This research aims to develop an inexpensive ocean observation instrument with a project name is NOBEL (Nusantara Oceanography Backdoor Experiment Laboratory)-BOX. The device can be installed on all types of vessels and placed in a specific area to map the water conditions, providing accurate data for managing a marine area specifically to water quality. The principle of NOBEL-BOX is to attach sensors in a container connected to a microcontroller and then measure directly. The method employed includes experimental design, laboratory and field tests, and data evaluation to produce the necessary system and instruments. The design process involves the construction of the instrument and the fabrication of the device. This process includes creating three-dimensional sketches/drawings, designing microcontrollers and data transmission systems, and power capacity. The sensors attached to this device are surface water temperature, air temperature, salinity, dissolved oxygen, pH, and TDS. The testing phase includes data validation, testing of the device in the laboratory, and field testing. The laboratory and field tests showed that the device worked and provided data that can be used to analyze the seawater condition spatially and over time.

This paper introduces an advanced method that integrates contingent valuation and machine learning (CVML) to estimate residents’ demand for reducing or mitigating environmental pollutions and climate change. To be precise, CVML is an innovative hybrid machine-learning model, and it can leverage a limited amount of survey data for prediction and data enrichment purposes. The model comprises of two interconnected modules: Module I, an unsupervised learning algorithm, and Module II, a supervised learning algorithm. Module I is responsible for grouping the data into groups based on common characteristics, thereby grouping the corresponding dependent variable, whereas Module II is in charge of demonstrating the ability to predict and the capacity to appropriately assign new samples to their respective category based on input attributes. Take a survey on the topic of air pollution in Hanoi in 2019 as an example, we found that CVML can predict households’ willingness– to– pay for polluted air mitigation at a high degree of accuracy (i.e., 98%). We found that CVML can help users reduce costs or save resources because it makes use of secondary data that is available on many open data sources. These findings suggest that CVML is a sound and practical method that could be widely applied in a wide range of fields, particularly environmental economics and sustainability science. In practice, CVML could be used to support decision-makers in improving the financial resources to maintain and/or further support many environmental programs in years to come.

Developing a low-cost wireless energy meter with power quality measurement for smart grid applications represents a significant advancement in efficient and accurate electrical energy monitoring.In increasingly complex and interconnected electrical systems, this device is essential in various applications, such as smart grids, enabling real-time energy usage monitoring. In such a context, smart meters expand the possibilities for sustainable and efficient energy use and promote better utilization of energy sources, especially non-renewable ones. According to the 2020 International Energy Agency (IEA) research, non-renewable energy sources represent 65% of the global total. The smart meter developed in this work is built based on the ESP32 microcontroller and easily accessible components, using a user-friendly development platform that offers a cost-effective solution while ensuring reliable performance. The main objective of the smart meter development was to enhance the software and simplify the hardware. Unlike traditional meters that perform electrical parameter calculations using complex hardware circuits, this project performed the calculations directly on the microcontroller. This approach reduced the complexity of the hardware, simplifying the meter’s design. With the high-performance processing capability of the microcontroller, efficient and accurate calculations of electrical parameters were achieved without the need for additional circuits. This software-driven approach with simplified hardware brought benefits such as reduced production costs, lower energy consumption, and increased meter development and updates flexibility. Furthermore, the integrated wireless connectivity in the microcontroller enables the transmission of collected data to remote monitoring systems for later analysis. The developed smart meter offers cost, energy efficiency, and flexibility benefits. It represents an innovative, efficient, and versatile solution for smart grid applications.

Criticism has been directed towards the traditional methods of management accounting employed by organizations thus far, as they fail to adequately address the growing need for competitiveness and sustainable development in a globalized environment. This criticism stems from both economic factors, such as insufficient monitoring of material, energy, and escalating overhead costs, and environmental concerns, where inadequate attention is given to vital information regarding significant environmental aspects, impacts, risks, and damages arising from the organization's activities and processes. Additionally, these methods do not sufficiently support the implementation of a functional environmental management system (EMS). Drawing upon our research in EMS standardization following the international standards ISO 1400X, as well as our knowledge gained during the development of the Slovak Technical Standard (STN) for ISO 14051, this paper presents an innovative model of environmental management accounting. This model is based on the cost accounting of material and energy flows within organizations. It serves as an enhancement framework for organizations with an existing EMS built according to the EN ISO 14001 model, as well as for organizations lacking a formalized EMS based on this standard. Within this paper, we specify, decompose, and apply the new model, focusing on the cost accounting of material-energy flows in a hypothetical organization. We meticulously examine the ten key steps of the model's structure, particularly in terms of costing and the allocation of costs to material, energy, system, and waste management.

Hillslope and mountain roads are often the source of erosion, which in turn can lead to larger landslides and other types of mass-movements. For this reason, low-cost repeat surveys that can be done by practitioners and not solely scientists and engineers is essential. To solve this issue the present contribution shows the testing and applicability of a mix of SfM-MVS and low-cost SLAM technology to provide erosion information. The low-cost SLAM generated between 300 ~ 900 points per square meter, while SfM-MVS created between ~11,000 to 900,000 points. The density is however a trade-off against a spatially varying error, although the variability at the road-cut scale only ranges from 2 cm vertically to 2.5 cm in x,y,z based on the C2C algorithm. Furthermore, this error tends to be more important in the higher section of the cutout (further away from the sensor and at a flatter angle). It is thus possible to measure small-scale roadside change, providing that the change is in the range of >5 cm (adding the maximum potential error twice) and providing that the road-cut is such that the sensors can be brought close to the surface (camera or sensor on a pole). The authors suggest that it can be a solution for state and agencies with limited funding and that cannot afford regular laser or for roadside that are difficult to access.

Skid resistance of a road pavement surface is the force developed when tyre is prevented from rotating slides along the pavement sur-face. This property comes from the combination of the macro- and micro-texture of pavement. The skid resistance of an asphalt pave-ment is an important parameter influencing driving safety on a road, since there is a proven relationship between skid resistance and accident parameters. The paper deals with the measurement principle of pavement skid resistance (surface friction) including longitudinal and transverse friction. A high number of measuring devices of skid resist-ance are also introduced highlighting their advantages and limitations. Besides, the measurement policies in the European Union and in Hun-gary are outlined. Pavement surface texture is investigated dealing with the levels of surface tex-ture, the most common measuring tech-niques, the macro-texture features of asphalt types, as well as the Hungarian regulation in the field. As a related topic, the aggregate properties and their implication in the relevant Hungarian specification are introduced briefly, as well. Some outcomes of the EU’s COST Ac-tion 354 on the development of unified European macro-roughness and skid resistance performance indicators and indices are also presented.

The study sought to investigate the effect of Multi-choice Goal programming in project Portfolio Analysis, using Lagos State Project Portfolio from between 2017-2021 as a study case. Specifically, the objectives examined the effectiveness of multi-choice goal programming in analyzing the project Portfolio of Lagos State Government and investigated the possible lapses in the use of Multi-choice Goal programming in analyzing the project Portfolio of Lagos State Government. Data were gathered from the State financial Statements and analyzed using Lingo-17 Software. Based on the analysis, the study discovered that the goals formulated can be optimally and minimally attained as the positive deviational and negative deviational variables depict zero. This indicates that the cost incurred on the project portfolio was absolutely minimized during the period and at the same time; Lagos State Government derived the most preferred benefits from it. The study therefore concluded that the State minimized the project costs and at the same time maximized benefits while meeting its overall objectives.

The cost to train a basic qualified U.S. Navy fighter aircraft pilot is nearly $10M. The training includes primary, intermediate, and advanced stages, with the advanced stage involving extensive flight training and being very expensive as a result. Despite the screening tests in place and early-stage attrition taking place, 4.5% of aviators undergo attrition in this most expensive stage. Key reasons for aviator attrition include poor flight scores, voluntary withdrawals, and medical causes. Reduction in late-stage attrition offers several financial and operational benefits to the U.S. Navy. To that end, this research leverages feature extraction and machine-learning techniques on the very sparse flight test grades of student aviators to identify those with a high risk of attrition early in training. Using about 10 years of historical U.S. Navy pilot training data, trained models accurately predicted 50% of attrition with a 4% false positive rate. Such models could help the U.S. Navy save nearly $20M a year in attrition costs. In addition, machine-learning models were trained that can recommend a suitable training aircraft type for each student aviator. These capabilities could help better answer the need for pilots and reduce the time and cost to train them.

This study quantified effluents generated during processing in three industry types, estimated the energy potential from the quantified effluents in form of biogas generation, and determined the economic viability of the biogas recovered. Data were procured from relevant scientific publications to quantify the effluents generated from the production processes in the industry types examined using industrial process calculations. The effluent data generated was used on the 2-module biogas energy recovery model to estimate the bioenergy recovery potential within it. Economic and financial analysis was based on cash flow comparison of all costs and benefits resulting from its activities. The effluents generated average daily biogas of 2559 Nm³/gVS, having a daily potential combined heat and power of 0.52 GWh and 0.11 GWh respectively. The Life Cycle Analysis and cost-benefit analysis show the quantity of avoided emissions from using the effluents to generate heat and power for processes, and also the profitability of the approach. Conclusively, the study shows the use of biomass effluents to generate biogas for CHP is a viable one based on the technologies of a reciprocating engine, gas turbine, microturbine, and fuel cell. However, it is recommended that the theoretical estimation be validated using a field-scale project.

Intending to analyze the role of nuclear power in an integrated energy system, we used the IESA-Opt-N cost minimization model focusing on four key themes: system-wide impacts of nuclear power, uncertain technological costs, flexible generation, and cross-border electricity trade. We demonstrate that the Levelized Cost of Energy (LCOE) alone should not be used to demonstrate the economic feasibility of a power generation technology. For instance, under the default techno-economic assumptions, particularly the 5% discount rate and exogenous electricity trade potentials, it is cost-optimal for the Netherlands to invest in 9.6 GWe nuclear capacity by 2050. However, its LCOE is 34 €/MWh higher than offshore wind. Moreover, we found that nuclear power investments can reduce demand for variable renewable energy sources in the short term and higher energy independence (i.e., lower imports of natural gas, biomass, and electricity) in the long term. Furthermore, investing in nuclear power can reduce the mitigation costs of the Dutch energy system by 1.6% and 6.2% in 2040 and 2050, and 25% lower national CO₂ prices by 2050. However, this cost reduction is not significant given the odds of higher nuclear financing costs and longer construction times. In addition, this study has shown that lower financing costs (e.g., EU taxonomy support) considerably reduce the relevance of nuclear cost uncertainties on its investments. Furthermore, we demonstrate that the economic feasibility of national nuclear power investments can vary considerably depending on the cross-border electricity trade assumptions. Additionally, we found that lowering the cost of small modular reactors has more impact on their economic feasibility than increasing their generation flexibility. In conclusion, under the specific assumptions of this study, nuclear power can play a complementary role (in parallel to the wind and solar power) in supporting the Dutch energy transition from the sole techno-economic point of view.

Classification predictive modeling involves the accurate assignment of observations in a dataset to target classes or categories. There is an increasing growth of real-world classification problems with severely imbalanced class distributions. In this case, minority classes have much fewer observations to learn from than those from majority classes. Despite this sparsity, a minority class is often considered the more interesting class yet developing a scientific learning algorithm suitable for the observations presents countless challenges. In this article, we suggest a novel multi-class classification algorithm specialized to handle severely imbalanced classes based on the method we refer to as SAMME.C2. It blends the flexible mechanics of the boosting techniques from SAMME algorithm, a multi-class classifier, and Ada.C2 algorithm, a cost-sensitive binary classifier designed to address highly class imbalances. Not only do we provide the resulting algorithm but we also establish scientific and statistical formulation of our proposed SAMME.C2 algorithm. Through numerical experiments examining various degrees of classifier difficulty, we demonstrate consistent superior performance of our proposed model.

(1) Background: in epidemiological terms, it has been possible to calculate the savings in health resources and the reduction in health effects of COVID vaccines. From the point of view of economic evaluation, some studies have estimated its cost-effectiveness with the vaccination showing highly favorable results, which in some cases is cost-saving; (2) Methods: a cost-benefit analysis of the vaccination campaign in the North Metropolitan Health Region (Catalonia). An epidemiological model based on observational data and before and after comparison is used. The information on the doses used and the resources assigned (conventional hospital beds, ICU, number of tests) has been extracted from administrative data from the largest Primary Care provider in the region (Catalan Institute of Health). A distinction is made between the social perspective and the health system; (3) Results: the costs of vaccination are estimated at 137 million euros (€48.05/dose administered). This figure is significantly lower than the positive impacts of the vaccination campaign, which are estimated at 470 million euros (€164/dose administered). Of these, 18% corresponds to the reduction of ICU discharges, 16% to the reduction in conventional hospital discharges, 5% to the reduction in PCR tests and 1% to the reduction of RAT tests. Monetization of deaths and cases with sequelae avoided account for 53% and 5% of total savings, respectively. The benefit/cost ratio is estimated at 3.4 from a social perspective and 1.41 from a health system perspective. The social benefits of vaccination are estimated at €116.67 per dose of vaccine given (€19.93 from the point of view of the health system); (4) Conclusions: the mass vaccination campaign against COVID is cost-saving. From a social perspective, most of these savings come from the monetization of the reduction in mortality and cases with sequelae, although the intervention is equally widely cost-effective from the point of view of the health system thanks to the reduction in the use of resources. It is concluded that, from an economic perspective, the vaccination campaign has high social returns.

This paper presents a detailed description of three methodologies to electrify the rural households of small loads with reliable and economical method using solar energy as input source of power. It is observed that high DC voltage is better and economical solution rather than AC based solar systems and low voltage DC systems. In this paper, three methodologies were analysed to select the right combination of portable converters with low prices, which may further enhance the energy access and affordability to common people. The proposed three methodologies were tested through action research in collaboration with a team of scientists and technical experts of National Institute of Solar Energy. The comparative analysis is carried out on three different converters, various kind of house loads and types of power supply i.e. AC/DC. The performance, quality and economic evaluation has been carried out with the help of MATLAB SIMULINK and other measuring instruments specific for solar photovoltaic systems. The derived results were analysed and inferences made for use in village household loads and their income groups. Further, a three-step analytical framework on village-level power energy efficient power at affordable power preposition was presented. This study will help in the designing as well as in the performance and economic evaluation of converters based on solar photovoltaic systems of different technologies.

In this letter, we show an alternative low-cost fabrication method to obtain poly(dimethyl siloxane) (PDMS) microfluidic devices. The proposed method allows the inscription of micron resolution channels on polystyrene (PS) surfaces, used as a mold for the wanted microchip’s production, by applying a high absorption coating film on the PS surface to ablate it with a focused low-power visible laser. The method allows obtaining micro resolution channels at powers between 2 and 10mW and can realize any two-dimensional polymeric devices. The effect of the main processing parameters on the channel’s geometry is presented.

Real estate industry is related to the national economy and people's livelihood，characterized by a high degree of financial intensity. The enterprises in this industry need certain financial ability and large shareholder controlling ability to support their survival. However，due to the multiple adverse impacts of current state policies，banks and private capital，the credit crunch，the sudden decrease in withdrawn funds and the limitation of internal financing，the problem of capital restraint of real estate enterprises has become more and more serious. From the perspective of corporate governance，this paper studies the interaction among financial constraints，ownership concentration and corporate performance under different shareholding states by analyzing the quantitative characteristics of equity structure，and looks for the appropriate range of the largest shareholder holding ratio，which has considered the financial performance and risk. It is found that raising the ownership concentration can effectively ease the financing constraints and improve the performance of enterprises，both of which are significant under the state of high ownership concentration， while the financial constraints play a significant intermediary effect under the State of absolute holding， while in the decentralized state of ownership，there is a significant regulatory effect，and the interaction of the three will be different due to the size of the enterprise.

Background: There are major air pollution monitoring gaps in sub-Saharan Africa. Developing capacity in the region to conduct air monitoring in the region can help estimate exposure to air pollution for epidemiology research. The purpose of our study is to develop a land use regression (LUR) model using low-cost air quality sensors developed by a research group in Uganda (AirQo). Methods: Using these low-cost sensors, we collected continuous measurements of fine particulate matter (PM2.5) between May 1, 2019 and February 29, 2020 at 22 monitoring sites across urban municipalities of Uganda. We compared average monthly PM2.5 concentrations from the AirQo sensors with measurements from a BAM-1020 reference monitor operated at the US Embassy in Kampala. Monthly PM2.5 concentrations were used for LUR modeling. We used eight Machine Learning (ML) algorithms and ensemble modeling; using 10-fold cross validation and root mean squared error (RMSE) to evaluate model performance. Results: Monthly PM2.5 concentration was 60.2 µg/m³ (IQR: 45.4-73.0 µg/m³; median= 57.5 µg/m³). For the ML LUR models, RMSE values ranged between 5.43 µg/m³ - 15.43 µg/m³ and explained between 28% and 92% of monthly PM2.5 variability. Generalized additive models explained the largest amount of PM2.5 variability (R²=0.92) and produced the lowest RMSE (5.43 µg/m³) in the held-out test set. The most important predictors of monthly PM2.5 concentrations included monthly precipitation, major roadway density, population density, latitude, greenness, and percentage of households using solid fuels. Conclusion: To our knowledge, ours is the first study to model the spatial distribution of urban air pollution in sub-Saharan Africa using air monitors developed from the region itself. Non-parametric ML for LUR modeling performed with high accuracy for prediction of monthly PM2.5 levels. Our analysis suggests that locally produced low-cost air quality sensors can help build capacity to conduct air pollution epidemiology research in the region.

Economic burden due to premature mortality has a negative impact not only in health system even though in the society. The aim of this study was to estimate the potential years of work tenure lost (PYWL) due to Chagas disease in Colombia from 2010-2017. National data on mortality by sex and ages between 15 and 62 dues to Chagas from 2010 to 2017. The PYWL methodology was applied to assess the impact of Chagas disease in workers who suffer from them. In total, 1,261 deaths were analyzed in the study, of which 60% corresponded to males. The loss of labor productivity caused by Chagas disease was estimated at $29 million. Overall, 48,621 PYWL were lost, and there was an average of 21 years for all subjects with Chagas. Throughout the analyzed period, PYWL increased substantially, and it is necessary to continue with early detection programs to avoid premature death in working age population.

In power systems, flow allocation (FA) methods allow to allocate usage and costs of the transmission grid to each single market participant. Based on predefined assumptions, the power flow is split into isolated generator specific or producer specific sub-flows. Two prominent FA methods, Marginal Participation (MP) and Equivalent Bilateral Exchanges (EBE), build upon the linearized power flow and thus on the Power Transfer Distribution Factors (PTDF). Despite their intuitive and computationally efficient concept, they are restricted to networks with \emph{passive} transmission elements only. As soon as a significant number of \emph{controllable} transmission elements, such as High-voltage direct current (HVDC) lines, operate in the system, they loose their applicability. This work reformulates the two methods in terms of Virtual Injection Patters (VIP) which allows to efficiently introduce a shift parameter $q$, tuning contributions of net sources and net sinks in the network. Major properties and differences of the methods are pointed out. Finally, it is shown how the MA and EBE algorithm can be applied to generic meshed AC-DC electricity grids: Introducing a \emph{pseudo-impedance} which reflects the operational state of controllable elements, allows to extend the PTDF matrix under the assumption of knowing the current system's flow. Basic properties from graph theory are used for solving the pseudo-impedance dependent on the position in the network. This directly enables \emph{e.g.} HVDC lines to be considered in the MP and EBE algorithm. The extended methods are applied to a low-carbon European network model (PyPSA-EUR) with a spatial resolution of N=181 and an 18\% transmission expansion. The allocations of VIP and MP, show that countries with high wind potentials profit most from the transmission grid expansion. Based on the average usage of transmission system expansion a method of distributing operational and capital expenditures is proposed. Further it is shown, how injections from renewable resources strongly drive country-to-country allocations and thus cross-border electricity flows.

Buildings use 30-40 % of all energy resources and are thus their main consumers in modern society. Moreover, buildings require a vast amount of different raw materials. During the last two decades, several green building certifications have been created in order to consider social, economic and environmental aspects of sustainability of buildings. One of the most famous and widely used of these certifications is Leadership in Energy and Environmental Design (LEED). So far, the use of LEED has concentrated in the US and other developed countries. One reason that restricts the use of this point-based system certification in developing countries is the limited data about its costs. In this study, the extra cost of the certification process will be evaluated besides the changes needed in the design of the building to reach the points required by LEED. At the first stage, the number of points the case study earns in its current format (Scenario 1) were found out, then the cost difference of getting either the Certified (Scenario 2) or Silver (Scenario 3) level LEED certification for the building was studied. It was found that besides some technical considerations, filling the criteria of the Certified and Silver level increases the total costs of construction by 3.4% and 5.9%. Further improvement of the building’s energy efficiency would enable the attainment of a higher-level certification. The results of the study could help to promote the use of green building certifications in Western Asia.

Almost all single-family detached house in Canada consume huge electricity for space heating and domestic hot water (DHW) purposes. There are many possibilities to design an energy-efficient house. A solar water heating system can be used for domestic water and space heating. Water temperature can be kept constant always by connecting a heat pump or oil burner because solar energy is intermittent. Proper and optimized solar photovoltaic and collector design, tank design, heat pump selection, house insulation, total demand calculation in each section are essential. Energy-Efficient house design has been proposed with water heating and space heating system and compared with the existing system, solar PV based systems, and solar collector based system. The tracking and non-tracking based solar thermal collector based and the solar photovoltaic based system has been compared in this paper and investigate the suitable one for practically applicable and acceptable by the people. Simulation has been done by using the PolySun software. It found that by implementing the proposed PV based system with tracking is highly suitable considering lower cost, high output power, flexibility, easy installation.

In the industrial sector, the financial gain is generally considered at the cost of environmental degradation. The environmental protection and economic benefit are common conflicting objectives in many optimization problems. In any multi-criteria problem, the most challenging part is to optimize the conflicting goals. But if these objectives become non-conflicting, then the problem can be solved with ease. This paper is all about minimizing the environmental impact of industrial processes while maximizing the net economic benefit. There is a general misconception of linking the environmental protection with a financial loss. This is not always true; the Eco-Efficiency or Cleaner Production Technology (CPT) is a perfect example to disprove this notion. Economics of CPT regarding water savings for the textile-processing sector in Pakistan has been analyzed in this paper. Changes in water use before and after the implementation of water conserving technologies in the textile sector of Pakistan from a study conducted by the Cleaner Production Institute (CPI) are utilized to perform a cost-benefit analysis. Direct financial benefits that could be achieved through the application of these technologies are presented only. Other environmental benefits, though not described here, are of no less importance. The study results reveal that very high potential of water savings exists for most of the investments on CPT.

The objective of the prototype is to eliminate the polluting contamination of water sources, due to the leak of industrial waste without any kind of treatment, mainly generated by the industries and home sector. In this project, a prototype of water purification by plasma technology has been designed. The prototype will convert contaminated water into the plasma stream and eliminate the pathogens from the water by exposing it to ultraviolet radiation and plasma sterilisation. The polluted water will be accelerated at high speed using a water pump in order to convert it into a liquid-gas mixture for ease plasma generation. This process will be achieved when the electric supply from a source of alternating current (AC) is applied to the water by means of high voltage electrodes. After which, the mixture slows down to return into liquid form and the clean water is obtained. The whole process takes place without significantly raising the temperature also knows as non-thermal plasma. The device also has an automatic flow and pressure control system. Finally, a short feasibility study has been conducted on the water samples collected and report obtained from Chennai Metropolitan Water Supply and Sewage boards are reported. It has been concluded that this new plasma-based water treatment system will be more efficient and cheaper than the current wastewater treatment techniques and can be used in the future as the replacement of current secondary and tertiary treatments of industrial wastewater.

This study evaluates eight-year ownership costs for battery electric vehicles (BEV) versus non-plugin hybrid vehicles using forecasting to estimate future electricity and conventional gasoline prices and incorporating these in a multiple design of experiments simulation. Results suggest that while electric vehicles are statistically dominant in terms of variable costs over an 8-year life-span, high-performance hybrid non-plugins achieve variable fuel costs nearly as good as low-performing electric vehicles (those attaining only 3 miles per kilowatt hour) and that these hybrid acquisition costs are (on average) lower yet the vehicles retain higher residual values. In general, the six smallest ownership costs are split evenly between hybrid and electric vehicles; however, inflation for conventional regular gasoline is estimated to outstrip inflation per kilowatt hour. Thus, non-plugin hybrid cars are likely to require considerably more advanced engineering to keep pace.

In response to ever-increasing concerns regarding ecological degradation and societal impact, numerous stakeholders such as non-governmental organisations, government officials, end-users, the mass media, and community activists have compelled business organizations, particularly multi-national companies to lessen harmful greenhouse gases emissions associated with their production activities. Energy sector is one of the biggest harmful greenhouse gases emission producer, hence, decision-makers within the energy sector are forced to promote and build environmental friendly and sustainable power generation plants. To this end, the concept of sustainability in the electricity sector has attracted so much attention from academics and industrial practitioners over the last three decades. Albeit, an important number of sustainability assessment frameworks for electricity generating technologies are found in the current literatures, three major drawbacks have emerged from those frameworks. Firstly, there is a lack of a holistic and comprehensive sustainability assessment framework for different power generation plants. Secondly, from economic aspect, the most used model (Levelised Cost of Electricity) to measure the cost performance of various electricity generating technologies is biased and is not inclusive enough, because, it only considers the capital, Operations and Maintenance, and fuel costs. Hence, snubbing crucial elements to business decision. Lastly, in the current literature, there is no a single sustainability assessment model that considers all the phases of electrical energy’s lifecycle. Considering these flaws, the novelty of this study is the development of a new, holistic theoretical sustainability assessment framework for power generation plants. The developed theoretical model includes 19 impact categories, 52 potential indictors, and 10 end points environmental, economic, and social aspects.

Herein, the mechanical properties and carbonation durability of engineered cementitious composites (ECC) were studied. For cost-efficient utilization of ECC materials, polypropylene (PP) and hydrophilic polyvinyl alcohol (PVA) fibers were employed to cast different types of specimens. The compressive strength, Poisson’s ratio, strength-deflection curves, cracking/post-cracking strength, impact index, and tensile strain-stress curves of the two types of ECC materials, with different fiber contents of 0 vol%, 1 vol%, 1.5 vol% and 2 vol%, were investigated by conducting compressive tests, four-point bending tests, drop weight tests, and uniaxial tensile tests. In addition, the matrix microstructure and failure morphology of the fiber in the ECC materials were studied by scanning electron microscopy (SEM) analysis. Furthermore, carbonation test and steel corrosion after carbonization were employed to study durability resistance. The results indicated that for both PP fiber- and hydrophilic PVA fiber-reinforced ECCs, the compressive strength first increases and then decreases as fiber content increases from 0 vol% to 2 vol% and reaches the maximum at 1 vol% fiber content. The bending strength, deformation capacity, and impact resistance show significant improvement with increasing fiber contents. The ECC material reinforced with 2 vol% PP fiber shows superior carbonized durability with maximum carbonation depth of only 0.8 mm.

Clinical rehabilitation and sports performance analysis both require the objectification of movement. Kinovea^© is a free 2D motion analysis software that enables the establishment of kinematics parameters. This low-cost technology has been used in sports sciences, as well as clinical field and research work. Although it has been validated as a tool with which to assess time-related variables, this is not yet the case regarding angular and distance variables. The main objective of this study was to determine the validity and reliability of the Kinovea software in obtaining angular and distance data at different perspectives of 90°, 75°, 60° and 45°. For this purpose, a figure with 29 points was designed (in AutoCAD) and 24 frames analysed. Each frame was examined by three observers who each made two attempts. For each export data item, 20 angles and 20 distance variables were calculated, with intra- and inter-observer reliability also analysed. To evaluate Kinovea reliability and validity a multiple approach was applied involving the following analysis: -systematic error with a two-way ANOVA 2x4; -relative reliability with ICC and CV (95% confidence interval); -absolute reliability with Standard Error. The results thus obtained indicate that the Kinovea software is a valid and reliable tool that is able to measure accurately at distances up to 5 m from the object and at an angle range of 90°–45°. Nevertheless, for optimum results an angle of 90° is suggested.

This economic evaluation aims to support policy-making on the combined use of pre-exposure prophylaxis (PrEP) with HIV vaccines by evaluating the potential cost-effectiveness of implementation that would support the design of clinical trials for assessment of combined product safety and efficacy. The target study population is a cohort of men who have sex with men (MSM) in the United States. Policy strategies considered include standard HIV prevention, daily oral PrEP, HIV vaccine, and their combination. We constructed a Markov model based on clinical trial data and published literature. We used a payer perspective, monthly cycle length, a lifetime horizon, and a 3% discount rate. We assumed a price of $500 per HIV vaccine series in the base case. HIV vaccines dominated standard care and PrEP. At current prices,PrEP was not cost-effective alone or in combination. A combination strategy had the greatest health benefit but was not cost-effective (ICER=$463,448/QALY) as compared to vaccination alone. Sensitivity analyses suggest a combination may be valuable for higher-risk men with good adherence. Vaccine durability and PrEP drug prices were key drivers of cost-effectiveness. Results suggest that boosting potential may be key to HIV vaccine value.

In small-scale hydropower scheme, the most important component is electro-mechanical equipment. Since cost contribution of this component is high because hydrokinetic projects require negligible civil works. Turbine and alternator contribute major fraction of the hydrokinetic projects. Thus, there is a requirement to estimates the electromechanical equipment cost for the hydrokinetic hydropower scheme. The present paper investigates design parameters of the hydrokinetic turbines and intends to develop cost correlation which depends on most critical parameters of hydropower sites such as velocity and power capacity. In this present work, three zero head turbines are considered including straight blade Darrieus, two Stage Savonius, and Gorlov Helical. The size and cost of major components have been calculated based on material, manufacturing, research and design, and assembly costs. Based on cost and site parameters, cost correlation has been developed. The obtained cost has been validated with available zero head turbines in the market and installed projects. A techno-economic analysis has been carried out to select economical hydrokinetic turbine for river and canal application.

Magneto Hydrodynamic (MHD) propulsion presents an innovative and environmentally friendly alternative to conventional marine propulsion systems. By utilizing electromagnetic fields to generate thrust, MHD propulsion reduces both noise and air pollution, making it an appealing option for the future of commercial shipping. However, the widespread adoption of MHD technology has been limited by several significant challenges. High energy demands, particularly for generating the necessary magnetic fields, require advanced power sources such as nuclear reactors or large-scale batteries, which are impractical for most commercial vessels. Additionally, the cost of installing and maintaining the high-powered electromagnets and energy infrastructure remains prohibitively expensive. Furthermore, material durability is another concern, as system components such as electrodes are exposed to corrosion and wear due to their interaction with seawater and high electrical currents. By addressing these constraints, MHD propulsion has the potential to transform the commercial maritime industry, contributing to more sustainable and less polluting marine transportation. This paper explores the feasibility of overcoming these challenges through advancements in cost-effective energy efficient methods. We identify the primary limiting factor for the practical implementation of magnetohydrodynamic (MHD) propulsion systems as the magnetic field strength. Drawing comparisons with similar applications, such as magnetic resonance imaging (MRI) technologies, we explore advancements in generating strong, homogenous magnetic fields over large volumes. Insights from these research areas provide a foundation for improving the scalability and efficiency of magnetic field generation in MHD systems.

This study provides a detailed numerical assessment of the productivity of single-slope solar stills across key cities in the Caspian region, including Aktau, Atyrau, Astrakhan, Makhachkala, Baku, Tehran, and Turkmenbashi. Employing a mathematical model based on heat balance equations and the 4th-order Runge-Kutta method, we simulated the distillation process under various climatic conditions. The results reveal that productivity is significantly influenced by geographic location and local meteorological conditions. Tehran demonstrated the highest productivity across all seasons, with values of 1.75 × 10³ kg/(m²·year) and an efficiency of 0.53, due to its optimal solar irradiation and ambient temperature. In contrast, Atyrau and Makhachkala exhibited lower productivity, particularly in colder months, highlighting the effect of ambient temperature on solar still efficiency. The analysis identified optimal water depth at 2 cm and insulation thickness between 4 and 9 cm for enhancing productivity in continental climates like Aktau. Additionally, the lowest cost of distilled water was $0.024 per kilogram in Baku. These findings align with existing literature, validating the numerical model's accuracy. Future research will explore integrating solar stills with other renewable and fossil fuel-based technologies.

The implementation of Emissions Trading System (ETS) poses significant challenges for the Indian maritime sector. This paper investigates the regulatory implications of ETS, focusing on how these regulations impact compliance and operational practices within the Indian shipping sector. It also analyzes the economic effects, including the financial burden and competitive dynamics faced by the Indian shipping companies in a global context. Additionally, the paper explores various adaptation strategies, emphasizing technological innovations and operational adjustments essential for navigating ETS requirements. By integrating recent industry data and case studies, this study offers a comprehensive overview of how the Indian shipping industry can effectively address the evolving regulatory landscape of ETS.

Artificial General Intelligence (AGI) represents a pivotal advancement in AI with far- reaching implications across technological, ethical, and societal domains. This paper addresses the following: (1) an in-depth assessment of AGI’s transformative potential across different sectors and its multifaceted implications, including significant financial impacts like workforce disruption, income inequality, productivity gains, and potential systemic risks; (2) an examination of critical ethical considerations, including transparency and accountability, complex ethical dilemmas and societal impact; (3) a detailed analysis of privacy, legal and policy implications, particularly in intellectual property and liability, and (4) a proposed governance framework to ensure responsible AGI development and deployment. Additionally, the paper explores and addresses AGI's political implications, including national security and potential misuse. By analyzing and considering computer science, philosophy, economics, and policy perspectives, we offer a multidisciplinary view of AGI’s challenges and opportunities, advocating for proactive measures to align AGI development with human values and societal interests.

At the end of the dry season in early March to early April each year, extensive agricultural biomass waste burnings occur throughout insular mainland Southeast Asia. During this biomass burnings period, smoke aerosols blanket the whole regions and were transported and dispersed by predominant westerly and south westerly winds to southern China, Taiwan and as far southern Japan and the Philippines. The extensive and intense burnings coincided with some wildfires in the forests due to high temperature making the region one of the global hot spots of biomass fires. In this study, we focus on the effect of pollutants emitted from biomass burnings in March 2019 at the height of the burnings period to the exposed population and their health impact. The Weather Research Forecast- Chemistry (WRF-Chem) model was used to predict the PM2.5 concentration over the simulating domain and health impacts are then assessed on the exposed population in the four countries of Southeast Asia, namely Thailand, Laos, Cambodia and Vietnam. Using the health impact based on log-linear concentration response function and Integrated Exposure Response (IER), the results show that at the peak period of the burnings from 13 to 20 March 2019, Thailand experienced the highest impact, with an estimated 2170 premature deaths. Laos, Vietnam, and Cambodia followed, with estimated mortalities of 277, 565, and 314 deaths respectively. However, when considering the impact per head of population, Laos exhibited the highest impact, followed by Thailand, Cambodia, and Vietnam. The results highlight the significant health impact and associated economic cost of agricultural waste burnings in Southeast Asia at the end of the dry season. And hence policy makers should take these into account to design measures to reduce the negative impact of widespread burnings on exposed population in the region.

The advancement of mechanization in forestry has bolstered productivity within the sector while simultaneously precipitating impacts necessitating deeper comprehension for the attainment of heightened levels of environmental sustainability in forestry management. The aim of this study was to apply an instrument for assessing the environmental damage and impacts of forest harvesting in commercial Eucalyptus plantations, based on a combination of methodologies. The environmental impact assessment methodology for forest harvesting was applied by combining interaction network approaches and impact assessment matrices. This methodology was then applied to a commercial Eucalyptus plantation in the municipality of São Pedro, state of São Paulo, Brazil. The results show that timber transportation activities had the highest positive and negative environmental indices. The most significant negative impacts refer to the depletion of water resources and erosion, while the positive impacts are regional development and job creation. The most significant changes, both positive and negative, were observed in a greater number of environmental impacts for the physical and anthropic environment, with fewer for the biotic environment. The combination of methodologies has potential as an expedient tool for planning forestry operations, providing practical results for decision-making, making production more sustainable and applicable to different crops and production systems.

The study is aimed at developing a resilient benchmarking system for small hotels in Ukraine, designed to ensure their survival and growth amid global disruptions and local crises. Given the severe challenges associated with the COVID-19 pandemic and military actions, the resilience of the regional tourism business becomes particularly relevant. The methods used in the study, including factor and cluster analysis, the method of taxonomy, and dendrograms, have enabled the development of development programs for two clusters of hotels: those located in areas with increased military risk and those in relatively safe territories. The taxonomic analysis revealed significant differences in managerial practices and operational efficiency, largely determined by the geographic location of the hotels. Hotels in active combat zones experience a 40% reduction in tourist flow and financial instability, while hotels in safe areas demonstrate a 30% higher level of customer satisfaction. The application of advanced security systems and modern marketing techniques has led to a 40% reduction in incidents.

The assessment of Photovoltaic (PV) systems, a key component of sustainable energy solutions, is of paramount importance in the pursuit of a greener future. This abstract provides an overview of environmental assessments associated with PV systems, considering their life cycle, energy production, and ecological impact. The study explores various methodologies and tools employed in the evaluation of PV systems’ environmental performance, addressing issues such as material sourcing, manufacturing, installation, and end-of-life disposal. Additionally, it delves into the trade-offs and challenges in achieving sustainability objectives in the solar energy sector. By critically examining the environmental implications of PV systems, this research contributes to informed decision-making and the sustainable integration of solar power into our energy landscape.

Measuring the impact of land use regulation on the land market involves identifying and classifying relevant impact factors related to the land market. The objective of this study was to identify land market impact factors in the context of the introduction of land use regulation in Nepal. A desktop review was carried out to identify preliminary set of impact factors, which were reclassified through intuitive analysis based on the degree of thematic closeness. Perspective-based impact factors were identified through the qualitative analysis of primary data collected through semi-structured interviews with the Nepalese land market stakeholders. These independently derived impact factors were compared with the desktop literature review impact factors resulting in fourteen land market impact factors across four dimensions, including transaction cost, valuation, mortgage availability, taxation, and compensation across the economic dimension; lot size, subdivision restrictions, and coordination across the institutional dimension; awareness, expectation and proximity across the social dimension; and risk reduction, quality of residential land, and suitability of zoning classification across the environmental dimension. There was significant overlap and commonality across factors identified from both the literature review and semi-structured interviews. The land market impact factors determined in the study may be adapted and generalized across other countries and could be utilized to better understand the impacts of land policy decisions on urban planning and development. Further research is recommended on the process to operationalize the use of these factors to quantify the impact of land use regulation on different land markets.

The European Green Deal is the policy established by the EU against global environmental problems. However, it is closely concerned with the countries carrying import and export activities with the EU. Because it is planned to impose an obligation to declare greenhouse gas emission inventories of products imported into the EU, the EU will soon begin to assess the CBAM on imports from carbon-intensive industries. This regulation is expected to affect Turkey-EU trade relations. One of the industries that Turkey has export relations with the EU is the building materials industry. For this reason, it will have had necessary to provide sustainability criteria for the export of building materials. However, there is no database in Turkey where the inventory data of building materials is declared. In Turkey, within the scope of EU Green Deal action plans, it is aimed to create different structures for environmental information of building materials in the medium and long term. In light of such information, within the scope of this study it is aimed to create reference information for building material databases to be developed in Turkey. The study recommends weight and normalization reference values following the life cycle assessment methodology defined by ISO 14040 and ISO 14044 international standards. Twelve environmental impact categories accepted in the literature, including greenhouse gas emissions, have been considered as the environmental impacts of building materials. Semi-structured interviews were held with twenty-one industry stakeholders in Tur-key to determine the weight reference values. The results obtained from the semi-structured interviews were combined using the AHP method. An extensive research study was conducted on Turkey’s national inventory data to determine normalization reference values. Environmental impact calculations were carried out for different building materials in a case study to present the importance of regional adaptation of the determined reference values. The recommended reference information may be used in greenhouse gas emission declarations under the EU Green Deal and other potential environmental impact declarations from building materials.

This research investigates the socio-economic impact of rattan cane processing on the livelihoods of practitioners in Ibadan Metropolis. It comprehensively assesses raw material availability, examines factors influencing demand and supply, and evaluates sales to determine the profitability of rattan processors. With the snowball and total enumeration sampling methods, the study engages with a diverse group of rattan processors. The study reveals a concerning 36% acknowledgement of a decline in the availability of canes attributed to deforestation and over-exploitation. Notably, it emphasizes that rattan product manufacturing in Ibadan Metropolis operates at a craft level, employing tools like saws, hammers, knives, gas cylinders, pinches, and tape rules, impacting production efficiency and product longevity. Additionally, the research underscores the absence of government support and credit facilities reported by all respondents. The Market Margin analysis highlights varying profitability levels across rattan products, with chairs exhibiting notably higher profitability (32.22%) than others. Individual profit margins for specific products, such as chairs (145.00%), underscore the significance of processing costs, production time, and market demand. The study offers strategic recommendations, advocating for district-level service centres for training, technology transfer, and support to enhance skills and technical efficiency. It calls for government intervention through incentives, facilities, tax breaks, and policies to fortify the rattan trade, improving production standards and unlocking export market potential. Furthermore, financial support, technical assistance, and mechanization should be employed to reduce production costs and time, ultimately elevating income generation in the rattan industry. This research contributes valuable insights to the discourse on sustainable livelihoods and economic viability within the rattan sector.

The study examines the return performance and resilience to market volatility of the recently introduced Environment, Social/Sustainable, and Governance (ESG) weight-tilted Hang Seng Index (HSIESG) compared to its parent, the Hang Seng Index (HSI). The ESG-infused index has a higher mean return and lower return volatility than the parent index, although the differences are statistically and economically insignificant, a result consistent with the high correlation between the two index returns. Most important, the ESG weighted-tilted index is more resilient to volatility spikes than the parent index and therefore has lower downside risks. The overall results show that stocks with high ESG ratings are less susceptible to trading pressures triggered by volatility-induced turnovers. The paper contributes to the literature by providing significant incremental information on the emerging market for ESG-related equity products in Hong Kong.

It was believed that the construction of large dams in the upper tributary basin of the Chao Phraya River (CPR) has caused sediment load reduction in the CPR system by 75–85%. Based on historical and observed river flow and sediment data measured during 1922–2019, we investigated the effects of three large dams, the Sirikit, Naresuan, and Khwae Noi dams, on river runoff and sediment loads supplied to the CPR using Mann-Kendall (MK) test and the double mass curve (DMC). Results from this study revealed that the Nan River contributed approximately 40% and 57% of the runoff and total sediment load (TSL) to the CPR, respectively. The water regulation by the Naresuan diversion dam has mainly caused the reduction on annual runoff and TSL downstream of each dam. The results from this study also reveal that the sediment load observed at the headwater (C.2) of the CPR still increased after the construction of the Sirikit dam due to the expansion of irrigation areas downstream of the dam and the channel improvement in the lower reach of Nan River. After the three major dams were operated, the sediment load at C.2 reduced by 31% compared that discharging during the pre-construction period.

This study investigates the impact of the economic downturn induced by the COVID-19 pandemic on nutritional health disparities among women. The research aimed to understand how economic challenges have influenced dietary choices, access to nutritious food, and overall nutritional well-being in different socioeconomic groups of women.Utilizing a mixed-methods approach, the study combined quantitative data from national health and economic databases with qualitative insights from interviews conducted with a diverse group of women. The quantitative analysis focused on trends in nutritional health indicators and economic variables pre- and post-pandemic. Simultaneously, the qualitative component explored personal experiences and perceptions related to nutrition and economic hardships during the pandemic.The findings revealed a significant correlation between the economic downturn and worsening nutritional health among women, especially in low-income and marginalized communities. Women in these groups reported reduced access to healthy food options, increased reliance on less nutritious food due to budget constraints, and a general decline in dietary quality. This decline was less pronounced in higher-income groups, highlighting a clear disparity.Moreover, the study observed that the pandemic exacerbated pre-existing nutritional inequalities, with vulnerable groups experiencing a more pronounced impact. The research also noted that community support systems and public health interventions played a crucial role in mitigating some of these effects.In conclusion, the COVID-19 pandemic has not only posed a direct health challenge but has also indirectly affected women's nutritional health through economic strain. The study underscores the need for targeted nutritional support and economic policies that prioritize the health of women, particularly those from socioeconomically disadvantaged backgrounds.

Geoenvironmental engineering involves defining solutions for complex problems, such as containment systems management, contaminant transport control, wastewater management, remediation of contaminated sites and valorization of geomaterials and wastes. In the last years, energy harvesting (EH) - or energy scavenging - methods and technologies have been developed to reduce the dependence on traditional energy sources, namely fossil fuels, and nuclear power, also responding to the increase of energy demands for human activities and to fulfill sustainable development goals. EH in geoenvironmental works and surrounding soil and water environment include a set of processes for capturing and accumulating energy from several sources considered deemed wasted or unusable associated to soil dynamics, stress and strain of geomaterials, hydraulic, vibrations, biochemical, light, heating and wind sources can be potential EH systems. Therefore, this work presents a review of the literature and critical analysis on the main opportunities for EH capturing, accumulating and use in geoenvironmental works, among basic electric concepts and mechanisms, analyzing those works in complex conditions involving biological, chemical, mechanical, hydraulic, and thermal coupled actions, concluding with the main investigation and challenges within geoenvironmental aspects for EH purpose.

Climate change and net zero are expected to have a significant impact on eco-nomic activity in developing countries, and thus, on the financial cash flow of organizations. In this paper, we propose a procedure for assessing the financial impact of climate change on organizational cash flow in developing countries. Our study contributes to the research on scenario-based climate change impact analysis. The applied research design is exploratory and mainly qualitative, constructing financial impact scenarios combined with narratives to determine the impact of climate change on individual cash flow line items. The narra-tives rationalize cash flow variations and enrich the estimates by adding expert knowledge, in the form of financial micro-stories and descriptions to the pro-posed procedure. We present a hypothetical citrus farming company in a de-veloping country to demonstrate the application of the proposed measurement procedure. The results obtained suggest that (1) the procedure is practical be-cause financial cash flows are known and widely used in financial analysis and because they can be implemented in a spreadsheet; that (2) the procedure pro-vides flexibility because it can be adapted to suit the specific needs of different companies, depending on a selected climate change scenario, the company’s exposure to climate change, its resilience and vulnerability; (3) that the prac-tice of thinking in terms of financial worst-case, base-case and best-case sce-narios, which reflect different levels of uncertainty, helps to model the finan-cial impacts of climate change and provides a forward-looking perspective on how much a company’s cash flow could change.

In the process of construction and operation of the pumping station, a large number of axial flow pumps pose a threat to the survival of fish in the natural environment. Through experiments, the survival conditions of many kinds of fish in extreme positive and negative pressure environment are explored, and -40 kPa is established as the low pressure damage threshold, which is used as a reference to analyze the probability of fish damage caused by pumps. The immersion boundary-lattice Boltzmann numerical method (LES-IB-LBM) based on large eddy simulation is used to optimize the parameters of the prototype runner of axial flow pump. A new type of runner with leading edge thickness of 11.4mm and blade cutting angle of 18 ° is optimized. This kind of runner has both hydraulic performance and fish-passing ability. The runner of the fish-friendly axial flow pump effectively guides the fish into the low risk area and reduces the risk of friction, shear and impact damage to the fish. the total impact mortality ratio of the runner of the axial flow pump before and after optimization is close to 7:1. The pressure damage analysis shows that the lowest negative pressure of the fish is -59.2 kPa, which is higher than -64.5 kPa of the prototype pump, and the highest positive pressure of the body surface is 11.8 kPa, which is lower than the 43.8 kPa of the prototype pump. Shear damage analysis shows that the fish will not be damaged by shear in the prototype pump and fish-friendly pump.

In recent research, a growing body of empirical evidence, suggests that prosocial impact at work can play a significant role in enhancing creativity and innovativeness. Drawing from Self Determination Theory, we hypothesized that basic psychological needs and benevolence satisfaction could serve as a mediating factor in the relation between employees perceived social impact and innovative work behavior and creativity, thus illuminating the manner in which contentment of psychological needs fosters inventive proclivities within the organizational milieu. Results from a study in Greece and Canada (N=528) showed that both perceived social impact and prosocial motivation are positively associated with innovative work behavior and creativity while autonomy and competence satisfaction mediate the relation between perceived social impact and the work outcomes examined within this study. Moreover, prosocial motivation was found to moderate the relation between benevolence satisfaction and innovativeness. Findings extend prior research on the role of prosociality on creative behavior at work and provide supporting evidence for the organizations that encourage and support employees’ initiatives to make a positive difference in the lives of others.

Polymethyl methacrylate (PMMA) polymer is widely used in various fields today. In order to thoroughly reveal the structural impact performance of PMMA materials, this paper is based on the comprehensive Johnson-Cook constitutive model and damage failure model, and accurately confirm the J-C constitutive and damage failure model parameters of PMMA through material test data, The dynamic process of steel bullet impacting PMMA plate structure is analyzed by finite element software ABAQUS. The calculation results show that the numerical simulation results in this paper are in good agreement with the experimental test data. Therefore, the feasibility and accuracy of the impact analysis of PMMA structures based on J-C constitutive and damage failure models in this paper are studied. Finally, based on the J-C constitutive model and damage failure model, the variation of the residual velocity of the bullet with the PMMA plate thickness is analyzed in depth, that is, the results show that the residual velocity of the bullet has a certain linear relationship with the plate thickness.

Solar energy is a renewable and sustainable source of energy, and it has been used in addition of specified technologies, such as solar cooling. The demand for cooling continues to increase in line with environmental changes and a greater desire for human comfort. This study primarily aimed to analyze the performance of an evaporative cooling system powered by solar energy and assess the economic and environmental impact of this system. In this system, hot, dry incoming air is used to evaporate water, thus transforming part of its sensitive heat into latent heat without any variation in enthalpy or total heat. The outputted air then has a lower temperature and a greater humidity, thus providing a more comfortable, healthier environment for its users. To achieve this study’s objectives, numerical code was developed and implemented in MATLAB. The obtained results reveal that the efficiency of the evaporative cooler exceeds 90%, with maximum efficiency being reached at a high wet-bulb depression, while minimum efficiency was observed when the dry air has a high relative humidity and a low dry-bulb temperature.

The effects of partial replacement of Si by Al on the microstructure, tensile properties, and Charpy impact toughness were investigated using 0.2%C-Si/Al-Mn-Cr-B TRIP-aided martensitic steels. The impact toughness was related to the microstructural and mechanical properties. The partial re-placement decreased the volume fraction of retained austenite and increased the mechanical stabil-ity, accompanied by softening and an increase in the volume fraction of the primary martensite. Resultantly, the partial replacement decreased strength and ductility. The impact absorbed energy (value) at 25 C was slightly increased by the partial replacement. The increased impact absorbed energy was mainly caused by high crack/void propagation energy due to the softened primary martensite and a small contribution of the stabilized retained austenite. The 50% shear fracture ductile-to-brittle transition temperature was marginally raised by the partial replacement. The raised transition temperature was mainly associated with an increase in a unit crack path of qua-si-cleavage/cleavage fracture.

This paper analyzes the relationship between corporate social responsibility (CSR) and social impact within the oil industry in an emerging market (Peru). Following the emerging literature on the social impact field, and using the case study methodology, the findings show that the expected positive relationship is diverse depending on several factors. Specifically, the results reveal that companies must focus on community and environmental responsibility, together with stakeholders' involvement to achieve social impact. However, when companies are weak in any of the previous factors, then the social impact is only partially accepted or even withdrawn.

Purpose To analyse diagnostic and therapeutic impact of molecular imaging TNM (miTNM) stage obtained with 18F-DCFPyL versus 18F-Fluorocholine in head to head comparison in biochemical recurrence (BCR) prostate cancer (PCa). Material and methods Patients with BCR PCa after radical treatment with previous 18F-Fluorocholine-PET/CT (negative or oligometastatic disease) were derived to 18F-DCFPyL-PET/CT. Patients were classified attending to: grade group, EAU classification, PSA, PSA doubling time (PSAdt) and PSA velocity (PSAvel). The overall detection rate (DR) and miTNM stage according to PROMISE criteria were assessed for both radiotracers and also correlation (Kappa). The influence of PSA and kinetics on both PET/CT (DR and miTNM) and predictive value of unfavourable kinetics on miTNM were determined. Cut-off PSA, PSAdt and PSAvel values able to predict PET/CT results were determined. Change in miTNM and treatment derived of 18F-DCFPyL information with respect to 18F-Fluorocholine was also evaluated. Results We studied 138 patients. 18F-DCFPyL showed a higher DR than 18F-Fluorocholine (64.5% vs 33.3%). 18F-DCFPyL and 18F-Fluorocholine detected T in 33.3% versus 19.6%, N in 27.5% versus 13.8%, M in 30.4% versus 8.7%. Both tracers DR showed significant associations with PSA and PSAvel. Significant association was only found between miTNM and PSA on 18F-Fluorocholine-PET/CT (p=0.033). For 18F-Fluorocholine and 18F-DCFPyL PET/CT a PSAdt cut-off of 4.09 and 5.59 months, respectively, were able to predict M stage. 18F-DCFPyL changed therapeutic management in 40/138 patients. Conclusion 18F-DCFPyL provides a higher DR and superior miTNM staging than 18F-Fluorocholine in restaging BCR, especially with high PSA and unfavourable PSA kinetics, showing a fair agreement to 18F-Fluorocholine.

Identifying the psychological impacts of the COVID-19 pandemic on the general population can help with planning and preparing for future pandemics. The main objective of the current study was to examine the psychological impacts of the COVID-19 pandemic among Iranian citizens in 2021. The current study used an online cross-sectional survey via web-based questionnaires among Iranian citizens. Perceived feelings during the COVID-19 pandemic were measured by the validated self-reported inventory by Reynolds et al, 2007. The collected data were analyzed using descriptive statistics, including percent, mean, and standard deviation, as well as inferential statistics, including, correlation, chi-square, independent t-test, and analysis of variances (ANOVA) by SPSS.22. The average age of the respondents was 34.62 (±11.86). The most frequent feelings experienced by Iranian citizens were boredom (44.0%), nostalgia (40.6%), anxiety (35.3%), and nervousness (34.1%). Female participants, younger respondents, unemployed, single individuals, and those living in the urban areas experienced significantly higher levels of the negative feelings. The results of the study showed the high prevalence of the psychological effects of the COVID-19 pandemic in Iranian citizens, particularly among women, single, and unemployed people. Therefore, we need to develop more supportive and consultative packages for future pandemics as well as implement psychological rehabilitation after the crises.

Smart flexible materials with piezoresistive property are increasingly used in sensors field. When embedded in structures, they would allow an in-situ structural health monitoring and damage assessment of impact loading such as crash, bird strikes and ballistic impacts. However, this could not be achieved without a deep characterization of the relation between piezoresistivity and mechanical behavior. The aim of this paper is to study the potential use of the piezoresistivity effect of a conductive foam made of a flexible polyurethane matrix filled with active carbon for integrated Structural Health Monitoring (SHM) and damage assessment applications. To do so, PolyUrethane Foam filled with Active Carbon, namely PUF-AC, are manufactured and tested under quasi-static compression tests and under Dynamic Mechanical Analyser (DMA) with in-situ measurement of its resistivity during tests. A relation is proposed for describing the evolution of the resistivity versus strain and stress. In addition, a demonstrative experiment of a PUF-AC sample subjected to low velocity impact assesses the interest of this type of materials for damage assessments.

When bone substitutes materials are used, the increased risk of infection due to the none-autogenic graft is often discussed. The treatment of large bone defects in geriatric patients, often with osteoporotic bone, often presents major challenges to surgery. Bone substitutes materials are available indefinitely without the risk of additional surgery in each patient. Can bone substitutes materials be used without hesitation in the geriatric patient?Eighty-three patients suffered traumatic fractures of the distal radius, proximal humerus, and proximal tibia. The defect zones were filled with bone substitute material based on nanocrystalline hydroxyapatite (NHA) or calcium phosphate (CP). For comparison, a reference group without the void filling with a bone graft substitute (void defects, ED) was studied. All patients over the age of 64 years were retrospectively evaluated for complications and radiological outcomes. Results: 106 patients sustained traumatic fractures of the distal radius (68.9%), proximal humerus (5.7%), and proximal tibia (22.6%). No differences in infections were found when comparing the groups (p=1.0). retrospectively evaluated. The KEM group had a nonsignificantly lower rate of pseudarthrosis (p=0.09). A relative risk (RR) reduction of complications of 32.64% was observed when bone grafting material was used. The NNT for the prevention of complications was 5.99. When bone healing was assessed by radiological images, some follow-up studies showed significant differences in fracture bridging (p<0.001).Conclusions: In contrast to previous studies, bone substitutes materials can support the healing process in geriatric patients without relevant disadvantages. With an NNT of 6 regarding complications, many patients could benefit from bone graft augmentation. No increased infection rate was observed.

When bone substitutes materials are used, the increased risk of infection due to the none-autogenic graft is often discussed. The treatment of large bone defects in geriatric patients, often with osteoporotic bone, often presents major challenges to surgery. Bone substitutes materials are available indefinitely without the risk of additional surgery in each patient. Can bone substitutes materials be used without hesitation in the geriatric patient? Eighty-three patients suffered traumatic fractures of the distal radius, proximal humerus, and proximal tibia. The defect zones were filled with bone substitute material based on nanocrystalline hydroxyapatite (NHA) or calcium phosphate (CP). For comparison, a reference group without the void filling with a bone graft substitute (void defects, ED) was studied. All patients over the age of 64 years were retrospectively evaluated for complications and radiological outcomes. Results: 106 patients sustained traumatic fractures of the distal radius (68.9%), proximal humerus (5.7%), and proximal tibia (22.6%). No differences in infections were found when comparing the groups (p=1.0). retrospectively evaluated. The KEM group had a nonsignificantly lower rate of pseudarthrosis (p=0.09). A relative risk (RR) reduction of complications of 32.64% was observed when bone grafting material was used. The NNT for the prevention of complications was 5.99. When bone healing was assessed by radiological images, some follow-up studies showed significant differences in fracture bridging (p<0.001). Conclusions: In contrast to previous studies, bone substitutes materials can support the healing process in geriatric patients without relevant disadvantages. With an NNT of 6 regarding complications, many patients could benefit from bone graft augmentation. No increased infection rate was observed.

Background: Cyber security is used to save the important data from getting hacked, took by some unknown access. It makes our environment a safe place for us to work out or share our information and since privacy is almost everyone’s top priority, there must be the surety of saving the private information. Methods: In today’s world cyber security is really important as there are now many devices, websites, new technology which makes it a lot easier for hackers to get into anyone’s files which stores crucial data. However, many steps can be reserved to protect the data such as; educating your employees with how to prevent your files or computers from getting hacked, avoid clicking on websites that don’t seem safe, use firewalls, antimalware system and most importantly make sure to keep passwords that are hard to guess and try to go for a face recognition instead of pins and passcodes. Results and Conclusion: This all does not only makes using technology safe but also favors your business for the safety of employees working within the organization. The customers being assured about their data being safe, improves productivity since viruses can slow down computers which may trigger the focus of staff during the working hours. It could save your system from adware; the links of websites are also prevented from slow internet speeds. There is no denying at the fact that cyber security is a major requirement for everyone who is indulged with technology today.

The size of the home is considered the most critical issue affecting the quality of life. This can be measured by the quality of the house and its environment. This paper highlights the housing quality index at the urban axis level in Bistrita-Nasaud county and the impact of socioeconomic activities on the index. The Household Quality of Life Index (HQLI) was calculated based on 11 variables. Household quality of life comprises two distinctive indices: the Housing Conditions Index (HCI) and the Asset Possession Index (API). The study found a variation in the housing condition variables across the urban focus areas. The impact of the socioeconomic environments is disproportionately dependent on the social-economic profile of the urban environments that make up the Urban Axis and the related economic activities that take place in the area of influence in the administrative-territorial units and the variability of the Housing Conditions Index This can be measured by the quality of the house and its environment. This paper highlights the housing quality index at the urban axis level in Bistrita-Nasaud county and the impact of socioeconomic activities on this index. The study area comprises territories in the municipality of Bistrita. The socio-economic context of the area has been the basis for understanding the patterns of evolution identified in the urban landscape, and being a driver in ongoing urban transformations and processes. The impact of the socio-economic is disproportionate to the socioeconomic profile of the urban environments that make up the Urban Axis and the economic activities related to the economic activities taking place in the catchment area in the administrative-territorial units, hence the variability of the Housing Conditions Index which is a system Housing Quality Indicator (HQI) is a tool for measuring and evaluating housing systems based on quality and not just cost.

The change in the value of the breaking energy is discussed here for selected steel grades used in building structures after subjecting the samples made of them to episodes of heating in the steady-state heating regime and then cooling in a simulated fire conditions. These changes were recorded based on the instrumented Charpy impact tests, in relation to the material initial state. The S355J2+N, 1H18N9T steels and also X2CrNiMoN22-5-3 duplex steel were selected for detailed analysis. The fire conditions were modelled experimentally by heating the samples and then keeping them for a specified time at a constant temperature of: 600°C (first series) and 800°C (second series), respectively. Two alternative cooling variants were investigated in the experiment: slow cooling of the samples in the furnace, simulating the natural fire progress, without any external extinguishing action, and cooling them in water mist simulating an extinguishing action by a fire brigade. The temperature of the tested samples was set at the level -20oC and alternatively at the level + 20oC. The conducted analysis is aimed at assessing the risk of sudden, catastrophic fracture of load-bearing structure made of steel degraded as a result of a fire previously occurred with different development scenarios.

Business School rankings are “big business”, influencing donors and potential students alike, holding much sway over decanal and faculty priorities, particularly with respect to the curriculum as well as the focus and destination of research publications (i.e., in so called “top” journals). Over the past several years, the perverse effects of these priorities have begun to be acknowledged and new ratings and ranking systems have emerged. One promising new comer is the Positive Impact Rating (PIR), which uniquely and exclusively focuses on student perceptions of their business school’s priorities and the learning experience. In addition, it organizes schools by tier, in an effort to foster collaboration and continuous improvement, as opposed to ranked competition. If this new approach is to achieve its stated objective and help shift the focus of business schools to developing future business leaders and research output in alignment with a more sustainable world (and the United Nations Sustainable Development Goals), it is essential that the metrics used be – and perceived as - both valid and reliable. The current research aims to make a contribution in this regard, analyzing the results at one business school in detail and making recommendations for strengthening these aims. Results show that the parametric properties of the survey are highly interrelated suggesting that the predictive utility of the separate elements within scale could be improved. Additionally, biases in scores may exist dependent on where the responses are collected and who solicited them, as well as the students’ perception of their overall academic experience and on socio-cultural factors.

Shelter-in-place policies and the closure of non-essential workplaces intended to disrupt transmission of the SARS-COV-2 virus are effective approaches to combating COVID-19. They have, however, caused record levels of unemployment in the United States, raising questions of whether mitigation is more societally damaging than the disease. Here we use a coupled epidemiological-economic model to estimate the impact on employment of an unmitigated, business-as-usual approach to the pandemic. We compared unemployment between March-August 2020 in ten Californian socio-economic systems (SESs) to unemployment forecast by a model of industrial sector inter-dependencies subjected to unmitigated outbreaks of COVID-19. We found that economic losses are unavoidable because disease-driven losses propagate economically through SESs, amplifying losses to the disease. While model forecasts are generally lower than actual unemployment, jobs savings would come at the cost of greatly increased worker mortality. The costs would also be disproportionately greater among smaller and inland SESs.

Purpose: A novel coronavirus, SARS-CoV-2 was identified as the cause of COVID-19 eventually led to the declaration of Public health emergency of international concern and a pandemic by WHO due to its exponential global spread. Present study was conducted to investigate the impact of second wave of pandemic on mental wellbeing and social behaviors among university students of Pakistan during this crucial period of COVID-19 infection. Methods: A cross-sectional analytical study was designed to evaluate the psychosocial impact during the current COVID-19 outbreak among the students of The Islamia University of Bahawalpur, Pakistan. Snowball sampling or chain referral sampling procedure was adopted to recruit the participants in the study. Verbal informed consent was taken from all participants before recruitment in the study irrespective of their gender, age and socioeconomic status. Results: Mental health of university students during COVID-19 epidemic was affected to a varying degree revealing that 26.66% were recorded to have mild, 27.15% moderate and 17.04% suffering from severe anxiety out of total 1029 students. Students who were residing in urban areas with parents and having a steady family income were negatively associated and found protective factors against anxiety. However, having a relative or an acquaintance infected with COVID-19 was an independent risk factor for experienced anxiety. Positively associated factors with the level of anxiety symptoms included economic stressors, effects on daily-life, and academic delays whereas social support was negatively correlated with anxiety in COVID-19 related stressors. Conclusion: Public health emergencies and such pandemic are exerting serious psychological impacts on university students. It is recommended that the higher authorities should plan better policies to reduce this impact for the provision of high quality and timely crisis-oriented psychological services to university students.

When different strain hardening and strain rate sensitive materials are used for scaled model and prototype, the traditional pure geometrical similarity laws of solid mechanics will fail. Although correcting the basic scaling factors of velocity, density and geometry have been developed to compensate for the material distortion in recent non-geometric scaling works, it is difficult to be widely used because of its inherent indirect (depending on the structural strain and strain rate responses) and inexact (having significant prediction errors for prototype) defects. In this paper, a framework of material similarity, based on the new suggested material dimensionless numbers and the ‘Material number vs. strain/strain-rate’ function curves, are further developed, which represents the objective requirement of similarity theory for the basic mechanical properties of materials. It is demonstrated what is similitude materials of solid mechanics and how to use the best similitude materials to overcome the non-scalabilities of materials for identical or different materials. The direct and exact solution of the basic correction factors is further obtained and therefore overcomes the previous inherent indirect and inexact defects radically. Based on the similarity evaluation of different materials of the classical constitutive models, the impacted structures of circular plate and crooked plate with strain hardening and strain rate sensitive materials are verified numerically. The results show the completely different materials can be exact similitude for various structural behaviors (strain, strain rate, stress and displacement) of time and space fields after using the best similitude materials; and the basic correction factors do not depend on the structural strain and strain rate responses. As a contrast, when the non-similitude materials are used, the similarity results are very sensitive to the selection of strain/strain-rate and often leads to failed predictions. In addition, for the material elastic and temperature effects, the proposed method is also discussed to be valid.

(1)Background: To perform a simulation-based analysis of 2019 COVID-19 outbreak and how would the electronic commerce indursty help China’s economy resilient. (2)Methods: As the epidemic continues, it is possible to use Computable General Equilibrium model to simulate the economic consequences and analyse the role of electronic commerce industy in COVID-19 outbreak. (3)Results: Estimates and models produced at the time of the outbreak suggested that COVID-19 could have a catastrophic effect on China’s economy. National statistics were examined for anomalies that corresponded to the timing of COVID-19 outbreak and, where possible, the size of any gain or loss found estimated. Our analysis suggests that the electronic commerce industry could help China’s economy to recover by stimulating consumption, improving technological level and expanding investment. (4)Conclusions: This exercise holds important lessons for estimating the electronic commerce’s role of similar events – such as pandemic influenza – and measures to recover the economy. We suggest that electronic commerce industry should be paid more attention in economic development, especially in epidemic time. The implications of our findings are discussed in the light of a prospective epidemic.

This paper establishes a prognosis of the long term environmental impact of various car subsidy concepts. The CO2 emissions of the German car fleet impacted by the purchase subsidies are determined. A balance model of the CO2 emissions of the whole car life cycle is developed. Consideration of production-, use- and End-of-Life processes are taken into account. The implementation of different subsidy scenarios directly affects the forecasted composition of the vehicle population and therefore the resulting life cycle assessment. All scenarios compensate the additional emissions required by the production pull-in within the considered period and hence reduce the accumulated CO2 emissions until 2030. The exclusive funding of BEVs is most effective with a break-even in 2025.

The current paper aims to study the impact properties of additively manufactured Maraging steel (1.2709) using laser powder bed fusion (PBF-L) processing. The specimens were manufactured using 3Dsystems ProX 300 equipment under constant specific power input, or Andrew Number. The interactions between the build strategy and parameters, such as Hatch spacing and Scan speed was, and the impact strength and fracture were investigated. The Impact energy anisotropy was also investigated parallel and perpendicular to the build direction. Instrumented impact testing was performed, and the fractography supported that the fusion zone geometry dictated the fracture behaviour. The influence from gaseous elements such as Nitrogen, Oxygen and Hydrogen was found insignificant at the levels found in the printed material.

This paper presents a numerical two-scale framework for the simulation of fiber reinforced concrete under impact loading. The numerical homogenization framework considers the full balance of linear momentum at the microscale. This allows for the study of microscopic inertia effects affecting the macroscale. After describing the ideas of the dynamic framework and the material models applied at the microscale, the experimental behavior of the fiber and the fiber-matrix bond under varying loading rates are discussed. To capture the most important features, a simplified matrix cracking and a strain rate sensitive fiber pullout model are utilized at the microscale. A split Hopkinson bar tension test is used as an example to present the capabilities of the framework to analyze different sources of dynamic behavior measured at the macroscale. The induced loading wave is studied and the influence of structural inertia on the measured signals within the simulation are verified. Further parameter studies allow the analysis of the macroscopic response resulting from the rate dependent fiber pullout as well as the direct study of the microscale inertia. Even though the material models and the microscale discretization used within this study are still simplified, the value of the numerical two-scale framework to study material behavior under impact loading is shown.

The ductile behavior of strain hardening cement-based composites (SHCC) under direct tensile load makes them promising solutions for applications where high energy dissipation is needed, such as earthquake, impact by a projectile, or blast. However, the superior tensile ductility of SHCC due to multiple cracking does not necessarily entail compressive and shear ductility. As an effort to characterize the behavior of SHCC under impact compressive and shear loading, relevant to the mentioned high-speed loading scenarios, the paper at hand studies the performance of a SHCC and its constituent cement-based matrices using the split-Hopkinson bar method. For compression experiments, cylindrical specimens with a length-to-diameter ratio (l/d) of 1.6 were used. The selected length of the sample led to similar failure modes under the quasi-static and impact loading conditions, which was necessary for a reliable comparison of the obtained compressive strengths. The impact experiments were performed in a split-Hopkinson pressure bar (SHPB) at a strain rate that reached 110 s-1 at the moment of failure. For shear experiments, a special adapter was developed for a split-Hopkinson tension bar (SHTB). The adapter enabled performing impact shear experiments on planar specimens using the tensile wave generated in the SHTB. Results showed a dynamic increase factor (DIF) of 2.3 and 2.0 for compressive and shear strength of SHCC, respectively. As compared to the non-reinforced constituent matrix, the absolute value of the compressive strength was lower for the SHCC. Contrarily, under shear loading, the SHCC yielded the higher shear strength than the non-reinforced matrix.

COVID-19, previously known as “2019 novel coronavirus”, is a newly discovered virus which causes severe acute respiratory syndrome, similar to corona virus 2 (SARS-CoV-2), which has spread widely through human to human contact and was declared a pandemic by the WHO in March 2020. To cope with this pandemic many countries have adopted nationwide lockdowns which restrict nonessential activities and encourage their populations to avoid public transport, work from hone wherever possible, and to maintain social distancing at all times. While the severity of these shutdowns has varied with country, large geographic regions of the world (including China, India, Iran, Italy, Spain, and the USA) have all been severely affected, not just in the loss of human life, but also in terms of the financial impacts the lockdowns will have on their countries future prosperityIt was previously reported that a number of environmental factors, including humidity and temperature, played an important role in development and spread of the SARS-Corona Virus infection with the virus retaining viability for over 5 days at temperatures between 22–25^oC and relative humidity (RH) of 40–50%. Whereas, elevated temperatures and higher RH (38^oC, and >95% RH) decreased virus viability (Kroumpouzos et al., 2020). The spread of COVID-19 started in low temperature areas of China, with major outbreaks subsequently occurring in Iran, Japan, Northern Italy and South Korea. These new virus epicenters all had similar temperature and latitude, along the 30-50^◦N” zone. Subsequently, the virus was spread to regions of elevated temperature, such as India, the Middle East and Thailand, due to international travel (Kroumpouzos et al., 2020).The aim of this study was to consider the environmental impact of COVID-19, including water pollution. The paper is divided into four sections. In the first section the current literature related to COVID-19 is reviewed. In the second section a recent update on COVID-19 globally is presented, followed by a descriptive impact on the environment in the third section and finally in the fourth section the COVID-19 related worldwide environmental impact is discussed. Since COVID-19 is a currently an active pandemic with no end in sight, while constructive conclusions can only be made on the limited data currently, which may be prone to high errors. However, these conclusions may provide some preliminary suggestions for assisting in assessing spreading patterns of COVID-19 across the globe, and may be of long-term significances with respect to assessing the environmental impact of lockdown policies due to COVID-19 worldwide.

Due to remarkable socioeconomic development, an increasing number of karst rocky desertification areas have been severely affected by human activities in southern China. Effectively analyzing human activities in karst rocky desertification areas is a critical prerequisite for managing and restoring areas with tremendous negative impacts from desertification. At present, a timely and accurate way of quantifying the spatiotemporal variations of human activities in karst rocky desertification areas is still lacking. In this communication, we attempted to quantify human activities from the corrected NPP-VIIRS nighttime light data from 2012 to 2018 based on statistical analysis. The results show that a significant increase of night lights could be clearly identified during the study period. The total nighttime lights (TL) related to severe karst rocky desertification (S) were particularly concentrated in Guizhou and Yunnan. The nighttime light intensity (LI) related to the S areas in Chongqing were the strongest due to its rapid socioeconomic development. The annual growth rate of nighttime lights (GL) has been slow or even negative in Guangdong because of its various karst rocky desertification restoration programs. This communication could provide an effective approach for quantifying human activities and provide useful information about where prompt attention is required for policy-making on the restoration of the karst rocky desertification areas.

Consideration of climate change in Environmental Impact Assessment (EIA) is a rather novel topic, which became partly mandatory through the revised EU Directive on EIA. Through a mixed-methods approach involving key-actors from EIA practice, decision making and climate adaptation planning, this study presents a transdisciplinary point of view on barriers and opportunities to tackle climate change adaptation in environmental assessment of large-scale projects. It is based on both a retrospective ex-post evaluation of existing practices in Austria and Germany as well as prescriptive examination and development of outcomes for practice through the development of a climate-fit toolkit that supports the incorporation of climate change impacts into EIAs. The scenario analysis applied with a back casting approach provided the opportunity to look beyond limitations related to legal compliance and partly lack of data identified by previous research. Three scenario narratives were elaborated based on nine key impact factors based on literature review, content analysis of EIA documents and interviews with EIA actors. The groups of actors carried out a prioritization of actions towards consideration of climate change in EIA. Finally, the actors were involved in co-production of an online tool-kit for Austrian and German EIA practice.

Human-generated, natural occurrences and other actions connected with the petroleum industry have contributed expansively to the contamination of numerous areas of human habitant, thus effecting overwhelming challenges and constraints to sustainable human health, development and the biosphere. The sites commonly associated include derelict pits, hydrocarbon onshore release sites, and places to where oil slicks exuding from offshore releases are blown onshore. In diverse cases, remediation is pertinent to restore the affected ambient. This is required despite the complexity and intricate pore structure and fluid trafficking trajectories of soil which are not easily amenable to remediation. Due to the complex assemblage of contaminated soil, it is necessary to conduct an encompassing site assessment by considering the potential impact of the environment and human health prior to proper selecting and implementing of a desirable remediation process. There are extant remediation methodologies which are effective and efficient for the clean up of contaminated shorelines and other petroleum-contaminated sites involving inter alia agricultural activities. There are salient variations in the techniques to expunge contaminants regarding spatiotemporal and pecuniary costs or considerations due to the gross environmental hazards entailed. This study provides for the opportunity to harness and sustain the capacity for the mitigation of untoward impacts, and induce the latitude for an enabling condition in sustainable human health, environment and development in the petroleum industry and other anthropogenic activities.

In some cases, the human impact on ancient landscapes has been so profound that local soils still remain significantly affected even after hundreds and thousands of years after ending impact. We studied the Late Bronze Age Muradymovo settlement located in Urals, Russia, aiming to estimate the consequences of the ancient people activity for environment. Despite the present humid climate, the modern soils inside the cultural layer of the study site contain more than 27% of gypsum at a depth of just 10 cm from the surface, and a microrelief of the study site is typical of a gypsum desert. The nearby background Chernozems are gypsum-free to a depth of 2 meters. According to the archaeological data, the ancient people belonged to the ‘Srubno-Alakul’ archaeological culture (1750–1350 yrs BC cal) and had a tradition of building their houses from gypsum rocks. At the present time, this area is still unsuitable for human settlement. The properties of modern soils inside the cultural layer of the study site are directly affected by the Late Bronze Age human activities. It has been identified on soil morphology, micromorphology and chemical properties of soils developed inside the cultural layer of the settlement.

In this paper, a numerical modeling of the impact, spreading, and eventually rebound of a viscoelastic droplet is reported. The numerical model is based on the volume of fluid (VOF) method coupled with the FENE-CR constitutive equations, and accounts for both the surface tension and the substrate wettability. The FENE-CR constitutive equations are used to model the polymer solution, while taking advantage of its rheological characterization. The comparison is performed between droplets of Newtonian solvent and a monodisperse polymer solution. The droplet impact on both hydrophilic and superhydrophobic substrate is analyzed through a detailed analysis of the spreading diameter evolution. It is found that while the droplet kinematic phase seems independent of the substrate and fluids properties, the recoiling phase is highly related to all of them. In addition, the model infers a critical polymer concentration above which the droplet rebound from a superhydrophobic substrate is suppressed. The simulation is of particular interest to ink-jet processing, and demonstrates the capability of the model to handle complex non-Newtonian droplet dynamics.

The welding performance of shipbuilding steel under large heat input could be improved greatly by the Mg addition to the steel, but the impact toughness of the HAZ is not stable. According to the three different thickness steel plates obtained in the industrial experiment, the large heat input welding was carried out by different heat input, and the impact toughness analysis, impact fracture analysis, metallographic microstructure analysis and inclusions analysis were carried out. The results showed that, the HAZ of three kinds of thickness plates induced a lot of IAF, with Mg addition, the inclusion dimension had been reduced effectively, and the IAF induced ability of the inclusions had also been improved. The difference of HAZ impact toughness with different welding heat input and different impact temperature is significant, in consideration of the influence of welding heat input and metallographic microstructure on impact toughness of HAZ, the welding heat load had far greater effect than metallographic microstructure on ductile-brittle transition temperature. At the same time, if the original metallographic microstructure of steel was coarse, the pinning effect of the inclusions would be reduced significantly, and the microstructure of HAZ would be coarsened and the impact toughness of HAZ would be decreased, so there is a certain matching relationship between the metallographic microstructure and the inclusion dimension.

This study aimed to evaluate the relationship between various asbestos exposure routes and asbestos-related disorders (ARDs). The study population comprised 11,186 residents of a metropolitan city who lived near asbestos factories, shipyards, or in slate roof-dense areas. ARDs were defined from chest X-rays indicating lower lung fibrosis (LFF), pleural disease (PD), and lung masses (LMs). Of the subjects, 11.2%, 10.4%, 67.2% and 8.3% were exposed to asbestos via occupational, household, neighborhood, and slate roof routes, respectively. Regarding adjusted confounders and other exposure routes, the odds ratio (OR) of PD from household exposure (i.e., living with asbestos-producing workers) was 1.9 (95% confidence interval: 0.9–4.2), and that of LLF and PD from neighborhood exposure, or residing near asbestos factories) for <19 or >20 years, or near a mine, were 4.1 (2.8–5.8) and 4.8 (3.4–6.7), 8.3 (5.5–12.3) and 8.0 (5.5–11.6), and 4.8 (2.7–8.5) and 9.0 (5.6–14.4), respectively. The ORs of LLF, PD, and LM among those residing in slate-dense areas were 5.5 (3.3–9.0), 8.8 (5.6–13.8), and 20.5 (10.4–40.4), respectively. Substantial proportions of citizens residing in industrialized city have potentially been exposed to asbestos, and various exposure routes are associated with the development of ARDs. Notably, this may be the first finding of the health effects of slate roof exposure. However, given the limitations of this study, including potential confounders such as socioeconomic status, further research is needed.

2.25Cr-1Mo steel with high strength at high temperatures and good hydrogen resistance is widely used for power generation boiler material in high temperature and pressure use environments. Following the test evaluation of the ASME Boiler and Pressure Vessel Code, specimens from the base metal of a boiler pipe were found to have impact toughness values of 285 and 21 ft-lb, which are drastically different values. The analysis of the fracture surface of the 21 ft-lb test specimen revealed MnS inclusions, and it was found that cracks initiated at the inclusions. Observation of the cross-section of the crack propagation front revealed that cracks propagated along the ferrite regions and precipitate voids. Inclusions were also found in the 285 ft-lb impact specimen; however, the volume fraction of the inclusions was significantly less than that of the 21 ft-lb specimen. It was also found that the ferrite and carbide content of the 285 ft-lb specimen was less than 21 ft-lb specimen. The reason that the inclusions, ferrite, and carbide content differed in the two adjacent impact test specimens was analyzed. The effects of micro-segregation, such as MnS inclusions on ferrite and carbide, were compared and analyzed.

Impact characterization of linear resonant actuator (LRA) is studied experimentally by newly developed drop tester, which can control various experimental uncertainty such as rotational moment, air resistance, secondary impact and so on. The feasibility of this test apparatus was verified by comparison with free fall test. By utilizing a high-speed camera and measuring the vibrational displacement of spring material, the impact behavior was captured and the damping ratio of the system was defined. Based on the above processes, the finite element model was established and the experimental and analytical results were successfully correlated. Finally, the damage of the system from impact loading can be expected by developed model and as a result, this research can improve the impact reliability of LRA.

Supermarkets in Port Harcourt (PH) city, Nigeria, predominantly rely on diesel electricity generation due to grid instability, leading to high cost of electricity prices. Although solar photovoltaic (PV) systems have been proposed as an alternative, these supermarkets have yet to adopt them, mainly due to high investment costs and a lack of awareness of the long-term financial and environmental benefits. This paper examines the technical and economic practicality of a PV system for these supermarkets using the PVsyst software and a spreadsheet model. Solar resources showed that PH has a daily average solar radiation and temperature of 4.21 kWh/m2/day and 25.73℃, respectively. Market Square, the supermarket with the highest peak power demand of 59.8 kW, and a 561 kWh/day load profile, was chosen as a case study. A proposed PV system with a power capacity of 232 kW, battery storage capacity of 34,021 Ah, a charge controller size of 100 A/560V, and an inverter with a power rating of 60V/75 kW has been designed to meet the load demand. The economic analysis showed a $266,936 life cycle cost, $0.12 per kWh levelized cost of electricity (LCOE), 4-year simple payback time, and a 20.5% internal rate of return (IRR). The PV system is feasible due to its positive net present value (NPV) of $165,322 and carbon savings of 582 tCO2/year.

This study examines the varying performance of low-cost sensors during seasonal smog events, particularly those resulting from solid fuel combustion linked to residential or industrial heating and other pollution sources. A prototype low-cost sensor node developed within this work was collocated for several months with reference equipment at an air quality monitoring station in the city of Ostrava (Moravian-Silesian region, Czech Republic). Deterioration of air quality during a pair of smog alert events, namely wintertime "Polish smog" episode and springtime "Saharan dust storm", which occurred through the extended period of co-location, was characterized quantitatively based on particulate matter and carbon monoxide concentrations measured by the low-cost sensors. These datasets were compared with reference data and model predictions provided by the Copernicus Atmospheric Monitoring Service. Selected low-cost sensors demonstrated linear response during Polish smog episode with highly correlated time series of carbon monoxide and particulate matter concentrations. However, when compared to reference data, the Sensirion SPS30 sensor is found to overestimate coarse particle (PM10) concentrations by the factor of two during winter evaluation period. On the other hand, oppositely biased PM10 response (underestimation by the factor of five) was determined from co-location measurement during Saharan dust storm event. This observation was attributed to combination of specific detection efficiency of the given low-cost sensor and shifts in particle size distribution due to seasonal change of ambient conditions.

Like many coastal lagoons in several countries, “Navío Quebrado” lagoon (La Guajira-Colombia) is a very delicate and precious environment; indeed, it is a nationally recognized Flora and Fauna Sanctuary. Several factors, amongst which climate change, are threatening its existence because of changes in the governing hydro-morphological processes. A first step to address this problem certainly is to understand its hydrological behavior and to be able to replicate via simulation its recent history and then infer likely futures. These potential futures will be marked by changes in the water input by its tributary, the Camarones River, and by a modified water exchange with the sea, according to a foreseen sea level rise pattern, as well as by a different evaporation from the free surface, according to temperature changes. In order achieve the required ability to simulate future scenarios, data on the actual behavior have to be gathered, i.e. a monitoring system has to be set up, which to date is not existent. Conceptually, designing a suitable monitoring system is not a complex issue and seems easy to implement. However, the environmental and socio-cultural and socio-economic context makes every little step a hard climb. An extremely simple –almost “primitive”- monitoring system has been set up in this case which is based on very basic measurements of current velocity and water levels and a direct participation of local stakeholders, first of all the National Park unit of the Sanctuary. All this may clash with the latest groovy advances of science, like in situ automatized sensors, remote sensing, machine learning and digital twins, and certainly several improvements are possible and desirable. But it has strong positive points: it is providing surprisingly reasonable data and…it is working at almost zero additional cost. Several technical difficulties made this exercise interesting in itself and worth being shared. Its novelty lies in showing how old, simple methods may offer a working solution to new challenges. This humble experience may be of help in several other similar situations across the world.

Interoperable multilayer networks are pivotal and transformative in public digital health. They not only enhance the delivery of healthcare services but also contribute significantly to improving public health outcomes. Through better accessibility, efficiency, and data-driven decision-making, these networks are revolutionizing how we approach public health. The seamless data exchange and integration they facilitate across different healthcare systems and providers can lead to substantial cost savings for public healthcare systems. These networks pave the way for a more efficient and effective healthcare system by reducing redundancies, optimizing resource allocation, and minimizing unnecessary procedures. This study combines the pressing issues of public digital health with the innovative interpretation of multilayer network theory, proposing practical solutions that inspire academics and practitioners alike.Public digital health is not just about improving care access and patient engagement. It is also about ensuring the financial sustainability of our healthcare systems. Innovative multilayer network analysis is critical in this game, improving interoperability and reducing spatial and temporal constraints. These networks streamline processes and improve efficiency by easing complex interactions within healthcare systems. This process, in turn, allows healthcare providers to deliver high-quality care at a lower cost, transforming, whenever possible, in-patients and out-patients into home-patients. Networks are a cornerstone and a beacon of hope for transforming public digital health. They are increasing the Value for Money and enhancing a patient-centric approach, making us optimistic about the future of healthcare.This study shows that revolutionizing digital healthcare methodologies has a profound impact on crucial elements like resilience, interoperability, and patient well-being within public health. The enhancement of interoperability facilitates the smooth flow of data exchange across various platforms and healthcare providers, fostering improved collaboration and decision-making processes and elevating the safety of patients.

Background: GaitSmart (GS) is a sensor-based digital medical device that can be used with the integrated app vGym to provide a personalised rehabilitation programme for older people undergoing total hip arthroplasty (THA) or total knee arthroplasty (TKA). This study aimed to determine whether the GS intervention used in the rehabilitation of older people undergoing THA or TKA is potentially cost-effective compared to the current standard of care (SoC). Methods: Decision-analytic modelling was conducted to estimate cost-effectiveness over a seventeen- week time horizon, from an NHS perspective. UK clinical and cost data from the GaitSmart randomised clinical trial was used to obtain input parameters, and sensitivity analysis was performed to address uncertainty. Results: Over a seventeen-week time horizon, GS incurred cost savings of £450.56 and a 0.02 gain in quality-adjusted life years (QALYs) compared to the SoC. These results indicate that GS is the dominant intervention because the device demonstrated greater effectiveness and lower costs. Probabilistic sensitivity analyses confirm the robustness of our results. Conclusion: GS appears to offer short-term efficiency benefits and demonstrates cost-effectiveness for the improvement of gait in people undergoing THA or TKA, compared to the SoC. These findings provided new evidence to inform policymakers.

Investments in small and medium-sized anaerobic digestion facilities have the potential to boost biogas production in Greece and other EU countries. This study aimed to evaluate the economic feasibility of anaerobic digestion facilities equipped with combined heat and power (CHP) units ranging from 50 to 400 kW, while treating livestock waste. For this purpose, data were gathered from various livestock operations (dairy cattle, poultry, swine, dairy sheep and goat) regarding their annual production, revenues, electricity and fuel usage, and waste generation. Waste samples were then collected and analyzed to assess their biochemical methane production potential. The capital and operational costs of anaerobic digestion facilities, from 50 and 400 kW, were calculated using the equations developed within the “eMT cluster” project. Findings indicate that current feed-in tariffs (FIT) of 0.21 € kWh-1 are insufficient to incentivize investment in anaerobic digestion facilities with capacities below 250 kW, highlighting the need for increased FIT rates or capital expenditure subsidies. Recommendations include shifting towards simplified technology and business models with reduced farmer involvement, coupled with supportive legislative framework and long-term electricity price guarantees. These measures are expected to foster the implementation of anaerobic digestion projects in the animal husbandry sector.

The use of e-scooters and e-bicycles has been on the rise with an increased medical and financial burden. Our purpose was to identify risk factors for increased medical charges and recommend appropriate steps. We studied a retrospective cohort presenting to a level 1 urban trauma center from January 2018 through July 2019 due to micro-mobility platforms. The primary outcome for comparison was total billing cost and a regression analysis was performed to identify variables associated with higher cost. A total of 4073 patients were identified. The majority of which were males (67%), aged 20-30 years (41%). Helmet use reported in only 39.8%. Radiologic studies were performed more often in the e-bicycle group. Total billing charges amounted to 28.3 million New Israeli Shekels ($7.9 million). Average total cost was higher for e-bicycle injuries and in non-helmet users. Other factors associated with higher cost were age, and time of day 12:00-18:00. We recommend that urban cities invest in infrastructure in advance to micro-mobility introduction, regulate the use of e-platforms, designate resources for education on personal protective equipment and prepare medical centers in advance.

In large-capacity energy storage systems, instructions are decomposed typically using an equalized power distribution strategy, where clusters/modules operate at the same power and durations. When dispatching shifts from stable single conditions to intricate coupled conditions, this distribution strategy inevitably results in increased inconsistency and hastened system aging. This paper presents a novel differentiated power distribution strategy comprising three control variables: the rotation status, the operating boundaries for both Depth of Discharge (DOD) and C-rates (C) within a control period. The proposed strategy integrates an aging cost prediction model developed to express the mapping relationship between these control variables and aging costs. Additionally, it incorporates the multi-colony particle swarm optimization (Mc-PSO) algorithm into the optimization model to minimize aging costs. The aging cost prediction model consists of three functions: predicting health features (HFs) based on the cumulative charge/discharge throughput quantity and operating boundaries, characterizing HFs as comprehensive scores, and calculating aging costs using both comprehensive scores and residual equipment value. Further, we elaborated on the engineering application process for the proposed control strategy. In the simulation scenarios, this strategy prolonged the service life by 14.62%, reduced the overall aging cost by 6.61%, and improved module consistency by 21.98%, compared with the traditional equalized distribution strategy. In summary, the proposed strategy proves effective in elongating service life, reducing overall aging costs, and increasing the benefit of energy storage systems in particular application scenarios.

This paper shows the firsts results of a methodological approach in which Remote Sensing technologies (Satellite imagery and LiDAR) combined with Least Cost Path algorithm on GIS are being tested like tools to overcome locally-based interpretations about populational patterns and communication routes during Flavian municipality in the Comarca de El Condado region. The identification of fourteen hypothetical communication routes between already known archaeological sites thanks to a recent archaeological prospection, and the confirmation of three sections of them and a new archaeological site demonstrate that the combined use of the aforementioned technics has a very useful to delve more deeply, more widely and at a variety of different scales into the relation between the twelve known/selected Early Roman Empire sites and the surrounding landscape in the right bank of Guadalén River. It is expected that, in next phases, this methodological approach will allow us to go deeper in analysing and understanding spatial distribution, exploitation, and interrelation between different Early Roman Empire sites, helping us to fill the aforementioned gaps in our understanding of the archaeological continuum in the region reducing time and costs.

Integration of 5D Building Information Modelling (BIM) into large rail projects has the potential to significantly enhance cost management and control. Nevertheless, 5D-BIM implementation has encountered difficulties stemming from technical, functional, and governance-related factors. This paper builds a conceptual framework to support financial decision-making, enhances project management, and promotes efficient project delivery. The framework encompasses a set of interrelated elements that include project governance, BIM policies and standards, digital platforms, BIM LOD, cost estimation classification, and continuous improvement. The proposed framework acknowledges the significance of project governance in guiding and organizing the implementation of 5D-BIM. Additionally, BIM policies and standards ensure the adherence to quality standards for the produced BIM models. Digital platforms serve as the basis for multiple users to generate, access, share, and exchange project information. BIM LOD promotes collaboration and coordination among all project stakeholders. Cost estimation classification aligns the estimation process with the development of project scope and financial decision-making. Continuous improvement plays a vital role in optimizing processes, enhancing efficiency, and achieving higher-quality outcomes. Moreover, it fosters stakeholder satisfaction, improves project performance, and nurtures a conducive environment for innovation and learning. The study analyses the framework utilization in the Victorian rail projects and identifies key implementation challenges. This can be summed up as follows: The main technical hurdles were the lack of current horizontal infrastructure standards for data exchange and the lack of compatibility with current cost management standards. Increased project complexity and the absence of clear project governance strategies and processes posed organizational challenges. A further validation of the framework in real-world rail projects was recommended to achieve the implementation goals.

Lump sum contracts are specific contracts used in the construction industry to give owners financial certainty and decrease risk while incentivizing contractors to finish projects on schedule and within budget. This article discusses the usage of lump sum contracts in construction projects, covering the types of projects appropriate for lump sum contracts, critical considerations for entering a lump sum contract, and the tender and preliminary expenses involved. The article also covers lump sum contracts' benefits and drawbacks and how owners and contractors allocate risk. Finally, the difficulties of implementing lump sum contracts, such as potential disagreements over scope changes and quality issues, are investigated. In general, lump sum contracts can be a valuable tool for managing construction projects, but their execution necessitates careful planning and collaboration to ensure successful completion.

This article summarizes Chinese framework for regulating artificial intelligence and integrates evolutionary game theory with cost-benefit analysis to establish a model and simulation. This framework is employed to analyze the behavioral trends among three distinct entities: governmental bodies, third-party independent institutions, and AI companies within the context of regulatory relationship. The findings indicate that: (1) The cost-benefit dynamics within the regulatory legal nexus significantly influence the behaviors of these entities; (2) Under the condition of normalized government regulation approaching full enforcement, the behavioral choices of third-party independent institutions and AI companies exhibit cyclical fluctuations.The paper draws two principal conclusions: (1) The regulatory framework need to be tailored to the specific risks presented by AI and the relative costs and benefits of legal enforcement in different jurisdictions. (2) From a cost-benefit standpoint, government intervention in AI regulation ought to be circumscribed, with government regulation focusing on critical legal risks. Other aspects of regulatory control should be delegated to cooperative legal framework that allows the participation of the independent third-party institution,which brings a nuanced and specialized approach to the governance of AI.

Inverse optimal control is a method for recovering the cost function used in an optimal control problem in expert demonstrations. Most studies on inverse optimal control have focused on building the unknown cost function through the linear combination of given features with unknown cost weights, which are generally considered to be constant. However, in many real-world applications, the cost weights may vary over time. In this study, we propose an adaptive online inverse optimal control approach based on a neural-network approximation to address the challenge of recovering time-varying cost weights. We conduct a well-posedness analysis of the problem and suggest a condition for the adaptive goal, under which the weights of the neural network generated to achieve this adaptive goal are unique to the corresponding inverse optimal control problem. Furthermore, we propose an updating law for the weights of the neural network to ensure the stability of the convergence of the solutions. Finally, simulation results for an example linear system are presented to demonstrate the effectiveness of the proposed strategy. The proposed method is applicable to a wide range of problems requiring real-time inverse optimal control calculations

As the international community attaches importance to environmental and climate issues, carbon dioxide emissions in various countries have been subject to constraints and limits. Carbon trading market, as a market tool to reduce greenhouse gas emissions, has gone through a development process from a pilot carbon market to a national carbon market in China. At present, the industries included in the national carbon market are mainly the electric power industry, and the carbon emissions of the electric power industry account for about 40% of the national carbon emissions. According to the construction history of foreign carbon markets, China's future carbon quota allocation will gradually transition from free allocation to auction allocation,the auction mechanism will bring a heavy economic burden to the electric power industry, especially the thermal power generation industry. Therefore, this study takes Guangdong Province as an example to optimize the power generation mix with the objective of minimizing the total economic cost after the innovative introduction of the carbon quota auction mechanism and constructs an optimization model of the power generation mix based on the auction ratio by comprehensively applying the system dynamics model and the multi-objective linear programming model and systematically researches the power generation structure under different auction ratios with the time scale of months and quantitatively evaluates the economic inputs needed to reduce the greenhouse gas emissions.The results of the study show that after comprehensively comparing the total economic cost, renewable energy development and carbon emissions, it is the most scientific and reasonable to set the auction ratio of carbon allowances at 20%, which achieves the best level of economic and environmental benefits.

Antimicrobial resistance (AMR) poses a global public health threat, with rates directly linked with consumption. The World Health Organization (WHO) AWaRе-classification aims to guide antibiotic use, which is influenced by drug availability, affordability, and economic policies. In Jordan, a high proportion of consumed antibiotics belong to the 'Watch' category. Data from the WHO-AWaRе classification, the Essential Medicines List, and the Jordan Food and Drug Administration were analyzed. Antibiotics for adults were classified and their cost per Defined Daily Dose (DDD) was determined, then affordability was assessed. In 2023, 43 injectable and 43 oral antibiotics for adults were registered in Jordan. 'Watch' antibiotics were the most common. 'Access' antibiotics had the lowest cost/DDD. 'Rеsеrvе' antibiotics were the most expensive with few generics. Injectable antibiotics had a negative correlation between cost and the number of alternatives. Affordability was higher for oral antibiotics compared to injectable ones. 'Rеsеrvе' antibiotics were generally unaffordable. This study highlights the need to promote 'Access' antibiotics over other categories by encouraging registration of missing ‘Access’ antibiotics and adjusting 'Watch' and 'Rеsеrvе' antibiotic prices. Competition among generics can lead to lower prices, increasing affordability and accessibility. We emphasize the importance of the AWaRе-classification in guiding antibiotic use in Jordan.

This publication aims to disseminate a step-by-step process that walks through the conceptualization and building of a low-cost (~ $150 monitoring device for airborne fine particulate matter (PM2.5), based on miniaturized sensors and components. Details on the implementation of the hardware and software are provided which facilitate the data acquisition, capture and analysis. The central components and their setup discussed in what follows include: the sensor device (called “P.ALP” – Ph.D. Air quality Low-cost Project), Arduino IDE (Integrated Development Environment) and R code (open-access software). A monitoring device for PM2.5, using low-cost sensors and technologies was successfully conceptualized, designed, and implemented. The P.ALP monitoring system was designed and developed to be a basic device, which can be further customized and implemented using the wide range of low-cost sensors available on the market.

Nanostructured transition metal nitrides (TMNs) have been considered a promising substitute for precious metal catalysts toward ORR due to their multi-electron orbitals, metallic properties, and low cost. To design TMNs catalysts with high catalytic activity toward ORR, the intrinsic features of the influencing factor on the catalytic activity toward ORR of nanostructured TMNs need to be investigated. In the paper, titanium nitride (TiN), zirconium nitride (ZrN), and hafnium nitride (HfN) nanoparticles (NPs) are high-efficiency and one-step synthesized by the direct current arc plasma. TiN, ZrN, and HfN NPs with an oxidation layer are applied as the catalysts of hybrid sodium-air batteries (HSABs). The effect of composition and structure attributes of TMNs on ORR catalysis is defined as follows. (ⅰ) Composition effect. With the increase of the oxygen content, the catalytic ORR capability of TMNs decreases progressively due to the reduction of oxygen adsorption capacity; (ⅱ) Structure effect. The redistribution of the density of states (DOS) of ZrN indicates higher ORR activity than TiN and HfN. HSABs with ZrN exhibit an excellent cyclic stability up to 137 cycles (about 140 h), an outstanding rate performance, and a specific capacity of 2817 mAh·g−1 at 1.0 mA·cm−2.

Blasting pattern optimization is an attempt to optimize blast design parameters aiming to achieve optimum fragmentation, reduce mining operating costs as well as environmental side consequences. The present study aims to propose a multi-objective optimization model, employing artificial intelligence and metaheuristics, to simultaneously minimize the mine-to-crusher operating costs and the impact of blasting consequences including fly-rock and back-break. To achieve the purpose of the study, a multi-variable regression model was developed to model total costs from drilling to crushing. In addition to the costs, multilayer perception neural networks were implemented to predict the blast-induced back-break and fly-rock as a function of collected variables such as number of holes, hole length, burden, spacing, hole slope, stemming, blasted rock per hole, powder factor, and charge per delay. High-precise estimations for both back-break and fly-rock were achieved with the average of 99% coefficient of determination for train, test and validation data sets. Then, the developed regression model and the neural networks were used in an optimization framework, employing Grasshopper algorithm, to find the optimum blast design satisfying practical constraints. The proposed model was tested on a lead-zinc open-pit mine, where 1032 blasting patterns were recorded and analyzed. The results of the optimization model provide a Pareto set of solutions, such that any of these solutions can be implemented according to the strategy of the mining operation management team. The blast pattern with the lowest cost, result in a relatively high fly-rock and back-break, while the pattern with low fly-rock and back-break raises the cost by 20.13 % compared the minimum cost blast design.

The established classical method of treating oil refinery effluent is flotation followed by biological treatment. Membrane bioreactors (MBRs) offer more advanced treatment, producing a clarified and potentially reusable treated effluent, but demand robust pretreatment to remove oil and grease (O&amp;G) down to consistent, reliably low levels. An analysis of a full-scale conventional oil refinery ETP based on flotation alone, coupled with projected performance, energy consumption and costs associated with a downstream MBR, have demonstrated satisfactory performance of flotation-based pre-treatment. The flotation processes, comprising an API separator followed by dissolved air flotation (DAF), provided ~90% removal of both total suspended solids (TSS) and O&amp;G coupled with 75% COD removal. The relative energy consumption and cost of the pretreatment, normalised against both the volume treated and COD removed, was considerably less for the API-DAF sequence compared to the MBR. The combined flotation specific energy consumption in kWh was found to be almost an order of magnitude lower than for the MBR (0.091 vs 0.86 kWh per m3 effluent treated), and the total cost (in terms of the net present value) around one sixth that of the MBR. However, the nature of the respective waste streams generated and end disposal of waste solids differs significantly between the pretreatment and MBR stages.

In Brazil, the production of corn and soybeans has been growing significantly in recent years. Considering that the strategies for investing in the production of these crops are conditioned by production costs and risk, the objective of this article is to propose a simulation model that indicates the trend of production costs for these commodities, considering the dispersion and correlation of selected key variable prices and corn and soybean production costs practiced between the years of 2018 and 2022. Fifty specialized companies in the state of Sao Paulo, Brazil, provided data for this study, and Monte Carlo simulations showed that the total cost of corn production/ha is between USD 600.00 and USD 1150.00, with a level of certainty of 84.7%, and soybeans are in the range of USD 260.00 to USD 420.00, with a level of certainty of 86.4%. The model evidenced a trend of decreasing production costs for the crops for the 2023/24 and 2024/25 harvests, as the input with the greatest impact (fertilizer) is showing a downward trend. On the other hand, costs related to labor, soybean seed, and fungicides are showing an upward trend, while dolomitic limestone corrective remains stable.

The main problem in pursuing multiple extended targets tracking is distinguishing the origins of the measurements. The association of measurements to the possible origins within the target’s extent is difficult, especially for the occlusions or the detection blind zone which cause the intermittent measurements. To solve the problem, a hierarchical network-based tracklets data association algorithm is proposed. At the low level, the min cost network flow model is used to extract possible tracklets from the divided measurement set. At the high-level, the trajectories are estimated from the tracks produced by the previous low level network. The experimental results show that the hierarchical network-based tracklets data association algorithm outperforms the JPDA and RFS-based method when the measurement is intermittently unavailable.

The alteration of natural land cover to impervious surfaces during development increases stormwater runoff. Stormwater Control Measures (SCMs) are used to manage water quantity and enhance water quality by restoring the hydrologic cycle altered by development. Often, SCMs have an outflow pipe to handle overflows or to manage the release of water detained when infiltration is not possible. Traditionally, these are static controls (e.g. a small orifice is used to restrict the volume of outflow), however, these systems can be improved by instituting real-time controls (RTC). RTC improve the functionality of SCMs by dynamically controlling outflows to adjust to environmental conditions. A major impediment to the widespread implementation of RTC is the high cost of installation and operation. This study utilized machine learning methods to develop a forecasting approach for the implementation of low-cost RTC that were implemented on a programmable gate of the outlet structure of a multi-stage basin in southeastern Pennsylvania. The goals were to decrease the peak flow exiting the basin during rain events, increase the volume of water detained, decrease the number of overtopping events, maintain healthy vegetation in the basin, and protect the downstream vegetation from erosion. Multiple popular data science algorithms were evaluated including multiple linear regression and long short-term memory. These algorithms were used with a dataset, which consisted of four years of historical sensor data, collected in 5-minute intervals, to train models to predict water levels to optimize operations. The accuracy of 30 models with three different methods of handling missing values were compared. A long short-term memory model configured with a 30-minute lead time produced the best results. Having an approximate same lag time of 30 minutes for the contributing drainage area of the SCM provided a sufficient RTC functioning period to improve the performance of the outlet structure.

Fluorescence microscopy is an important tool for disease diagnosis, often requiring costly optical components, such as fluorescence filter cubes and high-power light sources. Due to its high cost, conventional fluorescence microscopy cannot be fully exploited in low-income settings. Smartphone-based fluorescence microscopy becomes an interesting low-cost alternative, but raises challenges in the optical system. We present the development of a low-cost inverted laser fluorescence microscope, that uses a smartphone to visualize the fluorescence image of biological samples. Our fluorescence microscope uses a laser-based simplified optical filter system, that provides analog optical filtering capabilities of a fluorescence filter cube. Firstly, we validated our inverted optical filtering by visualizing microbeads labeled with three different fluorescent compounds or fluorophores, commonly used for disease diagnosis. Secondly, we validated the disease diagnosis capabilities, by comparing the results of our device with those of a commercial fluorescence microscope. We successfully detected and visualized Trypanosoma cruzi parasites, responsible of the Chagas infectious disease, and the presence of Antineutrophil cytoplasmic antibodies of the ANCA non-communicable autoimmune disease. The samples were labeled with the fluorescein isothiocyanate (FITC) fluorophore, one of the most commonly used for disease diagnosis. Our device provides a 400 X magnification and is at least two orders magnitude cheaper than conventional commercial fluorescence microscopes.