or under continuous operation needs further improvement. Research is ongoing to address these challenges and develop advanced cellulose membranes with enhanced performance. This article reviews the microstructure, fabrication methods, and potential applications of cellulose membranes, providing some critical insights into processing-structure-property relationships for current state-of-the-art cellulosic membranes that could be used to rationally improve their performance.

The present study aimed to date the death of Korean red pine (Pinus densiflora Siebold & Zucc.) at Sogwang-ri, where is famous for the Korean red pine forests in Korea, using a dendrochronological method, and to investigate the effect of climatic factors on the death and growth decline of the pine trees. For the study, 51 Korean red pines were selected from three stands (Jungmi-gol A, Jungmi-gol B, and Neonam-gol) in the study area, and for the ring-width measurement the increment cores were extracted from the selected trees. The stand chronologies for the Jungmi-gol and Neonam-gol were established using the mean values of individual time series of the living trees from each site, and the chronologies were further used for cross-dating with individual time series of the dead trees to date their death. The abrupt growth reduction was also investigated based on the consecutive ring-width growth decline trend to know the vitality during the living. Results revealed that the red pines at Jungmi-gol A were dead in 2020, at Jungmi-gol B in 2012 or 2013, and at Neonam-gol in 2019. So, it was verified that the death of the red pines at the study sites occurred from high to low elevations. In the analysis of the role of climate in the annual ring growth, the trees at Jungmi-gol A and B located on the southwest slope showed more ring-width growth when precipitation in April, July, and August were increasing, at Neoman-gol located on the north slope when precipitation only in July was increasing. Although the correlations of the ring-width growths at the sites with temperature and precipitation were somehow different from each other, Drought Stress Index (DSI) from May to September at all sites were all negative. Therefore, water stress caused by increased temperature and decreased precipitation is the most significant climatic parameter for the death or growth decline of the pine trees in the study area.

Productivity has been modest in construction due to many reasons, especially project-specific operations and fragmentation. The main aim of this research is to map the challenges towards industrialization in construction through a comparative study of the manufacturing industry. An inductive approach and a qualitative method are followed by applying semi-structured inter-views, and node-based thematic analysis. The key elements of industrialization in mature man-ufacturing companies are benchmarked. The findings indicate a specific order of steps for indus-trialization in construction. Data and information management with achieved systematization have been sources of productivity in manufacturing companies. However, the systematization starts from the offering, construction companies need to define their offering, that is the products, and define their respective data. Only accurate and good quality data on products enables the systematization of order delivery processes, which are the engine for a company’s success or failure. Continuous efficiency improvement is only possible with systematized processes and respective data related to the processes. The originality involves clarifying the industrialization of construction companies via products, respective data, and processes considering industrialized manufacturing companies.

Long COVID is a phenomenon in which individuals experience persistent symptoms after recovering from COVID-19. The symptoms are discovered to be unique for every individual and can affect multiple organs systems in the body. This study aims to identify effective natural remedies for long COVID by analyzing hub genes associated with the symptoms of the condition and evaluating the repurposed drugs catered and used for treating the symptoms of long COVID. The most common and prevalent symptoms of long COVID were identified; Fatigue, Shortness of Breath, Loss of Smell, Headache, Brain Fog, Chest Pain, Insomnia, Heart Palpitations, Dizziness, Joint Pain, Depression, Anxiety, Tinnitus, Loss of Appetite. Hub genes for each of the symptoms provided an insight on the key biological pathways of the symptom. Repurposed drugs identified, provided the template to identify the natural compounds with similar structure as a potential therapeutic drug. The natural compounds were retrieved using fingerprint search of the repurposed drugs from the NPASS Database. The findings of this study suggest several natural remedies for each symptom based on the molecular docking of the hub gene and natural compound using iGEMDOCK. The identified natural remedies may hold promise in treating long COVID, but further research is required to explore the efficacy and effectiveness of the proposed natural compounds. The results of the study pose important implications for the development of effective treatments for long COVID.

Xinjiang, as a climate-sensitive region in northwest China, holds significant importance in studying extreme weather events for agricultural production and socioeconomic development. Based on data from 52 meteorological stations and 23 extreme climate indicators in Xinjiang from 1960 to 2021, the 5a moving average, linear trend estimation, climate tendency rate, and GIS spatial analysis methods are used to analyze the distribution and change characteristics of extreme climate. The results revealed that from 1960 to 2021, TXx, TNx, TXn, TNn, FD, ID, TN10p, TX10p, and WSDI showed a significant upward trend, while SU, TR, TN90p, TX90p, and CSDI showed a significant downward trend. What is more, the range of changes in the extreme temperature indexes from 1990 to 2021 is higher than that of 1960 to 1989. The high-value areas of extreme indexes are primarily concentrated in the northern part, while the high-value areas of cold event indexes are mainly distributed in the southern part. The upward/downward trends all accounting for over 80.00% of the entire region. The Rx1day, Rx5day, PRCPTOT, R95p, R99p, R10, R20, and R25 showed a significant growth trend from 1960 to 2021, and the range of change in the extreme precipitation indexes from 1990 to 2021 was lower than that from 1960 to 1989. Moreover, their high-value areas and high climate tendency areas are mainly concentrated in the northern and western parts of Xinjiang, with upward trends all accounting for over 71.00% of the entire region. The research results provide theoretical foundations for formulating climate risk strategies in the northwest region of China.

Transcriptomes from stem-inoculated Scots pine saplings were analyzed to identify unique and enriched H. annosum transcripts in the early stages of infection. Comparing different time points since inoculation identified 131 differentially expressed H. annosum gene with p values ≤ 0.01. Our research supports the results of previous studies on the Norway spruce – Heterobasidion annosum s.l. pathosystem, indicating the role of carbohydrate- and lignin degradation genes in pathogenesis at different timepoints post inoculation and the role of lipid metabolism genes (including but not limited to the delta-12 fatty acid desaturase gene previously reported to be an important factor). The results of this study indicate that malic enzyme could be a potential gene of interest in the context of H. annosum virulence.

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.

Dogs population increased exponentially in the past decade while the birth rate decreased gradually, especially in the developed countries. Due to dogs’ characteristics of loyalty and obedience, most pet owners treated their dogs as their family members or companions, therefore they are willing to select and purchase safe, high quality and nutrition dog food to their dogs. However, there is lack of regulation or standard to reinforce the production or distribution of dog food in Hong Kong. Consequently, this paper will first review the relationship between high dog population and declining birth rate, types of dog food and typical ingredients. Secondly, the potential health implications of various “toxic” food ingredients for dog, hazard analysis of dog food and its testing methods will be investigated. Lastly, this paper will summarize worldwide existing standards and regulations, quality and hygiene management system related to dog food safety to provide insights and suggestions to Hong Kong government about the importance and urgency of implementing regulations and policies to oversee the imported and local manufactured dog food.

Long COVID, also referred to as post-COVID-19 condition, describes the lingering after-effects of COVID-19 infection. This complex syndrome encompasses a range of symptoms affecting physical well-being, cognitive function, and mental health. The widespread impact of long COVID extends beyond individual sufferers, placing significant strain on healthcare systems and causing far-reaching socioeconomic consequences. The scale of this multifaceted health crisis is unparalleled in recent history, affecting millions globally and challenging our understanding of post-viral conditions. This study aimed to explore the possibility of targeting G protein-coupled receptors (GPCRs) signaling pathway-related genes as therapeutics for long COVID using computational analysis. Targeting GPCRs and their signaling pathways holds significant promise for long COVID therapeutics, as these receptors are involved in crucial physiological processes including immune regulation, inflammation control, and neurotransmission. All of these are implicated in the diverse and persistent symptoms of long COVID, potentially offering a versatile approach to addressing multiple aspects of the condition simultaneously. This study identified 21 genes associated with GPCRs for further exploration as potential targets. Notably, GPCR genes such as HRAS, KRAS, GNAQ, and GNA11 were determined to be possible therapeutic targets for the pathophysiological mechanisms of long COVID. FDA-approved drugs targeting these genes were identified, including Binimetinib, Cabozantinib, Selumetinib, Panitumumab, Cetuximab, Adagrasib, Tipifarnib, and Sotorasib. Additionally, Naltrexone and Tipifarnib have emerged as potential new treatment options for long COVID. SARS-CoV-2 may hijack GPCR signaling pathways to dysregulate lung ion and fluid transport, contributing to the pathophysiology of pulmonary edema in patients with COVID-19. Further research is required to translate the findings into the development of therapeutics and to investigate the relationship between viral infection and GPCR signalling.

This paper aims to assess whether the financial performance of Portuguese companies that publish sustainability reports (SR) differs from the financial performance of companies that do not publish SR. We use two methodological procedures. First, we conducted a univariate analysis to test the differences in the financial performance according to the disclosure of SR. Second, we conducted a multivariate analysis using a multiple linear regression that explains financial performance by the disclosure of SR, as well as control variables such as sector, size, leverage, growth, and liquidity. Findings indicate that financial performance of companies that disclose SR does not significantly differ from the financial performance of companies that do not disclose SR. The results are robust to both methodological procedures, as well as to the sample split by sectors.

Retrospective information about the climate system and plant ecophysiology are key inputs in climate and Earth system modelling. Dendrochronology provides such information with large spatiotemporal coverage, and stable carbon isotope (¹³C/¹²C) analysis across tree-ring series is among the most advanced dendrochronological tools. For the past seventy years, this analysis was performed on whole molecules, and, to this day, ¹³C/¹²C variation is attributed to ¹³C discrimination during CO₂ diffusion into leaves and assimilation by rubisco. By contrast, ¹³C discrimination by post-rubisco processes is presumed constant. Here, recently reported results on the first dataset of intramolecular ¹³C discrimination in tree rings were synthesised by variance component analysis. The emerging picture is not consistent with the classical (DR-discrimination-centred) concepts and practices of carbon-isotope dendrochronology. Specifically, leaf and stem post-rubisco discrimination each account for more variation in the data than diffusion-rubisco discrimination, i.e., post-rubisco discrimination is not constant. Furthermore, diffusion-rubisco discrimination is used widely as proxy of leaf intrinsic water-use efficiency (iWUE), a key determinant in the responses of global biogeochemical cycles to climate change. However, since diffusion-rubisco discrimination is a small component of the total data variance, whole-molecule analysis yields confounded iWUE estimates, yet intramolecular analysis will likely offer solutions. Lastly, all currently observed ¹³C/¹²C-climate relationships are attributed to diffusion-rubisco discrimination. However, here, relationships with temperature and radiation derive from leaf-level post-rubisco discrimination while relationships with precipitation derive from stem-level post-rubisco discrimination. That said, advances in mass spectrometry may soon make intramolecular ¹³C/¹²C analysis broadly available taking carbon-isotope dendrochronology to the next level.

This paper presents a comprehensive study on applying machine learning (ML) techniques for real-time credit card fraud detection. With the increasing prevalence of credit card fraud, financial institutions face the challenge of detecting fraudulent transactions promptly and accurately. This research evaluates various ML models, including Logistic Regression, Decision Trees, Random Forests, XGBoost, and Deep Convolutional Neural Networks, for their effectiveness in identifying fraudulent activities. Utilizing PySpark for processing large-scale transaction data, the study highlights the importance of real-time analysis, adaptive learning, and handling imbalanced datasets. Key findings reveal that XgBoost, with its balance of accuracy and complexity, emerged as the most promising model, achieving an accuracy rate of 98% in detecting fraudulent transactions. The paper further discusses the potential of ensemble methods, graph-powered systems, and intelligent sampling in enhancing fraud detection capabilities. Challenges such as overfitting, data access, and the need for real-time analysis are addressed. Future research directions include implementing models in live transaction environments, expanding model applicability across financial platforms, and developing advanced anomaly detection methodologies. This study underscores the pivotal role of machine learning in safeguarding financial transactions against fraud, offering significant implications for consumers, financial institutions, and the broader financial ecosystem.

Food insecurity remains a significant developmental challenge, particularly in rural areas, despite ongoing efforts to mitigate it. To better understand the persistence of this issue, this study investigates the causes of household food insecurity and the coping strategies employed by households in Zorkor Goo, Zorkor Kanga, and Zorkor Kadaare communities within the Bongo District of Ghana’s Upper East Region. A descriptive cross-sectional survey was conducted, sampling 196 households systematically from these communities. Data were collected through a structured questionnaire covering socio-demographic factors, causes of food insecurity, household coping strategies, and dietary intake. The analysis was performed using SPSS (Version 21.0), with food security status and dietary intake assessed via the FANTA Household Food Insecurity Access Scale (HFIAS) and Food Consumption Score, respectively. The study identified key factors contributing to food insecurity, including soil infertility, poor rainfall patterns, food spoilage, extravagant funerals, large family size, high unemployment, and lack of education. Major coping strategies among households included meal skipping, reducing meal size and frequency, migration, asset sales, and basket weaving. The findings revealed that 96.4% of households were severely food insecure, and over two-thirds (67.3%) exhibited poor dietary consumption. Notably, household employment levels, education, and food consumption scores were significantly associated with food insecurity (P<0.001). These findings underscore the need for targeted interventions addressing the root causes of food insecurity and enhancing household resilience in these communities.

This research article investigates the application of artificial intelligence (AI) in human resource management (HRM) within the Australian commercial media sector, addressing a critical gap in scholarly knowledge. Despite increasing interest in AI’s potential, there is a lack of consensus among managers and HR professionals regarding its application. Through qualitative interviews with 22 industry professionals, this study examines perceptions, understanding, and the potential for leveraging AI across key HRM functions. Findings indicate significant potential for AI to enhance HRM processes, particularly in job/work design and staffing. This research contributes to a deeper understanding of AI’s role in HRM and offers insights to optimize organizational efficiency and effectiveness in the Australian commercial media sector.

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.

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.

Using the data of China's A-share non-financial listed enterprises from 2015 to 2022 as a sample, this study empirically tests the impact of ESG rating disagreement on corporate debt financing cost. The results show that ESG rating disagreement significantly increases the debt financing costs of enterprises, and this result still holds after a series of robustness tests such as replacing the dependent and independent variables and PSM approach. Meanwhile, the heterogeneity analysis reveals that non-state-owned enterprises and enterprises in non-heavily polluting industries are more sensitive to ESG ratings disagreement. In addition, mechanism analysis shows that corporate financing constraints mediate the relationship between ESG rating disagreements and debt financing costs. The findings of this study provide important evidence for understanding the impact of ESG rating disagreement on the costs of debt financing, enrich the meaning of ESG rating disagreement, and reveal the significance of unifying ESG rating standards.

Amidst the escalating demands for efficient Intelligent Transportation Systems (ITS) management, the significance of accurate Origin-Destination (OD) data prediction has emerged as a paramount concern, given its indispensable role in the ITS domain. This paper introduces a groundbreaking integrated model, creatively fusing the unique strengths of Graph Convolutional Network (GCN), Residual Neural Network (ResNet), and Long Short-Term Memory Network (LSTM) into a single architecture, which we name GraphResLSTM, to exploit road segment average speed data for OD prediction. Compared with conventional road segment traffic volume data, average speed data is more readily available, significantly reducing the data accessibility barrier for OD prediction. We underscore the critical role of selection of key road segments and employs a combination of Entropy Weight Method and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) to identify 41 representative road segments from the experimental road network. In the experimental phase, we simulate 86400000 seconds of traffic conditions, generating a total of 1440000 data records. Utilizing these simulated data, we design and conduct comparative experiments between models as well as data types. The results demonstrate that both the GraphResLSTM model and road segment average speed data outperform other models and data types notably in OD prediction tasks.

Background: Although the link between environmental lead exposure and several patterns of cardiovascular disease (CVD) has been reported, its association with silent myocardial infarc-tion (SMI) has not been explored. We aimed to assess the association between blood lead levels (BLL) and risk of SMI. Methods: This analysis included 7283 (mean age 56.1±2.52 years, 52.5% women) participants from the Third National Health and Nutrition Examination Survey who were free of clinical CVD. Ascertainment of BLL done using graphite furnace atomic absorption spectrophotometry. SMI defined as ECG evidence of MI without history of MI. Multivariable lo-gistic regression analysis was used to examine the association between SMI and BLL. Results: SMI was detected in 120 participants corresponding to an unweighted prevalence of 1.65%. Par-ticipants with higher levels of BLL had higher prevalence of SMI (0.4%, 0.9%, and 2.4% across BLL tertiles; p-value

The recent mountain pine beetle outbreaks have caused widespread mortality among lodgepole pine trees in western North America, resulting in a reduced population of surviving trees. While previous studies have focused on the cascading impacts of these outbreaks on the physiology and growth of the surviving trees, there remains a need for a comprehensive study into the interactions among various physiological traits and the growth in post-outbreak stands. Specifically, the relationship between chemical (primarily terpenes) and anatomical (mainly resin ducts) defences, as well as the allocation of non-structural carbohydrates (NSCs) to support these defence modalities, is poorly understood. To address these gaps, we conducted a field survey of surviving lodgepole pine trees in post-mountain pine beetle outbreak stands in western Canada. Our retrospective analysis targeted to determine correlations between the post-outbreak concentrations of monoterpenes, diterpenes, and NSCs in the phloem, and the historical resin duct characteristics and growth traits before and after the outbreak. We detected strong correlations between post-outbreak concentrations of monoterpenes and historical resin duct characteristics, suggesting a possible link between these two defence modalities. Additionally, we found a positive relationship between NSCs and the total concentrations of monoterpenes and diterpenes, suggesting that NSCs likely influence the production of these terpenes in lodgepole pine. Furthermore, historical tree growth patterns showed strong positive correlations with many individual monoterpenes and diterpenes. Interestingly, while surviving trees had enhanced anatomical defences after the outbreak, their growth patterns did not vary before and after the outbreak conditions. The complexity of these relationships emphasizes the dynamics of post-outbreak stand dynamics and resource allocation in lodgepole pine forests, highlighting the need for further research. These findings contribute to a broader understanding of conifer defenses and their coordinated responses to forest insect outbreaks, with implications for forest management and conservation strategies.

Our aim is at three feels: i) study safe haven properties of sectoral CDS; ii) study hedging effectiveness and measure the diversification benefits of introducing them in both Conventional and Islamic stock markets portfolios and iii) compare these results with VSTOXX, Gold and Bitcoin indices. To do this, we estimate time varying hedge ratio using (A)DCC and GOGARCH models and then deduce hedging effectiveness.. We follow the same methodology for each sub-period and each refit to test the effect of bubble crypto currency and COVID-19 pandemic on hedging effectiveness. Empirical results show the superiority of industrial CDS sectoral index to hedge both conventional and Islamic stock markets whether long or short term despite that their performances were reduced slightly during the second and third periods. VSTOXX and Others sectoral CDS indices offers Hedging performances that are generally better than Bitcoin and gold.

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.

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.

Background. The Stanford Health Assessment Questionnaire-Disability Index (HAQ) is widely used to measure functional ability in persons with Rheumatoid Arthritis (RA). The instrument was developed with limited involvement from persons with RA, and their perception of the instrument has not been studied in depth. The aim of this study was to explore how persons with RA experience the use of the HAQ in care. Methods. Forty persons with RA were purposefully recruited to participate in semi-structured interviews. The interviews were then analyzed qualitatively using thematic analysis. Results. The participants questioned the relevance of the HAQ but nevertheless experienced that the instrument had a profound effect on their understanding of health and how care is delivered. The analysis resulted in three themes: Problems with individual items, meaning of the summative score, and effects on care and health perceptions. Conclusions. To make the HAQ relevant to persons with RA, it needs to be revised or to include an option to select items most meaningful to the respondent. To ensure relevance, the HAQ update should preferably be co-created by researchers, clinicians and persons with RA.

Recycling waste products is an environmental-friendly activity that can bring benefits to accompany, saving manufacturing costs and improving economic efficiency. For the beer industry, recycling bottles can reduce manufacturing costs and reduce the industry's carbon footprint. This paper presents a model for a multi-objective collection-distribution center location and allocation problem in a closed loop supply chain for the beer industry, in which the objective is to minimize total costs and transportation pollution. Uncertainties in the form of randomness and fuzziness are jointly handled in this paper to ensure a more practical problem solution, for which returned bottle sand unusable bottles are considered fuzzy random variables. A heuristic algorithm based on priority-based global-local-neighbor particle swarm optimization (pb-glnPSO) is applied to ensure reliable solutions for this NP-hard problem. A case study on a beer operation company is conducted to illustrate the application of the proposed model and demonstrate the priority-based global-local-neighbor particle swarm optimization.

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.

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

In the extended malfatti problem, the numbers of circles in the triangle are triangle numbers, because the tangency properties among the internal circles and 3 sides of the triangle have special type of structure. That is, the corner circle tangent to two sides of the triangle and two boundary circles, the boundary circles tangent to one side of the triangle and 4 other circles, and the inner circles always tangent to 6 other circles. The circles we find in extended general Malfatti’s problem has the property that the smallest radius and the largest radius for the circle has big difference. In this paper, we proposed algorithms to solve the problem that the tangency properties among circles and sides of triangle is not fixed, so that the number of circles in the triangle is not necessary a triangle numbers. The purpose of this change is try to find the radii of the circles in the triangle is within a small range.

It has been empirically observed that the upper tail of the firm size distribution follows either the Pareto distribution or the Zipf distribution, with both patterns being explained by Gibrat’s law (Gabaix, 1999). This article analyzed firm revenue data from China, spanning from 2005 to 2013, to examine the whole range of the firm size distribution. Our empirical analysis revealed that firm revenue data over these years is well fitted by a three-parameter generalized Pareto distribution, with the fitted parameters indicating a dichotomy: The size distribution of large-sized firms, namely the upper tail, is asymptotically characterized by a Pareto distribution or a Zipf distribution, whereas the size distribution of smaller and medium-sized firms is approximated by an exponential distribution. This finding suggests that Gibrat’s law should be extended to account for the emergence of a generalized Pareto distribution.

Driving factors of smart city construction are exploratively studied by grounded theory method based on text sources from journal papers in SCI and CSSCI databases. Initial scale of driving factors about smart city construction is obtained on the basis of above analyses. This paper modifies measuring items of the initial scale with a small sample pretest and reliability test, then forms final items by exploratory factor analysis. According to the above scale, questionnaires are designed to obtain empirical data, and confirmatory factor analysis is used to verify further validity and reliability of the scale. The results show that driving factors of smart city construction include three main dimensions: problem-oriented factors, business- or technology-driven factors and endogenous development requirements.

The search of DNA polymorphisms useful for the genetic improvement of dairy farm animals lasts more than 40 years with relevant findings in cattle for milk traits, where the best combination of alleles for dairy processing have been found in casein genes and in the DGAT1. Nowadays, similar results are not reached yet in river buffaloes despite advanced genomic technologies and accurate phenotype records are available. The aim of the present study was to investigate and validate in a larger buffalo population the effect on six milk traits of four single nucleotide polymorphisms (SNP) in the CSN1S1, CSN3, SCD and LPL genes, previously reported to be associated with or affect dairy traits in smaller populations often belonging to one farm. A total of 800 buffaloes were genotyped. Daily milk yield (dMY, kg), protein yield (dPY, kg) and fat yields (dFY, kg), fat and protein contents (dFP, % and dPP, %), somatic cell count (SCC, 103cell/ml) and urea (mg/dl) were individually recorded in each month for the whole lactation from 2010 to 2021. A total of 15,742 individual milk test day records (2,496 lactations) were available on 680 buffalo cows with 3.6±1.7 parity (from 1 to 13) and 6.1±1.2 test day records per lactation on average. Three out four SNP in CSN1S1, CSN3 and LPL were associated with at least one of analyzed traits. In particular, the CSN1S1 (AJ005430:c.578C&gt;T) gave favorable associations with all yield traits (dMY, p=0.022; dPY, p=0.014 ; dFY, p=0.029) and SCS (p=0.032), whereas the CSN3 (HQ677596: c.536C&gt;T) positively associated with SCS (p=0.005) and milk urea (p=0.04). Favorable effect on dMY (p=0.028), dFP (p=0.027) and dPP (p=0.050) were observed for the LPL. Conversely, SCD did not show any association with milk traits. This is the first example of confirmation study carried out in the Mediterranean river buffalo for genes of economic interest in dairy field and it represents a very important indication for the preselection of young bulls destined to breeding programs in the view of a more sustainable dairy production.

Natural gas hydrate, also known as combustible ice and mainly composed of methane, it is identified as the potential clean energy in the 21th century. Due to its large reserves, gas hydrate can ease problems caused by energy resource shortage and has gained attention around the world. In this paper, we focus on the exploration and development of gas hydrate as well as discussing its status and future development trend in China and abroad, then we analyze its opportunities and challenges in China from four aspects: resource, technology, economy and police with five forces model and PEST method. The results show, China has abundance gas hydrate resource; however the backward technologies and inadequate investment has seriously hindered the future development of gas hydrate, so China should establish relevant cooperation framework and intuitional arrangement to attract more investment as well as breaking through technical difficulties to make gas hydrate commercialization as soon as possible.

This paper proposes an Integrated Fire Management (IFM) framework that can be used to support communities and resource managers in finding effective and efficient approaches to prevent damaging fires, as well as maintain desirable fire regimes in Kenya. Designing and implementing an IFM approach in Kenya calls for a systematic understanding of the various uses of fire and the underlying perceptions and traditional ecological knowledge of the local people. The here proposed IFM framework allows an evaluation of the risks posed by fires, while balancing them with their beneficial ecological and economic effects, and thus developing effective fire management approaches. A case study of the proposed IFM framework was conducted in Gathiuru Forest that is part of the larger Mt. Kenya Forest Ecosystem. Focus group discussions were held with key resource persons, primary and secondary data on socio-economic activities were studied, fire and weather records were analyzed and the current fire management plans were consulted. Questionnaires were used to assess how the IFM is implemented in the Gathiuru Forest Station. The results show that the proposed IFM framework is scalable and can be applied in places with fire-dependent ecosystems as well as in places with fire-sensitive ecosystems in Kenya. The effectiveness is dependent on the active participation, formulation and implementation of the IFM activities by the main stakeholder groups (Kenya Forest Service (KFS), Kenya Wildlife Service (KWS), and the Community Forest Associations (CFA)). The proposed IFM framework helps in implementing cost-effective approaches to prevent damaging fires and maintain desirable fire regimes in Kenya.

The impact of the agriculture net carbon effect is crucial for climate change mitigation, yet research on this topic in China is currently insufficient. In this study, the carbon balance ratio (CBR) is used to characterise the net carbon effect of agriculture, and the spatial Durbin model and threshold regression model were used to empirically analyse the CBR with a sample of 30 provincial-level regions in China from 2004 to 2019. The aim was to investigate the mechanism of the influence of the land management scale (SCALE) of farming on the CBR. The results of the study indicate that national farming generally has a net sink effect, with large changes in interannual fluctuations; the expansion of SCALE has an obvious role in increasing sinks and reducing emissions, and has a positive spatial spillover effect on CBR, with significant spatial heterogeneity. Furthermore, as the SCALE expands, the direction of the influence of rural residents’ income and education level on CBR changes in the opposite direction, showing a significant non-linear relationship characteristic. However, in most areas, the SCALE threshold has not yet been reached, suggesting that the improvement in education levels can still offset the negative impact of economic income growth in the short term. The study recommends paying attention to the role of farmers’ human capital, changing production methods, and implementing carbon peaking and carbon neutrality goals to enhance the net sink effect of the farming industry, which would be achieved by increasing land management scale.

Brand trust holds significant importance in the Mongolian cashmere industry. This study examines the primary predictors of customer brand trust among Mongolian cashmere users. Our research includes a survey with 800 participants and 20 individual interviews conducted among both Mongolian cashmere users and non-users. We utilized an online platform to reach participants, employing convenience sampling. The study investigates the relationship between various factors and brand trust within the context of Mongolian cashmere brands. Through a combination of in-dividual interviews and statistical analysis, we test six hypotheses. The findings indicate that higher brand quality, a strong reputation, greater transparency, effective issue resolution, commitment to environmental and social responsibility, and pre-purchase research on such commitments positively impact brand trust. Key insights gleaned from the interviews emphasize the importance of quality, reputation, transparency, customer service, and environmental responsibility in shaping consumer trust. These results offer valuable implications for cashmere brands aiming to bolster consumer trust and foster loyalty.

Sixteen crossbred rabbits (New Zealand white x Thai native breed) were randomly divided into two groups of eight to determine the effects of daily oral administration of crude extracts from the butterfly pea (Clitoria ternatea L.) from weaning (at 35 days) to slaughter (at 90 days). Effects on phagocytic activity of blood polymorphoneuclear leukocytes, serum biochemistry, meat quality, muscular lipid peroxidation, apparent digestibility, and gut histology were studied. The results revealed that the phagocytic function of circulating leukocytes (75 and 90 days) and alveolar macrophages (90 days) did not differ between the two groups. At slaughter, treated rabbits had lower blood urea nitrogen concentrations and higher liver weight than control rabbits (p &lt; 0.05). After chilling at 4°C for 24 hours, lower meat pH and alteration of meat color (a brighter, less yellow, Hue angle and color saturation) were observed in the treated group (p &lt; 0.05). Furthermore, lipid peroxidation (measured at 3, 5 and 7 storage days) in the meat of treated rabbits was lower than in controls (p &lt; 0.05). Apparent digestibility (analyzed at 46 days for four days) revealed improved organic matter and ether extracts in the treated group (p &lt; 0.05), whereas gut histology was unaffected. In conclusion, butterfly pea extract supplementation did not affect phagocytic function but led to a modification in meat color, delayed lipid peroxidation, and improved digestibility.

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.

Under the development mode of deep blending of emerging technologies, major projects are important for enhancing China's comprehensive national strength and independent innovation capability. Based on the social benefits of major projects, and considering the influence of reputation factors, an evolutionary game model composed of a general contractor and subcontractor of major projects is established. This paper studies the influence of various factors including the income distribution coefficient of collaborative innovation, and MATLAB simulation software is analyzed the influence of factors on an evolutionary path. The results show that a reasonable income distribution coefficient can promote the game behavior of both parties to the direction of active collaborative innovation; The reduction of innovation cost and the improvement of spillover technology absorption capacity can make innovation subjects tend to choose active collaborative innovation strategies. In addition, reputation loss above a specific threshold can effectively inhibit free-riding behavior and encourage innovation subjects to choose active collaborative innovation strategies. The research results provide reference for promoting major projects' technology innovation.

The intelligent elderly care model places new demands on the skills of aging caregivers. The identification and development of forward-thinking, high-potential nursing talent is essential for advancing the intelligent elderly care sector. Multi-benchmarking selection and competitive analysis is used to evaluate and identify nursing personnel in the rehabilitation treatment center of a hospital in Hefei City. Using proxy evaluation and democratic evaluation model, three types of benchmarks such as single item benchmark, individual benchmark and group benchmark were identified and selected, and the professional nursing team was constructed based on the evaluation results. Different from the traditional other-directed evaluation, multi-benchmarking selection and competitive analysis is self-directed evaluation, this method is more conducive to the identification of high-potential and digital nursing talents by intelligent elderly service organizations, and facilitates the formation of professional nursing teams with complementary strengths and cross-fertilization, so as to satisfy the diversified and multi-level needs of the elderly for intelligent elderly services.

In the present research, it has been tried to use the applied research bases in the field of business management with marketing orientation. Since there were wide and diverse spectrums in the field of economic activities in Isfahan province and Isfahan city, the model of economic sectors was used to categorize these activities. In this regard, the specific goals of the research in all five sectors of the model of economic sectors, drawing and prioritizing the components (indices) of the 7P marketing mix model in each of these economic sectors by the related individuals and members of subgroups of guilds and non-governmental organizations in Isfahan province, have been formed, specified and determined. Collecting data for processing and answer the question of this research, after designing a questionnaire in accordance with the AHP model standard, by spending a period of 2 months from September 2022 and ended in November 2022 , from the statistical population has been done.

Against the backdrop of the European Union's commitment to achieve climate neutrality by 2050, efforts to improve energy efficiency are being intensified. The manufacturing industry is a key focal point of these endeavors due to its high final electrical energy demand, while simultaneously facing a growing shortage of skilled workers crucial for meeting established goals. Expert systems (ESs) offer the chance to overcome this challenge by automatically identifying potential energy efficiency improvements and thereby playing a significant role in reducing electricity consumption. This paper systematically reviews state-of-the-art approaches of ESs aimed at improving energy efficiency in industry, with a focus on manufacturing. The literature search yields 1668 results, of which 62 articles published between 1987 and 2024 are analyzed in depth. These publications are classified according to the system boundary, manufacturing type, application perspective, application purpose, ES type, and industry. Furthermore, we examine the structure, implementation, utilization, and development of ESs in this context. Through this analysis, the review reveals research gaps, pointing toward promising topics for future research.

Graph visualization has evolved significantly over time, offering various methods to effectively represent complex data structures. This study introduces a novel force-directed algorithm for 3D data visualization, specifically tailored for commonly used datasets in linked data visualization. Leveraging the power of graph theory and methods, the algorithm aims to address the challenge of creating visually appealing and easily interpretable visualizations, which often necessitate advanced user interaction. By utilizing 3D space and the Unity Engine, the research successfully visualizes data and develops interactive visualizations, overcoming limitations of basic force-directed implementations. The main contribution of presented research is in the force-directed algorithm with springs and controlled placement as a visualization technique that combines the use of springs and attractive forces to stabilize a graph in a 3D environment. The combination of these two techniques also helps prevent the formation of a single large cluster within the graph, enhancing the overall visualization quality.

The increasing availability of longitudinal data (repeated numerical observations of same units at different times) requires the development of flexible techniques to automatically detect common errors in such data. Besides obvious and easily identifiable cases, such as missing or out-of-range data, large longitudinal dataset often present problems not easily traceable by the techniques used for generic datasets. In particular, elusive and baffling problems are i) inversion of one or more values from one unit to another; ii) anomalous jumps in the series of values, iii) errors in the timing of the values due to a recalculation operated by the data providers to compensate previous errors. This work proposes a statistical-mathematical approach based on a system of indicators that is able to capture the complexity of the described problems by working at the formal level, regardless of the specific meaning of the data. The proposed approach identifies suspect erroneous data and is applicable in a variety of contexts. We implement this approach in a relevant database of European Higher Education institutions (ETER) by analyzing Total academic staff, that is one of the most important variables, used in empirical analysis as proxy of size and also considered by policy makers at European level.

The European Union Water Framework Directive (2000/60/EC; WFD) aims to achieve a good ecological and chemical status of all bodies of surface water by 2027. The development of an integrated guidance on surface water chemical monitoring (e.g. WFD Guidance Document No. 7/19) has been transferred into national German law (Ordinance for the Protection of Surface Waters, OGewV). For the majority of compounds, this act requires a monthly sampling frequency to assess the chemical water quality status of a surface water body. To evaluate the representativeness of the sampling strategy under the OGewV, high-frequency online monitoring data is investigated under different sampling scenarios and compared with current, monthly grab sampling strategy. About 23 million data points were analyzed for this study, three chemical parameters (dissolved oxygen, nitrate-nitrogen, chloride concentration) and discharge data were selected from four catchment of different sizes ranging from 51391 km² to 84 km² (Elbe, Vereinigte Mulde, Neiße and two stations at Lockwitzbach). In this paper we proposed short-term online-monitoring (STOM) as a sampling alternative. STOM considers the placement of online sensors over a limited duration and return interval. In general we: (I) compare the results of conventional grab sampling with STOM, (II) investigate the different performance of STOM and grab sampling using discharge data as proxy for analyzing event-mobilized pollutants and (III) investigate the related uncertainties and costs of both sampling methods. Results show, that STOM outperforms grab sampling for parameters where minimum/maximum concentrations are required by law as the probability to catch a single extreme value is higher with STOM. Furthermore, parameters showing a pronounced diurnal pattern, like dissolved oxygen, are also captured considerably better. The performance of STOM showed no substantial improvements for parameters with small concentration variability, as nitrogen-nitrate The analysis of discharge events as a proxy parameter for event-mobilized pollutants proved that the probability of capturing samples during events is significantly increased by STOM.

Urbanization is considered as a main indicator of regional economic development due to its positive effect on promoting industrial development; however, many regions, especially developing countries, are troubled by its negative effect — the aggravating environmental pollution. Many researchers have indicated that rapid urbanization stimulated the expansion of industrial production scale and increased industrial pollutant emissions. However, this judgement contains a grave deficiency in that urbanization not only expands industrial production scales but can also increase industrial labour productivity and change the industrial structure. To modify this deficiency, we first decompose the influence which urbanization impacts on industrial pollutant emissions into the scale effect, the intensive effect and the structure effect by using the Kaya Identity and the LMDI Method; second, we perform an empirical study of the three effects’ impacts by applying the spatial panel model with data from 282 Chinese cities between 2003 and 2013. Our results indicate that (1) there are significant reverse U-shapes between Chinese urbanization rate and its industrial pollutant emissions; (2) the scale effect and the structure effect have aggravated Chinese industrial waste water discharge, sulphur dioxide emissions and soot (dust) emissions, while the intensive effect has generated a decreasing and ameliorative impact on that aggravated trend. The definite relationship between urbanization and industrial pollutant emissions depends on the combined influence of the scale effect, the intensive effect and the structure effect; (3) there are significant spatial autocorrelations of industrial pollutant emissions between Chinese cities, but the spatial spillover effect from other cities does not aggravate local urban industrial pollutant emissions, we offer an explanation to this contradiction that the vast rural areas surrounding Chinese cities have served as sponge belts and have absorbed the spatial spillover of cities’ industrial pollutant emissions. According to the results, we argue that this type of decomposition of the influence into three effects is necessary and meaningful, it establishes a solid foundation for understanding the relationship between urbanization and industrial pollutant emissions, and effectively helps to meet relative policy making.

This study critically reviews the situation of Language teachers in China, who transited from on-campus face-to-face teaching to online teaching within one week of the official school opening dates, which were postponed due to the outbreak of the COVID-19 pandemic. Presently, the study is located in an interpretive research paradigm, and hence, it takes the human experiences within educational setups to be subjective and complex. In so doing, to elude this educational transition and its complex dynamics, the research uses such qualitative research methods as document analysis and interviews. Painting a picture of great variety in individual experiences and challenges, strategies, and outcomes not only across these two specific institutions but, certainly, more broadly through the lens of previous case studies. First, it values depth over breadth and tries to unearth the varied experiences, challenges, innovations, and achievements faculty made during their transition into online Language Teaching. The results of the study are believed to be able to contribute quite a rich, detailed, and multi-faceted perspective to the pool of knowledge for the complexities of online Language education in China, which may be of interest to educators, policymakers, and researchers.

Poplars (Populus spp.) are of significant ecological and economic importance. Long-term breeding efforts were aimed mainly at obtaining fast-growing and productive plants and less considered resistance to pests. This study aimed to identify the patterns of susceptibility to Saperda carcharias (Linnaeus, 1758) (Coleoptera: Cerambycidae) among Populus hybrids and pure species, considering crossing combinations and some other traits. In our research, 35 clones of poplar species and hybrids of the Ukrainian and foreign selection from the Aigeiros, Tacamahaca, and Leucoides sections were tested in 2021 and 2023 in the plantation created in 2014 in Eastern Ukraine. S. carcharias preferred infesting clones with earlier foliage development, larger diameters, and height increments. Specifically, hybrids with American maternal lineage showed the highest susceptibility, whereas those with European maternal lineage or Asian origin displayed lower infestation levels or resistance, respectively. This variability suggests a complex relationship between genetic background and pest resistance, underlining the need for a deeper understanding of the factors influencing susceptibility to S. carcharias. Selecting native species clones or creating mixed clone plantations could enhance the resilience of poplar plantations to pest threats.

Background: Subclinical myocardial injury (SCMI) is associated with an increased risk of poor cardiovascular disease (CVD) outcomes. Understanding the underlying risk factors for SCMI is crucial for the prevention and management of CVD. We hypothesized that atherogenic dyslipidemia, a combination of high triglycerides (TG) and low high-density lipoprotein cholesterol (HDL-C), is associated with an increased risk of SCMI. Methods: This analysis from the third National Health and Nutrition Examination Survey (NHANES-III) included 7,093 participants (age 59.3±13.4 years, 52.8% women, and 49.4% White) free of CVD. Atherogenic dyslipidemia was defined as TG ≥150 mg/dL and HDL-C <40 mg/dL in men or <50 mg/dL in women. A validated electrocardiographic-based cardiac infarction injury score (CIIS) ≥10 was considered positive for SCMI. Multivariable logistic regression analysis was used to examine the association of different combinations of TG and HDL-C groups, including atherogenic dyslipidemia with SCMI. Results: About 22.5% (n=1,594) of participants had atherogenic dyslipidemia, and 26.3% (n=1862) had SCMI. Compared to participants with normal TG and normal HDL-C, those with atherogenic dyslipidemia had a higher prevalence of SCMI (31.2% vs. 23.9%, p-value <0.001). In a multivariable logistic regression model, atherogenic dyslipidemia was associated with the highest odds of SCMI followed by high TG/normal HDL-C, then low HDL-C/normal TG [OR (95% CI): 131 (1.14,1.52), 1.13 (0.97,1.33), and 1.01(0.86,1.20), respectively). Conclusions: Atherogenic dyslipidemia is associated with a higher risk of SCMI, which highlights the role of nontraditional risk factors in the development of subclinical CVD.

The elasticity of red blood cells (RBCs) is crucial for their ability to fulfill their role in the blood. Decreased RBC deformability is associated with various pathological conditions. This study explores the application of machine learning to predict the elasticity of RBCs using both image data and detailed physical measurements derived from simulations. We simulated RBC behavior in a microfluidic channel. The simulation results provided the basis for generating data on which we applied machine learning techniques. We analyzed the surface-area-to-volume ratio of RBCs as an indicator of elasticity, employing statistical methods to differentiate between healthy and diseased RBCs. The Kolmogorov–Smirnov test confirmed significant differences between healthy and diseased RBCs, though distinctions among different types of diseased RBCs were less clear. We used decision tree models, including random forests and gradient boosting, to classify RBC elasticity based on predictors derived from simulation data. The comparison of the results with our previous work on deep neural networks shows improved classification accuracy in some scenarios. The study highlights the potential of machine learning to automate and enhance the analysis of RBC elasticity, with implications for clinical diagnostics.

Long COVID, characterized by persistent symptoms lasting from weeks to several months following initial infection with Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), significantly impacts individuals' quality of life and poses a potential long-term burden on global healthcare systems. Clinical manifestations of Long COVID are diverse, affecting multiple systems including respiratory, cardiovascular, neurological, and gastrointestinal. While the precise mechanisms underlying Long COVID remain unclear, emerging evidence suggests involvement of various bodily pathways. This study employs bioinformatics tools to investigate the mechanisms of Long COVID and identify potential drug targets associated with key pathways, including inflammation, cytokines, immune response, coagulation, mitochondrial function, oxidative stress, and autonomic dysfunction. Tools such as the National Center for Biotechnology Information (NCBI) database, PubChem, UniProt, and iGEMDOCK were utilized for pathway analysis and molecular docking. The analysis aimed to identify drug molecules that may interact with target proteins to regulate or inhibit pathway activity, yielding several potential drug candidates for further investigation.