An updated inventory of marine opisthobranch(Mollusca,Gastropoda)from the territorial waters of the Republic of Mauritius

Island ecosystems support diverse aquatic invertebrate communities comprising endemic taxa.Documentation of existing species is important for conservation.In this study,a checklist of marine opisthobranch from the Republic of Mauritius is presented.A combination of benthic surveys(50 m×5 m in triplicates),rover diving techniques and photo documentation were used over two years(2018–2020)within 35 sheltered and unsheltered lagoons.Morphological and molecular analysis were used for identification.Species composition within sheltered and unsheltered areas in Mauritius was estimated using the Bray-Curtis similarity.The checklist featured 117 species belonging to 61 genera and 28families,of which 13 are new records.The findings increased the knowledge of opisthobranch diversity from the Mauritius by 15.4%.Among the listed species,the distribution range of Cyerce nigra,Actinocyclus papillatus,and Phyllidia picta extended from the Western Pacific to the South Western Indian Ocean.Molecular analysis of the undescribed Gymnodoris sp.showed it resembled Gymnodoris sp.from Hawaii and were different by a genetic distance value of 10.6%.The species richness and evenness were higher within the sheltered regions of Mauritius which harboured the food resource of opisthobranch.These areas as compared to unsheltered regions were heavily populated,suggesting the probable influence of wave actions on opisthobranch diversity and abundance.The order Nudibranchia was reported as most speciose,with 86 species.The Sacoglossa and Nudibranchia were observed only on macroalgae and sponges respectively.High abundance was also recorded on shipwrecks which are the most common form of artificial reefs.With the inclusion of observations from previous studies,201species belonging to 94 genera and 36 families are now known from the Mauritius.

Intelligent Decision-Making in Warehouse Management: How AI Automation Improves Inventory Tracking, Order Fulfillment, and Logistics Efficiency Compared to Drone Technology

This paper analyzes how artificial intelligence (AI) automation can improve warehouse management compared to emerging technologies like drone usage. Specifically, we evaluate AI’s impact on crucial warehouse functions—inventory tracking, order fulfillment, and logistics efficiency. Our findings indicate AI automation enables real-time inventory visibility, optimized picking routes, and dynamic delivery scheduling, which drones cannot match. AI better leverages data insights for intelligent decision-making across warehouse operations, supporting improved productivity and lower operating costs.

A Note on an Order Level Inventory Model with Varying Two-Phased Demand and Time-Proportional Deterioration

The main purpose of this paper is to generalize the effect of two-phased demand and variable deterioration within the EOQ (Economic Order Quantity) framework. The rate of deterioration is a linear function of time. The two-phased demand function states the constant function for a certain period and the quadratic function of time for the rest part of the cycle time. No shortages as well as partial backlogging are allowed to occur. The mathematical expressions are derived for determining the optimal cycle time, order quantity and total cost function. An easy-to-use working procedure is provided to calculate the above quantities. A couple of numerical examples are cited to explain the theoretical results and sensitivity analysis of some selected examples is carried out.

Using machine learning algorithms to estimate stand volume growth of Larix and Quercus forests based on national-scale Forest Inventory data in China

Estimating the volume growth of forest ecosystems accurately is important for understanding carbon sequestration and achieving carbon neutrality goals.However,the key environmental factors affecting volume growth differ across various scales and plant functional types.This study was,therefore,conducted to estimate the volume growth of Larix and Quercus forests based on national-scale forestry inventory data in China and its influencing factors using random forest algorithms.The results showed that the model performances of volume growth in natural forests(R^(2)=0.65 for Larix and 0.66 for Quercus,respectively)were better than those in planted forests(R^(2)=0.44 for Larix and 0.40 for Quercus,respectively).In both natural and planted forests,the stand age showed a strong relative importance for volume growth(8.6%–66.2%),while the edaphic and climatic variables had a limited relative importance(<6.0%).The relationship between stand age and volume growth was unimodal in natural forests and linear increase in planted Quercus forests.And the specific locations(i.e.,altitude and aspect)of sampling plots exhibited high relative importance for volume growth in planted forests(4.1%–18.2%).Altitude positively affected volume growth in planted Larix forests but controlled volume growth negatively in planted Quercus forests.Similarly,the effects of other environmental factors on volume growth also differed in both stand origins(planted versus natural)and plant functional types(Larix versus Quercus).These results highlighted that the stand age was the most important predictor for volume growth and there were diverse effects of environmental factors on volume growth among stand origins and plant functional types.Our findings will provide a good framework for site-specific recommendations regarding the management practices necessary to maintain the volume growth in China's forest ecosystems.

An open-accessed inventory of landslides triggered by the M_(S)6.8 Luding earthquake,China on September 5,2022

This study constructs a preliminary inventory of landslides triggered by the M_(S) 6.8 Luding earthquake based on field investigation and human-computer interaction visual interpretation on optical satellite images.The results show that this earthquake triggered at least 5007 landslides,with a total landslide area of 17.36 km^(2),of which the smallest landslide area is 65 m^(2)and the largest landslide area reaches 120747 m^(2),with an average landslide area of about 3500 m^(2).The obtained landslides are concentrated in the IX intensity zone and the northeast side of the seismogenic fault,and the area density and point density of landslides are 13.8%,and 35.73 km^(-2) peaks with 2 km as the search radius.It should be noted that the number of landslides obtained in this paper will be lower than the actual situation because some areas are covered by clouds and there are no available post-earthquake remote sensing images.Based on the available post-earthquake remote sensing images,the number of landslides triggered by this earthquake is roughly estimated to be up to 10000.This study can be used to support further research on the distribution pattern and risk evaluation of the coseismic landslides in the region,and the prevention and control of landslide hazards in the seismic area.

Spatial distribution and inventory of natural gas hydrate in the Qiongdongnan Basin,northern South China Sea

Natural gas hydrate is a potential clean energy source and is related to submarine geohazard,climate change,and global carbon cycle.Multidisciplinary investigations have revealed the occurrence of hydrate in the Qiongdongnan Basin,northern South China Sea.However,the spatial distribution,controlling factors,and favorable areas are not well defined.Here we use the available high-resolution seismic lines,well logging,and heat flow data to explore the issues by calculating the thickness of gas hydrate stability zone(GHSZ)and estimating the inventory.Results show that the GHSZ thickness ranges between mostly~200 and 400 m at water depths>500 m.The gas hydrate inventory is~6.5×109-t carbon over an area of~6×104 km2.Three areas including the lower uplift to the south of the Lingshui sub-basin,the Songnan and Baodao sub-basins,and the Changchang sub-basin have a thick GHSZ of~250-310 m,250-330 m,and 350-400 m,respectively,where water depths are~1000-1600 m,1000-2000 m,and2400-3000 m,respectively.In these deep waters,bottom water temperatures vary slightly from~4 to 2℃.However,heat flow increases significantly with water depth and reaches the highest value of~80-100 mW/m2 in the deepest water area of Changchang sub-basin.High heat flow tends to reduce GHSZ thickness,but the thickest GHSZ still occurs in the Changchang sub-basin,highlighting the role of water depth in controlling GHSZ.The lower uplift to the south of the Lingshui sub-basin has high deposition rate(~270-830 m/Ma in 1.8-0 Ma);the thick Cenozoic sediment,rich biogenic and thermogenic gas supplies,and excellent transport systems(faults,diapirs,and gas chimneys)enables it a promising area of hydrate accumulation,from which hydrate-related bottom simulating reflectors,gas chimneys,and active cold seeps were widely revealed.

Inventory of Host Plants and Parasitoids of the Fall Armyworm (FAW), Spodoptera frugiperda (JE Smith), in the Southern Agricultural Zone of Niger

The fall armyworm, Spodoptera frugiperda (JE Smith), is a polyphagous pest reported in sub-Saharan Africa since 2016 and has expanded rapidly in almost Africa. In Niger, Spodoptera frugiperda (JE Smith) is considered like a major pest of maize, to which it causes significant damage, in a context where proven control methods against this moth remain almost non-existent. The objective of the present study was to determine the economic importance of FAW through the damage caused to the different host plants and to identify the parasitoids of this caterpillar. The study was conducted in the southern agricultural zone of Niger, specifically in the regions of Dosso, Maradi, Tahoua and Zinder. FAW eggs and caterpillars were collected from six villages in each region and then incubated and reared in the entomology laboratory of INRAN in Maradi. The rate of infestation of the different crops by FAW was determined as well as the observation of the beneficiaries. The results obtained indicate the presence of FAW on millet with an attack rate varying from 45.7% to 68%, sorghum with 47.2% to 62.25% and sesame with 9.7%. This work also revealed an oophagous parasitoid, Telenomus remus with 138 ± 23 and larval parasitoids, Cotesia sp with 16 ± 1 maximum number of individuals emerged from the collected material. Also, it was identified the parasitoid Cotesia icipe with a rate of parasitism from 4.6% to 5.75%;the Charops ater whose rate of parasitism varies from 4.5% and 12.25% but for Chelonus insularis with 17.25% and Tachnidae with 53%. These very interesting results will constitute a basis for the development of biological control and a component of an agroecological management strategy of caterpillar.

Screening Tree Species for Carbon Storage Potential through Urban Tree Inventory in Planted Vegetation

Urban tree inventory is a great tool for gathering data that can be used by different end users. This study attempted to chart the species diversity in planted areas and measure their tree diameter at breast height to screen them for the carbon storage potential. A total of 2860 trees belonging to 36 species were recorded in the planted vegetation in parks and avenue plantation. The dominant species were Azadirachta indicia (25.5%), Conocarpus erectus (19.2%), Ficus spp. (15.5%), Tabebuia rosea (9.2%), Peitophorum pterocarpum (9.0%) and the remaining represents (21.6%) of the tree identified in this study. It was found that the highest contribution of carbon sequestration (CO2 equivalent) is dominated by the Ficus spp. (30.3%) with a total of 3399.3 tCO2eq, followed by Azadirachta indicia (25.4%) with a total of 2845.2 tCO2eq and Conocarpus erectus (20.4%) with a total of 2286 tCO2eq. The entire area has the capability to sequester around 11,213.3 tCO2eq and on average of 3.9 ± 0.1 tCO2eq. In accordance with the findings, it is imperative for the preservation of a sustainable environment to have vegetation that has the capacity to store carbon. The study suggests, there is potential to increase carbon sequestration in urban cities through plantation programs on existing and new land uses and along roads.

Inventory of the Thermo-Physiological Behavior of Fabrics—A Review

A comprehensive literature review was performed to create an inventory of thermal-physiological quantities for fabrics from different fiber materials, material blends, and fabric structures. The goal was to derive over-arching concepts that cannot be seen by the individual studies alone. Equations of best fits suggest non-linear changes for fabric thickness, thermal and water-vapor resistance with changes in material blend ratio. Air permeability decreases with increasing fabric density and fabric weight wherein the degree of decrease differs among fabric materials, blend ratio, and fabric structure. Water-vapor transmission rates strongly depend on fabric thickness, material, and blend, but marginally depend on fabric structure as long as the fabric and material thickness remain the same.

Methods of vendor managed inventory control in supply chain management

By inducing the typical inventory control problem - the bullwhip effect, this paper presents vendor managed inventory (VMI) control methods on the basis of traditional methods of inventory management methods, constructs a VMI mathematics model, and analyzes the influence of VMI on inventory cost and channel profit. Finally, a special case is studied to verify that VMI is an effective supply chain strategy that can not only increase channel profit of supplier and customer but also improve full channel coordination, thereby reducing the bullwhip effect.

Vendor managed inventory and bullwhip effect

This paper studies that the bullwhip effect of order releases and the amplifications of safety stock arise within the supply chain even when the demand model is ARIMA(0, 1, 1) and the forecast method used is a simple exponentially weighted moving average. It also examines a vendor managed inventory (VMI) program to determine how it can help alleviate such negative effects, and gives the theoretical proofs and numerical illustrations. The results show that the effects with VMI are better than the effect without VMI in demand forecasting and safety stock levels, etc.

Modelling of debris-flow susceptibility and propagation: a case study from Northwest Himalaya

The geological and geographical position of the Northwest Himalayas makes it a vulnerable area for mass movements particularly landslides and debris flows. Mass movements have had a substantial impact on the study area which is extending along Karakorum Highway(KKH) from Besham to Chilas. Intense seismicity, deep gorges, steep terrain and extreme climatic events trigger multiple mountain hazards along the KKH, among which debris flow is recognized as the most destructive geohazard. This study aims to prepare a field-based debris flow inventory map at a regional scale along a 200 km stretch from Besham to Chilas. A total of 117 debris flows were identified in the field, and subsequently, a point-based debris-flow inventory and catchment delineation were performed through Arc GIS analysis. Regional scale debris flow susceptibility and propagation maps were prepared using Weighted Overlay Method(WOM) and Flow-R technique sequentially. Predisposing factors include slope, slope aspect, elevation, Topographic Roughness Index(TRI), Topographic Wetness Index(TWI), stream buffer, distance to faults, lithology rainfall, curvature, and collapsed material layer. The dataset was randomly divided into training data(75%) and validation data(25%). Results were validated through the Receiver Operator Characteristics(ROC) curve. Results show that Area Under the Curve(AUC) using WOM model is 79.2%. Flow-R propagation of debris flow shows that the 13.15%, 22.94%, and 63.91% areas are very high, high, and low susceptible to debris flow respectively. The propagation predicated by Flow-R validates the naturally occurring debris flow propagation as observed in the field surveys. The output of this research will provide valuable input to the decision makers for the site selection, designing of the prevention system, and for the protection of current infrastructure.

Developing kNN forest data imputation for Catalonia

The combined use of LiDAR(Light Detection And Ranging)scanning and field inventories can provide spatially continuous wall-to-wall information on forest characteristics.This information can be used in many ways in forest mapping,scenario analyses,and forest manage-ment planning.This study aimed to find the optimal way to obtain continuous forest data for Catalonia when using kNN imputation(kNN stands for“k nearest neighbors”).In this method,data are imputed to a certain location from k field-measured sample plots,which are the most similar to the location in terms of LiDAR metrics and topographic variables.Weighted multidimensional Euclidean distance was used as the similarity measure.The study tested two different methods to optimize the distance measure.The first method optimized,in the first step,the set of LiDAR and topographic variables used in the measure,as well as the transformations of these variables.The weights of the selected variables were optimized in the second step.The other method optimized the variable set as well as their transformations and weights in one single step.The two-step method that first finds the variables and their transfor-mations and subsequently optimizes their weights resulted in the best imputation results.In the study area,the use of three to five nearest neighbors was recommended.Altitude and latitude turned out to be the most important variables when assessing the similarity of two locations of Catalan forests in the context of kNN data imputation.The optimal distance measure always included both LiDAR metrics and topographic variables.The study showed that the optimal similarity measure may be different for different regions.Therefore,it was suggested that kNN data imputation should always be started with the optimization of the measure that is used to select the k nearest neighbors.

Dominant woody plant species recognition with a hierarchical model based on multimodal geospatial data for subtropical forests

Since the launch of the Google Earth Engine(GEE)cloud platform in 2010,it has been widely used,leading to a wealth of valuable information.However,the potential of GEE for forest resource management has not been fully exploited.To extract dominant woody plant species,GEE combined Sen-tinel-1(S1)and Sentinel-2(S2)data with the addition of the National Forest Resources Inventory(NFRI)and topographic data,resulting in a 10 m resolution multimodal geospatial dataset for subtropical forests in southeast China.Spectral and texture features,red-edge bands,and vegetation indices of S1 and S2 data were computed.A hierarchical model obtained information on forest distribution and area and the dominant woody plant species.The results suggest that combining data sources from the S1 winter and S2 yearly ranges enhances accuracy in forest distribution and area extraction compared to using either data source independently.Similarly,for dominant woody species recognition,using S1 winter and S2 data across all four seasons was accurate.Including terrain factors and removing spatial correlation from NFRI sample points further improved the recognition accuracy.The optimal forest extraction achieved an overall accuracy(OA)of 97.4%and a maplevel image classification efficacy(MICE)of 96.7%.OA and MICE were 83.6%and 80.7%for dominant species extraction,respectively.The high accuracy and efficacy values indicate that the hierarchical recognition model based on multimodal remote sensing data performed extremely well for extracting information about dominant woody plant species.Visualizing the results using the GEE application allows for an intuitive display of forest and species distribution,offering significant convenience for forest resource monitoring.

Spatiotemporal Characteristics of Atmospheric Brown Carbon Emis-sion from Combustion Sources in China

Atmospheric Brown Carbon(BrC)with strong wavelength-dependence light-absorption ability can significantly affect radiat-ive forcing.Highly resolved emission inventories with lower uncertainties are important premise and essential in scientifically evaluat-ing impacts of emissions on air quality,human health and climate change.This study developed a bottom-up inventory of primary BrC from combustion sources in China from 1960 to 2016 with a spatial resolution at 0.1°×0.1,based on compiled emission factors and detailed activity data.The primary BrC emission in China was about 593 Gg(500-735 Gg as interquartile range)in 2016,contributing to 7%(5%-8%)of a previously estimated global total BrC emission.Residential fuel combustion was the largest source of primary BrC in China,with the contribution of 67%as the national average but ranging from 25%to 99%among different provincial regions.Signi-ficant spatial disparities were also observed in the relative shares of different fuel types.Coal combustion contribution varied from 8%to 99%across different regions.Heilongjiang and North China Plain had high emissions of primary BrC.Generally,on the national scale,spatial distribution of BrC emission density per area was aligned with the population distribution.Primary BrC emission from combus-tion sources in China have been declined since a peak of~1300 Gg in 1980,but the temporal trends were distinct in different sectors.The high-resolution inventory developed here enables radiative forcing simulations in future atmospheric models so as to promote bet-ter understanding of carbonaceous aerosol impacts in the Earth's climate system and to develop strategies achieving co-benefits of hu-man health protection and climate change.

Analysis of the inflection points of height-diameter models

The inflection point is an important feature of sigmoidal height-diameter(H-D)models.It is often cited as one of the properties favoring sigmoidal model forms.However,there are very few studies analyzing the inflection points of H-D models.The goals of this study were to theoretically and empirically examine the behaviors of inflection points of six common H-D models with a regional dataset.The six models were the Wykoff(WYK),Schumacher(SCH),Curtis(CUR),HossfeldⅣ(HOS),von Bertalanffy-Richards(VBR),and Gompertz(GPZ)models.The models were first fitted in their base forms with tree species as random effects and were then expanded to include functional traits and spatial distribution.The distributions of the estimated inflection points were similar between the two-parameter models WYK,SCH,and CUR,but were different between the threeparameter models HOS,VBR,and GPZ.GPZ produced some of the largest inflection points.HOS and VBR produced concave H-D curves without inflection points for 12.7%and 39.7%of the tree species.Evergreen species or decreasing shade tolerance resulted in larger inflection points.The trends in the estimated inflection points of HOS and VBR were entirely opposite across the landscape.Furthermore,HOS could produce concave H-D curves for portions of the landscape.Based on the studied behaviors,the choice between two-parameter models may not matter.We recommend comparing seve ral three-parameter model forms for consistency in estimated inflection points before deciding on one.Believing sigmoidal models to have inflection points does not necessarily mean that they will produce fitted curves with one.Our study highlights the need to integrate analysis of inflection points into modeling H-D relationships.

Spatial distribution characteristics of climate-induced landslides in the Eastern Himalayas

Climate warming is constantly causing hydro-meteorological perturbations,especially in high-altitude mountainous regions,which lead to the occurrences of landslides.The impact of climatic variables(i.e.,precipitation and temperature)on the distribution of landslides in the eastern regions of the Himalayas is poorly understood.To address this,the current study analyzes the relationship between the spatial distribution of landslide characteristics and climatic variables from 2013 to 2021.Google Earth Engine(GEE)was employed to make landslide inventories using satellite data.The results show that 2163,6927,and 9601 landslides were heterogeneously distributed across the study area in 2013,2017,and 2021,respectively.The maximum annual temperature was positively correlated with the distribution of landslides,whereas precipitation was found to have a non-significant impact on the landslide distribution.Spatially,most of the landslides occurred in areas with maximum annual precipitation ranging from 800 to 1600 mm and maximum annual temperature above 15℃.However,in certain regions,earthquake disruptions marginally affected the occurrence of landslides.Landslides were highly distributed in areas with elevations ranging between 3000 and 5000 m above sea level,and many landslides occurred near the lower permafrost limit and close to glaciers.The latter indicates that temperature change-induced freeze-thaw action influences landslides in the region.Temperature changes have shown a positive correlation with the number of landslides within elevations,indicating that temperature affects their spatial distribution.Various climate projections suggest that the region will experience further warming,which will increase the likelihood of landslides in the future.Thus,it is crucial to enhance ground observation capabilities and climate datasets to effectively monitor and mitigate landslide risks.

Accuracy of tree mapping based on hand-held laser scanning comparing leaf-on and leaf-off conditions in mixed forests

The use of mobile laser scanning to survey forest ecosystems is a promising,scalable technology to describe forest 3D structures at high resolution.To confirm the con-sistency in the retrieval of forest structural parameters using hand-held laser scanning(HLS),before operationalizing the method,confirming the data is crucial.We analyzed the per-formance of tree-level mapping based on HLS under differ-ent phenology conditions on a mixed forest in western Spain comprising Pinus pinaster and two deciduous species,Alnus glutinosa and Quercus pyrenaica.The area was surveyed twice during the growing season(July 2022)and once in the deciduous season(February 2022)using several scan-ning paths.Ground reference data(418 trees,15 snags)was used to calibrate the HLS data and to assess the influence of phenology when converting 3D data into tree-level attrib-utes(DBH,height and volume).The HLS-based workflow was robust at isolating tree positions and recognizing stems despite changes in phenology.Ninety-six percent of all pairs matched below 65 cm.For DBH,phenology barely altered estimates.We observed a strong agreement when comparing HLS-based tree height distributions.The values exceeded 2 m when comparing height measurements,confirming height data should be carefully used as reference in remote sensing-based inventories,especially for deciduous species.Tree volume was more precise for pines(r=0.95,and rela-tive RMSE=21.3–23.8%)compared to deciduous species(r=0.91–0.96,and relative RMSE=27.3–30.5%).HLS data and the forest structural complexity tool performed remark-ably,especially in tree positioning considering mixed forests and mixed phenology conditions.

赛德西港用于船舶减排的陆上可再生能源供电评估

Air pollution from shipping is becoming a critical issue,particularly in dense hub port cities.One proposed solution to minimize ship-based emissions at ports is the implementation of an Onshore Power Supply(OPS)system.OPS allows ships to shut off their auxiliary engines and instead connect to the port grid.While there have been numerous studies conducted on ports in Europe and the United States,little research has been done on Egyptian ports.Therefore,this paper aims to investigate the feasibility of implementing OPS at Port Said West Port in Egypt,aligning with Egypt Vision 2030’s goals for addressing climate change.The research primarily focuses on analyzing data collected from calling ships to generate socio-economic and cost-effectiveness analyses of OPS.To further enhance the environmental benefits of OPS,the paper proposes the use of solar energy as the OPS electricity source.The findings of the study revealed that by relying on the national grid,emissions can be reduced by 28%.Moreover,it is predicted that this reduction could reach 100%if electricity generation is solely based on solar energy.Additionally,the economic analysis demonstrates promising profitability,with a payback period of approximately two years.

Quantity-based shipment consolidation and stock replenishment policy with lead time in vendor-managed inventory system

In order to let the supplier make more reasonable supply decisions,an integrated continuous replenishment policy for the vendor-managed inventory system is presented,which considers the quantity-based shipment consolidation and stock replenishment with lead time.Then the system cost is analyzed and a mathematical model is built.Since the model is rather complex,the bounds of the optimal policy are first attained,then the problem is solved by a heuristic algorithm.Through experiments the relationship between the order lead time and the corresponding integrated policy is discussed,and the influence on the system cost is also analyzed.The results reveal that the lead time's influence on the system is more serious with the increase of the order lead time,the integrated policy with the order lead time is more reasonable and the optimal policy can minimize the total system cost.Finally,the parameter sensitivity of the model is analyzed.