This review explores the transformative impact of sensor technologies in early disease detection. It highlights recent advancements in nanotechnology-based sensors, particularly focusing on their applications in identifying disease biomarkers at early stages. The integration of these sensors with artificial intelligence for predictive analytics in healthcare is also discussed. Applications in cancer, infectious diseases, and chronic disease management underscore their diverse utility. Challenges in sensor technology adoption and future directions, including multiplexed sensors and wearable devices, are examined. This review underscores sensors' potential in enhancing disease management and patient outcomes.

Microplastic contamination, defined as plastic particles less than 5 mm in size, has become a major global environmental concern. This study synthesizes findings from carefully chosen studies undertaken in a variety of geographical areas and ecosystems, offering light on the multifaceted issues posed by microplastic pollution. These articles show the global prevalence of microplastics in both land and marine ecosystems. Seasonal fluctuations in microplastic abundance highlight the cyclical nature of this pollution, with larger amounts being reported during the summer months. Tourism, industrialization, and urbanization have all been highlighted as significant contributors to microplastic contamination, stressing the impact of man-made influences. Wastewater treatment plants (WWTPs) have been identified as hotspots for microplastic release into aquatic habitats, emphasizing the need for enhanced treatment procedures. Because microplastics can build up in the food chain, it underlines the possible health problems connected with microplastic pollution for both aquatic creatures and human consumers. Longitudinal studies, geographical variety, in-depth health and ecological impact evaluations, standardization, and effective mitigation methods should be prioritized in future studies. To address this worldwide issue, interdisciplinary collaboration among scientists, health professionals, politicians, and industrial stakeholders is required.

The broad and rapid application of new genomic techniques (NGTs) such as CRISPR-Cas has led to a fast increase in the number of genetically modified crops. With the recently published EU Commission’s (EC) regulatory proposal on plants generated with NGTs (July 5, 2023) the process of admission of such genetically modified plants is aimed to get accelerated and simplified. NGT plant applications shall be categorized based on their molecular characteristics into category 1 NGT (NGT1) and category 2 NGT (NGT2), which is in divergence from the current legislation centered around Directive 2001/18/EC. In the regulatory proposal NGT2 plants are envisioned to undergo a reduced risk assessment while for NGT1 plants no risk assessment is foreseen at all. To demonstrate were the path of the proposal leads to in practice, we applied the proposed criteria for categorization to a list of NGT plant applications that are currently in the commercialization pipeline. A key finding is that out of those plant applications affected by the EC proposal, 94% would be classified as NGT1 and thus would receive a market approval without risk assessment, monitoring and sufficient labeling provisions. The remaining 6% of applications would be classified as NGT2 plants for which, in deviation from the current regulation, an adapted risk assessment is proposed. A screening of intended traits in the pipeline highlights that certain NGT1 plants can pose similar environmental risks (e.g. invasiveness) compared to other genetically modified organisms (GMOs), as defined in Directive 2001/18/EC. For example, NGT1 applications based on RNAi technology can exhibit insecticidal effects with potential side effects on non-target organisms (i.e. other insects). Our quantitative and case-specific elaboration on how the current EC regulatory proposal would affect the environment, health or consumer protection will be informative for decision makers and politicians.

In many ports, the ship's speed is limited for the safety of navigation. At the same time, ship captains and port pilots choose the speed of the ship, but not higher than the permitted speed of the ship in the port, therefore the speed of the ship also depends on the experience of the ship captains and port pilots and the sailing conditions of the ship in specific conditions. Choosing the optimal speed of ships in port, including the effect of shallow depth, can reduce fuel consumption and ship emissions in ports, which is important for the development of green and sustainable ports. In all cases, shipping safety is the highest priority. The main objectives of the article are determining the optimal speed of ships in ports with low clearance, ensuring navigational safety, reducing fuel consumption and emissions, and at the same time creating a sustainable port. The article presents the methodology of optimal ship speed calculation, minimum ship controllable speed maintenance, fuel consumption and emission reduction methodology and their impact on sustainable and green maritime transport and port development. The developed methodology was tested on real ships and with the help of a calibrated simulator, sailing through harbor channels and harbor waters in low clearance conditions.

This research delves into the intricate dynamics underlying the impact of investments on global output, em-ploying the Leontief matrix as a robust analytical framework. Investments wield a profound influence on economies worldwide, with varying effects contingent upon investment types, development levels of coun-tries, and external factors such as trade conflicts and global shocks. The diverse range of investment forms, including physical capital, human capital, R&D, and technological investments, engenders distinct implica-tions for productivity, innovation, and efficiency. Developing and developed economies navigate unique tra-jectories, with investments playing a pivotal role in bridging infrastructure gaps, improving technology, and spurring growth. However, external disruptions, such as trade wars and global shocks, introduce an element of complexity, reshaping investment patterns and altering global output trajectories. This study centers on harnessing the Leontief matrix's prowess to evaluate the interplay of investments and global output, focusing on the Romanian economy. By analyzing input-output tables, encompassing 105 branches aggregated into 10 sectors, the research captures the intricate connections between economic segments. No-tably, the Romanian context reveals the volatility of the matrix coefficients', an outcome of ongoing transi-tional processes, technological advancements, and fluctuating relative prices. In unraveling the intricate threads weaving investments and global output, this study contributes to a nuanced comprehension of these multifaceted interactions. The findings underscore the significance of tailoring investment strategies to specific economic contexts and advocate for robust frameworks, such as the Leontief matrix, to inform pol-icy decisions and drive sustainable growth in an increasingly complex global economy.

In order to reduce the silique shattering loss of rapeseed mechanical harvesting process, based on the state of force on the silique during rapeseed harvesting reel branch, Ningza 1810, Zhenyou 8 and Fengyou 306 were used as research objects, and the experimental research on the factors affecting rapeseed siliques shattering was carried out using the swing impact method. The experimental analysis showed that: rapeseed varieties, silique moisture content, silique growth position, collision material, impact speed, force position and other factors had significant effects on silique shattering. The impact velocity was less than 1.5m·s-1, the difference on the effect of each factor on pod-shattering was not significant, it was not easy to shatter when the moisture content of rapeseed silique was higher. The impact resistance of the front side of rapeseed was 2 to 4 times that of the bonding surface of rapeseed petals, the shattering rate of the top rapeseed silique was twice that of the bottom siliques, when siliques were supported, they were more likely to shatter under external forces than when they were unsupported. The experimental study of the mechanical properties of rapeseed siliques was carried out using the impending fracture method, the experimental analyses showed that the support position and force position of the silique, the loading speed and the growth position of the silique had a significant effect on the mechanical properties of the silique. The maximum cracking force was higher and bending strength was stronger when the body of the silique was supported, which the range of the maximum cracking force were 3.05N to 4.16N, and the bending strength range were 8.48 MPa to 11.57 MPa. The maximum cracking force and bending strength of the silique was stronger when the front side of the silique petal was pressurized than when the bonding surface of the petal was pressurized. Based on Pearson's correlation and grey correlation analysis, the morphological characteristics of rapeseed siliques were ranked in order of their influence on the performance of siliques in terms of the angle between silique and stalk, stalk diameter, petal thickness, beak length, silique thickness, silique width and silique length. This study can be used as a reference for the design and optimization of the rapeseed harvesting reel branch mechanism and the selection of machine harvestable rapeseed varieties.

Magnetorheological elastomers (MREs) are a type of smart material that can change their mechanical properties in response to external magnetic fields. These unique properties make them ideal for various applications, including vibration control, noise reduction, and shock absorption. This paper presents an approach for modeling the impact behavior of MREs. The proposed model uses a combination of exponential functions arranged in a multi-layer Preisach model to capture the nonlinear behavior of MREs under impact loads. The model is trained using particle swarm optimization (PSO) and validated using experimental data from drop impact tests conducted on MRE samples under various magnetic field strengths. The results demonstrate that the proposed model can accurately predict the impact behavior of MREs, making it a useful tool for designing MRE-based devices that require precise control of their impact response. The model's response closely matches the experimental data, with a maximum prediction error of 10% or less. Furthermore, the interpolated model's response shows good agreement with the experimental data, with a maximum percentage error of less than 8.5%.

The assessment of an organization/product's environmental footprint is based on the protocols developed by the Joint Research Centre of the European Union, which take into account 16 impact categories. Among the categories covered are toxicity to freshwater ecosystems and to humans. Standard protocols use only chemical parameters as input data, preventing the true impact of entire complex mixtures, such as emissions discharged into the environment, from being determined. Biological assays allow us to bridge this gap: in the current study, assays were used to determine baseline toxicity towards aquatic organisms (green algae, luminescent bacteria, and crustaceans) as well as specific toxicity (mutagenicity and carcinogenicity). Expected impacts were compared using two approaches (standard and bioassay-centered results obtained are not always in line and, in general, the traditional method underestimates impacts). This demonstrates the importance of these assays and suggests that they be used in LCA (Life Cycle Assessment) protocols as well.

The Impact Chain framework for risk assessment has proven to be a robust and effective It is very useful to set up the conceptual framework associated to a given risk and allows accommodating naturally the different components that shape that However, the operationalization of the impact chain may not be straightforward, in particular due to the inherent uncertainties associated to the selected indicators and the assigned In this paper, we introduce an extension to the Impact Chain framework that allows to consider uncertainties in the different components of the risk In the framework of the UNCHAIN project, a web-based tool has been developed to ease the task of implementing that The tool has been applied to a case study on the loss of tourist attractiveness due to heat stress conditions on the Balearic island, Spain, to illustrate how uncertainties in different components of the impact chain can affect the robustness of the final risk Also, the tool provides an estimate of the sensitivity of the final risk to each component, which can be used to guide risk mitigation Finally, a proposal for the validation of the risk assessment is presented.

The effective management of Fusarium wilt of bananas (FW) depends on the knowledge of the disease dynamics in time and space. The objectives of this work were: To estimate disease intensity and impact, and to investigate the spatial and temporal dynamic of FW. Fields planted with Silk (n = 10), Pome (n = 17) or Cavendish (n = 3) banana subgroups were surveyed in Brazil, totaling 95 ha. In each field, all plants were visually assessed and diseased plants were georeferenced. The incidence of FW and the impact of the disease on yield on a regional scale were estimated. Spatial patterns were analyzed using quadrat- and distance-based methods. FW incidence ranged from 0.09 to 41.42%, being higher in Silk fields (median = 14.26%). Impacts of epidemics on yield ranged from 18.4 to 8,192.5 kg.ha-1.year-1, with a median of 935.2 kg.ha-1.year-1. The higher economic impact of the disease was observed on Silk cultivar with a median loss of US$ 910.5 ha-1.year-1. Overall, estimated losses increased on average by US$ 109.8 ha-1.year-1 at each 1% of incidence. Aggregation of FW was detected by all analytical methods in 13 fields (1 of Cavendish, 11 of Pome and 1 of Silk). In the other 17 fields, at least one analytical method did not reject the null hypothesis of randomness. One field (5 ha), composed of six plots, was selected for spatial and temporal studies during two years with bi-monthly assessments. A sigmoidal curve represented the FW progress and the Gompertz model best fitted disease progress. The level of aggregation varied over time, and evidence of secondary infection to neighboring and distant plants were detected. FW is a widespread problem in Brazil and yield losses can be of high magnitude. Epidemiology-based management strategies can now be better established.

This study addresses the causal links between external factors and the main hydro-climatic variables. There is a gap in the literature on the description of a complete chain in addressing the structures of direct causal links of solar activity on the terrestrial variables. This is why, the present study uses the extensive facilities of the application of information theory in view of recent advances in different fields. Also, by other methods (e.g. neural networks) first are tested the existence non-linear links of solar-terrestrial influences on hydro-climate system. The results are promising related to the solar impact on terrestrial phenomena which is discriminant in space-time domain. The implications prove robust for determining the causal measure of climate variables under direct solar impact which makes it easier to consider solar activity in climate models, by appropriate parametrizations. This study found that hydro-climatic variables are sensitive to solar impact only for certain frequencies (periods) and these have a coherence with the Solar-Flux only for some lags of the Solar-Flux (in advance).

The 2019-2020 summer wildfire event on the east coast of Australia was a series of major wildfires occurring from November 2019 to end of January 2020 across the states of Queensland, New South Wales (NSW), Victoria and South Australia. The wildfires were unprecedent in scope and the extensive extend of the wildfires has caused smoke pollutants transported not only to New Zealand but across the Pacific Ocean to South America. At the height of the wildfires, smoke plumes were injected into the stratosphere at height up to 25km and hence transported across the globe. Based on meteorological and air quality simulation using WRF-Chem model, air quality monitoring data collected during the bushfire period and remote sensing data from MODIS and CALIPSO satellites, the extend of the wildfires and the pollutant transport, and their impacts on air quality and health on exposed population in NSW can be analysed. The results showed that WRF-Chem model using Fire Emission Inventory from NCAR (FINN) predicts the dispersion and transport of pollutants and the predicted concentration of PM2.5 and other pollutants from wildfires reasonably well when compared with ground-based and satellite data. The impact on health endpoints such as mortality, respiratory and cardiovascular diseases hospitalisation across the modelling domain is then estimated. The estimated health impact is comparable with previous study based only on observation data, but the results in this study provide much more detailed spatially and temporally with regards to the health impact from the 2019-2020 wildfire.

Dust storms originating from Central Australia and western New South Wales frequently cause high particles concentration at many sites across New South Wales, both inland and along the coast. This study focussed on a dust storm event in February 2019 which affect air quality across the state as detected at many ambient monitoring stations in the Department of Planning, Industry and Environment (DPIE) air quality monitoring network. The WRF-Chem (Weather Research and Forecast Model – Chemistry) model is used to study the formation, dispersion and transport of dust across the state of New South Wales (NSW, Australia). Wildfires also happened in northern NSW at the same time of the dust storm in February 2019, and their emissions are taken into account in WRF-Chem model by using Fire Inventory from NCAR (FINN) as emission input. The model performance is evaluated and is shown to predict fairly accurate the PM2.5 and PM10 concentration as compared to observation. The predicted PM2.5 concentration over New South Wales during 5 days from 11 to 15 February 2019 is then used to estimate the impact of the February 2019 dust storm event on three health endpoints namely mortality, respiratory and cardiac diseases hospitalisation rates. The results show that even though as the daily average of PM2.5 over some parts of the state, especially in western and north western NSW near the centre of the dust storm and wild fires, are very high (over 900 µg/m3), the population exposure is low due to the sparse population. The top five Statistical Area Level 4 regions with the most impact in term of mortality, respiratory diseases hospitalisation and cardiac disease hospitalisation are Far West and Orana, Newcastle and Lake Macquarie, New England and North West, Sydney – Inner South West and either Central Coast (mortality) or Sydney – Parramatta (respiratory diseases hospitalisation) or Sydney – Inner West (cardiac diseases hospitalisation). Generally, the health impact is similar in order of magnitude to that caused by biomass burnings events from wildfires or from hazardous reduction burnings (HRBs) near populous centres such as in Sydney in May 2016. One notable difference is the higher respiratory diseases hospitalisation for this dust event (161) compared to fire event (24).

Context: COVID-19 pandemic and the Lockdown implemented as a measure to contain the virus spread has taken a toll over the psychological well-being of the people especially the young adults, the confinement along with the environment of a highly infectious pandemic around the induvial are put under great stress.Aims: The current study aims to assess the psychological impact and perceived stress due to COVID-19 lockdown in Young Adult population of India.Settings and Design: It is a cross sectional, observational study.Methods and Material: The survey was conducted using Google forms involving snowball sampling technique which obtained 267 responses in total. (IES-R) and (PSS) scales were used for the study.Statistical analysis used: Descriptive analysis were performed on the sociodemographic parameters and the comparison of means were done by Chi-square test in SPSS Statistic 21.0 (IBM SPSS Statistics, New York, United States). Results: The mean IES-R and PSS scores obtained for the population in this study was 25.64±18.95 and 18.27±6.10 respectively. Out of the 267 respondents in total 61.4% (n=164) of them were males. Maximum of the respondents 62.54%(n=167) belonged to the age group of 18-23 with mean age being 23.14± 2.913. 92.5% of the respondents were unmarried and only 26.6% belonged to the rural part of India. Females, younger individuals were found to have higher IES-R and PSS scores. Conclusions: There is significant psychological burden and stress on the young Indian population with females and younger individuals particularly students are the most vulnerable population.

Silty clay modified by fly ash and crumb rubber is a kind of sustainable subgrade filler which has good anti-freeze-thaw resistance stability but wake vibration isolation performance. The objective of this study was to improve the vibration isolation of the modified soil and investigate the vibration isolation effect of the composite subgrade structure of XPS plates and the modified soil by indoor impact test. First, the vibration isolation performance of silty clay, modified soil and composite subgrade structure was respectively evaluated. Second, the effect of XPS plate’s thickness and vibration intensity on the vibration performance of the composite subgrade structure were evaluated. Third, the vibration isolation performance of the test groups under the condition of freeze-thaw cycles was assessed. The results show that the vibration isolation performance of subgrade can be effectively improved by setting XPS plates. The composite subgrade structure has certain vibration isolation effect, especially in vertical direction. Considering vibration isolation performance and costs, 5cm is the optimum XPS plates thickness. The composite subgrade structure has great vibration isolation performance under the condition of freeze-thaw cycles, so it is suitable to be applied in road subgrade in seasonal frozen regions.

In this study, we first present a time-fractional L$\hat{e}$vy diffusion equation of the exponential option pricing models of European option pricing and the risk-neutral parameter. Then, we modify a particular L$\hat{e}$vy-time fractional diffusion equation of European-style options. Introduce a more general model from the models based on the L$\hat{e}$vy-time fractional diffusion equation and review some recent findings regarding of the Europe option pricing of risk-neutral free.

The construction industry is responsible for 40 to 45% of primary energy consumption in Europe alone. Therefore, it is essential to find new materials with a lower environmental impact in order to attain sustainable housing. This study aims to determine and compare the environmental impact of two clay samples forming a basis for the manufacture of traditional brick, a standard material in building construction; traditional red clay brick and a brick based on clay mixed with a biological ingredient. The samples of fired clay were manufactured at the laboratory scale, the results being valid exclusively as indicators for the extrapolation of the analysis to other studies. The results of the environmental impact of these formulations have been examined through an evaluation of life-cycle analysis (LCA), observing that the incorporation of biological pore forming agents led to a decrease of around 15 to 20% of all impact categories studied. Thus, the suitability of using biological-based additives in clay bricks was confirmed both for their constructive characteristics (lighter material) and increased energy efficiency (better thermal insulation) considering the environmental point of view.

As the natural resources are getting exhausted, the concept of sustainable development of region has received increasing attentions, especially for resource-dependent cities. In this paper an innovative method that emergy analysis and IPAT (Human Impact Population Affluence Technology) model were combined in order to analyze the quantitative relationship of economic growth and energy consumption and further to evaluate its overall sustainability level. Taiyuan, a traditional, resource-dependent city in China, is selected as the case study region. The main results show that total emergy of Taiyuan increased from 9.023× 1023 sej in 2007 to 9.116× 1023sej in 2014, with 38% reduction on non-renewable emergy and 125% growth on imported emergy. Regional emergy money ratio (RMB) was reduced by 48% from 5.31× 1013sej/$ in 2007 to 2.74× 1013sej/$ in 2014, indicating that the increasing speed of consuming resources and energy was faster than the increase of GDP, and that Taiyuan’s money purchasing power declined. The lower emergy sustainability index (ESI) indicates that Taiyuan was explored and produced large quantities of mineral resources, which puts more stress on the environment as a consequence, and that this is not sustainable in the long run. The IPAT analysis demonstrates that Taiyuan sticks to the efforts of energy conservation and environmental protection, in order to promote regional sustainable development, it is necessary to take an integrated effort. Policy insights suggest that resourceful regions should improve include energy and resource efficiency, optimizing energy and resourceful structure.

Background: This research examines how market knowledge and artificial intelligence (AI) interact in different market designs such as business-to-business (B2B) settings while taking emerging technologies and the changing digitalization landscape into account. Objective: The main goal is to understand how AI affects market knowledge in different market designs such as business-to-business (B2B) contexts, taking into account language barriers, practical difficulties, and the revolutionary effects on decision-making and customer interactions. Result: They underscore the transformative potential of artificial intelligence (AI) by highlighting how it shapes market knowledge, encourages customized approaches, and improves marketing efficacy in the business-to-business (B2B) space. Conclusion: In order to create a path for responsible AI integration in B2B marketing, the study concludes with recommendations for standardized terminology related to AI, practical insights into implementation challenges, and ethical issues.

Modular inversion on large operands is a time-consuming calculation used in elliptic curve cryptography. Its hardware implementation requires extensive hardware resources such as lookup tables and registers. We investigate state-of-the-art modular inversion algorithms and evaluate the performance and cost of the algorithms and their hardware implementations. We then propose a high-radix modular inversion algorithm aimed at short execution time and low hardware cost. We present a detailed radix-8 hardware implementation based on 256-bit primes in Verilog HDL and compare its cost performance with other implementations. Our implementation on the Altera Cyclone V FPGA chip uses 1227 ALMs (Adaptive logic modules) and 1037 registers. The modular inversion calculation takes 3.67 microseconds. The AT (Area time) factor is 8.30, outperforming other implementations. We also present an implementation of elliptic curve cryptography using the proposed radix-8 modular inversion algorithm. The implementation results also show that our modular inversion algorithm is more efficient in area time than other algorithms.

Our decisions today are part of an economic thinking which aims to fix problems and increase the efficiency of capital markets. Our economy is still largely approached as a system which is unre-lated to our environment or social returns like community well-being, social cohesion, equitable distribution of resources, etc. To fully incorporate sustainability into cost-benefit analysis, busi-nesses need to change their financial approach, showcasing how long-term thinking impacts prof-its. Money's value is not static and evolves with time and technology. We propose to introduce a variation of cost/benefit and present value calculations. Precise calculations reveal the benefits of long-term strategies over short-term profit focus. As financial systems evolve and technology advances, the accurate calculation of money's time value will continue to play a crucial role in shaping economic landscapes and guiding financial choices. In this article, we demonstrate that updated calculations could be a key enabler to visualize the benefits of long-term thinking or “patient finance” as compared to a short-term focus on profit. We introduce the concept and calculation of a Present Future Value (PFV).

Cloud data centers play a vital role in modern computing infrastructure, offering scalable resources for diverse applications. However, managing costs and resources efficiently in these centers has become a crucial concern due to the exponential growth of cloud computing. User applications exhibit complex behavior, leading to fluctuations in system performance and increased power usage. To tackle these obstacles, we propose the consolidation of virtual machines (VMs) to optimize resource utilization and reduce operational costs. This paper introduces a novel algorithm, leveraging the Modified Artificial Feeding Birds Algorithm (ModAFBA), to manage cost optimally in cloud data centers by reducing power usage while meeting service level agreements (SLAs). The ModAFBA algorithm, inspired by bird foraging behavior, dynamically adjusts solutions based on environmental changes. Comprehensive experimental evaluation demonstrates the superiority of the proposed algorithm in terms of power usage, migration count, and SLA compliance. Through this research, we aim to advance cost management and resource optimization in cloud data centers, providing a valuable tool for cloud service providers to enhance infrastructure efficiency and achieve cost savings.

Grain supply is the lifeblood of a country, and the stability of the supply chain is a crucial prerequisite for ensuring national grain security. This paper draws on the definition of resilience in physics and economics. It takes supply chain fracture resilience, impact resilience, and synergy resilience as the secondary indicators. It constructs a comprehensive evaluation indicator system of the grain supply chain resilience, measures the resilience indicator of China's grain supply chain from 1996 to 2021, and analyzes the role of supply, cost, exchange rate, and other external shocks in influencing the resilience of China's grain supply chain on this basis. The study found that the overall level of China's total grain supply chain resilience has been growing year by year and can be divided into three stages: low-level stabilization stage, continuous growth stage, and high-level stabilization stage. Grain supply chain fracture resilience has been growing steadily, grain supply chain impact resilience fluctuation is more obvious, and grain supply chain synergy resilience has been changing more gently. In the inquiry of the impact of external shocks on the resilience of China's grain supply chain, it was found that world grain exports and the RMB exchange rate have a significant positive impact on China's grain supply chain resilience level, while the international oil price has a significant negative impact. Based on this, the paper puts forward suggestions for ensuring stable production and supply in the grain market, improving the structure of foreign trade in grain, and actively coping with international commodity price shocks.

The efficiencies of conventional triple organic Rankine cycle (TORC) configurations are restricted by the low critical temperatures of common working fluids (＜320 °C). This paper proposes a high-temperature TORC system. A near-azeotropic mixture biphenyl/diphenyl oxide (BDO), which has stellar track record in the high-temperature ORC applications, is innovatively adopted as the top and middle ORC fluid simultaneously. The top cycle evaporation temperature is up to 400 °C in view of BDO’s high stability. Five conventional organic fluids are chosen for the bottom ORC. Fundamentals of the TORC system are illustrated, and mathematical models are built. Thermo-economic performance is investigated. The results hint that the optimal thermodynamic property is achieved on the use of benzene as the bottom fluid. The maximum thermal and exergy efficiencies are respectively 40.86% and 74.14%. The largest irreversible loss occurs inside the heat exchanger coupling the middle and bottom ORCs, accounting for above 30% of the total entropy generation. The levelized energy cost (LEC) is 0.0284 $/kWh. Given the same heat source condition, the TORC system can boost efficiency by 1.02% and drive down LEC by 0.0024 $/kWh compared with a BDO mixture-based cascade ORC.

Pacing strategy refers to the distribution of effort and speed throughout the race to achieve optimal performance. The study aims to understand whether the choice of pacing strategy in swimming depends on the length of competitions and how sex, age, and performance level influence this strategy. Participants were the finalists of the 800-meter and 1500-meter freestyle events at the elite and junior world championships in 2022-2023. Race outcomes and pacing parameters were compared between the two distances and across different groups of swimmers. Swimmers in both distances did not break world records. Pacing strategy generally followed a U-shape with significant differences in the frequency and duration of speed changes between the two distances. The 800-meter exhibited more frequent changes in acceleration while the 1500-meter events generally followed a more consistent time series pattern. There were differences in pacing strategies between males and females and between junior and elite swimmers. Swimmers closer to world records showed more consistent pacing patterns compared to those farther from records. The study suggests that pacing strategies are influenced by race distance, sex, age, and performance level. The research highlights the complex interplay between physiological and psychological factors that shape a swimmer's decision-making during a race.

The aim of this paper is to study the main areas in which Artificial Intelligence (AI) will impact the field of project management that relates to cost, risk and scheduling. The research model was based on a previous study of the ten project management knowledge areas presented in the PMI’s PMBOK 6th edition where project schedule-, cost- and risk management knowledge areas were identified as being the ones most likely to be affected by the development of AI. A group of experts that participated in the study agreed that AI will affect the project management profession in the future. Different elements of the three knowledge areas were considered to be affected more by AI than others. The schedule baseline is the element believed to be affected the most out of the project schedule management elements. For project cost management, the estimation of resource cost is believed to be affected the most. In the case of project risk management, the application of AI will have the strongest impact on the probability and impact format.

Energy poverty is a severe electrification challenge in Nigeria that has affected the country's economic development. The gap between electricity demand and supply continues to increase, and a large percentage of the populace affected are rural dwellers. With the prevailing electricity inaccessibility in rural, renewable energy sources (RES) present viable option for addressing rural electrification challenge. However, RES like solar have intermittency challenges since they are weather dependent. Thus, a hybrid microgrid system (HMS) comprising solar, hydropower and battery storage was sized as a feasible solution for electrifying rural areas in Nigeria. For the HMS sizing, a site was selected, whose residential load demand was estimated. The renewable energy generation potential for the selected resources at the site as well as grid reliability were assessed. Hybrid Optimisation Model for Multiple Energy Resources (HOMER) was used to size the HMS. An economic analysis was performed to determine the levelised cost of energy (LCOE) and net present cost (NPC). For the sized HMS, two scenarios were considered: off-grid and grid-connected HMS configurations. The results showed that the LCOE of the off-grid and grid-connected configurations are $0.101/kWh and $0.046/kWh, respectively. The NPC of the off-grid and grid-connected configurations are $227,307 and $145,282, respectively.

The recent happenings in the world such as flood and wild forest fire were as a result of climate effect as being envisaged by scientists. It is urgent now to adopt a source of energy that will eliminate this effect on our universe. Solar energy is the major energy means that is abundant which could be utilized. In this review, the prospects of solar energy exploration were studied in Nigeria which include assessments, economic viability and hybrid systems. Findings show higher potential in the North as compared to the Southern region. Additionally, potential of offshore solar energy system was simulated by considering 2002, 2003 and 2004 data sets from Era5-land base. Their monthly mean, seasonal changes and annual mean value were estimated. The algebraic annual solar radiance for 2002, 2003 and 2004 were 34,914.732 kWh/m2, 33,898.316 kWh/m2 and 34,338.324 kWh/m2. Suggestions were made due to the present status of solar energy utilization that will enhance its maximum usage and development. One of these is the establishment of a functioning financial scheme and the database for all renewable energy systems. When all these are put in place, the energy supply will increase, climate effect will be reduced, and the economy will be boosted.

The building and construction sector has a significant impact on CO2 emissions and pollutants released into the atmosphere, which contribute to climate change. The EPDB Directive mandates the achievement of a minimum energy class E for all residential buildings by 2030 and an energy class D by 2033. Particularly, in Italy, about 86% of the existing building stock predates the enactment of any energy laws and regulations, making it imperative to apply the energy efficiency interventions. This paper provides a support decision tool for the identification of standardized interventions on the envelope, air conditioning system and domestic hot water production. The study is focused on a specific construction period class (1976 - 1990) in six different climatic zones. The methodological approach is based on a cataloguing phase and definition of Ante Operam energy classes, on the case study identification, the energy requalification interventions identification, solutions simulations, cost estimation. By simulating standardized interventions for each climatic zone, a range of possible combinations will be identified. The most advantageous ones will be determined based on a cost-benefit analysis, considering the potential class jump achieved. The research result is a matrix of energy efficiency interventions that is applicable to each climatic zone and can be extended to the existing housing stock.

Low-cost particulate matter (PM) sensors offer an excellent opportunity to improve our knowledge about this type of pollution. Their size and their cost, which support multi-node network deployment, along with their temporal resolution, enable them to report fine spatio-temporal resolution for a given area. These sensors have known issues across performance metrics. Generally, the literature focuses on the PM mass concentration reported by these sensors but some models of sensors also report particle number concentrations (PNC) segregated into different PM size bins. In this study, 8 units of each Alphasense OPC-R1, Plantower PMS5003 and Sensirion SPS30 have been exposed, under controlled conditions, to short-lived peaks of PM generated using two different combustion sources of PM, exposing the sensors to different particle size distributions to quantify and better understand the low cost sensors performance across a range of relevant environmental ranges. The PNC reported by the sensors were analysed. This study showed that there is added value in directly using the PNC reported by the sensors instead of the mass concentrations, which could aid the efforts to calibrate these sensors to a known accuracy. It demonstrated that all sensors tested here could track the fine temporal variation of PNC, that the Alphasense OPC-R1 could closely follow the variations of size distribution between the two sources of PM, and it showed that particle size distribution and composition are more impactful on the sensors measurement than relative humidity.

In recent years, the discharge of various emerging pollutants, chemicals and dyes in water and wastewater has represented one of the prominent human problems. Since water pollution is directly related to human health, highly resistant and emerging compounds in aquatic environments will pose many potential risks to the health of all living. Therefore, water pollution is a very acute problem that has constantly increased in recent years with the expansion of various industries. Consequently, choosing efficient and innovative wastewater treatment methods to remove contaminants is crucial. Among advanced oxidation processes, electrochemical oxidation (EO) is the most common and effective method for removing persistent pollutants from municipal and industrial wastewater. However, there are still many gaps despite the great progress in using EO to treat real wastewater. This is due to the lack of comprehensive information on the operating parameters which affect the process and its operating costs. In this paper, among various scientific articles, the impact of operational parameters on the EO performances, a comparison between different electrochemical reactor configurations, and a report on general mechanisms of electrochemical oxidation of organic pollutants have been reported. Moreover, an evaluation of cost analysis and energy consumption requirements have also been discussed. Finally, the combination process between EO and another important advanced oxidation technology, PEC, called photoelectrocatalysis (PEC), has shortly been discussed and reviewed. This article showed that there is a direct relationship between important operating parameters with the amount of costs and the final removal efficiency of emerging pollutants. Optimal operating conditions can be achieved by paying special attention to reactor design, which can lead to higher efficiency and more efficient treatment. The rapid development of EO for removing emerging pollutants from impacted water and its combination with other green methods can result in more efficient approaches to face the pressing water pollution challenge.

The solar water heater must be integrated into future residential buildings as the main energy source, which will subsequently reduce the energy cost of water heating. An original configuration for efficient Domestic Hot Water "DHW" storage tank is developed and experimentally evaluated under Saharan climate. This novel DHW configuration includes a hybrid (solar and electric) energy system with a flat plate solar collector coupled with an electric heater. Additionally, phase change material "PCM" mixture that is composed of paraffin wax and animal fat with a melting temperature between 35.58°C and 62.58°C and latent heat between 180 and 210 kJ/kg is integrated into this novel tank configuration. The experimental results indicated that hot water production by using latent heat storage could be economically attractive. In this proposed configuration, one liter of hot water may cost around 0.1362 DZD/liter (i.e., 0.00096 US$/liter) compared to 0.4431 DZD/liter for the conventional water heater, an average energy cost savings of 69.26%. On a yearly basis, the average energy cost savings may reach up to 80.25% if optimal tilt for the solar collector is adopted on a monthly basis. The flat plate collector may be vulnerable to convective heat transfer, and therefore, other solar collectors such as vacuum tube collectors may provide enhanced energy performance.

The increasing regulatory pressure to monitor and reduce GHG emissions and air pollutants requires cost-effective methods for their surveillance. The most common technique used for scientific investigations on gas concentration monitoring in barns are accurate but expensive and with a complex maintenance. This research study analysed the potential use of a low-cost portable measurement devices for the measurement of ammonia (NH3) and carbon dioxide (CO2) concentrations in an open dairy barn. A comparison between gas concentrations acquired at different heights from the floor by using portable devices and those acquired by a photoacoustic infrared multigas spectroscope (i.e., reference measurement) in the same sampling locations was carried out to determine the precision of the low-cost portable devices. The performance of low-cost portable devices was statistically analysed by the application of the one-way analysis of variance, correlation analysis and regression analysis. The results showed a significant difference between gas concentration values at various heights from the floor for both NH3 and CO2. The correlations between concentrations acquired by the low-cost portable device and the INNOVA were statistically significant (r=0.83; P<0.001) for gas concentrations monitored at 0.4 m from the floor. Compared with the reference measurement device, the low-cost devices were effective in the monitoring of NH3 concentrations at 0.40 m from the floor though it underestimated them in the barn at increasing of the height from the floor, whereas the device was not adequate for CO2 concentrations. In detail, the relative measurement error of the low-cost devices compared to INNOVA was reduced close to the floor during NH3 concentration measurements. Within these limitations, this device could be useful for monitoring NH3 concentration in the barn and to assess variation in NH3 concentrations mainly related to the animal occupied zone. Further efforts are needed in this field of research to identify low-cost device that could simplify emission estimation from open dairy barns.

Large quantities of real-time particle data are becoming available from low-cost particle monitors. However, it is crucial to determine the quality of these measurements. The largest network of monitors in the United States is maintained by the PurpleAir company, which offers two monitors: PA-I and PA-II. PA-I monitors have a single sensor (PMS1003) and PA-II monitors employ two independent PMS5003 sensors. We determine a new calibration factor for the PA-I monitor and revise a previously published calibration algorithm for PA-II monitors (ALT-CF3). From the PurpleAir API site, we downloaded 83 million hourly average PM2.5 values in the PurpleAir database from Washington, Oregon, and California between January 1, 2017 and September 8, 2021. Daily outdoor PM2.5 means from 194 PA-II monitors were compared to daily means from 47 nearby Federal regulatory sites using gravimetric Federal Reference Methods (FRM). We find a revised calibration factor of 3.4 for the PA-II monitors. For the PA-I monitors, we determined a new calibration factor (also 3.4) by comparing 26 outdoor PA-I sites to 117 nearby outdoor PA-II sites. These results show that PurpleAir PM2.5 measurements can agree well with regulatory monitors when an optimum calibration factor is found.

Green credit policy as an important tool to guide China's sustainable economic development, how to effectively play the function of capital deployment and improve the efficiency of industrial green innovation is an important issue facing the construction of ecological civilization. This paper uses China's Green Credit Guideline introduced in 2012 as a quasi-natural experiment , based on relevant panel data of industries from 2007 to 2018, uses the Super-SBM model including non-expected output to measure the green innovation efficiency of 35 industries in China, and constructs the PSM-DID model to explore how green credit policy impact on the green innovation efficiency of heavily polluted industries, the results show that : green credit policy significantly contributes to green innovation efficiency of heavily polluted industries with a lag. Further study finds that green credit policy pushes heavily polluted industries to improve green innovation efficiency by increasing financing cost and R&D investment; meanwhile, the heterogeneity test shows that the higher the state-owned share of industry, the greater the promoted effect of green credit policy on green innovation efficiency of heavily polluted industries. Finally, in order to accelerate the implementation of green credit policy and promote the green innovation efficiency of heavily polluted industries, relevant countermeasures are proposed from three aspects: banks, enterprises and government.

This study aims to develop a performance evaluation system that can facilitate performance evaluation at region, hospital, and department levels to enable better cost management for sustainable development. A multi-level system of performance evaluation informs a hierarchical assessment of cost management from regions to hospitals to departments using diagnosis-related group (DRGs). Various metrics are developed employing the variances between targets and actuals where targets are determined from two perspectives: benchmarking using external regional prices and change management using internal data. Targets for the latter are statistically based and specifically incorporate variability. The model is applied to two hospitals, twenty departments, nine DRGs and 1071 inpatients. The analyses indicate that the approach can provide a practical evaluation tool that allows for particular characteristics at multiple levels. The system provides macro-micro and external-internal perspectives in performance, enabling high-level variances to be decomposed thereby identifying sources of performance variability and financial impact.

Land and property development process include a series of multifaceted activities ranging from purchasing to converting it for development purposes and everything in between. The process itself encompasses multiple stakeholders, drivers, and contributions from diversified public and private actors and transaction cost arises out of their complex interaction. Transaction costs incurred during any kind of human interactions (i.e. transactions). Every actor involved in the process wishes to maximize his achievement under various constraints and hence institutional arrangement (i.e. set of humanly devised rules to administer the constraints) is necessary for efficient management of the development process. Therefore, to devise an optimum outcome out of economic and social transactions in the property development process, cooperative and competitive relationships between individuals should be understood from a broader socio-political and governance structure. In this research, it is critically argued that land and property development process should implicate a multifaceted set of formal and informal rules or institutional arrangement to govern the intrinsic interaction, action and thereby reducing the related transaction cost. The argument is further reproachfully evaluated and implicated in the urban development process through the myopic lens of Transit-oriented development (TOD) pathway. A vigilant combination of descriptive and explanatory research approach is adopted to analyze the connection between theory and practice.

Climate change from anthropogenic activities represents a serious threat to life in earth. Agriculture releases significant emissions of greenhouse gases (GHG), but also offers low-cost opportunities to mitigate GHG emissions. This paper assesses agricultural GHG emissions in Aragon, one important and representative region for agriculture in Spain. The Marginal Abatement Cost Curve (MACC) approach is used to analyze the abatement potential and cost-efficiency of mitigation measures under several scenarios, with and without taking into account the interaction among measures and their transaction costs. The assessment identifies the environmental and economic outcomes of different combinations of measures. Moreover, we develop future mitigation scenarios for agriculture toward the year 2050, in order to assess the impacts on GHG emissions. Results highlight the importance of assessing biophysical processes in mitigation measures, and the significant effects of interactions between measures that reduce the abatement potential and worsen the cost-efficiency. The inclusion of transaction costs provides a better ranking of measures and a more accurate estimation of implementation costs. The scenario analysis shows how the combinations of measures could reduce emissions and promote sustainable agriculture in the future. Keywords: Climate change; mitigation measures; biophysical processes; cost-efficiency; abatement costs; transaction costs; policy scenarios.

Uniform illumination is a key requirement in different research fields. However, this requirement is often difficult to achieve when high intensity is required at the same time. Recent advancements in LED lamps allow nowadays for compact and economical solutions. In this work we present a suitable solution for various laboratory purposes requiring stable, uniform and high intensity illumination. The system is composed of four identical high power white LED arrays of 30 mm diameter each, placed on a supporting and cooling structure having a minimum volume of 26 cm x 26 cm x 8 cm. A numerical model has been developed, based on a ray tracing software, in order to simulate the performances. These have then been experimentally validated with measurements of the power density map, carried out with a 1% uncertainty pyranometer. Data show that the built system is very stable over time and provides an illumination uniformity higher than 98%, on a surface of 50 mm radius, which reduces to 95% on a surface of 75 mm radius. The power density can be adjusted in the 390-1360 W m-2 range, not affecting uniformity.

Users of meteorological forecasts are often faced with the question of whether to make a decision now based on the current forecast or whether to wait for the next and hopefully more accurate forecast before making the decision. One would imagine that the answer to this question should depend on the extent to which there is a benefit in making the decision now rather than later, combined with an understanding of how the skill of the forecast improves, and information about the possible size and nature of forecast changes. We extend the well-known cost-loss model for forecast-based decision making to capture an idealized version of this situation. We find that within this extended cost-loss model, the question of whether to decide now or wait depends on two specific aspects of the forecast, both of which involve probabilities of probabilities. For the special case of weather and climate forecasts in the form of normal distributions we derive a simulation algorithm, and equivalent analytical expressions, for calculating these two probabilities. We apply the algorithm to forecasts of temperature and find that the algorithm leads to better decisions relative to three simpler alternative decision-making schemes. Similar problems have been studied in many other fields, and we explore some of the connections.

The concept of geostationary VHTS (Very High Throughput Satellites) is based on multibeam coverage with intensive frequency and polarization reuse in addition to the use of larger bandwidths in the feeder links, in order to provide high capacity satellite links at a reduced cost per Gbps in orbit. The dimensioning and design of satellite networks based on VHTS imposes the analysis of multiple trade-offs to achieve an optimal solution in terms of cost, capacity and figure of merit of the user terminal. In this paper, we propose a new method for sizing VHTS satellite networks based on an analytical expression of the forward link CINR (Carrier-to-Interference-plus-Noise Ratio) that is used to evaluate the trade-off of different combinations of system parameters. The proposed method considers both technical and commercial requirements as inputs including the constraints to achieve the optimum solution in terms of the user G/T, the number of beams and the system cost. The cost model includes both satellite and ground segments. Exemplary results are presented with feeder links using Q/V bands, DVB-S2X and transmission methods based on CCM and VCM (Constant and Variable Coding and Modulation, respectively) in two scenarios with different service areas.

Vulnerability is the big issue of the small inland urban centers exposed to the risk of depopulation. In the climate and in the context of an increasing seismic risk in the center-northern part of Italy, seismic vulnerability can become the determinant cause of the final abandonment of a small town. In some Italian regions, as well as Emilia Romagna, municipalities are implementing seismic vulnerability reduction policies based on the Emergency Limit Condition that has become a basic reference for ordinary land planning. This study proposes a valuation planning approach to the seismic vulnerability reduction carried out within the general planning framework concerning the Faentina Union, a group of five small towns located in the south-western part of the Province of Ravenna, Italy. The approach consists of three main stages: knowledge – the typological, constructive and technological description of the buildings specifically concerning their vulnerability degree; interpretation – the analyses aimed to outline a range of hypotheses about the damages in case of seism; planning – identifying the works intended to definitely reduce the vulnerability of the buildings. This stage includes a cost modelling tools aimed at outlining the trade-off between the extension and the intensity of the vulnerability reduction works, given the budget.

In the U.S. 44% of low-income households struggle to pay their utility bills, affecting their ability to afford necessities such as food and health expenses. Several government and utility funded energy efficiency programs exist to assist those experiencing energy insecurity. In Salt Lake City, Utah, there is a high demand for, but low availability of, energy efficiency services in underserved neighborhoods creating an opportunity for creative community-based programs to fill this inherent gap. This pilot project, involving the exchanging of LED bulbs in Salt Lake City, highlights the development of a community-based energy efficiency program that aims to bring energy savings to a uniquely targeted portion of the city and determines its feasibility in addressing energy insecurity at a larger scale. Through the 8-month project duration, 1,432 bulbs were exchanged at 23 events reaching 181 households in low-income areas. Through a year of use, these bulbs are estimated to save residents approximately 18,219 USD in electricity bills and reduce CO2 emissions from power plants by 122.23 metric tons, in addition to a savings of 4,400 USD in social cost of carbon as defined by the U.S. Environmental Protection Agency. Since this pilot reached less than 1% of households, we extrapolated a reach of 2%, 5%, and 7.5% and found substantial potential decreases in power plant emissions and financial savings. As this project is ongoing and being expanded, we discuss relevant findings that will help shape future community-based models so that they are appropriately deployed and more effective in alleviating local energy insecurity.

Background: New open-source electric-grid planning models have the potential to improve power system planning and bring a wider range of stakeholders into the planning process for next-generation, high-renewable power systems. However, it has not yet been established whether open-source models perform similarly to the more established commercial models for power system analysis. This reduces their credibility and attractiveness to stakeholders, postponing the benefits they could offer. In this paper, we report the first model intercomparison between an open-source power system model and an established commercial production cost model. Results: We compare the open-source Switch 2.0 to GE Energy Consulting’s Multi Area Production Simulation (MAPS) for production-cost modeling, considering hourly operation under 17 scenarios of renewable energy adoption in Hawaii. We find that after configuring Switch with similar inputs to MAPS, the two models agree closely on hourly and annual production from all power sources. Comparing production gave a coefficient of determination of 0.996 across all energy sources and scenarios, indicating that the two models agree on 99.6% of the variation. For individual energy sources, the coefficient of determination was 69–100. Conclusions: Although some disagreement remains between the two models, this work indicates that Switch is a viable choice for renewable integration modeling, at least for the small power systems considered here. Although some disagreement remains between the two models, this work indicates that Switch is a viable choice for renewable integration modeling, at least for the small power systems considered here.

This paper analyses and evaluate the precision and the accuracy the capability of low-cost terrestrial photogrammetry by using many digital cameras to construct a 3D model of an object. To obtain the goal, a building façade has imaged by two inexpensive digital cameras such as Canon and Pentax camera. Bundle adjustment and image processing calculated by using Agisoft PhotScan software. Several factors will be included during this study, different cameras, and control points. Many photogrammetric point clouds will be generated. Their accuracy will be compared with some natural control points which collected by the laser total station of the same building. The cloud to cloud distance will be computed for different comparison 3D models to investigate different variables. The practical field experiment showed a spatial positioning reported by the investigated technique was between 2-4cm in the 3D coordinates of a façade. This accuracy is optimistic since the captured images were processed without any control points.

The implementation of green logistics can reduce the increasingly serious environmental damage and improve the operating efficiency of logistics enterprises. In this paper, through quantitative calculation of logistics negative external costs, including traffic accidents, atmospheric pollution, noise pollution, and pollution caused by packaging, we build accounting standards to calculate the negative external cost of logistics enterprises that mainly generate pollution from transportation and packaging. Then, assuming the impetus provided by government implementing incentive mechanisms in a free market economy, we establish a ‘Logistics Negative External Cost Internalizing’ model, which quantitatively analyzes the relationships among freight rate, freight volume, investment of enterprise for implementing green logistics, and the subsidy given by government, under the comparison of the two modes ‘enterprise operates first’ and ‘government subsidizes first’. The results show that the introduction of ‘government subsidizes first’ incentive mechanism and increasing freight rate are helpful in promoting the process of logistics market greening.

Background: COVID-19 pandemic has pulled us all a few steps back, were we never shake hands or hug each other when we meet our friends and family after a gap, but instead we greet them by saying Namaste and joining our hands together. As we all know, COVID-19 spreads through air and the only way to shield ourselves is by maintaining a safe distance from one another. Methodology: In order to conduct a meta-analysis on the number of COVID-19 cases in Kerala and India, the data was retrieved from various sites hosted by the government bodies. The data for analysis was collected from May 2020 to July 2020. The average number of days required to reach every 5000 fresh cases were also calculated using this data. COVID-19 has affected all the economy holistically regardless of financial, behavioral, or societal aspects. Conclusion: Lifting of the lockdown in a step by step process keeping in mind the necessities for the nation was a thoughtful act, but the people who mistook this opportunity and did not remain in quarantine after coming from abroad was recognized as the reasons behind the sudden and uncontrolled rise in the number of COVID-19 cases in Kerala, India. The government authorities had no other option but to lift the restrictions to reduce the economic burdens that had already affected the daily wage worker and farmers prompting them to give up their lives.

Background/Objectives: The COVID-19 pandemic has had a profound impact on mental health worldwide, especially exacerbating Eating Disorders (EDs). This study aims to evaluate changes in the presentation and severity of EDs before and after the pandemic at the Regional Residential Center "Mariconda" in Salerno. Methods: This retrospective cohort study analyzed data from 162 patients admitted to the Regional Residential Center "Mariconda" between December 2018 and December 2023. The onset of pandemic restrictions in mid-2020 was the dividing line for the pre-COVID and post-COVID groups. Demographic and clinical data were collected, including age, gender, education level, previous hospital admissions, admission diagnosis, body mass index (BMI), and comorbidity with other psychiatric illnesses. Results: Of the 162 subjects, 115 were admitted post-pandemic. The post-pandemic group was significantly younger, had lower education levels, more frequent previous hospitalizations, and a higher frequency of severe comorbidity with other mental illnesses compared to the pre-COVID group. No significant differences were found between the groups regarding gender distribution, initial diagnosis upon admission, average and length of hospital stay. Conclusion: The COVID-19 pandemic has exacerbated the presentation and severity of EDs, particularly among younger individuals with lower educational attainment. These findings underscore the urgent need for targeted, integrated treatment approaches for EDs, especially global crises. Further research is essential to understand the long-term effects of the pandemic on EDs and to refine treatment strategies to better support affected individuals.

In an era of relentless technological change, accelerating the pace of innovation has become a critical imperative for organizations seeking to maintain a competitive edge and drive meaningful societal progress. Yet the complex, multifaceted innovation process - from ideation to commercialization - is often plagued by bottlenecks and challenges that can significantly delay the time-to-market for groundbreaking new products, services, and business models. This pioneering article represents an outstanding contribution to the burgeoning field of innovationology, offering a comprehensive, evidence-based playbook for shortening the innovation lifecycle. Drawing on an extensive review of over 180 cutting-edge academic sources, the paper maps the key stages of the innovation process - idea generation, concept development, prototyping, testing, and launch - and illuminates cutting-edge strategies that innovators can deploy to overcome common barriers and drive transformative innovations to market more quickly. From advanced idea generation techniques like design thinking and AI-powered creativity to innovative approaches to rapid prototyping, virtual experimentation, and data-driven commercialization, this seminal work equips scholars, practitioners, and policymakers with a transformative, globally-relevant toolkit for accelerating the innovation lifecycle. By synthesizing insights from a diverse array of industries and disciplines, the article establishes a new benchmark for innovationology research and empowers organizations to cultivate the conditions needed to translate visionary ideas into game-changing realities with unprecedented speed and efficiency. As the world grapples with ever-more complex, interconnected challenges, the ability to rapidly develop and scale breakthrough innovations has never been more crucial. This paradigm-shifting contribution to the field of innovationology represents a vital resource for driving the bold, disruptive changes needed to shape a more sustainable, equitable future.

Background: Amidst escalating climate change concerns, sustainable practices in healthcare logistics are critical. This study evaluates the ecological impact and efficiency of medical sample transportation using drones, combustion cars, and electric cars across diverse terrains and weather in Liechtenstein and Switzerland. Methods: We conducted a comparative analysis of CO2 emissions, transport distances, and delivery times for each transportation modality. Data collection involved multiple runs under various conditions, with CO2 emissions measured using onboard systems, and transport efficiency assessed via GPS tracking and time monitoring. Results: Combustion cars exhibited the highest average CO2 emissions at 159.5 grams per kilometer (g/km). Electric cars emitted significantly less, averaging 3.43 g/km, which is 2.15% of combustion car emissions. Drones were the most environmentally friendly, with an average emission of 0.09 g/km, representing 0.07% of combustion car emissions and 2.6% of electric car emissions. Drones also showed substantial transport efficiency, covering 17% shorter distances on flat terrain and 24% shorter in mountainous areas, with time savings ranging from 13% to 80%. Conclusion: This study highlights the potential of drone technology to revolutionize healthcare logistics by reducing environmental impact and optimizing operational efficiency. Integrating drones can significantly lower the carbon footprint, improve sample integrity, and enhance patient care, promoting a greener, more resilient healthcare system.

Artificial intelligence (AI) has emerged as a transformative force in retail operations, promising significant advancements in efficiency, customer engagement, and competitive positioning. This research explores the integration of AI in retail, focusing on its potential benefits, challenges, and future trends. Through a comprehensive analysis, the study identifies key motivations for AI adoption, such as enhancing operational efficiency, optimizing inventory management, and personalizing customer interactions. Retailers are increasingly leveraging AI to streamline processes and gain insights into consumer behavior, which allows for more strategic decision-making and improved customer experiences. However, the implementation of AI is not without its obstacles. High costs, the need for specialized skills, and concerns about data privacy and ethical use of AI present substantial challenges. The impact of AI on employment, including job displacement and the necessity for reskilling, is also a critical consideration. Future trends suggest that AI will continue to drive innovation in retail, with developments in AI-powered analytics, autonomous retail solutions, and sustainability initiatives shaping the industry’s future. The study highlights the importance of addressing these challenges and ethical considerations to fully realize the potential of AI in retail. The findings emphasize the need for retailers to adopt a strategic approach to AI integration, balancing innovation with responsible practices to achieve long-term success. Overall, this research provides valuable insights into the evolving role of AI in retail and offers a foundation for understanding the complexities and opportunities associated with its adoption.

The safety concerns associated with current lithium-ion batteries are a significant drawback. A short-circuit within the battery's internal components, such as those caused by a car accident, can lead to ignition or even explosion. To address this issue, a polymer shear thickening electrolyte, free from flammable solvents, has been developed. It comprises a star-shaped oligomer derived from a trimethylolpropane (TMP) core and polyether chains, along with the inclusion of 20 wt.% nanosilica. Notably, the star-shaped oligomer serves a dual function as both the solvent for the lithium salt and the continuous phase of the shear thickening fluid. The obtained electrolytes exhibit an ionic conductivity of 10-6 S cm-1 at 20 °C and 10-4 S cm-1 at 80 °C, with a high Li+ transference number (t+ = 0.79). A nearly thirtyfold increase in viscosity to a value of 1187 Pa s at 25 °C and a critical shear rate of 2 s-1 were achieved. During impact, this electrolyte could enhance cell safety by preventing electrode short-circuiting.

Dairy farming dates to the early Neolithic era in Europe, Middle East, Asia, and Africa. Over time, the industry has evolved from small ranches and farmhouses milk processing units to large, intensive dairy farms and big, high-tech processing units. Despite the industry’s contribution to the economy and food production, multiple studies have tainted cattle farming as a main contributor to climate change. This study explores literature in dairy farming and sustainable development goals (SDGs) to identify current scholarly developments since the formulation and adoption of SDGs in 2015 and themes for future research. This paper argues that dairy animals are not the source of sustainability shortfalls as depicted by multiple scholarly articles, but human processes associated with dairy farming. Data was analyzed using R package, Excel, NVIVO, and VoS Viewer. A review of literature shows that dairy farming and its contribution to sustainability has gained more intensive scientific interest since 2015. Moreover, livestock management, feed production and management, stakeholder’s management, logistics and supply chain management, and waste management are the sources of environmental adversities associated with dairy farming. Notably, these are human processes developed from commercialization of dairy farming and involve multiple stakeholders across the supply chain. While solutions are embedded within these processes, innovation emerges as a key driver of sustainability and source of opportunities to strengthen sustainability in dairy farming sector and achieve the sustainable development goals.

This study investigates green logistics strategies adopted by businesses to enhance sustainability within their supply chains, particularly focusing on reducing carbon footprints and environmental impacts associated with transportation and distribution activities. Through qualitative analysis and interviews with supply chain managers, industry experts, and sustainability officers, this research provides insights into motivations, strategies, challenges, and benefits related to green logistics practices. Key findings highlight the critical role of aligning green logistics with corporate sustainability goals. Advanced technologies such as telematics, GPS tracking, and data analytics are instrumental in optimizing transportation routes, reducing fuel consumption, and minimizing emissions. Additionally, strategies like using eco-friendly vehicles, energy-efficient warehouse operations, and implementing reverse logistics are identified as effective means to mitigate environmental impacts. Collaboration and partnerships with suppliers, logistics providers, and stakeholders are essential for sharing best practices and fostering joint initiatives. Despite benefits such as cost savings and enhanced corporate reputation, challenges include high initial investments, the absence of standardized metrics for environmental impact, and regulatory complexities. However, regulatory compliance also acts as a driver for green logistics adoption and presents opportunities for improving sustainability performance. The integration of sustainability goals with logistics strategies underscores the interdisciplinary nature of sustainable supply chain management. This holistic approach encompasses aspects of entrepreneurship, emotional intelligence, marketing, and supplier relationship management.

This research project aimed to address the growing concern about methane emissions from seaweed by developing a Convolutional Neural Network (CNN) model capable of accurately predicting these emissions. The study used PANDAS to read and analyze the dataset, incorporating statistical measures like mean, median, and standard deviation to understand the dataset. The CNN model was trained using the ReLU activation function and mean absolute error as the loss function. Model performance was evaluated through MAPE graphs, comparing the mean absolute percentage error (MAPE) between training and validation sets and between true and predicted emissions, and analyzing trends in yearly greenhouse gas emissions. The results demonstrated that the CNN model achieved a high level of accuracy in predicting methane emissions, with a low MAPE between the expected and actual values. This approach should enhance our understanding of methane emissions from Sargassum, contributing to more accurate environmental impact assessments and effective mitigation strategies.

The article aims to present the results of research on anaerobic digestion (AD) of waste wafers (WF - control) and co-substrate system - waste wafers and cheese (WFC - control), combined with digested sewage sludge. The aim of the study was to evaluate the physicochemical parameters of the chitosan/perlite (Ch/P; 3:1) carrier material and to verify its effect on the directions of change of the bacterial microbiome, removal kinetics of organic matter and AD process efficiency. The experiment was conducted in a laboratory, in a periodical mode of operation of bioreactors, under mesophilic conditions. The results of analyses of morphological-dispersive, spectroscopic, adsorption, thermal and microbiological properties confirmed that the tested carrier material can be an excellent option to implement in biotechnological processes, especially in anaerobic digestion. The microstructural properties of the carrier were influenced by both components: perlite determined the development of the specific surface area, while chitosan shaped the porosity of the system. The thermal properties were determined by the less heat-resistant component, present in a threefold higher weight proportion, i.e. chitosan. The evaluation of quantitative and qualitative changes in the genetic diversity of bacterial communities, carried out using Next Generation Sequencing (NGS), showed that the material has a modifying effect on the bacterial microbiome. Amount of bacteria from phyla Actinobacteria, Bacteroidetes, Campilobacterota, Chloroflexi, Euryarchaeota, Planctomycetes, and Proteobacteria decreased while Firmicutes, Synergistetes, and Thermotogae increased during the course of the experiment. The shapes of the FT-IR spectra indicated a dependence of the degradation rate on both the presence of the carrier and the cosubstrate system. Monitoring of the course of AD was carried out by measuring key parameters for the stability of the process: pH, VFA and VFA/TA ratio (volatile fatty acids/total alkalinity). As a result, an increase in the volume of biogas/methane produced, under the influence of the carrier, was recorded for WF-control by 12.05% and for WFC-control by 19.16%. The volume of methane for the WF-control increased from 351.72 m3 Mg-1 VS to 411.14 m3 Mg-1 VS, while for the cosubstrate sample it increased from 476.84 m3 Mg-1 VS to 518.08 m3 Mg-1 VS, confirming the validity of combining the respective cosubstrate with microbial carrier in anaerobic bioreactor.

Shared e-scooters are gaining popularity as a solution for first and last-mile connectivity in urban areas. This study conducted in Doha, Qatar, aimed to understand how weather and land use patterns affect e-scooter usage, utilizing various datasets. Given the novelty of micromobility systems in Qatar and their sensitivity to the local hot and humid climate, the study employed a comprehensive analytical approach. Analysis revealed that e-scooter demand spikes on weekends, particularly in the afternoon. Influential land use factors include proximity to universities and employment centers, while a higher presence of Qatari females in an area correlates with reduced usage, pointing to cultural influences. Weather conditions, especially humidity and extreme temperatures, significantly impact e-scooter demand. The study employed the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm for trip clustering and the Random Forest (RF) algorithm to model trip counts, considering temperature and humidity. Insights showed humidity as a critical predictor of hourly e-scooter trip counts. These findings underscore the importance of considering weather, land use, the relationship between land use characteristics and weather variations, and finally cultural factors in optimizing e-scooter services. By integrating data analysis and machine learning, the study offers valuable insights for enhancing urban mobility and transportation planning in GCC countries, promoting sustainable urban mobility.

The open data market is growing, driven by laws requiring governments to make data publicly available. However, the true value of this market lies in the products and services derived from open data. In Slovenia, the open data market has been growing since 2015, especially in the field of mobility. Platforms such as Avtolog.si facilitate the retrieval of vehicle data via vehicle identification numbers, while tocen.si, ojpp.si, and brezavta.si aggregate mobility data, focusing on sustainable and micromobile public transportation services. These platforms use open government data and private sector open data, and are freely accessible. This study examines the use of open data in the Slovenian mobility applications and similar solutions developed in other countries. By mapping these applications, we gain valuable insights into potential advances in the field, and provide a useful resource for future developers worldwide. By focusing on the socio-economic impact of these innovations, the study identifies methods for evaluating such applications. This approach highlights how transforming open data into user-friendly products and services can deepen our understanding of their broader societal and economic benefits. While open data is valuable in its own right, its full transformative potential is realized through innovative products that support sustainable lifestyles and promote environmentally conscious behavior.

As the largest contributor of carbon emissions in China, the building sector currently relies mostly on enterprises’ own effort to report carbon emissions, which usually results in challenges related to information transparency and workload for regulatory bodies, who play an otherwise vital role in controlling the building sector’s carbon footprint. In this study, we established a novel regulatory model known as QCEPM (Quantitative Carbon Emission Prediction Model) by conducting multiple linear regression analysis using the quantities of concrete, rebar, and masonry structures as independent variables, and the embodied carbon emissions of a building as the dependent variable. We processed the data in the detailed quantity list of 20 multi-story frame-structure buildings and fed them to QCEPM for solution. Comparison of the QCEPM-calculated results against the time-consuming and error-prone manual calculation results suggested a mean absolute percentage error (MAPE) of 2.26%. Using this simplified model, regulatory bodies can efficiently supervise the embodied carbon emissions in multi-storey frame structures by setting up carbon quota for a project in its approval stage, allowing the construction enterprise to carry out dynamic control over the audited three most important building materials throughout a project’s planning and implementation phase.

The review emphasizes the importance of the petroleum refining industry in Iraq, highlighting the environmental obstacles it presents. The industry's significant role in the country's economy necessitates a crucial evaluation and improvement of the environmental impact of oil refining in Iraq. The study scrutinizes the progression of crude oil refining techniques, from basic distillation to advanced technologies like vacuum distillation, thermal cracking, and catalytic cracking, all of which are associated with environmental damage. The refining processes produce hazardous gases such as carbon dioxide, carbon monoxide, sulfur oxides, and nitrogen oxides. Refineries also consume substantial amounts of water and discharge effluent, causing environmental issues. It highlights that significant solid waste is produced throughout the refining process, contributing to the environmental difficulties caused by the petroleum industry in Iraq. The paper proposes several approaches to enhancing the environmental performance of Iraq's petroleum refining processes through a comprehensive environmental impact assessment, focusing on identifying problematic areas, measuring environmental damage, and proposing solutions. It provides insights into the types of waste generated in Iraqi refineries, such as polluted hydrocarbon liquids, and highlights the environmental impact of leaks and spills from oil and byproducts, affecting groundwater and soil quality. The paper suggests reusing consumable auxiliary agents and mineral elements in other industrial fields to minimize waste generation, as well as employing advanced technologies like artificial intelligence and machine learning for efficient refining processes and reduced effluents. By offering suggestions to policymakers, industry stakeholders, and environmental regulators in Iraq, the study aims to inform sustainable practices and environmental management strategies within the petroleum refining sector, contributing to improved environmental outcomes and sustainable development in the industry.

Wearable antennas are increasingly vital in modern healthcare, facilitating continuous monitoring and communication for medical purposes. These antennas are integrated into wearable devices, allowing for the transmission and reception of vital health data, such as heart rate, body temperature, and blood pressure, to medical professionals or monitoring systems in real time. This instantaneous data transmission enables timely interventions, thus improving the quality of healthcare services. This review aims to comprehensively synthesize recent research from reputable databases, focusing on the functionality and effectiveness of wearable antennas in medical applications. The primary objectives of each study were categorized, including antenna design and integration, the performance of medical wearable antenna technology, safety considerations in wearable antenna biomedicine, and the use of antennas in clinical applications and their impact. This synthesis provides valuable insights for researchers and practitioners aiming to develop and deploy wearable antennas in medical settings, ultimately enhancing healthcare delivery and patient outcomes.

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

In the face of 21st-century environmental challenges, including climate change, migration pressures, and waste disposal issues, certain healthcare sectors, notably dentistry, pose a significant global environmental footprint with concerns about carbon emissions and waste production. This scoping review delves into the paradox that healthcare, while dedicated to safeguarding health, inadvertently contributes to environmental degradation through waste accumulation and disposal. The analysis of relevant pertinent literature emphasizes the imperative for an environmentally sustainable approach to dental waste measurement, disposal methods, and comprehensive education for stakeholders. Aligned with the World Conference on Sustainable Development and the United Nations' 2030 Agenda, the study adhered to PRISMA-ScR scoping review guidelines and Institute of Medicine's recommendations. The study utilized Medline/PubMed and international organization websites for data retrieval, employing Boolean operator queries in Medline. A systematic literature analysis identified six cross-sectional studies (2004-2018) highlighting variations in dental waste composition due to the lack of standardized identification methods. This heterogeneity underscores the need for a proposed research model, emphasizing comprehensive methodologies and healthcare staff training in waste management. The study advocates for environmentally conscious dental practices, contributing to broader sustainable healthcare goals through sharing of a research protocol for dental solid waste management.

Crop digital twin is redefining traditional farming practices, offering unprecedented opportunities for real-time monitoring, predictive and simulation analysis, and optimization. This research embarks on an exploration of the synergy between precision agriculture, crop modeling, and regression algorithms to create a digital twin for augmenting farmers the concentration and composition prediction-based crop nutrient recovery. This captures the holistic representation of crop characteristics, considering the intricate relationships between environmental factors, nutrient concentrations, and crop compositions. However, the complexity arising from diverse soil and environmental conditions makes nutrient content analysis expensive and time-consuming. This paper presents the result of a predictive digital twin case study that employs six regression algorithms namely Elastic Net, Polynomial, Stepwise, Ridge, Lasso, and Linear Regression to predict rice nutrient content efficiently, particularly considering the coexistence and composition of multiple nutrients. Our research findings highlight the superiority of the Polynomial Regression model in predicting nutrient content, with a specific focus on accurate nitrogen percentage prediction. This insight can be used for nutrient recovery intervention by knowing the precise amount of nutrient to be added into the crop medium. The adoption of the Polynomial Regression model offers a valuable tool for nutrient management practices in the crop digital twin, potentially resulting in higher-quality rice production and a reduced environmental impact. The proposed method can be replicable in other low-resourced crop digital twin system.

Background and objectives Osteoporotic vertebral compression fractures, particularly prevalent among the aging population, constitute a pressing global healthcare challenge. They often are unnoticed until severe pain and disability manifest, making them an underdiagnosed and undertreated condition. Patients who suffer from these fractures not only endure excruciating pain but also face diminished quality of life, impaired mobility, and the risk of respiratory compromise due to altered spinal alignment. The economic repercussions of vertebral compression fractures are substantial. Hospitalization costs, rehabilitation expenses, long-term care, and indirect costs related to lost productivity significantly burden healthcare systems and society at large , offering a promising avenue for enhancing patient outcomes and relieving the healthcare and societal financial strain posed by vertebral compression fractures. Kyphoplasty, a minimally invasive surgical technique, offers rapid pain relief, vertebral height restoration and improved function. Recognizing the importance of early intervention, this manuscript highlights the potential of kyphoplasty in mitigating both clinical and economic consequences. By addressing these fractures more effectively, kyphoplasty may potentially reduce long-term healthcare expenses and indirect costs. Material and Methods 66 randomized patients were divided into 2 treatment groups (kyphoplasty using single balloon and double balloon), analyzing radiographic and clinical parameters during 12 months of follow-up. Results Patients treated with double balloon obtained greater anterior and posterior vertebral height after reduction and a better local vertebral angle. Both groups of patients obtained clinical improvement after treatment (VAS and ODI), with no differences between the groups. Conclusion Patients treated with double balloon kyphoplasty had better radiographic results. This revision manuscript delves into the critical dimensions of these fractures, encompassing demographics, clinical consequences, challenges in diagnosis and management, and their profound economic impact. This manuscript serves to introduce an improved kyphoplasty technique to guide to the multifaceted nature of osteoporotic vertebral compression fractures and their management.

Low Emission Zone (LEZ) is an important mechanism to limit the negative environmental impact of traffic pollution in an urban area. Car traffic in the zone is strictly regulated to meet certain technical and environmental criteria. Such solutions of urban environment protection are gaining popularity, especially in European conurbations. Nowadays there are above 300 zones in Europe, mostly established in Italy and Germany. So far there are no LEZs in Eastern and Central European countries. However, the Warsaw authorities are just discussing launching of a LEZ in the city in 2024. According to the ongoing project, the zone is to be launched gradually, in five phases between 2024 and 2032, with the traffic criteria being tightened accordingly. The progressive emission reductions of the predominant pollutants in LEZ in consecutive phases were estimated on the basis of the Real Driving Emissions (RDE) test, carried out in 2020 by the ICCT. The gradually reduced emissions of NOx and PM were subsequently utilized as the input dataset for the regional CALPUFF model predictions, to calculate concentrations of these main traffic-induced pollutants within the LEZ area. The direct effect of the zone activation is reduction of air pollution in the urban center, which is mostly exposed to the pollution risk due to heavy traffic. Computer simulations reveal that implementation of the zone in the case of Warsaw will result in a quite significant reduction of NOx concentrations within the LEZ, such that its mean concentration in the zone becomes comparable to the rest of the conurbation. However, it will bring only a slight reduction of PM2.5 concentrations. This situation is due to the long-standing dominance of coal combustion in the Polish economy, which also applies to the urban municipal sector. As a consequence, particulate matter emissions of this sector are clearly predominant over the road traffic contribution. Balance can be achieved once the ongoing de-carbonization of the national economy has been completed.

The production of photovoltaic modules is increasing to reduce greenhouse gas emissions. However, this results in a significant amount of waste at the end of their lifespan. Therefore, recycling these solar panels is important for environmental and economic reasons. However, collecting and separating Crystalline silicon, Cadmium Telluride, and Copper indium gallium selenide panels can be challenging, especially in underdeveloped countries. The innovation of this work is to develop a process to recycle all solar panel waste. The dissolution of all metals is studied through the leaching process as the main step of the flowchart. In the first step of leaching, silver can be recovered 98% by 0.5 nitric acid. Then, the second and third step involves the use of glycine for base metals dissolution, followed by the leaching of valuable metals with hydrochloric acid. The effect of parameters such as initial pH, acid concentration, solid/liquid ratio, and Hydrogen peroxide concentration are studied. The results show that Cu, Pb, Sn, Zn, Cd, In, Ga, and Se can be recovered until about 100% under optimal conditions. The optimal conditions for the dissolution of Cu, Zn, and Cd were the glycine concentration of 0.5 M, Temperature of 25 ˚C, the solid/liquid ratio of 10 gr/l, and 1% of Hydrogen peroxide. The optimized glycine concentration for the leaching of lead and tin was 1.5M. Indium and gallium were recovered to 100% by the use of 5M Hydrochloric acid, S/L=10gr/l, and T=45 ˚C. Separation of selenium and tellurium occurred by 0.5 M HCl at a temperature of 60 ˚C. Additionally, for the first time, a general outlook for the recycling of various end-of-life solar panels is suggested.

Even though, graphene family materials (GFMs) hold great promise for various applications, there are still significant knowledge gaps in ecotoxicology and environmental risk assessment associated with their potential environmental impacts. Here, we provide a critical perspective on published ecotoxicity studies of GFMs based on a meticulous bibliometric research. Analysis of the available data revealed that to date, ecotoxicity studies with GFMs have been carried out predominantly using freshwater species (82%), while their effects on marine or terrestrial or-ganisms are understudied. Three groups of organisms were found dominating ecotoxicological research: fish (39%), algae (20%) and arthropoda (20%) predominantly applying small, planktonic crustaceans. It was also revealed that despite their widespread detection in different environ-mental compartments the potential impacts of GFMs in complex test systems with hierarchical trophic organisation or in trophic transfer studies are significantly under-represented. One of the main causes was identified as the difficulties in the physicochemical characterisation of GFMs in complex terrestrial test systems or in aquatic micro- and mesocosm studies containing a sediment phase. The lack of tools for adequate characterisation of GFMs in these complex test systems may discourage researchers from conducting experiments under environmentally relevant test condi-tions. However, in the coming years, fundamental research about these complex test systems will con-tinue to better understand the mechanism behind GFMs toxicity affecting organisms in different environmental compartments and to ensure their safe and sustainable use in the future.

Polymer-based composites represent a separate class of materials in demand by the industry. In comparison with other polymers, UHMWPE is characterized by exceptionally high wear and impact resistance. In this work, compacts were produced from UHMWPE GUR 4120 powder with the addition of Nanoscale Detonation Carbon (NDC) and Single-Walled Carbon Nanotubes (SWCNTs) on a hydro-pneumatic impact device by the Cyclic Impact Compaction (CIC) technique. The obtained samples were subjected to wear resistance test, their hardness and tensile strength were measured. Studies have shown that the addition of NDС and SWCNTs in UHMWPE leads to an increase in hardness by 6.4 and 19.6%, to a decrease in wear by 11.5 and 38.5% and a decrease in the coefficient of friction by 10 and 20%, respectively. However, the tensile strength of UHMWPE decreases by 11.7 and 40.4% and elongation decreases by 11.9 and 30.1% with the addition of NDС and SWCNTs, respectively.

Seasonal changes and varying degree of corneal hydration has been linked to excimer laser corneal ablation rates. Use of PMMA as calibration material in refractive lasers is well established. However, PMMA ablation may be equally affected by seasonal variations in temperature and humidity, intern affecting the calibration process. Aim of this work is to analyze the effect of seasonal changes in PMMA Performance using SCHWIND AMARIS laser system. PET and PMMA ablations conducted in climate-controlled environment with 826 consecutive AMARIS systems manufactured over 6 years were retrospectively analyzed. Lasers were stratified depending on seasons and months of the year. Metrics like Single laser pulse fluence, nominal number of laser pulses, mean performance, standard deviation, and technical performance of system were compared to global average values. Cyclic winter-summer variation was confirmed with seasons winter and summer showing statistically significant variations with respect to global values. Metric technical performance showed deeper PMMA ablation performance in summertime, with maximum seasonal deviation of 6%. Results were consistently confirmed in seasonal as well as monthly analyses. These findings could help minimize variance among laser systems by implementing compensation factors depending on seasons such that laser systems installed worldwide follow the same trend line of variation.

The COVID-19 pandemic has profoundly changed the world and human society in various ways. Infection-related figures are key indicators of the direct impact of the pandemic. In addition to the virus, many vulnerable populations (e.g. patients with complex health conditions) have suffered direct or indirect health impacts, such as delayed treatment or diagnosis of cancer and severe mental illness due to stay-at-home restrictions, or financial issues. Studying these kinds of impacts can provide the community with insights and understanding of how the pandemic has affected patients with health conditions and help inform decision-makers design policies that support various vulnerable populations. This paper surveys analytical work conducted on how COVID-19 has impacted patients with complex health conditions. We categorise vulnerable populations into three specific cohorts: patients with chronic disease, patients with cancer and patients with end-of-life care. We then investigate how each of these cohorts has been impacted by the pandemic. We also identify each cohort from an analytics perspective by leveraging various data collection strategies and analytical methods. The goals of this paper are to provide evidence on how to improve the quality of life for vulnerable populations and to inform a national response strategy for future pandemics, which seems inevitable in a highly globalised economy.

As a rapidly growing coastal megacity, Shanghai is continuously threatened with land subsidence issues since 1920s. Land subsidence was controlled in 1960s, however in 1990s, unconscious and dangerous urban underground space (UUS) exploration and tunneling development are causing further land subsidence. It is imperative to study previous relations towards future adaptive and resilient scenario modelling and planning. There are multiple cause-effect factors determined in the urban built environment of Shanghai megacity. This paper presents the current evidence based on the relations of the multifactor of the spectrum. Methods consist of understanding the cause-effect relations and spatiotemporal from the crucial period of 1960-2020. Data are collected secondarily from multiple open sourced databases. The results determine Shanghai are highly influenced by the UUS development induced-subsidence, tunneling leakage and weak spatial modelling. Spatiotemporal pattern has shown a mixed positive-negative impact: population, land subsidence is growing in parallel distribution (positive) with tunneling leakage, construction of tunneling, metro system, UUS development, building price, reconstruction area, GDP growth, land price, arable land decrease and further tunnel settlement in Urban City Centre, Pudong New Area, Minhang, Baoshan and Songjiang districts. These results are useful for further adaptive and resilient scenario modelling and spatial planning.

This paper aims at addressing the exploitation of solid-state carriers for hydrogen storage, with attention paid both to the technical aspects, through a wide review of the available integrated systems, and to the social aspects, through a preliminary overview of the connected impacts from a gender perspective. As for the technical perspective, carriers to be used for solid-state hydrogen storage for various applications can be classified into two classes: metal and complex hydrides. Related crystal structures and corresponding hydrogen sorption properties are reviewed and discussed. Fundamentals of thermodynamics of hydrogen sorption evidences the key role of the enthalpy of reaction, which determines the operating conditions (i.e. temperatures and pressures). In addition, it rules the heat to be removed from the tank during hydrogen absorption and to be delivered to the tank during hydrogen desorption. Suitable values for the enthalpy of hydrogen sorption reaction for operating conditions close to ambient (i.e. room temperature and 1-10 bar of hydrogen) are close to 30 kJ·molH2 1. The kinetics of hydrogen sorption reaction is strongly related to the microstructure and to the morphology (i.e. loose powder or pellets) of the carriers. Usually, kinetics of hydrogen sorption reaction is rather fast, and the thermal management of the tank is the rate determining step of the processes. As for the social perspective, various scenarios for the applications in different socio-economic contexts of solid-state hydrogen storage technologies are described. As it occurs with the exploitation of other renewables innovative technologies, a wide consideration of the social factors connected to these processes is needed to assess the extent to which a specific innovation might produce positive or negative impacts in the recipient socio-economic system and to explore the potential role of the social components and dynamics in fostering the diffusion of the innovation itself. Attention has been addressed to the gender perspective, in view of the enhancement of hydrogen-related energy storage systems, intended both in terms of the role of women in triggering the exploitation of hydrogen-based storage as well as to the impact of this innovation in their current conditions, at work and in daily life.

Groundwater habitats in the Dinaric Karst are home to the only known cave-adapted genus of bivalve mollusks, which currently survives as three distinct species with a highly fragmented distribution. Over the past few decades, Congeria populations suffered a steep decline across their range, as a result of human activities. Here, we identify the most pressing issues concerning the current conservation status of Congeria and identify key priorities for its scientific study. The building of dams and other hydrotechnical constructions have led to a significant decrease in the water inputs that used to supply underground systems where Congeria lives, contributing to a reduction of the cave habitats feasible for bivalve settlement. This is relatively well documented in Popovo polje where, as a result of hydrotechnical interventions on Trebisnjica river, water level drop destroyed well over 99 % of Congeria kusceri population in Zira cave and caused their complete disappearance in other caves. Similar factors are likely to have also affected the sister species Congeria jalzici in the Lika region. In addition, the salinization of Neretva river, lack of proper wastewater management, intense agriculture, tourist exploitation, and additional hydrotechnical plans add to the ongoing decline of the quality of Congeria habitats. These threats are expected to least to drastic reductions in population size within the next few decades, and this situation might be further aggravated by the lack of regulations and active measures for Congeria protection, as well by the limited understanding of its biology. Future scientific studies should be aimed at better elucidating microhabitat preference, defining the range of tolerance of environmental parameters, and assessing the size and genetic viability of existing Congeria populations. However, we highlight that these efforts would most certainly require the collaboration of local authorities and the allocation of appropriate financial resources in order to be effective.

The overrun of transient power quality index caused by the large-capacity electric arc furnace (EAF) has become a prominent problem affecting the safe and stable operation of the power system. (1) In this paper, the relationship between arc furnace volt-age and current is derived based on the different stages of arc combustion, and the random variation of chaotic phenomenon of the arc voltage are simulated. Established an EAF model suitable for the study of transient power quality problems. (2) Take 50t AC EAF as an example to analyze the reactive power impact and the influence on the point of common coupling (PCC) voltage caused by the three-phase short circuit of the electrode. The results show that the experimental results are consistent with the theoretical analysis, verifying the correctness and effectiveness of the model. (3) When the three-phase short-circuit occurs, the reactive power impact is nearly 6 times that of normal operation, the short-circuit current is 2.66 times that of normal operation, and the effective value of the PCC voltage has dropped by 40.37%, which provides a theoretical basis for real-time compensation of impulsive reactive power and improvement of the transient power quality of the EAF.

In the last decades farm animals kept in confined and mechanically ventilated livestock buildings are increasingly confronted with heat stress (HS) due to global warming. These adverse conditions cause a depression of animal health and welfare and a reduction of the performance up to an increase of the mortality. To facilitate sound management decisions, livestock farmers need relevant arguments, which quantify the expected economic risk and the corresponding uncertainty. The economic risk was determined for the pig fattening sector based on the probability of HS and the calculated decrease in the gross margin. The model calculation for confined livestock buildings showed, that HS indices calculated by easily available meteorological parameters can be used for assessment quantification of indoor HS, which is so far difficult to determine. These weather-related HS indices can be applied not only for an economic risk assessment but also for a weather-index based insurance for livestock farms. Based on the temporal trend between 1981 and 2017, a simple model was derived to assess the likelihood of HS for 2020 and 2030. Due to global warming, the return period for a 90-percentile HS index is reduced from 10 years in 2020 to 3-4 years in 2030. The economic impact of HS on livestock farms was calculated by the relationship between an HS index based on the temperature-humidity index (THI) and the reduction of the gross margin. From the likelihood of the HS and this economic impact function, the probability of the economic risk could be determined. The reduction of the gross margin for a 10 year return period was determined for 1980 with 0.27 € per year and animal place and increased by the 20-fold to 5.13 € per year and animal place in 2030.

We present an updated design process for adapting and integrating existing cyber risk assessment approaches for impact assessment for the risk from IoT to the digital economy. The new design process includes a set of changes to the original standards (e.g. NIST) that are adapted for the IoT cyber risk in this paper. This paper also presents a new framework for impact assessment of IoT cyber risk, specific for the digital economy.

The spectra currently emerging from modern ground- and space-based astronomical instruments are of exceptionally high quality and resolution. To meaningfully analyse these spectra researchers utilise complex modelling codes to replicate the observations. The main inputs to these codes are atomic data such as excitation and photoionisation cross sections as well as radiative transition probabilities, energy levels and line strengths. In this publication the current capabilities of the numerical methods and computer packages used in the generation of these data are discussed. Particular emphasis is given to Fe-peak species and the heavy systems of tungsten and molybdenum. Some of the results presented to highlight certain issues and/or advances have already been published in the literature, while other sections present, for the first time, new recently evaluated atomic data.

Background: This study aimed to evaluate the burden and impact of cardiac and cerebrovascular disease (CCD) on hospital inpatients with type 1 diabetes mellitus (T1DM). Methods: This is a retrospective nationwide cohort study of people with T1DM with or without CCD in the US National Inpatient Sample between 2016 to 2019. The in-hospital mortality rates, length of stay (LoS) and healthcare costs were determined. Results: A total of 59,860 T1DM patients had a primary diagnosis of CCD and 1,382,934 did not. The median LoS was longer for patients with CCD compared to no CCD (4.6 vs. 3 days). Patients with T1DM and CCD had greater in-hospital mortality compared to those without CCD (4.1% vs. 1.1%, p<0.001). The estimated total care cost for all patients with T1DM with CCD was approximately US $326 million. The adjusted odds of mortality compared to patients with non-CCD admission was greatest for intracranial hemorrhage (OR 17.37, 95%CI 12.68-23.79), pulmonary embolism (OR 4.39, 95%CI 2.70-7.13), endocarditis (OR 3.46, 95%CI 1.22-9.84), acute myocardial infarction (OR 2.31, 95%CI 1.92-2.77) and stroke (OR 1.47, 95%CI 1.04-2.09). Conclusions: The burden of CCD in patients with T1DM is substantial and significantly associated with increased hospital mortality and high healthcare expenditures.

Background & Objective: Acne Vulgaris is a common skin disease, with 5 % prevalence in Pakistan and has a significant physiological, psychological, emotional and social impact on the population, our study aims to estimate its annual economic burden per person in Pakistan.Methodology: Economic burden was estimated by using Cost of Illness analysis. All types of costs, including direct medication costs, direct non-medical costs and indirect costs due to loss of work. Data were collected from online databases and literature reviews. Results: Cost of illness analysis estimated that the cost of Acne Vulgaris treatment varies for mild, Moderate and severe acne. Acne is causing an annual economic burden of PKR 1,92,366 PKR per person (direct cost is PKR 1,86,366 and indirect cost 6,000) for an expensive treatment and PKR 53,550 (direct cost is PKR 47550 and indirect cost 6,000) for a cheap treatment. Limitation: Being an LMIC, Pakistan needs extensive study on the pharmacoeconomic analysis of Acne for proper and rational allocation of resources. Conclusion: Acne has a significant economic burden in Pakistan. Even with significant impact of Acne, there is very minute work done about Acne in Pakistan.

Many advanced technologies have been employed in the high performance active matrix displays including liquid crystal displays, organic light emitting diode displays, and micro light emitting diode displays. On the other side, there have existed the strong demand on the cost reduction and it is one of low cost schemes to integrate the driver circuit in a panel based on thin film transistor technologies. This paper reviews overall concept, operation principles, and various circuit approaches in shift registers for the scanning pulse generation. In addition, it deals with the implementation of additional functionalities in gate drivers to support pixel compensation, multi-line driving, in-cell capacitive touch screen, pixel sensing, and adaptive scanning region control.

This study investigates the potential benefits of integrating coarser particle size distributions (PSDs) of 45-106 µm into laser-based powder bed fusion of metals (PBF-LB/M), aiming to reduce costs while maintaining quality standards. Despite the considerable advantages of PBF-LB/M in producing intricate geometries with high precision, the high cost of metal powders remains a barrier to widespread adoption. By exploring the use of coarser PSDs, particularly from electron beam-based powder bed fusion of metals (PBF-EB/M), significant cost-saving opportunities are identified. Through comprehensive powder characterization, process analysis, and mechanical property evaluation, this study demonstrates that PBF-LB/M can effectively utilize coarser powders while achieving comparable mechanical properties to those produced with 20-53 µm PSD. Adaptations in process parameters enable the successful processing of coarser powders, maintaining high-relative density components with minimal porosity. Additionally, market surveys reveal substantial cost differentials between PBF-LB/M and PBF-EB/M powders, indicating 40 % cost reduction potential by integrating coarser PSDs into PBF-LB/M. Overall, this study provides valuable insights into the economic and technical feasibility of printing with coarser powders in PBF-LB/M, offering promising avenues for cost reduction without compromising quality, thus enhancing competitiveness and adoption of the technology in manufacturing applications.

Lower limb exoskeletons have the potential to reduce the prevalence of work-related musculoskeletal disorders; however, they often lack user-oriented control strategies. Human-in-the-loop (HITL) controls can adapt an exoskeleton’s assistance in real-time, to optimize the user-exoskeleton interaction. This study presents a HITL control for a knee exoskeleton to minimize the users’ physical effort, a parameter innovatively evaluated by their interaction torque with the exoskeleton (a muscular effort indicator) and metabolic cost. This work innovates by estimating the user’s metabolic cost within the HITL control through a machine-learning model. The regression model was able to estimate the metabolic cost, in real-time, with a root-mean-squared error of 0.66 W/kg and a mean absolute percentage error of 26%, making faster (10s) and less noisy estimations than a respirometer (K5, Cosmed) device. The HITL reduced the user’s metabolic cost and interaction torque by 7.3% and 32.3%, respectively, when compared to a zero-torque control. The developed HITL control surpassed both a non-exoskeleton and a zero-torque condition regarding the user’s physical effort, even for a simple task such as slow walking. Furthermore, the user-specific control enabled a lower metabolic cost than the non-user-specific assistance. This proof-of-concept demonstrated the potential for HITL controls in assisted working.

Although numerous organizational researchers have acknowledged that COVID-19 shocks reduced the tourism industry’s financial performance, relevant literature remains scarce. Do tourism firms reduce corporate social responsibility (CSR) investments to decrease costs? The answer is unclear. This study fills the gap between stakeholder and cost stickiness theories. Based on a quasi-natural experiment of listed Chinese tourism companies from 2017 to 2021, the study finds that the COVID-19 shock caused tourism firms to increase strategic and decrease responsive CSR. In addition, tourism firms that adopted cost leadership strategies trimmed responsive CSR more than strategic CSR. Tourism firms with differentiated leadership strategies increased strategic and decreased responsive CSR. Tourism firms with higher levels of political connections increased responsive CSR, while tourism firms with higher organizational resilience increased strategic CSR. At the theoretical level, this study reveals the theoretical mechanism of the COVID-19 epidemic’s shock on tourism firms' adjustment of CSR from the perspective of cost stickiness. On a practical level, it helps inform tourism firms’ decision-making regarding CSR adjustments for sustainable development when they face widespread crisis scenarios.

Agarwood, a highly valuable fragrant wood of Aquilaria spp. has been widely used in perfumes, traditional medicine, religious rites and cultural activities. Due to the presence of aromatic and medicinal properties, the Agarwood has gained its economic importance in recent years. It has been providing livelihood and employment facilities to many people who are cultivating those. Owing to high demand and profitability, the people of Assam has started to cultivate Agarwood in a commercial manner in recent days. This study was conducted to examine the economic viability of Agarwood cultivation in Assam and to understand the problems of the cultivators to consider Agarwood cultivation as the primary source of income. The primary data were collected during the months of May and June, 2022 from 210 households using structured questionnaire. The cost-benefit analysis revealed that INR 100 of initial investment yields a net benefit of INR 623 which indicates that the return from the Agarwood cultivation among the cultivators are very high. Thus, the present study argues that the government should encourage the cultivators by providing the necessary support in the form of a legalized market, credit requirement, creating the awareness about the demand of Agarwood and making provision of training.

The purpose of this study is to analyze the causes of cost variation in natural disaster prevention projects (NDPP) in Gyeongsangbuk-do and to develop effective cost and risk management strategies. The study collected data by conducting an online survey of 244 stakeholders. Binary logistic regression was used to analyze the impact of various variables on cost deviation, in-cluding project management risk (PMR), project costing and execution risk (PCER), project exe-cution strategy risk (PESR), construction project risk (CPR), project cost and schedule risk (PCSR), project management challenges (PMC), and construction project subcontractor and safety man-agement (CPSSM). The results showed that PMR (OR=3.744, 95% C.I. [1.657, 8.457]), PCER (OR=5.068, 95% C.I. [2.236, 11.484]), PESR (OR=3.447, 95% C.I. [1. 853, 6.413]), CPR (OR=2.292, 95% C.I. [1.020, 5.151]), PCSR (OR=4.817, 95% C.I. [2.250, 10.317]), PMC (OR=2.954, 95% C.I. [1.452, 6. 013]), and CPSSM (OR=2.419, 95% C.I. [1.297, 4.515]) had statistically significant effects on cost deviation. In particular, PMR, PCER, and PESR were identified as the main causes, and the effects of additional factors such as CPSSM were also important. The developed predictive model plays an important role in helping stakeholders of NDPP to manage risks in advance and take appro-priate preventive measures to ensure the success of the project. This study empirically supports the theoretical hypotheses of previous studies and provides a clear understanding of the causes of cost deviation in NDPP, which provides important insights for strategic decision-making and provides a new perspective on risk management.

Improving HIV testing efficiency saves financial and material resources for health. We conducted a retrospective secondary data analysis of routinely collected HIV risk screening program data in Uganda, from October to November 2019, to determine the performance characteristics of the adolescent and adult HIV risk screening tool in public health facility settings. A total of 19,854 clients had been screened for HIV testing eligibility and tested for HIV. The overall positivity rate (cluster weighted prevalence of HIV) among those screened was 4.5% (95% CI: 4.1%-4.8%) versus 3.71% (95% CI: 3.06-4.50) among those not screened. The sensitivity and specificity of the risk screening tool were 90.7% (95% CI: 88.4%, 92.7%) and 75.8%, (75.2-76.4) respectively. With screening, the number needed to test to identify one PLHIV was reduced from 27 to 22. Although risk screening would have led to a 24.5% (4,825/19,704) reduction in testing volume, 9.3% (68/732) of PLHIV would have been missed, being misclassified as not eligible for testing. The cost per PLHIV identified fell by 3% from $69 without screening to $66.9 with screening. We recommend the use of scientifically validated HIV risk screening tools, and a need to explore the use of HIV self-testing as a test for tirage.

Drowning remains a prominent global pediatric health concern, necessitating preventive measures such as educational initiatives for children and caregivers. This study aimed to assess the feasibility and educational effectiveness of an interactive puppet show centered on water safety for children and parents. A 30-minute original theater performance, featuring two actors and three puppets (a girl, a crab, and a lifeguard), was conducted. Subsequently, 185 children (aged 4 to 8) and their 160 parents (134 mothers and 26 fathers) participated in this quasi-experimental study. Pre- and post-show tests were administered to evaluate knowledge and behaviors in aquatic environments. Prior to the puppet show, 78% of children exhibited basic aquatic competency. Only 33% considered swimming alone risky. Following the intervention, 81.6% of children changed their perception of the risks of solo beach activities, showing improved knowledge of emergency number activation (from 63.2% to 98.9%, p<0.001). The intervention increased parents' intention to visit lifeguard-patrolled beaches and improved their CPR knowledge for drowning victims by 58.8%. In conclusion, a drowning prevention puppet show positively impacts children and parents, potentially enhancing safety behaviors during water-related leisure activities, warranting consideration as part of comprehensive drowning prevention strategies.

Aims: Multidisciplinary diabetic foot clinics have been shown to be an effective therapeutic model for reducing major amputations and mortality rates. The aim of this article is to assess the economic impact of the implementation of a multidisciplinary diabetic foot clinic in a hospital in Barcelona, Spain. Methods: Observational cost-benefit study of all subjects with diabetes admitted with the diagnosis of a major amputation due to diabetic foot from 2010 to 2020. Direct and indirect costs were compared before and after the introduction of the multidisciplinary diabetic foot clinic. Results: The individual cost per patient with diabetic foot was €14,768 before the implementation of the unit. After the implementation of the clinic, the expected cost was reduced to €5,985 due to a 40% reduction in the probability of hospitalization. Overall, the implementation of the clinic resulted in cost savings per patient valued at €8,783, of which, €7,165 are related to hospital benefits. Conclusion: The results of this analysis contributes to the evidence suggesting that multidisciplinary diabetic foot clinics are cost-effective, by demonstrating that they have a positive impact on patient health while also reducing the utilization of healthcare resources.

This comprehensive critical review shows how green buildings and energy optimization solutions improve indoor air quality as well as environmental quality (IEQ) and human health. Electricity-efficient green buildings create a better indoor atmosphere and save electricity. Green buildings improve occupant well-being through IAQ, thermal comfort, acoustics, and lighting. Renewable energy, green construction materials, and intelligent technologies increase sustainability and reduce environmental impact. Environmental hazards, architectural design, and societal variables can improve indoor air quality and subsequently reduce health risks linked with poor air quality. The evaluation highlights the pros and cons of low-cost IAQ sensors and equipment. These devices provide valuable insights, but calibration and validation using reference equipment are necessary to ensure accuracy and reliability. Architects, engineers, environmentalists, and policymakers are essential in creating green buildings with high IEQ. Prioritizing occupant comfort, health, and productivity ensures a sustainable and healthy future for future generations. However, continual monitoring and research are needed to improve green building practices and overcome construction problems. Intelligent models for continual on-field calibration of low-cost sensors may improve performance. Green buildings benefit occupants and the environment by optimizing energy use and using IAQ monitoring technologies.

Surgical methods for treating spasmodic torticollis include denervation and myotomy (DAM) of the affected muscles, and deep brain stimulation (DBS). This study reports on the long-term efficacy, prognostic factors, safety, and hospitalization costs following these two procedures. We collected data from 94 patients with spasmodic torticollis, of whom 41 and 53 were treated with DAM and DBS, respectively, from June 2008 to December 2020 at the Chinese People’s Liberation Army General Hospital. We used the Tsui scale and the global outcome score of the Toronto Western Spasmodic Torticollis Rating Scale (TWSTRS) to evaluate the preoperative and postoperative clinical condition in all patients. We also determined the cost of hospitalization, prognostic factors, and serious adverse events following the two surgical procedures. The mean follow-up time was 68.83 months (range = 13–116). Both resection surgery and DBS showed good results in terms of Tsui (Z = -5.103, P = 0.000; Z = -6.210, P = 0.000) and TWSTRS scores (t = 8.762, P = 0.000; Z = -6.308, P = 0.000). Compared with the DAM group, the preoperative (47.71, range 24–67.25) and postoperative (18.57, range 0–53) TWSTRS scores in the DBS group were significantly higher (Z = -3.161, P = 0.002). We found no correlation between prognostic factors and the patient age, gender, or disease duration for either surgical procedure. However, prognostic factors were related to the length of the postoperative follow-up period in the DBS surgery group (Z = -2.068, P = 0.039; Z = -3.287, P = 0.001). The mean hospitalization cost in the DBS group was 6.85 times that found in the resection group (Z = -8.284, P = 0.000). The total complication rate was 4.26%. We found, both resection surgery and DBS, showed good results in the patients with spasmodic torticollis. Although DBS had a greater impact on the quality of life and the degree of pain relief, it was more expensive. Prognostic factors were related to the length of the postoperative follow-up period in patients who underwent DBS surgery.

This venture targets giving a correspondence framework that can improve the legal framework execution and cooperation and furthermore make it simple to lead execution audits. This examination depicts restricted admittance to the right legitimate data just as admittance to courts and court administrations as the essential factors that limit admittance to equity. The basic target fundamental for accomplishing the point incorporates planning an online easy to understand data framework that would improve correspondence and association between legal professionals like legal advisors and disputants. The undertaking uses a subjective methodology that zeroed in on the substance investigation of essential information. Key members were court laborers like attorneys and agents, just as court clients like disputants. Pre-plan discoveries uncovered that current legitimate data frameworks – basically court sites – neglect to recognize their crowd by treating all court clients similarly comparative with the correspondence of lawful data. Likewise, the issue of admittance to equity includes the significant expense of equity also, restricted legal data that elevates admittance to equity. Post-plan discoveries from the online overview directed after the turn of events and execution of the IT curio uncovered a powerful and productive legitimate data framework. Exploration discoveries supported the advancement of an IT antiquity that records for huge shortcomings in current legal data frameworks, for example, their failure to pass on fitting legitimate data intelligently and productively. In outline, the investigation prescribes the reception of saw control to work fair and square of certainty of court clients and the differentiation between court clients to convey the right data to the right crowd.

Low-cost particle sensors are now used worldwide to monitor outdoor air quality. However, they have only been in wide use for a few years. Are they reliable? Does their performance deteriorate over time? Are the algorithms for calculating PM2.5 provided by the Plantower company for PurpleAir monitors accurate? We investigate these questions using continuous measurements of four monitors (8 sensors) under normal conditions inside and outside a home for 1.5-3 years. A recently-developed algorithm (called ALT-CF3) is compared to the two existing Plantower algorithms. The Plantower CF1 algorithm was shown to lose 25-50% of all indoor data due to the questionable practice of assigning zero to all concentrations below a threshold. None of these data were lost using the ALT-CF3 algorithm. About 92% of all data showed precision better than 20% using the ALT-CF3 algorithm, but only about 45-75% of data achieved that level using the Plantower CF1 algorithm. The limits of detection (LODs) using the ALT-CF3 algorithm were mostly under 1 ug/m3, compared to about 3-10 ug/m3 using the Plantower CF1 algorithm. The percent of observations exceeding the LOD was 53-92% for the ALT-CF3 algorithm, but only 16-44% for the Plantower CF1 algorithm. For indoor air in homes, the Plantower algorithms are inappropriate.

Highly nonlinear characteristics of lithium-ion batteries (LIBs) are significantly influenced by the external and internal temperature of the LIB cell. Moreover, cell temperature beyond the manufacturer’s specified safe operating limit could lead to thermal runaway and even fire hazards and safety concerns to operating personnel. Therefore, accurate information of cell internal and surface temperature of LIB is highly crucial for effective thermal management and proper operation of a battery management system (BMS). Accurate temperature information is also essential to BMS for the accurate estimation of various important states of LIB such as state of charge, state of health and so on. High capacity LIB pack, used in electric vehicles and grid-tied stationary energy storage system essentially consists of thousands of individual LIB cells. Therefore, installing a physical sensor at each cell especially at the cell core is not practically feasible from the solution cost, space and weight point of view. A solution is to develop a suitable estimation strategy which led scholars to propose different temperature estimation schemes aiming to establish a balance among accuracy, adaptability, modelling complexity and computational cost. This article presented an exhaustive review of these estimation strategies covering recent developments, current issues, major challenges, and future research recommendations. The prime intention is to provide a detailed guideline to the researchers and industries towards developing a highly accurate, intelligent, adaptive, easy to implement and compute efficient online temperature estimation strategy applicable to health-conscious fast charging and smart onboard BMS.

Environmentally friendly and low-cost sensors are needed for the next generation disposable electronics applications. Given its low-cost, availability and biodegradability, paper-based devices are a very promising. Here we demonstrate the fabrication of a tungsten disulphide (WS2) strain sensor on standard copy paper. The WS2 is deposited through direct abrasion of WS2 powder against the paper surface making the fabrication of the device low-tech and cost effective. The fabricated strain gauge devices present gauge factors up to ~70 for strains in the ± 0.5 % range. These values are ~ 9 times larger than that obtained on devices with the same geometry but using a graphite film instead a WS2 as a sensitive material. We demonstrate the potential of these WS2-on-paper strain gauges by integrating them directly on a paper cantilever to sense mass and forces. We show how this very simple device can detect sub-milligram masses. Moreover, we also demonstrate the capability of transducing motion in mechanical resonators by gluing a WS2-on-paper strain gauge on their surface.

Most people living in Europe's cities are still exposed to levels of air pollution deemed harmful by the World Health Organization. In the modern world, air pollution is the foremost concern because of its impact in human health and economy. This strong connection appears gaining a lot of concern, driven by new installed low-cost electrochemical sensors monitoring systems. Highly accuracy, real-time monitoring, daily and yearly statistics, data access from experts or simple users, low-cost equipment and forecasting needs, enforce the market to develop new air quality monitoring systems using advanced technologies and protocols. In this study, a comparison via low-cost electrochemical sensors and of static, fixed site measurement monitoring station, is taking place in Athens, Greece, along with the data quality and Air Quality Index (AQI) including data accuracy and quality of data concerning adverse health effects due to air pollution. The findings presented in this work, relate to different flexible and affordable alternatives adopted during the evaluation and calibration of low-cost gas sensors for the monitoring. The significance of the positive results is particularly useful, especially considering the founding for interference, environmental conditions affections and air quality information including indexes and health recommendations for a specific location.

Late pear cultivars, such as ‘Conference’, can be stored for a long period, with their storability depending on storage conditions. A three-year study (2011-2013) compared the impact of half-year storage using four technologies – normal atmosphere, normal atmosphere + 1-methylcyclopropene (1-MCP), controlled atmosphere, controlled atmosphere + 1-MCP – on the quality parameters of ‘Conference’ pears, such as mass loss, firmness, total soluble solids, acidity, antioxidant capacity, and the incidence of diseases and disorders. Additionally, the study analysed different storage conditions in terms of profitability, based on the market prices for pears in the seasons during which the pears were stored. The storage conditions had a very strong influence on the fruit quality parameters, and was found to affect most visibly the mass loss and the incidence of postharvest diseases and disorders. The storage of ‘Conference’ pears for 180 days in normal atmosphere is not economically viable, even if the fruit is subjected to 1-MCP treatment.

Background: The phase III KEYNOTE-604 study confirmed the benefit of pembrolizumab combined with chemotherapy in the first-line treatment of extensive-stage small-cell lung cancer(ES-SCLC). Intergrated the clinical benefits of pembrolizumab and its high cost into account, this study aim to assess the cost-effectiveness of adding pembrolizumab to standard first-line etoposide-platinum (EP) for patients with ES-SCLC from the the US payer perspective. Methods: A Markov model was developed to compared the costs and quality-adjusted life-years (QALYs) of pembrolizumab plus EP and placebo plus EP over a 10-year time horizon. Clinical efficacy, treatment utilization and safety data were pooled from the KEYNOTE-604 trial. Utilities were obtained from published resources. Costs were mainly collected from Medicare in 2020. Sensitivity analyses were performed to examined the robustness of our model. Results: Adding pembrolizumab to standard first-line EP, resulted in better effectiveness than the use of EP alone for ES-SCLC by 0.22 QALYs. Pembrolizumab plus EP was dominated economically by placebo plus EP, leading to an incremental cost-effectiveness ratio(ICER) of $334,373/ QALY. Deterministic sensitivity analyses indicated that the uncertainty in model parameters exerts no substantial effect on our results. Probability sensitivity analysis indicated that probabilities for pembrolizumab plus EP being cost-effective within a wide rang of willingness to pay were modest. Conclusion: From the US payer perspective, the first-line treatment for ES-SCLC with pembrolizumab plus EP was not cost-effective compare with placebo plus EP. Although pembrolizumab combination chemotherapy was beneficial to the survival of ES-SCLC, price reduction may be the necessary measure to improve its cost-effectiveness.

Mechanical ventilation systems, which are used for breathing support when a person is not able to do it by their own, requires a device for measuring the air flow to the patient in order to monitoring and a assure the magnitude establish by a medical staff. Flow sensors are the conventional devices used for the air flow measuring; however, there were not available in Peru, because of the international demand during COVID-19 pandemic. In this sense, a novel air flow sensor based on orifice plate and an intelligent transducer stage were developed as an integrated design. Advanced methodologies in simulations and experiments using specially designed equipment for this application were carried out. The obtained data was used for a mathematical characterization and dimensions validation of the integrated design. The device was tested in its real working conditions, it was implemented in a breathing circuit connected to a low-cost mechanical ventilation system based on cams. Results indicate that the designed air flow sensor/transducer is a low-cost complete medical device for mechanical ventilators able to provide satisfactorily all the ventilation parameters air flow, pressure and volume over time by measuring the air flow and calculating the others. Furthermore, this device provides directly a filtered equivalent electrical signal for a display or a computer.

Quality of Service (QoS) metrics deal with network quantities, e.g. latency and loss, whereas Quality of Experience (QoE) provides a proxy metric for end-user experience. Many papers in the literature have proposed mappings between various QoS metrics and QoE. This paper goes further in providing analysis for QoE versus bandwidth cost. We measure QoE using the widely accepted Mean Opinion Score (MOS) rating. Our results naturally show that increasing bandwidth increases MOS. However, we extend this understanding by providing analysis for internet access scenarios, using TCP, and varying the number of TCP sources multiplexed together. For these target scenarios our analysis indicates what MOS increase you get by further expenditure on bandwidth. We anticipate that this will be of considerable value to commercial organizations responsible for bandwidth purchase and allocation.