The aim of this paper is to examine the beneficial impact of feedback information in the dynamics of production-inventory control systems. Two production-inventory control system models are analyzed: APIOBPCS and 2APIOBPCS models. The simulation-based experiment designs were conducted by using the state-space equations of the two models. The bullwhip effect as measured by the variance ratio between the order rate and the consumption rate, and inventory responsiveness as measured by the Integral of Absolute Error between the actual and the target levels of inventory, are two metrics used to evaluate the performance of the production-inventory control system in response to a random customer demand. To ensure that both models work under optimal performance, multi-objective particle swarm optimization (MOPSO) is employed to address the problem of tuning the controller’s parameters. The simulation results show the 2APIOBPCS model outperforms the APIOBPCS model at achieving the desired bullwhip effect and being able to provide better inventory responsiveness. The improvement in the inventory responsiveness becomes more significant when the system operates under mismatched lead time and/or an initial condition.

This research investigates Haifa ports emissions’ contribution to the existing daily emission inventory level in the area. This research based on developed full bottom-up model framework that looks at the single vessel daily voyage through its port call stages. The main data sources for vessels movements used in this research are the Israel Navy`s movements log and Israel Administration of Shipping and Ports’ (ASP) operational vessel movements and cargo log. The Fuel Consumption (FC) data and Sulfur Content (SC) levels are based on an official Israel ASP survey data. Observation years in this research are 2010 - 2018 with focus on the Ocean-Going Vessels (OGV) type only. The results show that the vessel fleet calling at Israel ports is mainly vessels that have a lower engine Tier grade (i.e., Tier 0 and 1) which is considered a heavy contributor to Nitrogen Oxides (NOx) pollutant. The study recommends on an additional cost charged (selective tariff) to reflect the external social cost linked to the single vessel air pollution combined with supportive technological infrastructure and economic incentive tools (e.g. electric subsidy) to attract or influence vessel owners to assign vessels equipped with new engine Tier grades for calls at Israeli ports.

Ammonia is an atmospheric pollutant, predominantly emitted from agriculture, leading acidification and eutrophication of soil and water and contributing to secondary PM2.5. The implementation of accurate emission inventories with high spatial and time resolution plays a fundamental role in the development of air modelling simulation and in the impact assessment of actions for air quality improvement. The development and release of new algorithms and the increase of data availability are supporting the implementation of machine learning approaches in environmental and air quality data analysis. In this paper we present a methodology developed by the application of the Random Forest algorithm to bottom-up local emission inventories of ammonia to validate annual time series of ammonia emissions and calculate high resolution temporal profiles. The model has been trained and tested by the hourly measurements of ammonia concentrations and atmospheric turbulence parameters starting from a constant emission scenario. The initial values of emissions are calculated based on a bottom-up emission inventory detailed at the municipal basis and considering a circular area of about 4 km radius centered on measurement sites. By comparing predicted and measured concentrations, the emissions are modified, the model's training and testing are repeated, and the model converges to a very high performance in predicting ammonia concentrations and establishing an hourly time changing emission profile. The site-specific emissions profiles, estimated by the proposed methodology, clearly show a nonlinear relation with measured concentrations and allow to identify the effect of atmospheric turbulence on pollutant accumulation. The estimated time series well confirm the available data of the emission inventories and the monthly emission profiles have been compared with estimated data from satellite.

The assessment of rockfall risks on human activities and infrastructure is of great importance. Rock falls pose a significant risk to a) transportation infrastructure b) inhabited areas and c) Cultural Heritage sites. The paper presents a method to assess rockfall susceptibility at national scale in Greece, using a simple rating approach and GIS techniques. An extensive inventory of rockfalls for the entire country was compiled for the period between 1935 and 2019. The rockfall events that were recorded are those, which have mainly occurred as distinct rockfall episodes in natural slopes and have impacted human activities, such as roads, inhabited areas and archaeological sites. Through a detailed analysis of the recorded data, it was possible to define the factors which determine the occurrence of rockfalls. Based on this analysis, the susceptibility zoning against rockfalls at national scale was prepared, using a simple rating approach and GIS techniques. The rockfall susceptibility zoning takes into account the following parameters: (a) the slope gradient, (b) the lithology, (c) the annual rainfall intensity, (d) the earthquake intensity and (e) the active fault presence. Emphasis was given on the study of the earthquake effect as a triggering mechanism of rockfalls. Finally, the temporal and spatial frequency of the recorded events and the impact of rockfalls on infrastructure assets and human activities in Greece were evaluated.

Livestock farming is a major source of greenhouse gas and ammonia emissions. In this study, we estimate methane, nitrous oxide and ammonia emission from livestock sector in the Red River Delta region from 2000 to 2015 and projection to 2030 using IPCC 2006 methodologies with the integration of local emission factors and provincial statistic livestock database. Methane, nitrous oxide and ammonia emissions in 2030 are estimated at 132 kt, 8.3 kt and 34.2 kt, respectively. Total global warming potential is 9.7 MtCO_2eq in 2030, accounts for 33% greenhouse gas emissions from livestock in Vietnam. Pig farming is responsible for half of both greenhouse gases and ammonia emissions in the studied region. Other major livestock for greenhouse gas emission is cattle and for ammonia emission is poultry. Hanoi contributes for the largest emissions in the region in 2015 but will be caught up and surpassed by other provinces in 2030.

Methane is a greenhouse gas and identified as a key driver of near-term climate change. Bottom-up approaches estimate annual methane loss from US natural gas production and transport at 6 Tg, but recent studies suggest this may be an underestimate. To investigate this possibility, an equipment-based emissions inventory, using EPA emission factors, was developed to calculate methane emissions from oil and gas operations in the Delaware Basin, USA. Emission factors and activity data were then updated using contemporary and region-specific measurement data. The original inventory estimated emissions at 315 Gg CH4 y-1 (gas production-normalized rate of 0.6% loss), while the updated inventory estimated emissions of 1,500 Gg CH4 y-1 (2.8% loss). The largest changes resulted from large fugitive emissions from oil production (+430 Gg CH4 y-1), updating maintenance activity emissions (+214 Gg CH4 y-1), considering flaring inefficiency (+174 Gg CH4 y-1), and the inclusion of associated gas venting (+136 Gg CH4 y-1). This study suggests that a systematic underestimate probably exists in current bottom-up inventories and identifies sources currently missing or may be incorrect. We also strongly recommend that emission factors should be validated through direct comparison against measurement campaigns that include long-tail distributions typical of oil and gas activities.

Amphibian populations have been globally declining since at least 1990. In temperate forests of eastern North America, vernal pools offer important breeding habitats to many amphibians but are usually not considered for forest management because of their small size and temporary status. Effective monitoring and management of amphibians rely on accurate knowledge of their spatiotemporal distributions, which is expensive to collect due to the amount of fieldwork re-quired. In this study, we tested whether eDNA metabarcoding could identify the same amphibian communities as traditional inventory protocols. We collected eDNA samples in twelve vernal pools in the spring of 2019 and identified their communities by metabarcoding. At each pool, three traditional amphibian inventory methods were used in May and June 2019: call surveys using acoustic recorders, trapping, and active search surveys. In total, 13 amphibian species were detected, most of them being detected by both eDNA and traditional methods. We found that species ecology and behavior are key factors of its detectability by a specific method. As eDNA metabarcoding is comparatively inexpensive and presents a higher repeatability, we conclude that eDNA sampling should be considered for integration as a standard monitoring tool, after an initial assessment of amphibian diversity.

(1) We document the invertebrate fauna collected from 24 oak canopies in east and west Norway as a contribution to the Norwegian Biodiversity Information Centre’s “The Norwegian Taxonomy Initiative”. (2) A snap-shot inventory of the canopies was recorded by means of emitting a mist of synthetic pyrethroid into the canopies at night using a petrol-driven fogger, collecting the specimens in butterfly nets spread on the ground under the canopy. (3) Almost the entire catch of more than 6800 specimens was identified to 722 species. Out of 92 species new to the Norwegian fauna, 21 were new to science and additionally 15 were new to the Nordic fauna. Diptera alone constituted nearly half of the species represented with 61 new records (18 new species). Additionally, 24 Hymenoptera (one new species), six oribatid mites (two new species) and one Thysanoptera were new to the Norwegian fauna. (4) Our study emphasis the importance of oak tree as habitat both for a specific fauna and occasional visitors and it demonstrates that the canopy fogging technique is an efficient way to find the ‘hidden fauna’ of Norwegian forests. The low number of red listed species found reflects how poor the Norwegian insect fauna is still studied. Moreover, the implication of the IUCN red list criteria for new described or newly observed species is discussed.

We propose a new estimator for creating expansion factors for survey plots in the USDA Forest Inventory and Analysis program. This is a regularized version of the raking estimator widely used in sample surveys. The regularized raking method differs from other predictive modeling methods for integrating survey and ancillary data in that it produces a single set of expansion factors that can have general purpose use to produce small area estimates and wall-to-wall maps of any plot characteristic. This method also differs from other more widely used survey techniques, such of GREG estimation, in that it is guaranteed to produce positive expansion factors. We extend the previous method here to include cross-validation, and provide a comparison to expansion factors between the regularized raking and ridge GREG survey calibration.

Landslides around the main roads in the mountains not only cause fatal events but also cause ecosystem damage, including land degradation. This study aims to map the susceptibility of the landslides around the Saqqez-Marivan main rod of Kurdistan province, Iran, using ensemble Fuzzy logic with Analytic Network Process (Fuzzy Logic-ANP; FLANP), and with TOPSIS (Fuzzy Logic-TOPSIS; FLTOPSIS). A total of 100 landslides were first recognized by field surveys and then they were randomly divided into a 70% dataset (70 locations) and a 30% dataset (30 locations), respectively, for training and validating the methods. Eleven landslide conditioning factors, including slope, aspect, elevation, lithology, land use, distance to fault, distance to a river, distance to road, soil type, curvature, and precipitation were used. The performance of the methods was checked by the areas under the receiver operating curve (AUCROC). Results concluded that the prediction accuracy based on validating datasets were, respectively, 0.882 and 0.918 for FLANP and FLTOPSIS methods. Our findings demonstrated that although both models were known as promising techniques, the FLTOPSIS method had a better capacity for predicting the susceptibility of landslides in the studied area. Therefore, the susceptibility map developed by the FLTOPSIS method can be used for the proper management of areas with high landslide potential and also for managers and planners during the implementation of land allocation and development projects, especially in mountainous areas.

This article aims to serve as a guide for the construction of supply chain simulation models designed with a lean approach, using Promodel software. To achieve this, a supply chain was designed for a fictitious company located in the City of Celaya, Guanajuato and a set of suppliers located in different cities within the same State. It was used as a google tool to define the distances between each of the companies. As a final result, a representative model of a supply chain was obtained, as well as a methodology that allows the construction of lean supply chains regardless of the number of companies that comprise it. The effect of the variability in the delivery times between suppliers was incorporated into the simulation model, as well as an equation that calculates the pollution emissions of the vehicles that integrate the network that moves the products between the companies. With this work it is possible to represent networks of supply chains of real world companies, where the variability and contamination factor is included, to facilitate the decision making regarding the number of vehicles, inventory levels, quantities to be shipped, frequency in the shipments, etc. with the purpose of contaminating as little as possible and at the same time preventing interruptions in the supply chain using the least amount of resources possible.

Abstract: Landslide is a frequent natural hazard in Chittagong Hilly Areas (CHA), Bangladesh, which causes the loss of lives and damage to the economy. Despite that, an official landslide inventory is still lacking in this area. In this paper, we present a landslide inventory of this area prepared using the visual interpretation of Google Earth images (Google Earth Mapping), field mapping, and literature search. We mapped 730 landslides that occurred from 2001 to 2017. Different landslide attributes, including type, size, distribution, state, water content, future risk, and causes, are presented in the dataset. In this area, slide and flow were the two dominant types of landslides. Among five districts (Bandarban, Chittagong, Cox's Bazar, Khagrachari and Rangamati), most (54.93%) of the landslides occurred in Chittagong and Rangamati districts. About 45.09% of the landslides were small (<100 m2) in size while the maximum size of the detected landslides was 85201.6 m2. This dataset will help to understand the characteristics of landslides in CHA and provide useful guidance for policy implementation.

The Inventory Déjà Vu Experiences Assessment (IDEA) is the only screening instrument proposed to evaluate Déjà vu (DV) experience. Here we intended to validate the Italian version of IDEA (I-IDEA) and at the same time to investigate the incidence and subjective qualities of DV phenomenon in Italian healthy adult individuals on basis of an Italian multicentre observational study. In this study we report normative data on the I-IDEA, collected on a sample of 542 Italian healthy subjects aging between 18 to 70 years (average age 40, range 18-70) with a formal educational from 1-19 years. From September 2013 to March 2016 were recruited 542 healthy volunteers from ten outpatient neurological clinics in ItalyAll participants (i.e family members of neurological patients enrolled, medicine’s student, physicians) had no neurological or psychiatric illness and they gave informed consent to participate in the study. All subjects enrolled had self-administered the questionnaire and they are able to complete I-IDEA test without any support. In total 396 (73%) of the 542 healthy controls had DV phenomenon. The frequency of DV was inversely related to age as well as to derealisation, jamais vu, precognitive dreams, depersonalization, paranormal activity, remembering dreams, travel frequency and daydreams (all P<0.012). The Italian version of IDEA maintains good properties in Italian version, thus confirming that this instrument is reliable for detecting and characterising the DV phenomenon.

The aim of this study is to evaluate the efficacy of taking a daily supplement based on active compounds (AUDISTIM® Day Night: A D/N) in alleviating tinnitus-related disability, as suggested by previous real-life studies. This double-blind randomized placebo-controlled study was conducted in adult with mild to severe tinnitus receiving a 3 month-supplementation. Tinnitus-related handicap (THI), psychological stress (MSP-9) and sleep quality (PSQI) were assessed at baseline and during intervention, perceived impression of tinnitus improvement at the end of the follow-up. The full set analysis included 114 patients (59 A D/N, 55 placebo) aged 53.8±11.4 years, 58% women, with fluctuating (45%) or permanent (55%) tinnitus from 9.3±9.4 years. A D/N supplementation led to greater changes in THI (-13.2±16.0 vs -6.2±14.4, p=0.0158, Cohen’s d=0.44) at 3 months, especially with continuous tinnitus (-15.0±16.3 vs -4.6±12.8, p=0.0065), and, to a lesser extent, at 1 month (-9.8±13.1 for A vs -4.3±12.1, p=0.0213). PSQI significantly improved over time in both groups, MSP-9 only with A D/N. In lines with previous observational studies, both clinical (THI score &gt; 7 pts) and statistical (vs. placebo) improvement, more pronounced in permanent tinnitus, demonstrate the effectiveness of the combination of active compounds and support its use in the management of mild to severe tinnitus.

This paper is a detailed study on radio frequency identification (RFID) technology focused on its application for warehouse traceability in the company FRUMECAR. It highlights the importance of controlling the traceability of items in warehouses or logistics companies, highlighting the need for reliable, real-time information to effectively manage inventory and resolve incidents quickly. The paper discusses in depth what RFID is, its types, main components such as tags, readers, and antennas, as well as discussing how it works, advantages, disadvantages, and specific applications. It also examines factors such as return on investment, cost feasibility, and environmental factors that influence the implementation of RFID systems. The paper presents a practical approach, proposing RFID-based solutions to improve traceability in FRUMECAR, analyzing the current state of the company and its warehouses, with the aim of implementing an efficient system that allows real-time tracking of products.

Greenhouse gases (GHGs) trap heat in the atmosphere, causing the Earth's surface temperature to rise. The main GHGs are carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O), perfluorocarbons (PFCs), hydrofluorocarbons (HFCs), and sulfur hexafluoride (SF6). Human activities are increasing GHG concentrations rapidly, which is causing global climate change. Global climate change is increasing environmental and public health problems. To reduce greenhouse gas (GHG) emissions, identify where the emissions are coming from, develop a plan to reduce them, and then implement and monitor the plan to ensure that emissions are actually reduced. Anthropogenic global climate change has large and increasingly adverse economic effects. Cities emit the most GHGs due to fossil fuel burning and power usage. The four major GHG emitters are energy, transportation, waste management, and urban land use sectors. Organizations should prepare action plans to lower their GHG emissions and stop the worst consequences of climate change. These action plans require companies and local authorities to submit their GHG emissions reports on a yearly basis. A GHG emissions management system includes several processes and tools created by organizations to understand, measure, monitor, report, and validate their GHG emissions. Two of the most widely adapted frameworks for GHG inventory reporting are ISO 14064 and the GHG Protocol. This review paper aims to identify some of the key points of GHG inventory preparation and mitigation strategies.

The aim of this study was to use latent profile analysis to identify specific profiles of burnout syndrome in combination with work engagement and to identify whether job satisfaction, psychological well-being, and other sociodemographic and work variables affect the probability of presenting a profile of burnout syndrome and low work enthusiasm. A total of 355 healthcare professionals completed the Spanish Burnout Inventory, the Utrecht Work Engagement Scale, the Job Satisfaction Scale, and the Psychological Well-Being Scale for Adults. Latent profile analysis identified 4 profiles: 1) burnout with high indolence (BwHIn); 2) burnout with low indolence (BwLIn); 3) high engagement, low burnout (HeLb); and 4) in the process of burning out (IPB). Multivariate logistic regression showed that a second job in a government health care institution; a shift other than the morning shift; being divorced, separated or widowed; and work load are predictors of burnout profiles with respect to the HeLb profile. These data are useful for designing intervention strategies according to the needs and characteristics of each type of burnout profile.

The problem of blood delivery has been a critical concern in past decades. However, the unsteady behavior of blood donors, along with the uncertainties associated with demands in a crisis imposes several challenges for resource management. In this research work, we analyze the blood delivery behavior in a crisis modeled by coupled queues of patients and blood sources. Using Markovian modeling and results associated with G-network, we derive two critical probabilities, namely lack of unit of blood (unit of donations) or not having any storage capacity left to accept new donors' blood. We propose a closed-form solution to calculate the optimal blood storage size, and also we suggest sufficient conditions that guarantee the feasibility of the model. Finally, we conduct a sensitivity analysis to investigate the impact of model parameters on storage size and the proportion of time that storage is full.

This paper describes the use of an open source instrument called LANcube v2 to build a lighting device inventory. The LANcube v2 is and instrument having 5 color sensitive sensors, each on a face of a cube. The instrument can be mounted on a car roof in order to create a map of the artificial light at night while roaming the streets and roads. Based on the temporal variations of the detected signal on various cube’s faces, we developed a method of finding the position in 3D of each lighting device. The lamp spectral types can be determined thanks to the color balance of the raw Red (R) Green (G) Blue (B) and Clear (C) color bands. If one assumes a typical angular photometry of a lighting device with respect to its location, it is possible to estimate its luminous flux. Such information allows us to build a lighting devices inventory of a territory. One advantage of that new method is that it can provide information about the private lighting devices that are always excluded from public lighting inventories. We will compare the inventory extracted with that new methodology with an in situ lamp inventory made for two villages in Canada. This will allow us to emphasize the strengths and limitations of the method by comparing to the ground truth.

Vocational interest inventories are widely used by career counselors to help individuals to make career choices. The most common approach to assess vocational interests is based on verbal or textual stimuli. However, some of them are based on pictorials to overcome reading limits and provide additional information about the working environment and the activities related to a particular job. This article aims to present two studies on the development and first validation of the Multilingual Iconographic Professional Interest Inventory (MIPII) on two samples, one composed of 792 high-school students, and one composed of 366 middle school students. The inventory aimed to assesses the vocational interests of people over 19 areas by illustrations representing 95 jobs, five for each one, combined with their title in six different languages (Arabic, German, English, Spanish, French and Italian in this order). Both illustrations and titles are provided separately in the male and female version on the same page.

This paper proposes a novel formulation of the fuzzy newsvendor problem for inventory management applications. This new formulation allows the use of any profit function. A new credibility estimation is proposed to explore the neighborhood around the most impactful demand scenarios. Further, a simulation procedure was designed for the different demand scenarios, which allows the comparison of the proposed approach with probabilistic demand curves. The fuzzy newsvendor problem is solved using a modified genetic algorithm (GA), where an initialization mechanism with null values is proposed. The new formulation of the fuzzy newsvendor problem together with the modified GA have shown to improve the average profit up to 55% in problems with low-budget scenarios.

Precise individual tree or sample-based inventories derived from 3D point cloud data of mobile laser scanning can improve our comprehensive understanding of the structure, function, resilience, biodiversity, and ecosystem services of urban forests. This study assessed the performance of a hand-held laser scanning system for the extraction of tree position, diameter at breast height (DBH) and tree height (H) in an urban area. A total of 2083 trees of 13 species from 34 plots were analyzed. The results showed that the registration of tree positions using ground control points (GCPs) demonstrated high accuracy, with errors consistently below 0.4 m, except for a few instances. The extraction accuracy of DBH for all trees and individual species remained consistently high, with a total root mean square error (RMSE) of 2.06 cm (6.89%) and a bias of 0.62 cm (2.07%). Notably, broad-leaved trees outperformed coniferous trees, with RMSE and bias values of 1.86 cm (6%) and 0.76 cm (2.46%) compared to 2.54 cm (9.46%) and 0.23 cm (0.84%). Tree height extraction accuracy varied significantly among different species, with R2 values ranging from 0.65 to 0.92. Generally, both DBH and height were underestimated. Linear mixed effects models (LME) were applied to evaluate factors affecting the performance of HLS with the plot as a random factor. LME analysis revealed that plant type and terrain significantly influenced the accuracy of DBH and H derived from HLS data. Other fixed factors of plot area, tree density and trajectory length didn’t have significance. With a large sample size, we concluded that the HLS demonstrated sufficient accuracy in extracting individual tree parameters in urban forests.

This paper is a follow-up to a previous study that was conducted in a research forest and identified the optimal method for Diameter at Breast Height (DBH) estimation as a circular scanning and fitting ellipses to 4cm stem cross-sections at breast height. The aim of this study was to determine whether the identified method is applicable to natural boreal forests as well. The iPad Pro LiDAR scanner was used to acquire point clouds for 15 sites representing a range of natural boreal forest conditions in Ontario, Canada, and estimate DBH. The secondary objective was to determine if tested stand (Species composition, age, density, understory) or tree (Species, DBH) factors affected the accuracy of estimated DBH. Overall, estimated DBH values were within 1cm of actual DBH values for 78 of 133 measured trees (59%). An RMSE of 1.5cm (8.6%) was achieved. Stand age had a large effect (&gt;0.15) on the accuracy of estimated DBH values, while density, understory, and DBH had moderate effects (0.05-0.14). No trend was identified between accuracy and stand age. Accuracy improved as understory density decreased and as tree DBH increased. Inertial Measurement Unit (IMU) and positional accuracy errors with the iPad Pro scanner limit the feasibility of using this device for forest inventories.

This research work aims to develop a mathematical modeling for production-inventory systems. Two mathematical models are present: An infinite dimensional, partial differential equation (PDE), production level modeling is present and a finite dimensional system for a coupled dynamic pricing, production rate and inventory level ordinary differential equations (ODE), integrating a proper Lyapunov stability analysis of the dynamical system and simulations.

The mental and emotional health of an athlete is crucial for their performance and well-being. Sports-related stress can significantly impair that mental health. Yoga of Immortals (YOI) is a unique combination of specific yogic postures, breathing exercises, sound therapy &amp; meditation, which has demonstrated benefit in improving measures of mental health. This study used the Sports Mind Inventory (SMI) to examine whether YOI can improve the resilience of athletes to sports-related stress. Participants were a diverse set of athletes based in Mauritius who routinely engage in a wide range of athletic activities. Participants were randomly assigned to receive four weeks of YOI or no intervention. Both groups completed the SMI questionarie at baseline and again after four weeks. The YOI intervention significantly increased the total mean SMI scores by 14%, indicating improved sports resilience and psychological health. No improvement was observed in the control group. This study demonstrates that YOI is a promising intervention in improving sports related stress.

Due to the shortage of agricultural labor forces and rapid aging of farmers, the utilization of tractors is becoming popular and essential in Korea. Tractors can be classified into two types, a walking tractor called as a power tiller and a riding tractor. In this study, agricultural tractors including walking and riding types were categorized into 4 levels by rated output power. And diesel emission inventory of tractors was established and analyzed using 2011 and 2019 survey data in Korea. Emission inventory including CO, NOx, SOx, TSP(PM10), PM2.5, VOCs and NH3 were established using Tier 3 methodology. The total amount of emission using agricultural tractors was decreased about 13% from 2011 to 2019. The number of walking tractors were decreased by about 19% in 8 years, on the other hand that of riding tractors were increased by about 12%. However, the emission reduction is about 48% for walking tractors and the emission increment is about 5% for riding tractors. Thus, the total emission from agricultural tractors was decreased by about 16% in those periods. It is due to the decrease of 21% and 15% in the hours of use of walking and riding tractors, respectively, in 2019. Walking tractors mainly emit air pollutants from spraying and transporting. Riding tractors mainly 61% of total air pollutants emits from soil preparation and transporting operations. Geographic information system (GIS) was used to spatially assign air pollutants variables into 17 provinces and metropolitan cities in Korea. High emission generating regions and changes of emissions during 8 years were clearly seen in GIS analysis. High air pollutant emitting regions are mainly located in the western and southern regions of Korea, which have plenty of arable areas compared to other regions in Korea.

Net CO2 emissions and sequestration from European forests are the result of removal and growth of flora. To arrive at aggregated measurements of these processes at a country's level, local observations of increments and harvest rates are up-scaled to national forest areas. Each country releases these statistics through their individual National Forest Inventory using their particular definitions and methodologies. In addition, five international processes deal with the harmonization and comparability of such forest datasets in Europe, namely the IPCC, SOEF, FAOSTAT, HPFFRE, FRA (definitions follow in the article). In this study, we retrieved living biomass dynamics from each of these sources for 27 European Union member states. To demonstrate the reproducibility of our method, we release an open source python package that allows for automated data retrieval and analysis, as new data becomes available. The comparison of the published values shows discrepancies in the magnitude of forest biomass changes for several countries. In some cases, the direction of these changes also differ between sources. The scarcity of the data provided, along with the low spatial resolution, forbids the creation or calibration of a pan-European forest dynamics model, which could ultimately be used to simulate future scenarios and support policy decisions. To attain these goals, an improvement in forest data availability and harmonization is needed.

Today, non-expensive remote sensing (RS) data from different sensors and platforms can be obtained at short intervals and be used for assessing several kinds of forest characteristics at the level of plots, stands and landscapes. Methods such as composite estimation and data assimilation can be used for combining the different sources of information to obtain up-to-date and precise estimates of the characteristics of interest. In composite estimation a standard procedure is to assign weights to the different individual estimates inversely proportional to their variance. However, in case the estimates are correlated, the correlations must be considered in assigning weights or otherwise a composite estimator may be inefficient and its variance be underestimated. In this study we assessed the correlation of plot level estimates of forest characteristics from different RS datasets, between assessments using the same type of sensor as well as across different sensors. The RS data evaluated were SPOT-5 multispectral data, 3D airborne laser scanning data, and TanDEM-X interferometric radar data. Studies were made for plot level mean diameter, mean height, and growing stock volume. All data were acquired from a test site dominated by coniferous forest in southern Sweden. We found that the correlation between plot level estimates based on the same type of RS data were positive and strong, whereas the correlations between estimates using different sources of RS data were not as strong, and weaker for mean height than for mean diameter and volume. The implications of such correlations in composite estimation are demonstrated and it is discussed how correlations may affect results from data assimilation procedures.

Background: Asthma is a very prevalent disease with special characteristics during pregnancy, however little is known about its relation to the psychological wellbeing of women in this period; We aimed to know whether depression and anxiety symptoms are more frequent in asthmatic pregnant women. (2) Methods: Family Apgar (week 20), Edinburgh (weeks 20 and 32) and STAI (week 32) tests were administered to 738 pregnant women (81 asthmatics) in the NELA birth cohort; (3) Results: There were no significant differences between asthmatic and non-asthmatic pregnant women in any of the different tests at any of the time points. The mean? scores for the different tests and timepoints between asthmatic and non-asthmatic pregnant women were: Apgar 20, 17.9±2.2 vs. 10.0±2.2; Edinburgh 20, 6.7±4.2 vs. 6.9±4.3; Edinburgh 32, 5.9±4.4 vs. 5.6±4.3; and STAI 32, 16.7±8.4 vs. 15.8±8.3. The proportion of pregnant women out of the normal range score for any of the tests and time points was also similar in both populations. (4) Conclusions: Asthma is not associated with the psychological wellbeing among pregnant women.

Terrestrial laser scanning (TLS) provides detailed three-dimensional representation of the surrounding forest structure. However, due to close-range hemispherical scanning geometry, the ability of TLS technique to comprehensively characterize all trees and especially the upper parts of forest canopy is often limited. In this study, we investigated how much forest characterization capacity can be improved in managed Scots pine (Pinus sylvestris L.) stands if TLS point cloud is complemented with a photogrammetric point cloud acquired from above the canopy using unmanned aerial vehicle (UAV). In this multisensorial (TLS+UAV) close-range sensing approach, the used UAV point cloud data was considered feasible especially in characterizing the vertical forest structure and improvements were obtained in estimation accuracy of tree height as well as plot-level basal-area weighted mean height (H_g) and mean stem volume (V_mean). Most notably the root mean square error (RMSE) in H_g improved from 0.88 m to 0.58 m and the bias improved from -0.75 m to -0.45 m with the multisensorial close-range sensing approach. However, in managed Scots pine stands the mere TLS captured also the upper parts of the forest canopy rather well. Both approaches were capable of deriving stem number, basal area, V_mean, H_g and basal area-weighted mean diameter with a relative RMSE less than 5.5% for all of the sample plots. Although the multisensorial close-range sensing approach mainly enhanced characterization of forest vertical structure in single-species, single-layer forest conditions, representation of more complex forest structures may benefit more from point clouds collected with sensors of different measurement geometries.

Rice cultivation is the main agricultural activity in Asia, and it results in an enormous amount of agricultural waste, that are usually disposed-off by burning, causing serious environmental health and climate problems. Pakistan is the tenth largest producer in the world and the fourth exporter of rice in the world. To date, there are no studies targeting Pakistan’s profile monitoring its contribution in open burning emissions from rice straw waste. This study presents the characterization of PCDD/Fs, PCBs and PAHs emissions from three samples of rice straw collected from the main rice cultivation regions of Pakistan (Punjab, Sindh and Azad Jammu and Kashmir) in relation to the role of metal-based agricultural additives. PCDD/Fs, PCBs and PAHs emissions were quantified using a fixed grid system biomass plant and measuring the macro-pollutants online and sampling the organic micro-pollutants and metals in isokinetic mode, followed by ISO and CEN standards methods. In addition, role of agrochemicals was monitored in terms of metal catalysis resulted in POPs emissions. This study provides the characterization of most relevant group of pollutants (metals, PCDD/Fs, PCBs and PAHs) as a result of agricultural waste combustion. The rice straw samples from AJK showed high values of organochlorine micro-pollutants compared to the values followed by samples from Punjab and Sindh, i.e., respectively, ∑PCDD/F (2594>1493>856 pg/Nm³) and ∑PCB (41>38>30 pg/Nm³). Whereas the organic micro-pollutants ∑PAH, indicators of incomplete combustion (PICs), were recorded high in the samples from Sindh followed by Punjab and AJK. This study supports the idea that the phenomena that lead to the formation of PCDD/F and PCB are influenced not only by poor combustion but also by the presence of metal catalysts or precursors in the burned fuel.

Over the last few decades, the approach to geological and geomorphological surveys has changed remarkably. The advent of digital tools has allowed significant advances in the acquisition and management of survey data. In this paper, we demonstrate the development and testing of a new and effective digital survey method that allows for the fast acquisition and collaborative storage and management of data and information. This method was tested in collaboration with five universities for the mapping and classification of landslides in 249 survey areas in Central Italy and, more precisely, in the municipalities affected by the 2016 Central Italy Earthquake. Geological and geomorphological surveys were carried out in the field with tablet PCs, GPS, and cameras. The survey project for collecting field data was based on the structure of the Italian Landslide Inventory (IFFI) and the Territorial Resilience Central Apennines Earthquake Reconstruction (ReSTART) projects. The structure of the database and input forms were implemented for these aims. Moreover, the data and information were retrieved and organised in detailed records useful to the administrative entities.

While lidar-based forest inventory methods have been widely demonstrated, prediction of tree diameters with lidar is not well understood. The performance metrics typically used in studies for prediction of diameters can be difficult to interpret and may not support comparative inferences between sampling designs or study areas. We evaluate a variety of lidar and k nearest neighbor (k-NN) strategies for prediction of tree diameter distributions using two indices which are easier to interpret and compare. The indices are based on the coefficient of determination (R²), and root mean square deviation (RMSD). These indices facilitate comparisons with alternative (non-lidar) inventory strategies, and with other project areas. We evaluate k nearest neighbors (k-NN) dbh density (relative frequency by dbh class) prediction strategies with lidar for 190 training plots distribute across the 800 km² Savannah River Site in South Carolina, USA. We evaluate the performance of k-NN with respect to distance metrics, number of neighbors, predictor sets, and response sets. Amongst the examined strategies we found Mahalanobis distance with k = 3 neighbors performed best according to a number of criteria.

Wetlands are recognized as one of the world’s most valuable natural resources. With the increasing world population, human demands on wetland resources for agricultural expansion and urban development continue to increase. In addition, global climate change has pronounced impacts on wetland ecosystems through alterations in hydrological regimes. To better manage and conserve wetland resources, we need to know the distribution and extent of wetlands and monitor their dynamic changes. Wetland maps and inventories can provide crucial information for wetland conservation, restoration, and management. Geographic Information System (GIS) and remote sensing technologies have proven to be useful for mapping and monitoring wetland resources. Recent advances in geospatial technologies have greatly increased the availability of remotely sensed imagery with better and finer spatial, temporal, and spectral resolution. This chapter presents an introduction to the uses of GIS and remote sensing technologies for wetland mapping and monitoring. A case study is presented to demonstrate the use of high-resolution light detection and ranging (LiDAR) data and aerial photographs for mapping prairie potholes and surface hydrologic flow pathways.

Visual data on the geographic distribution of carbon storage helps policy maker to formulate countermeasures on global warming. However, Taiwan, as an island showing diversity in climate and topography, had lacked valid visual data in distribution of forest carbon storage between the last to forest surveys (1993-2015). This study established a model to achieve an estimation that capable to demonstrate the distribution of forest carbon storage. This model uses land use, stand morphology, carbon conversion coefficient databases accordingly for 51 types of major forest in Taiwan. An estimation on 2006 was conducted and shows an overall carbon storage of 165.65 Mt C, with forest carbon storage per unit area of 71.56 t C ha–1, where natural forests and plantations respectively contributed of 114.15 Mt C (68.9%) and 51.50 Mt C (31.1%). By assuming no change in land use type, the carbon sequestration from 2006 to 2007 by the 51 forest types was estimated to be 5.21 Mt C yr–1 using historical tree growth and mortality rates. The result reflects the reality of the land use status and event of coverage shifting with time by combining to the two forest surveys in Taiwan.

Background: New regional techniques can improve pain management after nephrectomy. Methods: This study was a randomized controlled trial conducted at two teaching hospitals. Patients undergoing elective open and laparoscopic nephrectomy were eligible to participate in the trial. A total of 100 patients were divided into a quadratus lumborum block (QLB) group and a control (CON) group. At the end of surgery, but while still under general anesthesia, unilateral QLB with ropivacaine was performed on the side of nephrectomy for patients in the QLB group. The main measured outcome of this study was oxycodone consumption via a patient-controlled anesthesia (PCA) pump during the first 24 hours following surgery; other measured outcomes included postoperative pain intensity assessment, patient satisfaction with pain management, and persistent pain evaluation. Results: Patients undergoing QLB needed less oxycodone than those in the CON group (34.5 mg [interquartile range 23–40 mg] vs. 47.5 mg [35–50 mg]; p<0.001). No difference between the groups was seen in postoperative pain intensity measured on the visual analog scale, except for the evaluation at hour 2, which was in favor of the QLB group (p=0.03). Patients who received QLB were more satisfied with postoperative pain management than the CON group. Persistent postoperative pain was assessed with the Neuropathic Pain Symptom Inventory (NPSI) at months 1, 3, and 6 and was found to be significantly lower in the QLB group at each evaluation (p<0.001). We also analyzed the impact of the surgery type on persistent pain severity, which was significantly lower after laparoscopic procedures than open procedures at months 1, 3, and 6. Conclusions: QLB reduces oxycodone consumption in patients undergoing open and laparoscopic nephrectomy and decreases persistent pain severity months after hospital discharge.

In this paper, we present an extended version of the Hadi-Vencheh model for multiple criteria ABC inventory classification. The proposed model is a nonlinear weighted product model (WPM) which determines a common set of weights for all the items. Our proposed nonlinear WPM incorporates multiple criteria with different measure units, without converting the performance of each inventory item in terms of each criterion into a normalized attribute value, thereby providing an improvement over the model proposed by Hadi-Vencheh. Our study mainly includes various criteria for ABC classification, and demonstrates an efficient algorithm for solving nonlinear programming problems in which the feasible solution set does not have to be convex. The algorithm presented in this study improves the solution efficiency of the Canonical Coordinates Method (CCM) algorithm substantially when applied to large scale, nonlinear programming problems. The modified algorithm was tested to compare our proposed model results to the results derived using the Hadi-Vencheh model and demonstrate the algorithm's efficacy. The practical implications of the study are to develop an efficient nonlinear optimization solver by optimizing the quality of existing solutions, thus improving time and space efficiency.

A model of a single-server queuing-inventory system (QIS) with catastrophes and a finite waiting room for consumer customers (c-customers) has been developed. When a catastrophe occurs, all inventory in the system's warehouse is destroyed, but c-customers in the system are still waiting for restocking. In addition to c-customers, negative customers (n-customers) are also taken into account, which displaces one c-customers (if any). The policy (?, ?) is used to replenish stocks. If upon arrival of the c-customer the inventory level is zero, then according to Bernoulli’s scheme this c-customer is either lost or joining the queue. The mathematical model of the studied QIS is constructed in the form of a two-dimensional continuous-time Markov chain (2D CTMC). Exact and approximate methods for calculating the steady-state probabilities of constructed 2D CTMCs are developed and closed-form expressions are derived for calculating the performance measures. The results of numerical experiments are presented, demonstrating the high accuracy of the developed approximate formulas, as well as the behavior of performance indicators depending on the reorder point. In addition, an optimization problem is solved to obtain the optimal reorder point to minimize the expected total cost.

Windthrow (i.e., trees broken and uprooted by wind) is a major natural disturbance in the Amazon. Images from medium-resolution optical satellites (mostly Landsat) combined with extensive field data have allowed researchers to assess patterns of tree mortality and monitor forest recovery over decades of subsequent succession in different regions. Although satellites with high spatial-resolution have become available for the Amazon in the last decade, they have not yet been employed for the mapping and quantification of windthrow tree-mortality. Here, we address how increasing the spatial resolution of satellites affects plot-to-landscape estimates of windthrow tree-mortality. We combined forest inventory data with Landsat 8 (30 m pixel), Sentinel 2 (10 m), and WorldView 2 (2 m) imagery over an old-growth forest in the Central Amazon that was disturbed by a single windthrow event in November/2015. Remote sensing estimates of tree mortality were produced with Spectral Mixture Analysis and analyzed together with forest inventory data using Generalized Linear Models. Windthrow tree-mortality measured in 3 transects (30 subplots) crossing the entire disturbance gradient was 26.9 ± 11.1% (mean ± 95% CI). Based on this ground truth, the three satellites produced reliable and statistically similar estimates (from 26.5% to 30.3% windthrow tree-mortality, p&lt;0.001). The mean-associated uncertainties decreased systematically with increasing spatial resolution (i.e., from Landsat 8 to Sentinel 2 and WorldView 2). However, the overall quality of fit of models showed the opposite pattern, which may reflect the influence of crown damage not accounted for in our field study, and fast-growing regeneration of leaf area. Among the satellites studied, Landsat 8 most accurately captured field observations of variations in tree mortality across the disturbance gradient (i.e., lower under- and/or overestimation from undisturbed to extremely damaged forest). Although satellites with high spatial-resolution can refine estimates of windthrow severity by allowing the quantification of individual tree damage and mortality, our results validate the reliability of Landsat imagery for assessing patterns of windthrow tree-mortality in dense and heterogeneous tropical forests. Although high-resolution imagery may improve estimates of tree damage and mortality, these should be validated using field data at compatible scales.

Gardens play a key role in the definition of the cultural landscape since they reflect the culture, identity and history of a people. They also contribute to the ecological balance of the city. Despite gardens have an historic and social value, they are not protected as much as the rest of the existing heritage, like architecture and archaeological sites. While methods of built-heritage mapping and monitoring are increasing and constantly improving to reduce built-heritage loss and the severe impact of natural disasters, the documentation and survey techniques for gardens are often antiquated, inventories are typically made by non-updated/updatable reports, and rarely they are on digital format and in 3D. This paper presents the preliminary results of a study on latest technology for gardens laser scanning. We compared static Terrestrial Laser Scanning and Mobile Laser Scanning point clouds, to evaluate their quality for documentation and the estimation of the tree attributes. The evaluation is based on visual observation and graphic comparison of the two point clouds acquired in different instances. Both methods produced useful outcomes for the research scope within their limitations. Terrestrial Laser Scanning is still the method that offers more accurate point clouds with a higher point density and less noise level. However, the more recent Mobile Laser Scanning is able to survey in less time, significantly reducing the costs for site activities, data post-production and registration. Both methods have their own restrictions that are amplified by site features, mainly the lack of plans for the geometric alignment of scans and for the Simultaneous Location and Mapping (SLAM) process. We also offer the results of a comparison of the functional range of the two machines, as well as for a comparison of their terrain information extraction capabilities.

Up to 56% of patients develop chronic postsurgical pain (CPSP) after coronary artery bypass grafting (CABG). CPSP can affect patients’ moods and decrease daily activities. The primary aim of this study was to investigate CPSP severity in patients following off-pump (OP)-CABG using the Neuropathic Pain Symptom Inventory (NPSI). This was a prospective cohort study conducted in a cardiac surgery department of a teaching hospital. Patients undergoing OP-CABG were enrolled in an erector spinae plane block (ESPB) group (n = 27) or a control (CON) group (n = 24). Before the induction of general anesthesia, ESPB was performed on both sides under ultrasound guidance using 0.375% ropivacaine. The secondary outcomes included cumulative oxycodone consumption, acute pain intensity, mechanical ventilation time, hospital length of stay, and postoperative complications. CPSP intensity was lower in the ESPB group than in the CON group 1, 3, and 6 months postsurgery (p < 0.001). Significant between-group differences were also observed in other outcomes, including postoperative pain severity, opioid consumption, mechanical ventilation time, and hospital length of stay in favor of the ESPB group. Preemptive ESPB appears to decrease the risk of CPSP development in patients undergoing OP-CABG. Reduced acute pain severity and shorter mechanical ventilation times and hospital stays should improve patients’ satisfaction and reduce perioperative complications.

Handedness reflects functional body and hemispheric lateralization, proving relevant to contextualize cognitive and behavioral profiles. This study aimed to determine the lateralization of the hands, feet and eyes in patients with various neurological disorders and to test the Edinburgh Handedness Inventory (EHI), a widely used handedness measure. The study design included a literature overview and a clinical study. The overview spanned the literature of the past 50 years since the EHI was developed. Three hundred seventy-four patients with immune-mediated (IM) or noIM neurological diseases and healthy subjects underwent personal and family history of handedness, stressful events and IM diseases, and EHI. The literature overview showed that handedness, which develops under the influence of genetic and acquired factors, is an indirect indicator of hemispheric lateralization; hemispheric lateralization is bidirectionally related to the immune system. In the clinical study, the EHI showed appropriate reliability and structural validity; its scores related to chronological age, onset age, and disease duration and helped distinguish patients with IM disease from other patients and healthy subjects. Therefore, handedness may change over the course of life under the influence of pathology and could predict IM neurological diseases. The EHI is an appropriate measure of handedness.

Life cycle assessment (LCA) is used frequently as a decision support tool for evaluating different design choices of products based on their environmental impacts. A life cycle usually comprises several phases of varying timespan. The amount of emissions generated from different life cycle phases of a product could be significantly different from one another. In conventional LCA, the emissions generated from the life cycle phases of a product are aggregated at the inventory analysis stage, which is then used as an input for life cycle impact assessment. However, when the emissions are aggregated, the temporal variability of inventory data is ignored, which may result in inaccurate environmental impact assessment. Besides, the conventional LCA does not consider the environmental impact of circular products with multiple use cycles. It poses difficulties in identifying the hotspots of emission-intensive activities with the potential to mislead conclusions and implications for both practice and policy. To address this issue and to analyse the embedded temporal variations in inventory data in a CE context, the paper proposes to calculate the emission intensity for each life cycle phase. It is argued that calculating and comparing emission intensity, based on the timespan and amount of emissions for individual life cycle phases, at the inventory analysis stage of LCA offers a complementary approach to the traditional aggregate emission-based LCA approach. In a circular scenario, it helps to identify significant issues during different life cycle phases and the relevant environmental performance improvement opportunities through product, business model and supply chain design.

Bhutan located in the Hindu Kush Himalayan (HKH) region consists of several glaciers and glacial lakes at higher elevations.  With the rapid change in global temperature, glaciers are found to melt at an accelerated rate. This rapid melt of glaciers gets accumulated in weak moraine walls forming a glacial lake, posing a major threat to the downstream communities. As per the Bhutan Glacial Lake Inventory 2021, Bhutan has 567 glacial lakes. Furthermore, the Phochhu basin has the maximum glacial lakes (0.05%) and of which 9 are PDGL. Hence a need for a time monitoring system is imminent. With the availability of free High-resolution Satellite Imageries and Advanced Remote Sensing tools, it has been a sine qua no for monitoring glacial lakes in high areas. Therefore, using the Google Earth Engine and Qgis Platform a semi-automated technique was used to generate glacial lake inventories for Phochhu Sub-basin for the year 2021.  We found out that there are 166 glacial lakes covering an area of 24.051 km2.

Mapping forest extent and forest cover classification are important for the assessment of forest resources in socio-economic as well as ecological terms. Novel developments in the availability of remotely sensed data, computational resources, and advances in areas of statistical learning have enabled fusion of multi-sensor data, often yielding superior classification results. Most former studies of nemoral forests fusing multi-sensor and multi-temporal data have been limited in spatial extent and typically to a simple classification of landscapes into major land cover classes. We hypothesize that multi-temporal, multi-censor data will have a specific strength in further classification of nemoral forest landscapes owing to the distinct seasonal patterns of the phenology of broadleaves. This study aimed to classify the Danish landscape into forest/non-forest and further into forest types (broadleaved/coniferous) and species groups, using a cloud-based approach based on multi-temporal Sentinel 1 and 2 data and machine learning (random forest) trained with National Forest Inventory (NFI) data. Mapping of non-forest and forest resulted in producer accuracies of 99% and 90 %, respectively. The mapping of forest types (broadleaf and conifer) within the forested area resulted in producer accuracies of 95% for conifer and 96% for broadleaf forest. Tree species groups were classified with producer accuracies ranging 34-74%. Species groups with coniferous species were the least confused whereas the broadleaf groups, especially Oak, had higher error rates. The results are applied in Danish National accounting of greenhouse gas emissions from forests, resource assessment and assessment of forest biodiversity potentials.

Hemodialysis (HD) patients experience significant psychological stress related HD. Coping strategies are utilized to help these patients cope with this stress. Prevalence of different types of coping strategies in relation with differential levels of HD-related stress scores and by demographics were assessed. HD patients aged 18 years and above who were seeking regular treatment in a specific HD-unit of a hospital in the upper Midwest, were surveyed between March 2020 and July 2020. Descriptive statistics, correlational analyses, and regression analyses were performed. Females could be expected to have emotional coping scores 8 units greater than males, as B = 8.017 for sex in the model, p =.000. Females could be expected to have coping scores 6.4 points higher than males when adjusted for stress levels, as: B = 6.351, p =.001 for sex in the model. For each 1point increase in Stress Score, the Coping Score could be expected to: increase 6.8 points when adjusted for Sex, where B = 6.751 and p =.000. Increased prevalence of emotional pre-occupation was only observed among female patients which increased with the increase in stress. Psychological interventions in female HD patients should be strongly considered in addressing emotional pre-occupation coping.

Historical forest management practices in the southwestern US have left forests prone to high intensity, stand-replacement fires. Effective management to reduce the cost and impact of forest-fire management and allow fires to burn freely without negative impact depends on detailed knowledge of stand composition, in particular, above-ground biomass (AGB). Lidar-based modeling techniques provide opportunities to reduce costs and increase ability of managers to monitor AGB and other forest metrics. Using Bayesian Model Averaging (BMA), we develop a regionally applicable lidar-based statistical model for Ponderosa pine and mixed conifer forest systems of the southwestern USA, using previously collected field data. The selected regional model includes a mid and low canopy height metric, a canopy cover, and height distribution term. It explains 72% of the variability in field estimates of AGB, and the RMSE of the two independent validation data sets are 23.25 and 32.82 Mg/ha. The regional model developed is structured in accordance with previously described models fit to local data, and performs equivalently to models designed for smaller scale application. Developing regional models for broad scale application provides a cost-effective, robust approach for managers to monitor and plan adaptively at the landscape scale.

The classical joint economic lot-sizing (JELS) policy in a single-vendor single-buyer system generates an equal production quantity in all cycles, where the input parameters remain static indefinitely. In this paper, a new two-echelon supply chain inventory model is developed involving a hybrid production system that simultaneously focuses on green and regular production methods with optimal allocation fraction of green and regular productions. Unlike the classical mathematical formulation, each cycle is independent from the previous one, and consequently, the input parameters can be adjusted to be responsive to the dynamic nature of demand rate and price fluctuation. A rigorous heuristic approach is used to derive a global optimal solution for a joint hybrid production system. The model accounts for carbon emissions from production and storage activities related to green and regular produced items along with transportation activity under a multi-level emission-taxing scheme. The results emphasize the significant impact of green production on emissions. That is, the higher the allocation fraction of green production the lower the total amount of emissions generated by the system, i.e., the system becoming more sustainable. Adopting a hybrid production method not only decreases the greenhouse gas (GHG) emissions dramatically, but also reduces the per unit time total cost when compared with regular production. One of the main findings is that the total system cost generated by the base closed-form formula of the proposed model is considerably lower than that of the existing literature i.e., 33.59% (16.13%) lower in the first cycle (subsequent cycles) when the regular production method is assumed. Moreover, the optimal production rate generated by the proposed model is the one that minimizes the emissions production function. Illustrative examples and special cases that reflect different realistic situations are compared to outline managerial insights.

The outstanding occurrences of red wood ant nests (RWA; Formica rufa-group) at two study sites in the Oberpfalz, NE Bavaria, Germany, could be confirmed by the 2023 re-inventory (a total of 5,393 nests and an increase by ≈8 %). RWA nests preferred mature and medium mature pine-dominated forests. Standing and downed dead wood do not negatively influence nests. Nest counts with woodpecker cavities and the diversity of the herb layer increased. The highly clustered nests in NW-SE direction suggests a strong interaction between nests distributions and the tectonic environment. Our developed (re-)inventory approach in combination with the comprehensive photo database is a valuable tool for a) monitoring the entire forest habitat including woodpecker cavities and dead wood in, at and around nests, and b) identifying GeoBio-Interactions. Such a comprehensive approach leads to more accurate and realistic counts of RWA nests during (re-)inventories and provides scientific evidence of the status quo of RWA occurrence. Our results disproved the unfounded and scientifically unsubstantiated blanket statement by laymen of the ASW Landesverband Bayern e.V. that RWA nest numbers in the Oberpfalz or even in the entire Bavaria are in sharp decline. We suggest seven to eight years as a suitable time interval for re-inventories.

Reducing GHG emissions is a crucial challenge in urban areas, characterized by high energy consumption and reduced exposure to nature. In this context, the urban green system could play a pivotal role. In the literature, scholars analyzed both the ability of species-specific and layout-specific green infrastructures to increase carbon sequestration and the best location sites of new green infrastructures, to increase the provision of overall ecosystem services. There is a lack of studies helping green urban planners and designers choose where and which green infrastructure to implement, based on vegetation species-specific performance and local carbon emissions of city components. This paper uses tree inventory data from a medium-sized city in central Italy (Perugia) to develop a spatial analysis of the urban parks' performance in carbon sequestration. Then, the method evaluates the carbon emission of a public city building to generate a spatialized balance between buildings’ demand and trees’ supply, to support local decisions about the best locations for new green infrastructures and the choice between species. The paper contributes to GIS-based tools that vary the recommended location sites and species for new green infrastructures based on the demanded ecosystem service.

We discuss two queueing-inventory systems with catastrophes in the warehouse. Catastrophes occur according to Poisson process and upon arrival of a catastrophe all inventory in the system is instantly destroyed. But consumer customers in the system (in the server or in the buffer) continue still waiting for the replenishment of the stock. The arrivals of the consumer customers follow a Markovian Arrival Process (MAP) and they can be queued in an infinite buffer. Service time of a consumer customer follows a phase-type distribution. The system receives negative customers whose have Poisson flows to service facility and upon arrival of a negative customer one consumer customer is pushed out from the system, if any. One of two replenishment policies can be used in the system: either (s,S) or (s,Q). If upon arrival of the consumer customer, the inventory level is zero, then according to the Bernoulli scheme, this customer is either lost (lost sale scheme) or join the queue (backorder sale scheme). The system is formulated by a four-dimensional continuous-time Markov chain. Steady state distribution is obtained using the matrix-geometric method. A comprehensive numerical study is performed on the performance measures under various replenishment policies. Finally, an optimization study is presented.

We re-inventoried red wood ant nests (RWA) in 12 study sites (≈1281 ha) in the Westeifel Volcanic Field, Germany, in an area-wide and integrated ecosystem approach after 12 years. Combined with the re-identification of previously mapped nests using a photo database, this approach leads to more accurate nest counts in (re)inventories. A total increase of nests from 1144 (2009) to 1252 (2021), and a dramatic one for the Heidberg site by 52-fold (2009) and 85-fold (2021) compared to a 1984 inventory was observed, contrasting with earlier postulations of a decline in RWA. Early to medium mature (41-80 years) and mature (³81–140 years) spruce forests were the preferred habitat. Large increase in small nest sizes suggests an increase in new nest settlements also in clearings, despite climate-induced forest dieback over the past decade. A decline in biodiversity in the herbaceous layer was observed; highly proliferating blackberries had no negative impact on RWA nests. Monitoring of woodpecker cavities in RWA nests is suggested as an indirect indicator tool for evaluating populations in forests. Positive ground movement rates caused by the Eifel plume are suggested another factor favoring nest settlements. This study contributes to the urgent update of statistically valid data required to a) effectively substantiate the status-quo of RWA occurrences, b) protect RWA as ecosystem engineers, c) advance understanding of Geo-Bio-Interactions in the wake of climate change, and c) contribute to the German government's Insect Conservation Action Program.

Historically, the evaluation and assessment of the clinical response to treatment for canine back pain is subjective and relies on owner and clinician assessment of pain. This study evaluated the use of sequential infrared thermal images as a measure of the response of canine patients with back pain to a prescribed series of photobiomodulation therapy (PBMT) treatments. Qualifying participants had histories of pain and dysfunction associated with spinal osteoarthritis or intervertebral disk disease, or of non-specific uni- or bilateral back pain along the paravertebral epaxial muscles. Each patient was initially thermally imaged prior to PBMT treatment and then received multiple PBMT treatments delivered to the appropriate spinal area on days 1, 2, 3, and 4. Participants were reimaged on day 7. Thermal images provided an objective measure of superficial temperature changes over the area of PBMT treatment of each patient after the PBMT regimen. The temperature correlated with statistically significant changes in Colorado State University Canine Chronic Pain Scale scoring (CPS) and owner assessment using the Canine Brief Pain Inventory (CBPI), which includes a Pain Severity Score (PSS) and Pain Interference Score (PIS). The correlation of objective thermal imaging data with more subjective outcome measures suggests thermal imaging may be a valuable additional tool in monitoring therapy outcome.