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.

This paper analyzes the mitigation of enteric methane (CH4) emissions from ruminants with the use of feed additives inhibiting of rumen methanogenesis to limit global temperature increase to 1.5 °C. A mathematical simulation conducted herein predicted that pronounced inhibition of rumen methanogenesis with pure chemicals or bromoform-containing algae can contribute to limit global temperature increase by 2050 to 1.5 °C only if widely adopted at a global level and considering an efficacy higher than obtained in most studies. Currently, the most important limitations to the adoption of antimethanogenic feed additives are probably increased feeding cost without a consistent return in production efficiency, and achieving sustained delivery of inhibitors to the rumens of non-supplemented, extensively ranging animals. Economic incentives, and changes in rumen microbial metabolism caused by inhibiting methanogenesis, could potentially be used to make the methanogenesis inhibition intervention cost effective. Also, the composition of the methanogenic community, and rate of disappearance of inhibitors of methanogenesis in the rumen can influence the effective dose of the inhibitors, and hence the cost of their adoption. Possible means for sustained delivery of antimethanogenic compounds to extensively grazing animals are discussed. Limitations and knowledge gaps of these approaches, and future research directions, are examined.

Although commercially-available low-cost air quality sensors have low accuracy, the sensor system are being used to collect the data for the regulation of PM2.5 emission caused by industrial activities or to estimate the personal exposure for PM2.5. In this work, to solve the accuracy problem of low-cost PM sensor, we developed a new PM2.5 calibration model by combining the deep neural network (DNN) optimized in calibration problem and a LSTM optimized in time-dependent characteristics. First, two datasets were generated to test the accuracy performance and generalization performance of the PM2.5 calibration machine learning (ML) model. The PM2.5 concentrations, temperature and humidity by low-cost sensor and gravimetric-based PM2.5 measuring instrument were sampled for a sufficiently long time. The proposed model was compared with benchmark (multiple linear regression model) and low-cost sensor results. For root mean square error (RMSE) for PM2.5 concentrations, the proposed model reduced 41-60% of error compared to the raw data of low-cost sensor, and reduced 30-51% of error compared to the benchmark model. R2 of ML model, MLR and raw data were 93, 80 and 59 %. Also, the developed model still showed consistent calibration performance when calibrated with new sensors in different locations. Low-cost sensors combined with ML model not only can improve the calibration performance of benchmark, but also can be applied to the sensor monitoring systems for various epidemiologic investigations and regulatory decisions.

This paper describes how to construct a low-cost magnetic levitation system (MagLev). The MagLev has been intensively used in the engineering education, allowing instructors and students to learn through hands-on experiments essential concepts, such as electronics, electromagnetism, and control systems. Built from scratch, the proposed MagLev depends only on simple, low-cost components readily available on the market. In addition to showing how to construct the MagLev, this paper presents a semi-active control strategy which seems novel when applied in the MagLev. Experiments performed in the laboratory have compared the proposed control scheme with the classical PID control. The corresponding real-time experiments illustrate both the effectiveness of the approach and the potential of the MagLev for education.

Proteins are the main active molecules of Life. While natural proteins play many roles, as enzymes or antibodies for example, there is a need to go beyond the repertoire of natural proteins to produce engineered proteins that precisely meet application requirements, in terms of function, stability, activity or other protein capacities. Computational Protein Design aims at designing new proteins from first principles, using full-atom molecular models. However, the size and complexity of proteins require approximations to make them amenable to energetic optimization queries. These approximations make the design process less reliable and a provable optimal solution may fail. In practice, expensive libraries of solutions are therefore generated and tested. In this paper, we explore the idea of generating libraries of provably diverse low energy solutions by extending Cost Function Network algorithms with dedicated automaton-based diversity constraints on a large set of realistic full protein redesign problems. We observe that it is possible to generate provably diverse libraries in reasonable time and that the produced libraries do enhance the Native Sequence Recovery, a traditional measure of design methods reliability.

Additive manufacturing (AM) is an emerged layer-by-layer manufacturing process. However, its broad adoption is still hindered by limited material options, different fabrication defects, and inconsistent part quality. Material extrusion (ME) is one of the most widely used AM technologies, and, hence, is adopted in this research. Low-cost metal ME is a new and AM technology used to fabricate metal composite parts using sintered metal infused filament material. Since the involved materials and process are relatively new, there is a need to investigate the dimensional accuracy of ME fabricated metal parts for real-world applications. Each step of the manufacturing process, from the material extrusion to sintering, might significantly affect the dimensional accuracy. This research provides a comprehensive analysis of dimensional changes of metal samples fabricated by the ME and sintering process, using statistical and machine learning algorithms. Machine learning (ML) methods can be used to assist researchers in sophisticated pre-manufacturing planning and product quality assessment and control. This study compares linear regression to neural networks in assessing and predicting the dimensional changes of ME made components after 3D printing and sintering process. The prediction outcomes using a neural network performed the best with the highest accuracy as compared to regression. The findings of this study can help researchers and engineers to predict the dimensional variations and optimize the printing and sintering process parameters to obtain high quality metal parts fabricated by the low-cost ME process.

Denture wearers often complain about jeopardized function and reduced quality of life due to lack of prosthesis’ retention. Implant retained mandibular overdentures, on 2 non-connected implants (2IOD) are well-proven solutions to overcome these issues. We prospectively assessed 69 patients and scrutinized clinical records until at least 7 years of function. Thirty-six were retained on Locator ® Abutments (LA) and thirty-tree on Ball Abutments (BA). Both systems were compared regarding the type, amount and total cost of required maintenance. One implants was lost, yielding 98.7% survival after 7 years. In total 438 technical issues occurred: 121 (27.35%) in BA and 317 (72.4%) in LA. Out of these, 343 events (78%) were solved chairside: 191 (43.6%) were replacements of retention caps, 113 (25.8%) were minor acrylic repairs, in 26 (5.9%) pressure ulcers had to be relieved and 13 (3%) related to abutments. LA required 179 insert replacements compared to 12 in the BA group. The overall initial treatment cost was 3850 euro. The average total maintenance cost in relation to the initial cost for the LA and BA groups was 19.11% (range 0% - 82.24%) and 18.91% (range 0% - 113.26%) respectively (P=0.540). Conclusions: The 7-years maintenance costs for a 2IOD is acceptable when the patient is regularly checked and professionally maintained. Most events are easily solvable chairside, but a few patients required more expensive interventions, regardless of the type of attachment used.

Since the beginning of the year 2020, the global healthcare system has been challenged by the threat of the SARS-COV 2 virus. Molecular, antigen, and antibody testing are the mainstay to identify infected patients and fight the virus. Molecular and antigen tests that detect the presence of the virus are relevant in the acute phase only. Serological assays detect antibodies to the Sars-CoV-2 virus in the recovering and recovered phase. Each testing methodology has its advantages and disadvantages. To evaluate the test methods, sensitivity (percent positive agreement - PPA) and specificity (percent negative agreement – PNA) are the most common metrics utilized, followed by the positive and negative predictive value (PPV and NPV), the probability that a positive or negative test result represents a true positive or negative patient. In this paper, we illustrate how patient risk and clinical costs are driven by false-positive and false-negative results. We demonstrate the value of reporting PFP (probability of false positive results), PFN (probability of false negative results), and costs to patients and healthcare. These risk metrics can be calculated from the risk drivers of PPA and PNA combined with estimates of prevalence, cost, and Reff number (people infected by one positive SARS COV-2).

Cost estimating based on building cost index plays an important role in project planning and management by providing accurate cost information. Recently, tremendous advances in cost estimating has been made but serious inaccuracies in it are still too frequently witnessed. This study aims to improve estimating accuracy for residential building costs in New Zealand. In this study, the New Zealand house prices index is involved in the transfer function models to produce forecasts of building costs for one-storey house, two-storey house, and town house in New Zealand. To demonstrate the effectiveness of the proposed models, this study compares the estimate results of the transfer function models with the univariate ARIMA models. The results indicate that the proposed transfer function models can achieve better outcomes than ARIMA models by considering the causality between building costs and New Zealand house prices. During the modelling process, the better cost estimation approach can be identified, and the movements of building costs are shown.

Acquisition of real-time hydraulic data is an essential component for flood forecasting. However, we frequently face difficulties in obtaining discharge data using classical contact methods during high magnitude floods and for systems experiencing rapid hydro-geomorphological adjustment. Therefore, we developed low-cost, non-contact sensors and platforms that are designed to overcome these difficulties. These advances enable flood flow properties to be monitored at multiple locations across a river catchment, at low-cost, and communicated in near real-time by using an image velocimetry method. This is an optics-based approach for stream flow measurement using commercially available near-infrared digital cameras to acquire video footage in full HD (30fps). Video footage is then subjected to optical flow tracking techniques based on cross-correlation, and feature-based tracking, enabling the displacement rates of detected features (for example natural foam, seeds, woody debris, and turbulent structures) to be computed. This manual provides step by step guidance to install an image-based gauging station. It contains the list of necessary components, the calibration process of a new camera and the assembly procedure of the system.

A lack of trust in Energy Service Company (ESCo) is the most critical factor affecting the development of Energy Performance Contracting (EPC) in China compared with other constraints. One cannot easily estimate the energy-saving performance of an EPC project. Under that condition, lack of trust may cause the Energy-Consuming Unit (ECU) to suspect the energy-saving performance promised by the ESCo, thus leaving potentially profitable projects without necessary funding. Currently, specific studies taking an across-projects viewpoint on annual energy-saving performance of EPC projects in multiple subsectors objectively and quantitatively are lacking. This paper studies the regression relationships of annual energy-saving quantity in terms of revamping cost and the regression relationships of annual cost saving in terms of revamping cost. The regression results show that there are statistically significant correlations in the above relationships in the nine subsectors investigated. This is significant for ESCos and ECUs because knowledge on energy-saving performance could contribute to EPC investment decisions and trust relationships between ESCos and ECUs. Then a multiple linear regression model of revamping cost is set up to analyze its influencing factors. The model indicates that the subsector the sample belongs to, financing, registered capital of the ESCo, and contract period have significant effects on revamping cost. Thus, policy implications regarding innovation of EE promotion technology, clarifying ESCos’ exit mechanism, innovation of financing mechanism, and improving the market credit environment for promoting investment in EPC projects are provided.

This study addresses the pressing energy constraints in nations like Bangladesh by proposing the implementation of photovoltaic (PV) microgrids. Given concerns about environmental degrada-tion, limited fossil fuel reserves, and volatile product costs, renewable energy sources are gaining momentum globally. Our research focuses on a grid-connected solar PV system model at Char Jazira, Lalpur, Natore, Rajshahi, Bangladesh. Through PVsyst simulation software, we assess the performance ratio (PR) and system losses, revealing an annual solar energy potential of 3375 MWh at Standard Test Condition (STC) efficiency. After considering losses, the system generates 2815 MWh annually, with 2774 MWh exported to the grid. We analyze an average PR of 78.63% and calculate a levelized cost of energy (LCOE) of 2.82 BDT/kWh [1 USD = 110 BDT]. The financial assessment indicates a cost-effective LCOE for the grid-connected PV system, with an annual gross income of 27744 BDT from selling energy to the grid and operating costs of 64060.60 BDT/year. Remarkably, this initiative can prevent 33074.061 tCO2 emissions over the project’s 25-year lifespan.

We aimed to directly compare the cost-effectiveness of the NELSON and NLST screening protocols, two trials influencing lung cancer screening implementation internationally. A decision-analytic model analyzing the cost-effectiveness of the NELSON protocols was manipulated to reflect the protocols of the NLST, while maintaining features specific to the Hungarian healthcare setting. In the NELSON protocol, there are three possible outcomes to the initial round of screening: positive, negative, or indeterminate, indicating an uncertain degree of suspicion for lung cancer. This pro-tocol differs from the NLST protocol, in which the only possible screening outcomes are positive or negative, with no indeterminate option. The NLST pathway for smokers aged 55-74 resulted in a 43 € increase in total average lifetime costs compared to the Hungarian screening pathway and re-sulted in a lifetime gain of 0.006 QALYs. The incremental costs and QALYs yield an ICER of 7875 €/QALY. Our results demonstrate that assigning any suspicious LDCT screen as a positive result (NLST protocol) rather than indeterminate (NELSON protocol) can yield a slight QALY gain that is worth the additional use of resources according to Hungary’s willingness to pay threshold. Our study provides insight on the cost-effectiveness, advantages, and disadvantages of various LDCT screening protocols for lung cancer and can assist other countries as they implement their screening programs.

Background: Atopic dermatitis (AD), a common dermatological condition, is often associated with significant economic and social burdens. Despite extensive studies globally, there is a gap in understanding the comprehensive impact of this condition in Romania. Objectives:This study evaluated the economic burden and quality of life impact of AD in Romania, considering both direct and indirect costs. Materials & Methods: A mixed methods cost of illness study was conducted from April to October 2023. A patient survey, expert reviews, and national health data analysis were used to assess direct medical costs (treatments, medical services), direct non-medical costs (transportation, accommodation), and indirect costs (productivity losses). Quality of life was evaluated using the EuroQol EQ-5D-5L instrument. Results: In 2022, the total cost of atopic dermatitis in Romania was 32.983.498 EUR for adults and 31.573.968 EUR for children. Adult costs included 24.534.603 EUR in direct and 8.429.884 EUR in indirect medical costs, with an average annual cost per patient of 447 EUR. Pediatric costs involved 15.026.781 EUR in direct and 16.516.410 EUR in indirect costs, averaging 290 EUR per patient annually. Conclusion: Care expenditure with AD management in Romania is substantially lower than in other countries, posing a significant economic burden, primarily driven by indirect costs. This study provides a foundation for future policy interventions and highlights the need for targeted management strategies for AD.

Considering the spectrum shortage problem of IoT devices, we introduce a collaborative spectrum sensing (CSS) framework in this paper to identify available spectrum resources, so that IoT devices can access it and meanwhile avoid causing harmful interference to the normal communication of the primary user (PU). However, in the process of the PU’s signal detection in IoT devices, the issue about the stopping time and decision cost arises. To this end, we propose a distributed cognitive IoT model, which includes two IoT devices independently using sequential decision rules to detect the PU. On this basis, we define the stopping time and cost function for IoT devices, and formulate an average cost optimization problem in CSS. To solve this problem, we further regard the optimal stopping time problem as a finite horizon problem, and solve the threshold of the optimal decision rule by dynamic programming. At last, numerical simulation results demonstrate the correctness of our proposal in terms of the global false alarm and miss detection probability, and it always achieves minimal average cost under various cost of each observation taken and thresholds.

Due to an urgent need for reducing the fast progressing climate changes, a rapid, standardized and replicable solution for the infrastructure restructuring of heat demand coverage of buildings at district and city level must be developed. As more and more communities in Europe, especially in Germany, are obligated to have plans for coverage of heat demand, city planners, energy agencies seek a tool, which will help them to design the first drafts of heating network routes, which can supply whole districts and cities with renewable energy. ArcGIS Pro Tools like Least-Cost-Path-Analysis (LCPA) and Closest Facility (CF) allow to find the shortest and “cheapest” way between the heat source and heat consumers in the analyzed areas. Starting from the community level, through the district, county, voivodeship and whole country levels, the replication of the methods for generating district heating (DH) network infrastructure can be achieved. The proposed LCPA and CF based methods help to design the most suitable and efficient DH networks in the analyzed areas. As only few open-source data input, like street networks and building footprints are needed, the methods can be implemented in all communities of the country of Poland. In this paper one example of one community of Wroclaw county, called Siechnice and surroundings is presented.

Resistance evolution during exposure to non-lethal levels of antibiotics is influenced by various stress responses of bacteria which are known to affect growth rate. Here, we aim to disentangle how the interplay between resistance development and associated fitness costs is affected by stress responses. We performed de novo resistance evolution of wild-type strains and single-gene knockout strains in stress response pathways using four different antibiotics. Throughout resistance development, the increase in minimum inhibitory concentration (MIC) is accompanied by a gradual decrease in growth rate, most pronounced with amoxicillin or kanamycin. By measuring biomass yield on glucose and whole-genome sequences at intermediate and final timepoints, we identified two patterns of how the stress responses affect the correlation between MIC and growth rate. First, single-gene knockout E. coli strains associated with reactive oxygen species (ROS) acquire resistance faster and mutations related to antibiotic permeability and pumping out occur earlier. This increases the metabolic burden of resistant bacteria. Second, the ΔrelA knockout strain which has reduced (p)ppGpp synthesis, is restricted in its stringent response, leading to diminished growth rates. The ROS-related mutagenesis and the stringent response are vulnerabilities within the fitness of resistant strains that can possibly be targeted to prevent development of resistance.

Installations of decentralised Renewable Energy Systems (RES) are becoming increasing popular as governments introduce ambitious energy policies to curb emissions and slow surging energy costs. This work presents a novel model for optimal sizing for decentralised renewable generation and hybrid storage system to create a Renewable Energy Community (REC), developed in Python. The model implements PV Solar and Wind Turbines combined with a hybrid battery and Regenerative Hydrogen Fuel Cell (RHFC). The electrical service demand is derived using real usage data from a rural island case study location. Cost remuneration is managed with an REC virtual trading layer, ensuring fair distribution among actors in accordance with the European RED(III) policy. A multi-objective Genetic Algorithm (GA) stochastically determines the system capacities such that the inherent trade-off relationship between project cost and decarbonisation can be observed. The optimal design results in an LCOE of 0.15€/kWh, reducing costs by over 50% compared with typical EU grid power, with a project IRR of 10.8%, simple return of 9.6%/year, and ROI of 9 years. Emissions output from grid only use is reduced by 72% to 69 gCO2e/kWh. Further research of lifetime economics and additional revenue streams in combination with this work could provide a useful tool for users to quickly design and prototype future decentralised REC systems.

Using the World Bank Global Findex Database for 91 countries in 2014, 2017, and 2021, we examine whether fintech levels influence bank performance and whether fintech’s interaction with GDP per capita causes differential effects on bank performance globally. Since fintech levels were already very high for rich countries when the World Bank started providing fintech development statistics in 2014, we estimate AbFintech by regressing fintech levels on GDP per capita by year. AbFintech is the difference between the fintech level and its fitted values. Then, using multiple regression analyses, we investigate the impact of AbFintech on bank performance worldwide, focusing on the differential effects of AbFintech and GDP levels on bank performance. We find AbFintech significantly increases bank performance, primarily in less developed countries. Specifically, AbFintech increases banks’ ROA in the least developed countries and net interest margin in the 75th percentile countries. Also, AbFintech decreases the cost-to-income ratio in the 75th percentile countries, while it increases the ratio in the most developed countries. AbFintech does not affect the ratio of noninterest income to total income, regardless of the level of economic development.

Spatial variation of indoor and outdoor PM2.5 within three states for a 5-year period is studied using regulatory and low-cost PurpleAir monitors. Most of these data were collected in an earlier study1 investigating the relative contribution of indoor-generated and outdoor-infiltrated particles to indoor exposures. About 260 regulatory monitors and ~10,000 outdoor and ~4,000 indoor PurpleAir monitors are included. Daily mean PM2.5 concentrations, correlations, and coefficients of divergence (COD) are calculated for pairs of monitors at distances ranging from 0 (collocated) to 200 km. We use a transparent and reproducible open algorithm that avoids use of the proprietary algorithms provided by the manufacturer of the sensors in PurpleAir PA-I and PA-II monitors. The algorithm is available on the PurpleAir API website under the name “PM2.5_alt”. This algorithm is validated using several hundred pairs of regulatory and PurpleAir monitors separated by up to 0.5 km. The PM2.5 spatial variation outdoors is homogeneous with high correlations to at least 10 km, as shown by the COD index under 0.2. There is also a steady improvement in outdoor PM2.5 concentrations with increasing distance from the regulatory monitors. The spatial variation of indoor PM2.5 indoors is not homogeneous even at distances <100 m. We document substantial differences between Federal Reference Methods (FRM) and Federal Equivalent Methods (FEM).

Currently, skin cancer is the most commonly diagnosed form of cancer in humans and is one of the leading causes of death in patients with cancer. Biopsy methods are an invasive research method and are not always available for primary diagnosis. Imaging methods have low accuracy and depend on the experience of the dermatologist. Artificial intelligence technologies can match and surpass visual analysis methods in accuracy, but they have the risk of a false negative response when a malignant pigmented lesion can be recognized as benign. One possible way to improve accuracy and reduce the risk of false negatives is to analyze heterogeneous data, combine different preprocessing methods, and use modified loss functions to eliminate the negative impact of unbalanced dermatological data. The paper proposes a multimodal neural network system with a modified cross-entropy loss function that is sensitive to unbalanced heterogeneous dermatological data. The accuracy of recognition in 10 diagnostically significant categories for the proposed system was 85.19%. The novelty of the proposed system lies in the use of cross-entropy loss when training the modified function with the help of weight coefficients. The introduction of weighting factors has reduced the number of false negative forecasts, as well as improved accuracy by 1.02-4.03 percentage points compared to the original multimodal systems. The introduction of the proposed multimodal system as an auxiliary diagnostic tool can reduce the consumption of financial and labor resources involved in the medical industry, as well as increase the chance of early detection of skin cancer.

This paper reviews the management of the COVID-19 crisis and the difficulty of cost estimation model, comparing centralized management or bureaucratic government coaction and the agile market alternative or spontaneous social coordination. This is a study of Political Economy and Health Economics from the perspective of Austrian Economics. We describe and compare the al-ternative models, which are adapted to the current crisis. The analysis is based on the theorem of the impossibility of the economic calculation under coactive systems, and other principles of economy. In this context we pay also attention to collateral problems of the centralized and coac-tive management. Finally we propose a solution based on dynamic efficiency and the constitutions of wellbeing economics

Advances in communication technologies have made the interaction of small devices, such as smartphones, wearables, and sensors, scattered on the Internet, bringing a whole new set of complex applications with ever greater task processing needs. These Internet of Things (IoT) devices run on batteries with strict energy restrictions. They tend to offload task processing to remote servers, usually to Cloud Computing (CC) in datacenters geographically located away from the IoT device. In such a context, this work proposes a dynamic cost model to minimize energy consumption and task processing time for IoT scenarios in Mobile Edge Computing environments. Our approach allows for a detailed cost model, with an algorithm called TEMS that considers energy, time consumed during processing, the cost of data transmission, and energy in idle devices. The task scheduling chooses among Cloud or Mobile Edge Computing (MEC) server or local IoT devices to better execution time with lower cost. The simulated environment evaluation saved up to 51.6% energy consumption and improved task completion time up to 86.6%.

Natural capital is the wealth of nations that give them the economic status they represent. Worldwide, vulnerable people depend on natural capital for employment, salaries, wealth, and livelihoods and, in turn, determine the developmental index of the nation to which they belong. The availability of ecological services is crucial for clean water and air, food and fodder, and agricultural development. In this short commentary, we have tried to sum up the ideas and discussions over natural capital's role in ascribing economic status to countries. We have discussed how the prosperity of humans is intertwined with the services ecosystems provide and how poor natural resource management (NRM) has adversely cost human well-being. The paper concludes that to ensure the current and future human well-being, an in-depth understanding of the services ecosystems provide, is essential.

Urban cities in sub-Saharan Africa (SSA) are faced with ambient air pollution. This is an important public health problem with models and limited monitoring data indicating high concentrations of pollutants such as fine particulate matter (PM_2.5). Going through most global air quality index maps, however, information about ambient pollution from SSA is scarce. We evaluated the feasibility and practicality of longitudinal measurements of ambient PM_2.5 using low-cost air quality sensors (Purple Air-II-SD) across thirteen locations in seven countries in SSA. Devices were used to gather data over a 30-day period with the aim of assessing the efficiency of its data recovery rate and identifying challenges experienced by users in each location. The median data recovery rate was 94% (range: 72% to 100%). The mean 24-hour concentration measured across all sites was 38 µg/m³ with the highest PM_2.5 period average concentration of 91 µg/m³ measured in Kampala, Uganda and lowest concentrations of 15 µg/m³ measured in Faraja, The Gambia. Kampala-Uganda and Nnewi-Nigeria recorded the longest periods with concentrations>250µg/m³. Power outages, SD memory card issues, internet connectivity problems and device safety concerns were important challenges experienced when using Purple Air-II-SD sensors. Despite some operational challenges, this study demonstrated that it is reasonably practicable and feasible to establish a network of low-cost devices to provide data on local PM_2.5 concentrations in SSA countries. Such data are crucially needed to raise public-, societal and policymaker awareness about air pollution across SSA.

A lot of previous research have proposed various frameworks and algorithms to optimize routes to reduce the total transportation cost, which accounts for over 70% of overall logistics cost. However, it is very hard to find the cases applied the mathematical models or algorithms to the practical business environment cases, especially daily operating logistics services like convenient stores. Most of previous research have considered the developing an optimal algorithm which can solve the mathematical problem within the practical time while satisfying all constraints such as the capacity of delivery and pick-up, and time windows. For the daily pick-up and delivery service like supporting several convenient stores, it is required to consider the unit transporting container as well as the demand, capacity of trucks, traveling distance and traffic congestion. Especially, the reusable transporting container, trays, should be regarded as the important asset of logistics center. However, if the mathematical model focuses on only satisfying constraints related delivery and not considering the cost of trays, it is often to leave the empty trays on the pick-up points when there is not enough space in the track. In this research, it has been proposed to build the mathematical model for optimizing pick-up and delivery plans by extending the general vehicle routing problem of simultaneous delivery and pickup with time windows while considering left-over cost. With the numerical experiments, it has been proved that the proposed model may reduce the total delivery cost. It may be possible to apply the proposed approach to the various logistics business which uses the reusable transporting container like shipping containers, refrigerating containers, trays, and pallets.

Cardiovascular disease (CVD) is considered to be one of the leading health issues in Thailand. CVD not only contributes to an increase in the number of hospital admissions year on year but also impacts on the rising health care expenditure for the treatment and long-term care of CVD patients. Therefore, this study is aimed at examining the impacts of risk reduction strategies on the number of CVD hospital admissions, Disability-Adjusted Life Years (DALYs) and the costs of hospitalisation. To estimate such impacts a CVD cost-offset model wasapplied using a Microsoft Excel spreadsheet. The number of the mid-year population was classified by age, gender and the CVD risk factor profiles from the recent Thai National Health Examination Survey (NHES) IV. This survey was chosen as the baseline population. The CVD risk factor profiles included age, gender, systolic blood pressure, total cholesterol, and smoking status. The Asia-Pacific Collaborative Cohort Study (APCCS) equation was applied to predict the probability of developing CVD over the next eight-year period. Estimates on the following were obtained from the model: (1.) the CVD events both fatal and non-fatal; (2.) the difference between the projected number of deaths and the actual number of deaths in that population; (3.) the number of patients who are expected to live with CVD; (4.) the DALYs from the estimated number of fatal and non-fatal events; (5.) the cost of hospital admissions. Four CVD risk strategy scenarios were investigated as follows: (1.) the do nothing scenario; (2.) the optimistic scenario; (3.) achieve the UN millennium development goal; and (4.) the worst-case scenario. The findings showed that over the next eight years there are likely to be 3,297,428 recorded cases of CVD; 5,870,049 cases of DALYs; and, approximately ฿57,000 million, ($1.9 billion), is projected as the total cost of hospital admissions. However, if the current health policy can reduce the levels of risk factors as defined in the optimistic scenario or such policy meets the specifications of the UN millennium development goal,there would be a significant reduction in the number of hospital admissions. These are estimated to be a reduction of 522,179 male and 515,416 female cases. With these results it is expected that health care costs would save approximately ฿9,000 million, ($298.3 million), for CVD and 900,000 million of DALYs over the next eight years. However, if there is an upward trend in the risk factors as predicted in the worst-case scenario, then there will be an increase of 428,220 CVD cases; consequently, DALYs cases may rise by 766,029 while the hospitalisation costs may increase by approximately ฿7,000 million, ($232.1 million). Based on our findings, reducing the levels of CVD risk factors in the population will drastically reduce: (1.) the number of CVD cases; (2.) DALYs cases; and (3.) health care costs. Therefore it is recommended that the health policy should enhance the primary prevention programs which would be targeted at reducing the CVD risk factors in the population.

This study attempts to investigate a method for creating an index from mobility data that not only correlates with the number of people who relocate to a place but also has causal influence on the number of such individuals. By creating an index based on human mobility data, it becomes possible to predict the influence of urban development on future residential movements. In this paper, we propose a method called the travel cost method for multiple places (TCM4MP) by extending the conventional travel cost method (TCM). We assume that the opportunity cost of travel time on non-working days reflects the convenience and amenities of a neighborhood. However, conventional TCM does not assume that the opportunity cost of travel time varies according to the departure place. In this paper, TCM4MP is proposed to estimate the opportunity cost of travel time with respect to the departure place. We consider such estimation to be possible due to the use of massive mobility data. We assume that the opportunity cost of travel time on non-working days reflects the convenience and amenities of the neighborhood. Therefore, we consider that the opportunity cost of travel time has a causal influence on future residential mobility. In this paper, the validity of the proposed method is tested using the smart card data of public transportation in Western Japan. Our proposed method is beneficial for urban planners in estimating the effects of urban development and detecting the shrinkage and growth of a population.

This study analyses the short-run hydro generation scheduling for the wind power differences from the contracted schedule. The approach for construction of the joint short-run marginal cost curve for the hydro-wind coordinated generation is proposed and applied on the real example. This joint short-run marginal cost (SRMC) curve is important for its participation in the energy markets and for economic feasibility assessment of such coordination. The approach credibly describes the short-run marginal costs which this coordination bears in “real life”. The approach is based on the duality framework of a convex programming and as a novelty combines the shadow price of risk mitigation capability and the water shadow price. The proposed approach is formulated as a stochastic linear program and tested on the case of the Vinodol hydropower system and the wind farm Vrataruša in Croatia. The result of the case study is a family of 24 joint short-run marginal cost curves.

To estimate the recreational value provided by the Foy’s Lake annually using the most applicable model for on-site data is the main objective of this study. Adhere to the objective of this study; Individual Travel Cost Method (ITCM) has been applied and Zero Truncated Poisson Regression Model has been found plausible among other models to estimate consumer surplus. Based on the findings of the study, an estimate of the consumer surplus or recreational benefits per trip per visitor can be recommended as BDT 5,875 or US $ 73.44 and counting the consumer surplus per trip per visitor, the annual recreational value (total consumer surplus) provided by the lake is found to be BDT 321 million or US $ 40.2 million.

At 100% penetration, intermittent renewable energy causes outages. To flatten the leaping supply and demand curves of renewable energy, an energy storage medium and carrier are required. The Hydrogen energy stotrage system (HydESS), which combines a proton exchange membrane (PEM)-based water electrolysis (WE) system and a fuel cell (FC), has the quickest response time, the longest duration, and the greatest storage capacity. HOMER tools for techno-economic analysis are used to evaluate the HydESS hybrid system. Kyiv, Ukraine, is appealing due to its spatial-temporal wind speed, solar irradiance, and energy security. With an LCOE of $0.245 kWh−1 and 9.3% fuel cell power output, the most feasible solution is onshore wind turbine-coupled PEMFC HydESS. As a result 1,000-kW wind power, 250-kW PEMFC, 250-kW PEMWE, and 441-kW BESS are optimized to serve a 3,900 kWh day academic residence load. When compared to a diesel generator and a BESS, the hybrid HydESS reduces CO2 emissions by 4,132 tons over a 25-year period. The LCOE of a hybrid HydESS system is determined by the SOFC lifetime rather than the PEMFC's 2% difference between 40,000 and 50,000 h with the same rated power. This study recommends accelerating PEMFC development with fluctuation-endurable materials and design.

The work presented in this paper presents several novel findings from a comprehensive analysis of about 50,000 Tweets about online learning during COVID-19, posted on Twitter between November 9, 2021, and July 13, 2022. First, the results of sentiment analysis from VADER, Afinn, and TextBlob show that a higher percentage of these tweets were positive. The results of gender-specific sentiment analysis indicate that for positive tweets, negative tweets, and neutral tweets, between males and females, males posted a higher percentage of the tweets. Second, the results from subjectivity analysis show that the percentage of least opinionated, neutral opinionated, and highly opinionated tweets were 56.568%, 30.898%, and 12.534%, respectively. The gender-specific results for subjectivity analysis indicate that for each subjectivity class, males posted a higher percentage of tweets as compared to females. Third, toxicity detection was performed on the tweets to detect different categories of toxic content - toxicity, obscene, identity attack, insult, threat, and sexually explicit. The gender-specific analysis of the percentage of tweets posted by each gender in each of these categories revealed several novel insights. For instance, for the sexually explicit category, females posted a higher percentage of tweets as compared to males. Fourth, gender-specific tweeting patterns for each of these categories of toxic content were analyzed to understand the trends of the same. The results unraveled multiple paradigms of tweeting behavior, for instance, the intensity of obscene content in tweets about online learning by males and females has decreased since May 2022. Fifth, the average activity of males and females per month was calculated. The findings indicate that the average activity of females has been higher in all months as compared to males other than March 2022. Finally, country-specific tweeting patterns of males and females were also performed which presented multiple novel insights, for instance, in India a higher percentage of the tweets about online learning during COVID-19 were posted by males as compared to females.

Let us assume that defence mechanisms are so strong that the average outcome of a hacking attack is unsuccessful. How to calculate the costs arising from false positives and false negatives in intruder detection? Is it better for the hacker to make fewer but more effective attacks rather than several but less effective attacks? How to calculate the difference between these alternative strategies?

A digital text abstract presents the essential information of an article, and we can find the trend and value of the research by analyzing it rigorously and digging up knowledge. Therefore, this study focuses on the abstracts of index journals in China and Taiwan from July 2010 to June 2020 (a total of 3,283 abstracts). Through the concepts of text mining and natural language processing (NLP), it constructs processes such as text retrieval, text segmentation and word cloud analysis, TF-IDF weight analysis, co-word analysis, network analysis, and trend analysis, and analyses a large amount of text data. The results show that the scope of research in China covers the fields of social sports and sports science, and research in Taiwan covers both natural and social sciences. The network diagram highlights the richness of sports-related research fields in the two regions, but research on sports philosophy is relatively rare. It is suggested that all disci-plines/departments should re-allocate the same resources, so as to show a balanced development trend and help expand a new chapter in the sports academic field.

The use of principal components analysis provided information about the main characteristics of teams, based on a set of indicators, instead of displaying individualized information for each of these indicators. In this work we have considered reducing an extensive data matrix to improve interpretation, using the principal components analysis. Subsequently, with new components and with a multiple linear regression, we have carried out a comparative analysis between the best and bottom teams of LaLiga. The sample consisted of the matches corresponding to the 2015/16, 2016/17 and 2017/18 seasons. The results showed that the best teams were characterized and differentiated from bottom teams in the realization of a greater number of successful passes and in the execution of a greater number of dynamic offensive transitions. The bottom teams were characterized by executing more defensive than offensive actions and showing a fewer number of goals, a greater ball possession time in the final third of the field. Goals, ball possession time in the final third of the field, number of effective shots and crosses are the main performance factors that influence the offensive success of football. This information allows us to increase knowledge about the key performance indicators in football.

Optinalysis, as a method of symmetry detection, is a new advanced computational algorithm that intrametrically (within elements) or intermetrically (between elements) computes and compares two or more multivariate sequences in an unclustered or clustered manner as a mirror-like reflection of each other (optics-like manner), hence the name is driven. Optinalysis is based by the principles of reflection and moment about a symmetrical line which detects symmetry that reflects a similarity measurement. Optinalysis is suitable for quantitative and qualitative data types, with or without replications, provided it conform the algorithmic requirements there provided. Optinalysis can be organized for geometrical, geostatistical and statistical analysis in one-way, two-way, or three-way approach. A simulation comparisons shows that Optinalysis is a simple alternative approach of multivariate analysis of sociometric, demographic, socio-demographic, psychometric, ecological, experimental, genomic, nanoparticle and shape morphometric data. Optinalysis of these data matrix shows very similar results or conclusions with some multivariate analysis such as skewness measure, one-way ANOVA, paired t-test, one sample t-test, Tukey’s multiple comparisons, BLAST sequence algorithmic analysis (percentages of identity, similarity, gabs, and positives, and the Needleman-Wunsch score), and Riemannian distance.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Pulmonary vein stenosis (PVS) causes a rare type of Pulmonary Hypertension (PH), by impacting the flow and pressure within the pulmonary vasculature resulting in endothelial dysfunction and metabolic changes. A prudent line of treatment in this type of PH would be targeted therapy in order to relieve the pressure and reverse the flow related changes. We used a swine model in order to mimic PH post PVS using pulmonary vein banding (PVB) of the lower lobes for 12 weeks to mimic the hemodynamic profile associated with PH and investigate the molecular alterations that provide an impetus for development of PH. Our current study aimed to employ unbiased proteomic and metabolomic analyses on both the upper and lower lobes of the swine lung to identify the regions with metabolic alterations. We detected changes in the upper lobes for the PVB animals mainly pertaining to fatty acid metabolism, ROS signaling and extracellular matrix remodeling, and small albeit significant changes in the lower lobes for purine metabolism.

A novel kind of power spectrum is constructed, the inter-spike spectrum, which transforms any signal into its spike-frequency domain. This method clearly shows the apparent cycles in the data and overcomes the problems for spike-train-like signals when using the obvious idea of Fourier-transforming it. We invent this instructive approach with the idea of transforming the τ-recurrence rate of a recurrence plot (RP), which often has a spiky appearance. The τ-recurrence rate is the density of recurrence points along diagonals of the RP, which are parallel to the main diagonal with a distance of τ. In this context the inter-spike spectrum can be interpreted as a nonlinear power spectrum of a potentially high dimensional system which constitutes the RP. The proposed measure is robust to noise and is able to detect and analyze bifurcations.

Background: Traditional omic analysis relies on p-value and fold change as selection criteria. There is an ongoing debate on their effectiveness in delivering systemic and robust interpretation, due to their dependence on assumptions of conformity with various parametric distributions.Here, we propose a threshold-free selection method based on robust, non-parametric statistics, ensuring independence from the statistical distribution properties and broad applicability. Such methods could adapt to different initial data distributions, contrary to statistical techniques based on fixed thresholds. Methods: Our work extends the Rank Products methodology with a neutral selection method of high information-extraction capacity. We introduce the calculation of the RP distribution’s entropy to isolate the features of interest by their contribution to the distribution’s information content. The aim is a methodology performing threshold-free identification of the differentially expressed features, which are highly informative about the phenomenon under scrutiny. Conclusions: Applying the proposed method on microarray (transcriptomic and DNA methylation) and RNAseq count data of varying sizes and noise presence, we observe robust convergence for the different parameterisations to stable cutoff points. Functional analysis through BioInfoMiner and EnrichR was used to evaluate the information potency of the resulting feature lists. Overall, the derived functional terms provide a systemic description highly compatible with the results of traditional statistical hypothesis testing techniques. The methodology behaves consistently across different data types. The feature lists are compact and information-rich, indicating phenotypic aspects specific to the tissue and biological phenomenon i nvestigated. Selection by information content measures efficiently addresses problems, emerging from arbitrary thresholding, thus facilitating the full automation of the analysis.

Sorghum has become increasingly popular for diverse applications, including food, feed, and bioenergy. The present study aimed to perform a bibliometric and content analysis on sorghum research in the industrial-agricultural sector to evaluate global research patterns, emerging topics, and future trends. The bibliometric analysis of the Scopus bibliographic metadata was done using Bibliometrix and Vosviewer. The results of this investigation show the descriptive analysis underscored fluctuation in research output and citation impact over the years. The identified four patterns through co-occurrence analysis encompassed central themes of sorghum research, including genetic expression and plant stress cluster, cereal crop and yield cluster, biomass and microbial bioprocessing, and metabolism and antioxidant cluster. The thematic map portrays a comprehensive view of cereal crops and yields, biomass and microbial bioprocessing, metabolism and antioxidant research, environmental remediation and sustainable agriculture practices, and the declining theme of starch. The findings provide valuable insights into the global pattern of sorghum research and emerging trends, thus directing future multidisciplinary research efforts for enhanced knowledge and sustainable practices in the industrial-agricultural sector.

In this paper we describe the potentialities of a tool for the visualization of experimental results directly on a three-dimensional model. The case study concerns the visualization of the results of a dynamic finite element analysis (FEA/FEM) applied to the calculation of seismic risk on works belonging to the Italian infrastructural heritage, specifically bridges, viaducts and overpasses. The project is based on finite element analysis performed on an exemplary set of 8 structures located on the Italian territory, performed by means of the open-source software framework OpenSees, according to the guidelines indicated in the Technical Standards for Construction NTC08. The application created for this project is classifiable as a webGIS, since all data are georeferenced and visualized on a map through an application executed through a browser. The graphical interface displays the interested works on the map of the Italian territory and allows to select them by mouse click. Following the selection, a 3D rendering of the model of the work and the surrounding terrain is shown, in which the results of the analysis are represented using color gradients directly on the three-dimensional model. The necessary tools are present for the selection of the type of result and for the animation in real time of the response of the work to the seismic action. The 3D representation is freely navigable by the user thanks to intuitive tools of panning, rotation and zoom through mouse and keyboard. The application takes advantage of HTML5, CSS and Javascript to show graphical features such as Cartesian diagrams of accelerograms used in modal analysis.

In Korea, the underground silo structure for low- and intermediate-level radioactive waste disposal facilities has been constructed and operated since 2014. Large-scale earthquakes occurred in 2016 and 2017, respectively, in Gyeongju and Pohang areas near the underground silo structures, and interest in the stability of the underground silo increased significantly. In this paper, one-dimensional free field analyses have been carried out before the three-dimensional silo dynamic analyses subjected to earthquake loadings. As an additional study, a new form of finite element equilibrium equation is derived in terms of relative motions, which is essentially the same equation as that expressed in terms of total motions where the base shear force is applied for earthquake load. The accuracy of conventional finite element solutions is evaluated by directly comparing them with closed-form solutions by frequency domain analysis such as SHAKE91.

The work of this paper presents multiple novel findings from a comprehensive analysis of about 150,000 tweets about exoskeletons posted between May 2017 and May 2023. First, findings from content analysis and temporal analysis of these tweets reveal the specific months per year when a significantly higher volume of Tweets was posted and the time windows when the highest number of tweets, the lowest number of tweets, tweets with the highest number of hashtags, and tweets with the highest number of user mentions were posted. Second, the paper shows that there are statistically significant correlations between the number of tweets posted per hour and different characteristics of these tweets. Third, the paper presents a multiple linear regression model to predict the number of tweets posted per hour in terms of these characteristics of tweets. The R2 score of this model was observed to be 0.9540. Fourth, the paper reports that the 10 most popular hashtags were #exoskeleton, #robotics, #iot, #technology, #tech #innovation, #ai, #sci, #construction and #news. Fifth, sentiment analysis of these tweets was performed using VADER and the DistilRoBERTa-base library. The results show that the percentage of positive, neutral, and negative tweets were 46.8%, 33.1%, and 20.1%, respectively. The results also show that in the tweets that did not express a neutral sentiment, the sentiment of surprise was the most common sentiment. It was followed by the sentiments of joy, disgust, sadness, fear, and anger. Furthermore, analysis of hashtag-specific sentiments revealed several novel insights, for instance, for almost all the months in 2022, the usage of #ai in tweets about exoskeletons was mainly associated with a positive sentiment. Sixth, text processing-based approaches were used to detect possibly sarcastic tweets and tweets that contained news. Finally, a comparison of positive tweets, negative tweets, neutral tweets, possibly sarcastic tweets, and tweets that contained news, in terms of different characteristic properties of these tweets are presented. The findings reveal multiple novel insights, for instance, the average number of hashtags used in tweets that contained news has considerably increased since January 2022.

The intestinal microbial community of aquatic animals is closely related to the environment and thus affects the health of aquatic animals. Here we investigated annual variation of intestinal bacteria community structures of Ruditapes philippinarum cultured using two different methods, bottom sown and raft hanging. Bacterial diversity of seawater and sediment in different seasons was examined as well. Correlation between R. philippinarum intestinal bacteria community structures and environmental factors was analyzed. The results showed that in the two aquculture areas where R. philippinarum was differentially cultured, bacteria community structure of seawater and sediment both altered with seasons. However, neither seasonal changes nor difference in culture methods could significantly affect the intestinal bacteria community structures of R. philippinarum. Additionally, in terms of the alpha diversity of intestinal bacteria communities, there was no significant difference in R. philippinarum cultured using the two methods. For benthic cultured clams, alpha diversity of the intestinal bacterial communities was often higher than that of the environmental bacterial communities. For raft hanging cultured calms, Alpha diversity of the intestinal bacterial communities was normally comparable with that of the environmental counterparts. Beta diversity analysis showed that the intestinal bacterial community structure of R. philippinarum was mainly determined by both deterministic and stochastic processes. Correlation analysis showed that the intestinal bacterial community structures of R. philippinarum were determined by a variety of environmental factors. For clams collected from the benthic culture area, the intestinal bacterial structure was more correlated with the physicochemical properties of sediment in comparing with those of seawater. For clams collected from the raft-culture area, the intestinal bacterial structure was mainly determined by the phsicochemical characteristics of seawater. This study helps us to better understand the bacterial community structure in complex aquaculture ecosystems, providing a reference for healthy shellfish aquaculture and ecosystem protection in aquaculture area.

Sequence analysis is a robust methodological framework that has gained popularity in various fields, including transportation research. It provides a comprehensive approach to understanding the dynamics and patterns of individual behaviors over time. In the context of the Metropolitan Region of Barcelona, applying sequence analysis to the mobility surveys offers valuable insights into the sequencing and order of travel activities and modes, shedding light on the complex interrelationship between individuals, their travel choices, and the built environment. The Barcelona travel surveys collect detailed data on individuals' travel behavior, such as trip purpose, duration, mode of transportation, and origin-destination pairs. Sequence analysis allows for examining travel behaviors as dynamic processes, unveiling travel patterns' underlying structure and evolution in a day. A data analytics methodological approach is described; it enables the identification of common travel patterns and the exploration of variations across different demographic groups or geographical regions. Sequence analysis reveals insights into the factors influencing mode choice and potential opportunities for sustainable transport interventions. The paper proposes a methodological approach to discover homogeneous travel behavioral segments from diaries included in travel surveys in order to refine transport policies to selected segments by transportation planners and authorities

Thanks to rapid development of artificial intelligence technology in recent years, the current artificial intelligence technology is contributing to many part of society. Education, environment, medical care, military, tourism, economy, politics, etc. are having a very large impact on society as a whole. For example, in the field of education, there is an artificial intelligence tutoring system that automatically assigns tutors based on student's level. In the field of economics, there are quantitative investment methods that automatically analyze large amounts of data to find investment laws to create investment models or predict changes in financial markets. As such, artificial intelligence technology is being used in various fields. So, it is very important to know exactly what factors have an important influence on each field of artificial intelligence technology and how the relationship between each field is connected. Therefore, it is necessary to analyze artificial intelligence technology in each field. In this paper, we analyze patent documents related to artificial intelligence technology. We propose a method for keyword analysis within factors using artificial intelligence patent data sets for artificial intelligence technology analysis. This is a model that relies on feature engineering based on deep learning model named KeyBERT, and using vector space model. A case study of collecting and analyzing artificial intelligence patent data was conducted to show how the proposed model can be applied to real-world problems.

Data can be illustrated in shapes, and the shapes could provide insight for data modeling and information extraction. Topological data analysis provides an alternative insight in biomedical data analysis and knowledge discovery with the algebra topology tools. In present work, we study the application of topological data analysis for personalized electrocardiographic signal classification toward arrhythmia analysis. Using phase space reconstruction technique, the signal samples are converted into point clouds for topological analysis facility. With topological techniques the persistence landscapes from the point clouds are extracted as features to perform the arrhythmia classification task. We find that the proposed method is robust to the training set size, with only a training set size of 20% percents, the normal heartbeat class are 100% recognized, ventricular beats for 97.13%, supra-ventricular beats for 94.27% and fusion beats for 94.27% within the corresponding experiments. The property of keeping high performance when using smaller training sample proves that the proposed method is especially applicable to personalized analysis. With the present study, we show that the topological data analysis technique could be a useful tool in biomedical signal analysis, and provide powerful ability in personalized analysis.

FinTech has been in the focus of the discussion for quite some time. However, the market share of FinTech companies is still relatively small in compared to more traditional financial services. The purpose of this paper is to analyse the status quo, current developments and challenges ahead for the Latvian FinTech sector. We combine three analyses: the political and legal, the economic, the social and the technological environment (PEST) analysis, an analysis of the size and performance of FinTech companies during the last 10 years, and a survey among FinTech companies. We find that the current status of regulation is one of the main obstacles to FinTech development, because it does not sufficiently consider FinTech-specific aspects. Problems in attracting skilled workforce, and an environment that is not very supportive of new developments in finance are further challenges and might explain at least part of the growth and performance difficulties. A revision, modernization and harmonization of regulation is essential to create a level playing field for all market participants: FinTech companies, traditional financial service providers and those originally traditional players that are integrating FinTech solutions in their business model. Further efforts are also required to foster the Latvia’s attractiveness for skilled workforce. We hope that this study helps increase the visibility of Latvian FinTech and contributes to the development of the new Latvian FinTech strategy.

This bibliometric review is aimed to analyze the top 100 most-cited publications in dentistry and to compare its outcomes with similar analysis done by Feijoo et al., [1]. A literature search was performed using the Elsevier's Scopus without any restriction of language, publication year or study design. Of 336381 articles, the top 100 were included based on their citation count which ranged from 638 to 4728 citations (Feijoo et al., 326 to 2050). Most productive decade was the 2000s with 40 articles on the list (Feijoo et al., 1980s: 26). Marx RE (7%) was the major contributor in this study (Feijoo et al., Socransky SS: 9%) and a whopping 48% articles generated from the USA. 26% of top 100 articles focused periodontology (Feijoo et al., periodontology: 43%), while 17% of the total were published in the Journal of Dental Research (Feijoo et al., Journal of Clinical Periodontology: 20%). Most of the publications were narrative reviews/expert opinion (36%), (Feijoo et al., case series: 22%) and were within the evidence level V (64%) (Feijoo et al., 54%). The citation count that a paper secures is not necessarily a reflection of research's quality, however, the current analysis provides latest citation trends in dentistry.

In this paper, Lie symmetry and Painlevé Techniques are applied to the SIRD (Susceptible, Infected, Recovered and Dead) model. A demonstration of the integrability of the model is provided to present an exact solution. The study revealed that nonlinear system passes Painlevé test and does not possesses complex chaotic behaviour. However, the system fails to pass the Painlevé test while constraints reach values equivalent to the corresponding complex chaotic behaviour. The two-dimensional Lie symmetry algebra and the commutator table of the infinitesimal generators are obtained. Lie symmetry analysis serves to linearize the nonlinear system and find the corresponding invariant solution.

This letter attempts to find splashing height of liquid-filled container drop impact to a solid surface by dimensional analysis (DA). Two solutions were obtained by both traditional DA and directed DA without solving any governing equations. It is found that the directed DA can provide much more useful information than the traditional one. This study shows that the central controlling parameter is called splash number $\mathrm{Sp}=\mathrm{Ga} \mathrm{La}^\beta=(\frac{gR^3}{\nu^2})(\frac{\sigma R}{\rho \nu^2})^\beta$, which is the collective performance of each quantity. The splash height is given by $ \frac{h}{H}=(\frac{\rho\nu^2}{\sigma R})^\alpha f[\frac{gR^3}{\nu^2}(\frac{R\sigma}{\rho\nu^2})^\beta]=\frac{1}{\mathrm{La}^\alpha}f(\mathrm{Ga}\cdot \mathrm{La}^\beta)$. From the physics of the splashing number, we can have a fair good picture on the physics of the liquid splashing as follows: the jets propagation will generate vortex streets from the container bottom due to sudden pressure increasing from drop impact (water-hammer effect), which will travel along the container sidewall to the centre of the container and subsequently excite a gravity wave on the liquid surface. The interaction between the gravitational force, surface force and viscous force is responsible for creating droplet splash at the liquid surface.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Together with display and input, storage and processing are requisite to computing. While storage technologies have improved tremendously in capacity and speed over the years, the basic principles enabling information storage into digital 1 and 0 remains the same: induction of phase change in the storage substrate. But recently, there has been much research into structural phase change material (SPCM) and exploration of its possible use in various types of memory storage applications. Despite unconventional use of structural change between amorphous and crystalline state as well as that between crystal structures for encoding information, key barriers for its widespread use remains access speed, capacity to cost ratio, and fidelity of storage. Hence, given the performance requirement of SPCM for memory applications, what are the material and design considerations that feed into translating a promising application into a practical reality? Given the important role of kinetic and thermal energy in structural organization of a phase change material, precise characterization of structural change in the material with external physical factors such as heat, voltage and current, is critical for storage material design. Next comes the precision at which individual memory cells for storing single bits of information could be defined reproducibly and at high fidelity using SPCM. In congruent with memory cell definition lies the equally important aspect of constraining the field characteristics used in modulating the phase state of the memory medium. Specifically, while heat is useful for mediating the “melting” of a crystalline material into its amorphous state, heat conduction is less useful for transferring the “switch command” from the effector to the memory material. More importantly, choice of structural phase change material for memory applications likely revolves around those where individual memory cells could be defined in a cross array format, which is amenable to high density information storage. Durability and fidelity of information storage are additional factors of design that favours selection of SPCM with phase change occurring at narrow operating windows without hysteresis over extended cycling. But, the most important requirement is speed of access. To this end, energetic cost of phase transitions might affect operation of the phase change memory at the system level: for example, usage of large current for high energy transition step may impact on device durability. Ultimately, there is a fundamental limit on the number of reproducible phase transitions in a SPCM before fidelity of information storage is no longer guaranteed. Hence, what are the drivers for uptake of phase change memories in consumer devices? Performance gains must be realized in access speed, storage capacity, form factor, and fidelity of information storage for practical application of structural phase change memories.

Over the last few decades there has been an exponential growth in IT, motivating IT professionals and scientists to explore new dimensions resulting in the advancement of artificial intelligence and its subcategories like computer vision, deep learning and augmented reality. AR is comparatively a new area which was initially explored for gaming but recently a lot of work has been done in education using AR. Most of this focuses on improving students understanding and motivation. Like any other project, the performance of an AR based project is determined by the customer satisfaction which is usually affected by the theory of triple constraints; cost, time and scope. many studies have shown that most of the projects are under development because they are unable to overcome these constraints and meet project objectives. We were unable to find any notable work done regarding project management for augmented reality systems and application. Therefore, in this paper, we propose a system for management of AR applications which mainly focuses on catering triple constraints to meet desired objectives. Each variable is further divided into subprocesses and by following these processes successful completion of the project can be achieved.

The worldwide interconnected objects, called Internet of Things (IoTs), have been increasingly growing in the last several years. Different social media platforms and devices are continuously generating data about individuals and facilitate the technological and the social convergence of their Internet-based data and services with globalized users. These social and device-related IoTs create rooms for data breaches as such platforms provide ability to collect private and sensitive data. We assert that data breaches are fundamentally failures of access control - most users are too busy or technically ill-equipped to understand access control policy expressions and decisions. We argue that this is symptomatic of globalised societies structured by the conditions of algorithmic modernity; an era in which our data is increasingly interdependent on, and enmeshed with, ever more complex systems and processes that are vulnerable to attack. Ethically managing data breaches is now too complex for current access control systems, such as Role-Based Access Control (RBAC) and Context-Aware Access Control (CAAC). These systems do not provide an explicit mechanism to engage in decision making processes, about who should have access to what data and when, that are involved in data breaches. We argue that a policy ontology will contribute towards the development of Ethical CAAC better suited to attributing accountability for data breaches in the context of algorithmic modernity. We interrogate our proposed Ethical CAAC as a theoretical construct with implications for future policy ontology models and data breach countermeasures. An experimental study on the performance of the proposed framework is carried out with respect to a more generic CAAC framework.

Renewable energy sources will play a central role in the sustainable energy systems of the future. Scenario analyses of such hypothesized energy systems require sound knowledge of the techno-economic potential of renewable energy technologies. Although there have been various studies concerning the potential of offshore wind energy, higher spatial resolution, as well as the future design concepts of offshore wind turbines, has not yet been addressed in sufficient detail. Here, we aim to overcome this gap by applying a high spatial resolution to the three main aspects of offshore wind potential analysis, namely ocean suitability, the simulation of wind turbines and cost estimation. A set of constraints is determined that reveal the available areas for turbine placement across Europe’s maritime boundaries. Then, turbine designs specific to each location are selected by identifying turbines with the cheapest levelized cost of electricity (LCOE), restricted to capacities, hub heights and rotor diameters of between 3-20 MW, 80-200 m and 80-280 m, respectively. Ocean eligibility and turbine design are then combined to distribute turbines across the available areas. Finally, LCOE trends are calculated from the individual turbine costs, as well as the corresponding capacity factor obtained by hourly simulation with wind speeds from 1980 to 2017. The results of cost-optimal turbine design reveal that the overall potential for offshore wind energy across Europe will constitute nearly 8.6 TW and 40.0 PWh at roughly 7 €_ct kWh^-1 average LCOE by 2050. Averaged design parameters at national level are provided in an appendix.

Gesture recognition has always been a technique to decrease the distance between the physical and the digital world. In this work, we introduce an Arduino based vehicle system which no longer require manual controlling of the cars. The proposed work is achieved by utilizing the Arduino microcontroller, accelerometer, RF sender/receiver, and Bluetooth. Two main contributions are presented in this work. Firstly, we show that the car can be controlled with hand-gestures according to the movement and position of the hand. Secondly, the proposed car system is further extended to be controlled by an android based mobile application having different modes (e.g., touch buttons mode, voice recognition mode). In addition, an automatic obstacle detection system is introduced to improve the safety measurements to avoid any hazards. The proposed systems are designed at lab-scale prototype to experimentally validate the efficiency, accuracy, and affordability of the systems. We remark that the proposed systems can be implemented under real conditions at large-scale in the future that will be useful in automobiles and robotics applications.

During recent years UAVs have been increasingly used in agriculture and forestry research and application. Nevertheless, most of this work has been devoted to improving accuracy and explanatory power, often at the cost of usability and affordability. We tested a low-cost UAV and a simple workflow to apply four different greenness indices to the monitoring of pine (Pinus sylvestris and P. nigra) after-fire regeneration in a Mediterranean forest. We selected two sites and masured all pines within a pre-selected plot. Winter flights were carried out at each of the sites, at two flight altitudes (50 and 100 m). Automatically normalizing images entered an SfM based photogrammetric software for restitution and the obtained point cloud and orthomosaic processed to get a canopy height model and four different greenness indices. Sum of pine DBH was regressed on summary statistics of greenness indices and canopy height model. ExGI and GCC indices outperformed VARI and GRVI in estimating pine DBH, while canopy height model slightly improved the models. Flight altitude did not severely affect model performance. Our results show that low cost UAVs may improve forest monitoring after disturbance, even in those habitats and situations were resource limitation is an issue.

This paper presents an optimized energy management strategy for Li-ion power batteries used on electric vehicles (EVs) at low temperatures. Under low-temperature environments, EVs suffer a sharp driving range loss resulted from the energy and power capability reduction of the battery. Simultaneously, because of Li plating, battery degradation becomes an increasing concern as temperature drops. All these factors could greatly increase the total vehicle operation cost. Prior to battery charging and vehicle operating, preheating battery to a battery-friendly temperature is an approach to promote energy utilization and reduce total cost. Based on the proposed LiFePO4 battery model, the total vehicle operation cost under certain driving cycles is quantified in the present paper. Then given a certain ambient temperature, a target temperature of preheating is optimized under the principle of minimizing total cost. As for the preheating method, a liquid heating system is also implemented on an electric bus. Simulation results show that the preheating process becomes increasingly necessary with a decreasing ambient temperature; however, the preheating demand declines as driving range grows. Vehicle tests verify that the preheating management strategy proposed in this paper is able to save total vehicle operation cost.

Badminton tactics refer to the techniques and strategies employed by players to win a match. Analyzing these tactics can help players improve their performance and outsmart their opponents. To study the tactics of top players, we use a gaming tree to analyze matches between two of the most powerful badminton players in history: Lin and Lee. By employing the Nash Equilibrium, we can discover the most beneficial strategies for both players, which reflect their most powerful techniques. Additionally, with the help of this gaming tree, we can precisely predict how players will implement their tactics. Empirical experimental results demonstrate that our proposed method not only evaluates and identifies each player's weaknesses and strengths but also has powerful capabilities to predict their tactics.

Transporter associated with antigen processing 1 (TAP1) gene codes for a transporter protein, which is responsible for tumor antigen presentation in the MHC I or HLA complex. A defect in the gene results in an inadequate tumor tracking. TAP1 may also influence multi drug resistance, which is an extreme threat in providing treatment by drugs which are chemotherapeutic. The gene of TAP1 was analyzed bioinformatically. It gave us prognostic data as a confirmation of whether it should be used as a biomarker. The expression level and pattern analysis were conducted using ONCOMINE, GENT2 and GEPIA2 online platforms. Samples with different clinical outcomes were investigated for expression and promoter methylation analysis was done in cancer vs normal tissues using UALCAN. The copy number alteration and mutation frequency and expression in different cancer studies were analyzed using cBioPortal. The PrognoScan and KM plotter survival analysis of significant data (p-value<0.05) was representing graphically. Pathway and Gene ontology analysis of gene correlated to TAP1 gene was presented using bar charts. After arranging the data in a single panel and correlating expression to prognosis, understanding mutational and alterations and comparing pathways, TAP1 may be a potential novel target to evade a threat against chemotherapy and the study gives new aspects to consider for immunotherapy in human breast, lung, liver and ovarian cancer.

The ANSYS 2024R1 student edition was used to create a finite element model of a basketball rim and backboard. This finite element model included the use of steel for the rim and its mount, a tempered glass backboard, and an aluminum frame behind the backboard. After a mesh was created, fixed support boundary conditions were applied to the four corners of the aluminum frame. The subsequently calculated mode shapes and frequencies were compared to empirical modal analysis previously done at the United States Military Academy at West Point, New York. Four mode shapes and frequencies agreed rather well between the theoretical finite element analysis and previously published empirical modal analysis, specifically where the rim was vibrating in the vertical direction, which was the direction that the accelerometer was aligned for modal analysis. However, three mode shapes missed by the empirical modal analysis were found where the vibration of the rim was confined to the horizontal plane, which was orthogonal to the orientation of our accelerometer.