Attribute reduction is a core technique in the rough set domain and an important step in data preprocessing. Researchers have proposed numerous innovative methods to enhance the capability of attribute reduction, such as the emergence of multi-granularity rough set models, which can effectively process distributed and multi-granularity data. However, these innovative methods still have many shortcomings, such as how to deal with complex constraints and how to perform multi-angle effectiveness evaluations. Based on the multi-granularity model, this study proposes a new method of attribute reduction, namely using multi-granularity neighborhood information gain ratio as the measurement criterion. This method combines both supervised and unsupervised perspectives, and by integrating multi-granularity technology with neighborhood rough set theory, constructs a model that can adapt to multi-level data features. This model can select the optimal granularity level or attribute set according to the requirements. Finally, the proposed method is experimented with on 15 UCI datasets against six other attribute reduction algorithms. The experimental outcomes, upon analysis and comparative review, substantiate the reliability and consistency of the attribute reduction approach introduced in this study.

Agriculture is an industry that heavily relies on the environment. The increasing frequency of ex-treme disasters attributable to climate change has adversely affected agricultural land and dis-rupted the stability of the supply of agricultural products. Therefore, we implemented a research program focused on current strategies for adapting to agricultural meteorological disasters. The primary goal was to actively promote disaster prevention information and techniques through information platforms and applications and to provide refined forecasting and an early warning system to enhance preparedness for and recovery from agricultural disasters. This initiative can transform passive mitigation into dynamic action by disseminating crucial information to farmers. We integrated meteorological data and crop characteristics to develop disaster prevention strate-gies for the predisaster, during-disaster, and postdisaster stages to empower farmers to implement autonomous disaster prevention, ensuring their profitability and minimizing the impact of disas-ters. This paper presents an agricultural disaster prevention system involving cross-disciplinary collaboration to establish an early warning system for crop disasters. However, the effectiveness of the early warning system relies on the availability of complete meteorological and crop infor-mation, necessitating high-density agricultural meteorological stations, refined weather forecast services, historical crop damage data, and ongoing research and development on disaster preven-tion technology. In the system, a diversified social media interface is used to provide local farmers with weather forecast information. When impending disasters are identified, the platform acti-vates disaster prevention measures to reduce agricultural losses. This article paper introduces Taiwan’s agricultural disaster prevention system as a complete disaster prevention strategy.

A typhoon-induced storm surge is considered one of the most severe coastal disasters in Taiwan. However, the combination of the storm surge and the astronomical tide called the storm tide can actually cause extreme flooding in coastal areas. This study implemented a two-dimensional hydrodynamic model to account for the interaction between tides and storm surges on the coast of Taiwan. The model was validated with observed water levels at Sauo Fish Port, Hualien Port, and Chenggong Fish Port under different historical typhoon events. The model results are in reasonable agreement with the measured data. The validated model was then used to evaluate the effects of the typhoon's intensity, bathymetric change, and the combination of the typhoon’s intensity and bathymetric change on the maximum storm tide and its distribution along the east coast of Taiwan. The results indicated that the maximum storm tide rises to 1.92 m under a typhoon with an intensity of a 100-year return period. The maximum storm tide increased from a baseline of 1.26 m to 2.63 m for a 90% bathymetric rise at Sauo Fish Port under the conditions of Typhoon Jangmi (2008). The combination of the intensity of a typhoon with a 100-year return period and a 90% bathymetric rise will result in a maximum storm tide exceeding 4 m, 2 m, and 3 m at Sauo Fish Port, Hualien Port, and Chenggong Fish Port, respectively. We also found that the distribution of the maximum storm tide on the east coast of Taiwan can expand significantly subject to the bathymetric rise.

Synergistic control of emissions of air pollutants and CO2 is critical to the dual challenges of air quality improvement and climate change in China. Based on the emission inventories of thermal power units in Beijing, Tianjin, and Hebei, this study analyzes the CO2 and NOX emission characteristics of these units, identifies and quantifies the synergistic drivers affecting these emission trends. Inventory data show that between 2010 and 2020, NOX emissions were reduced by 86.1%, while CO2 emissions were reduced by only 29.8%. Although significant progress has been made in reducing NOX emissions through measures such as end-of-pipe treatment, controlling CO2 emissions remains a difficult task. The index decomposition analysis reveals that economic growth is the main driver of CO2 and NOx emission growth, energy intensity reduction is the main driver of CO2 emission reduction, and end-of-pipe treatment is the main driver of NOX emission reduction. Currently, coal occupies about 87% of the energy consumption of thermal power units in the Beijing-Tianjin-Hebei, and remains the main type of energy for synergistic emissions, and the potential for emission reduction in the energy structure remains huge. For NOX emissions, it is expected that 90% of the reduction potential can be achieved through energy restructuring and end-of-pipe treatment. In conclusion, the high-precision unit-by-unit emission study confirms the effectiveness of the control policy for thermal power units in the region and provides some scientific reference for future policy formulation.

Considering characteristics of high river density, special underlying surface in plain river network region, and municipal drainage system function, river network drainage unit model is proposed, which is defined as a region surrounded by main river or embankment. Flood storage variety and control projects regulation of small rivers in each unit is simulated. With drainage catchment as an object, according to its drainage capacity, simplified municipal drainage model was developed. Coupling river network drainage unit model, simplified municipal drainage model and 2D flood routing model, urban flood analysis model for plain tidal river network region was developed, which could be applied to analyzing flood from upstream river, storm surge and local rainfall. Demonstration research was carried out in Puxi flood protected area in Shanghai.

The phenomenon of missing data can be seen everywhere in reality. Most typical attribute reduction models are only suitable for complete systems. But for incomplete systems, we can’t obtain the effective reduction rules. Even if there are a few reduction approaches, the classification accuracy of their reduction sets still needs to be improved. In order to overcome these shortcomings, this paper firstly defines the similarities of intra-cluster objects and inter-cluster objects based on the tolerance principle and the mechanism of knowledge granularity. Secondly, attributes are selected on the principle that the similarity of inter-cluster objects is small and the similarity of intra-cluster objects is large, and then the knowledge granularity attribute model is proposed under the background of clustering; Then, the IKAR algorithm program is designed. Finally, a series of comparative experiments about reduction size, running time and classification accuracy are conducted with twelve UCI data sets to evaluate the performance of IKAR algorithms, then the stability of Friedman test and Bonferroni-Dunn test are conducted. The experimental results indicate that the proposed algorithms are efficient and feasible.

With the large-scale application of Internet of Things (IoT) in the medical field, eHealthcare has attracted wide attention. With the help of IoT devices, eHealthcare allows doctors to monitor patients’ health conditions and make recommendations accordingly. The addition of cloud computing greatly saves Healthcare Center’s (HC) computing resources. In order to provide privacy and fine-grained access control, Attribute-b ased encryption (ABE) is widely favored. HC usually operates multiple medical institutions in different regions, and each medical institution can be regarded as a attribute authority. Therefor, multi-authority (MA) ABE is usually concerned to encrypt the patient data. However for existing MA-ABE algorithms, the ciphertext is long and the efficiency of encryption and decryption is low. These problems will greatly increase the computing burden of patients and seriously affect the efficiency of data transmission. Hence we propose a decentralized key-policy MA-ABE in eHealthcare. The proposed algorithm can achieve decentralization, that is, it does not need central authority. Meanwhile, compared with existing MA-ABE algorithms, the length of ciphertext generated by our algorithm is shorter, and the efficiency of encryption and decryption is significantly improved. With the help of Java Pairing Based Cryptography, we carried out the experiments which show that our algorithm can effectively reduce the time required for encryption and decryption.

Spirit of science is one of the important components of science education theory. It not only directly affects the level of science education, but also indirectly affects the selection, education and evaluation of scientific researchers, and the development of science and technology in a country or region. Although more than a century ago, the researchers began to discuss the topic of spirit of science. However, to date, the research of the definition, attribute, structural model and its characteristics of the spirit of science has not yet made a breakthrough. Based on the detailed literature review, the related theoretical analysis and the research of the structural model of the spirit of science, this paper puts forward the new definition, the attribute and the establishment of the structural model of the spirit of science, and analyzes the match the situation from new structural model of the spirit of science and the scientific nature published by the American Society for the Advancement of Science. The results of this study are of great significance for raising the level of scientific education and cultivating future scientific researchers, enhancing their motivation and skills in innovation in scientific research and promoting the development of future scientific undertakings.

Over the last decade, the field of Human Attribute Recognition (HAR) has dramatically changed, mainly due to the improvements brought by deep learning solutions. This survey reviews the progress obtained in HAR, considering the transition from the traditional hand-crafted to deep-learning approaches. The most relevant works on the field are analyzed concerning the advances proposed to address the HAR's typical challenges. Furthermore, we outline the applications and typical evaluation metrics used in the HAR context. Finally, we provide a comprehensive review of the publicly available datasets for the development and evaluation of novel HAR approaches.

With the rapid development of intelligent logistics and network environment, it has become an urgent problem to efficiently and accurately handle and analyse the huge amount of uncertain decision information. Traditional decision making methods often fail to make the best use of complex and incomplete information, especially in the field of cyber defence. To address these problems, this paper introduces a new mathematical tool, discrete multidimensional Z-numbers (MZs), for expressing and dealing with uncertainty and reliability in network defence decisions. In this paper, we first introduce the synthesis method of discrete multidimensional Z-numbers, which allows us to consider the uncertainty and reliability of multi-source information in network defence strategy. Then, the hidden probability of MZs is calculated by using the hidden probability model, and the multidimensional Z+−number is proposed, which improves the expressiveness of the model in dealing with information uncertainty. Based on MZs, we define a variety of utility functions and build a multi-attribute group decision framework for network defence around these functions. The MZs provides a novel perspective for analysing and designing response strategies, especially against highly adaptive and covert attacks. The method in this paper is verified by a case of network security assessment of an intelligent logistics company. The results show that this method can significantly improve the accuracy and efficiency of decision making, demonstrating its unique advantages and wide application potential in network defence. This integrated discrete multidimensional Z-number group decision method is not only suitable for intelligent logistics decision making, but also provides an efficient decision support tool for network defence, which helps to improve the intelligence and adaptability of network security defence.

Objectives: In order to solve the problem that most of the existing research focuses on the binary tactile attributes of objects,which ignores the tactile attribute strength and category recognition,an attribute strength and category recognition method based on convolutional neural network matrix-label is proposed. Methods:Firstly,in the data preparation stage,we preprocess the raw data and determine the matrix labels to build the haptic dataset.Secondly,in the feature extraction stage,we fuse the haptic data of two fingers and use the convolutional neural network to extract the attribute strength features.Finally,in the attribute strength and category recognition stage,all channel haptic data is fused to predict the attribute strength and category.Results:We compared with the multi-label convolutional neural network method in terms of elastic strength,hardness strength and category,and compared the attribute strength recognition capabilities of the two methods using novel objects outside the haptic dataset.The results show that the accuracy of the last 20 iterations of the matrix-label method has an average elastic strength of 96.73%,hardness strength of 97.34%,and category of 96.67%.The performance is better.When the Euclidean distance between the prediction of the novel object and the real label is less than 1,the accuracy of the elastic strength is best to reach 100%,and the hardness strength is best to reach 100%.The performance is better. Conclusions:The effectiveness of the method has been verified.Comparing with the convolutional neural network method,our method can effectively recognize the attribute strength and category of objects.

Community-Based Disaster Risk Reduction (CBDRR) plays a vital role in strengthening the resilience of local communities against both natural and human-made disasters. This paper seeks to examine the role of CBDRR in enhancing the resilience of local communities to both natural and man-made disasters. It underscores the critical importance of active community involvement across all stages of disaster management, from preparedness and mitigation to response and recovery. The paper also explores the key factors that contribute to the successful implementation of CBDRR, including institutional backing, capacity development, inclusive strategies, and the use of modern technologies. Additionally, it discusses the challenges and opportunities in building strong partnerships between local communities and external stakeholders, which are crucial for ensuring the sustainability of these efforts. Finally, the paper offers recommendations aimed at improving the effectiveness of CBDRR initiatives, drawing on insights gained from past disaster experiences. The findings of this study point to the importance of continuous monitoring and evaluation of CBDRR programs to maintain their relevance and ensure their effectiveness as risks evolve. Incorporating lessons learned from past disasters into future planning also plays a crucial role in helping communities enhance their resilience over time. Ultimately, by encouraging collaboration between communities, governments, and various organizations, CBDRR lays the foundation for a comprehensive disaster management framework that is equipped to meet future challenges and supports long-term sustainable development.

Hyperspectral image and LiDAR image fusion plays a crucial role in remote sensing by capturing spatial relationships and modeling semantic information for accurate classification and recognition. However, existing methods, like Graph Convolutional Networks (GCNs), face challenges in constructing effective graph structures due to variations in local semantic information and limited receptiveness to large-scale contextual structures. To overcome these limitations, we proposed a invariant attribute-driven binary bi-branch classification (IABC) method which is a unified network that combines binary Convolutional Neural Network (CNN) and GCN with invariant attributes. Our approach utilizes a joint detection framework that can simultaneously learn features from small-scale regular regions and large-scale irregular regions, resulting in an enhanced structured representation of HSI and LiDAR images in the spectral-spatial domain. This approach not only improves the accuracy of classification and recognition but also reduces storage requirements and enables real-time decision-making, which is crucial for effectively processing large-scale remote sensing data. Extensive experiments demonstrates the superior performance of our proposed method in hyperspectral image analysis tasks. The combination of CNNs and GCNs allows for accurate modeling of spatial relationships and effective construction of graph structures. Furthermore, the integration of binary quantization enhances computational efficiency, enabling real-time processing of large-scale data. Therefore, our approach presents a promising opportunity for advancing remote sensing applications using deep learning techniques.

This article addresses reinsurance decision making process, which involves the insurance company and the reinsurance company, and is negotiated through reinsurance intermediaries. The article proposes a decision flow to model the reinsurance design and selection process. In contrast to existing literature on pure proportional reinsurance or stop-loss reinsurance, this article focuses on the combination into Proportional-Stop-loss reinsurance design which better addresses interest of both parties. In terms of methodology, the significant contribution of the study is to incorporate Multiple Attribute Decision Making (MADM) into modelling the reinsurance selection. The Multi-Objective Decision Making (MODM) model is applied in designing reinsurance alternatives. Then MADM is applied to aid insurance companies in choosing the most appropriate reinsurance contract. To illustrate the feasibility of incorporating intelligent decision supporting system in reinsurance market, the study includes a numerical case study using simulation software @Risk in modeling insurance claims, and programming in MATLAB to realize MADM. Managerial implications could be drawn from the case study results. More specifically, when choosing the most appropriate reinsurance, insurance companies should base their decision on multiple measurements instead of single-criteria decision making models for their decisions to be more robust.

Green synthesis of silver nanoparticles (AgNPs) using raw Whey has never been reported. Silver nanoparticle formation involves a reduction reaction of silver salt solution with Whey extract. Extract and metal salt concentrations, and temperature can control this reaction. In this work, incubation period, extract, AgNO3, and NaOH concentrations were used as the independent factors using central composite design under response surface methodology. The aim was to elucidate the influence these factors have on the measured UV-Vis absorption spectra fitted with a Voigt function, and to optimize the conditions for nanoparticle formation. The fitting parameters, peak wavelength (λ0), peak area (A), and Full Width at Half Maximum (FWHM), were the responses. Silver nanoparticle formation was only possible in an alkaline environment (pH 10). The peak wavelength and the FWHM are influenced mainly by extract and AgNO3 concentrations. In contrast, the peak area is influenced by a total of 33.54 % from the interaction terms of AgNO3 and extract concentrations with NaOH. Metallic AgNPs were formed for the following parameter ranges: a) Whey (0.4 – 1.6 % v/v), b) AgNO3 (0.15 – 0.6 mM), c) NaOH (10 – 50 mM), and d) Time (20 – 60 min). Silver oxide nanoparticles were also observed.

As argued by ‘Declaration of Concrete Environment (2010)’ of Korea and ‘Declaration of Asian Concrete Environment (2011)’ of six Asian countries, concrete as a single material has lately shown extremely large impact on environmental issues such as climate change. Assessment of environmental impact from concrete material and production has considerable importance. Concrete is a major material used in the construction industry that emits a large amount of substances with environmental impacts during its life cycle. Accordingly, technologies for the reduction in and assessment of the environmental impact of concrete from the perspective of Life Cycle Assessment must be developed. At present, the studies in relation to greenhouse gas emission from concrete are being carried out globally as a countermeasure against climate change. In this study, a sustainable concrete mix design algorithm was designed using correlation analyses, and its carbon emission and cost reduction performances were assessed. Using correlation analyses, the concrete strength, w/b and s/a ratios, and CO₂ emissions were identified as major variables of concrete mix design that influenced other variables. Also, this study aims to evaluate the CO₂ emission reduction performance of the algorithm-deduced sustainable concrete mix design, and therefore, the CO₂ emissions of the sustainable concrete mix design are compared with those of the actual concrete mix design applied to the construction of the office building A in South Korea.

Emphasizing the importance of acoustic attenuation in maintaining compliance with stringent noise regulations and enhancing workplace safety, the analysis covers the theoretical and practical aspects of sound attenuation in a turboengine testing stand. This paper presents a preliminary analysis and also an evaluation of a procedure for projecting a noise attenuator for industrial application and especially for turboengine test stands. While primarily focusing on the static acoustic behavior of the attenuator, considerations were also made regarding flow dynamics, Mach number-dependent attenuation, pressure drop, and self-generated noise aspects to provide a comprehensive perspective in selecting the optimal design configuration. The study investigates different calculation methods for assessment of the noise reduction for linear and staggered baffles, applied on a scaled reduced model of attenuator. Thus, the critical parameters and projecting requirements necessary for effective noise reduction in high-performance turboengine testing environments will be evaluated in a down-scaled model. Key factors examined include the selection of design parameters and configurations from various options. Advanced computational methods, like analytic and finite element analysis (FEM) are utilized to predict the acoustic performance and identify potential design optimizations. Experimental validation is performed to corroborate the simulation results, ensuring the reliability and efficiency of the attenuator. The findings indicate that a well-designed sound attenuator module can significantly reduce noise levels without compromising the operational performance of the turboengine inside a test cell.

D-dimer testing in combination with clinical assessment is commonly used to diagnose suspected DVT and pulmonary embolism. However, as D-dimer levels are physiologically higher in elderly patients, this test loses clinical specificity in the setting of geriatric patients. The aim of our study was to investigate the changes in D-dimer levels with age and the specificity of the D-dimer test in the diagnosis of pulmonary embolism. 1617 elderly patients consecutively admitted to the Geriatric Emergency Care Department of the INRCA-IRCCS hospital between January 2013 and February 2014 and for whom D-dimer results were available were included. The D-dimer immunoturbidimetric assay was performed on a cs-2100i Sysmex Siemens using a conventional cut-off level of 500 µg/L. The median D-dimer level was 1200 [550-2990] µg/L FEU, and the percentage of patients with D-dimer levels below the conventional cut-off was only 22.1%. As expected, there was a significant age-related increase in D-dimer, even when sex was considered, and a significant decrease in the specificity of D-dimer assay with age. The most established use of D-dimer testing is in the diagnosis of deep vein thrombosis or pulmonary embolism. Our study confirms the need for an age-adjusted cut-off for D-dimer testing in geriatric patients.

Military Internet of Things (MIoT) has emerged as a new research area in military intelligence. The MIoT frequently has to constitute a federation-capable IoT environment when the military needs to interact with other institutions and organizations or carry out joint missions as part of a coalition such as in NATO. One of the main challenges of deploying MIoT in such an environment is to acquire, analyze, and merge huge amounts of data from many different IoT devices and disseminate it in a secure, reliable, and context-dependent manner. In this work, we present a comprehensive solution for secure data dissemination based on the integration of Hyperledger Fabric's distributed registry technology, Apache Kafka message broker, and data processing microservices implemented using the Kafka Streams API library and applying attribute-based encryption (ABE), a cryptographic approach that facilitates controlling access to information based on data and user attributes rather than traditional methods that rely on specific keys. In the work, we built an experimental setup, implemented an access control scheme designed for mobile components, and performed an evaluation of its performance. Performance tests have confirmed the suitability of the developed cryptographic method for attribute-based data access control in MIoT systems, considering the federations of military networks. The results indicate that the proposed cryptographic scheme is viable for the number of attributes typically assumed to be used in battlefield networks, offering a good trade-off between security and performance for modern cryptographic applications.

Product attribute extraction is an growing field in e- commerce business, with several applications including product ranking, product recommendation, future assortment planning and improving online shopping customer experiences. Under- standing the customer needs is critical part of online business, specifically fashion products. Retailers uses assortment planning to determine the mix of products to offer in each store and channel, stay responsive to market dynamics and to manage inventory and catalogs. The goal is to offer the right styles, in the right sizes and colors, through the right channels. When shoppers find products that meet their needs and desires, they are more likely to return for future purchases, fostering customer loyalty. Product attributes are a key factor in assortment planning. In this paper we present PAE, a product attribute extraction algorithm for future trend reports consisting text and images in PDF format. Most existing methods focus on attribute extraction from titles or product descriptions or utilize visual information from existing product images. Compared to the prior works, our work focuses on attribute extraction from PDF files where upcoming fashion trends are explained. This work proposes a more comprehensive framework that fully utilizes the different modalities for attribute extraction and help retailers to plan the assortment in advance. Our contributions are three-fold: (a) We develop PAE, an efficient framework to extract attributes from unstructured data (text and images); (b) We provide catalog matching methodology based on BERT representations to discover the existing attributes using upcoming attribute values; (c) We conduct extensive experiments with several baselines and show that PAE is an effective, flexible and on par or superior (avg 92.5% F1-Score) framework to existing state-of-the-art for attribute value extraction task.

The Pythagorean fuzzy set (PFS), which is characterized by a membership and a non-membership degree and the square sum of them is less or equal to one, can act as an effective tool to express decision makers’ fuzziness and uncertainty. Considering that the Heronian mean (HM) is a powerful aggregation operator which can take the interrelationship between any two arguments, we study the HM in Pythagorean fuzzy environment and propose new operators for aggregating interval-valued Pythagorean fuzzy information. First, we investigate the HM and geometric HM (GHM) under interval-valued intuitionistic fuzzy environment and develop a series of aggregation operators for interval-valued intuitionistic fuzzy numbers (IVIFNs) including interval-valued intuitionistic fuzzy Heronian mean (IVIFHM), interval-valued intuitionistic fuzzy geometric Heronian mean (IVIFGHM), interval-valued intuitionistic fuzzy weighted Heronian mean (IVIFWHM) and interval-valued intuitionistic fuzzy weighted geometric Heronian mean (IVIFWGHM). Second, some desirable and important properties of these aggregation operators are discussed. Third, based on these aggregation operators, a novel approach to multi-attribute decision making (MADM) is proposed. Finally, to demonstrate the validity of the approach, a numerical example is provided and discussed. Moreover, we discuss several real-world applications of these operators within policy-making contexts.

Most machine learning and deep learning algorithms can only use low-dimensional data as input, but the data that must be processed in practical applications is diverse and irregular. There are two main problems with big dynamic data. (1) The size of the embedding table grows linearly with the vocabulary size, resulting in massive memory consumption. (2) For different newly added vocabularies. To solve these two problems, this paper proposes a novel embedding algorithm that can learn attribute associations with entities based on deep and hash algorithms. Taking movie data as an example, the encoding method and the specific flow of the algorithm are presented in detail, and the effect of dynamic reuse of data models is realized. Compared with the four existing embedding algorithms which can fuse entity attribute information, the deep hash embedding algorithm proposed in this paper has obvious optimization of time and space complexity.

Arylmagnesium and -calcium reagents are easily accessible, however, ether degradation processes limit the storability especially of the calcium-based heavy Grignard reagents. Ortho-bound substituents with phosphanyl donor sites usually block available coordination sites and stabilize such complexes. The reaction of bromo-2,6-bis(diphenylphosphanylmethyl)benzene (1a) with magnesium in tetrahydrofuran yields [Mg{C₆H₃-2,6-(CH₂PPh₂)₂}₂] (2) after recrystallization from 1,2-dimethoxyethane. However, the similarly performed reduction of bromo- (1a) and iodo-2,6-bis(diphenylphosphanylmethyl)benzene (1b) with calcium leads to ether cleavage and subsequent degradation products. α-Deprotonation of THF yields 1,3-bis(diphenylphosphanylmethyl)benzene. Furthermore, the insoluble THF adducts of dimeric calcium diphenylphosphinate halides, [(thf)₃Ca(X)(µ-O₂PPh₂)]₂ [X = Br (3a), I (3b)], precipitate verifying ether decomposition and cleavage of P-C bonds. Ether adducts of calcium halides [such as [(dme)₂(thf)CaBr₂] (4)] form supporting the initial Grignard reaction and a subsequent Schlenk-type dismutation reaction.

Treating central giant cell granuloma (CGCG) is challenging due to high recurrence rates and variable therapy responses. This study examines the efficacy of various treatments in reducing CGCG recurrence. A literature review explored outcomes of surgical excision, curettage, intralesional corticosteroid injection, and adjuvant therapy, considering factors like lesion location, size, and histological features. Aggressive surgical techniques such as en bloc resection were found to potentially lower recurrence rates compared to conservative approaches. However, treatment should be tailored to individual patient needs. Further research is needed to confirm these findings and improve treatment strategies. A concise literature review was conducted using PubMed, MEDLINE, and Google Scholar, focusing on papers published from 1986 to 2024. Search terms included "central giant cell granuloma," "recurrence," "treatment modalities," and "surgical excision". Studies reporting recurrence rates and treatment outcomes for CGCG were analyzed. Twenty-nine studies were reviewed, including six studies on surgical excision and curettage, eight studies on intralesional corticosteroid injections, six studies on calcitonin therapy, five studies on interferon alpha therapy and four studies on the therapy with denosumab. Analysis indicated that aggressive surgical treatments like en bloc resection were associated with lower recurrence rates compared to conservative methods. Predictors of recurrence included lesion size (>3 cm), location (mandible), and aggressive histopathological features. Aggressive surgical excision combined with nonsurgical methods may lower recurrence rates, while conservative techniques remain viable in some cases. Further prospective research is needed to validate these findings and enhance CGCG treatment options.

Hydrogen sulfide is present in active or extinct volcanic areas (mofettas). The habitable premises in these areas are affected by the presence of hydrogen sulfide, which, even in low concentrations, gives off a bad to unbearable smell. If the living spaces considered are closed enclosures, then a system can be designed to reduce the concentration of hydrogen sulfide. This paper presents a membrane way to reduce the hydrogen sulfide concentration to acceptable limits using a cellulosic derivative-propylene hollow fiber based composite membrane module. The cellulosic derivatives considered were carboxymethyl–cellulose (NaCMC), cellulose acetate (CA), methyl 2–hydroxyethyl–cellulose (MHEC), and 2–hydroxyethyl–cellulose (HEC). In the permeation module, hydrogen sulfide is captured with a solution of cadmium that forms cadmium sulfide, usable as a luminescent substance. The composite membranes were characterized by SEM, EDAX, FTIR, FTIR 2D maps, thermal analysis (TG and DSC) and from the perspective of hydrogen sulfide air removal performance. To determine the process performances, the variables were: the nature of the cellulosic derivative–polypropylene hollow fiber composite membrane, the concentration of hydrogen sulfide in the polluted air, the flow rate of polluted air, and the pH of the cadmium nitrate solution. Pertraction efficiency was maximum for the sodium carboxymethyl–cellulose (NaCMC)-polypropylene hollow fiber membrane, a hydrogen sulfide concentration in the polluted air of 20 ppm, polluted air flow rate of 50 L/min, and a pH of 2 and 4.

The shear stress generated by wall turbulence is the main cause of wall friction resistance in turbulent flow through pipes. This paper investigates the impact of inserting rest periods (regions of constant Reynolds number) within the pulsating operating cycle of velocity on the fully turbulent flow at large time-averaged Reynolds numbers, using the Large Eddy Simulation (LES) method. The study aims to explore the effect of increasing rest periods within pulsations on drag resistance. The dimensionless shear stress and drag reduction rates during different time periods of rest were analyzed. Numerical simulation results indicate that the pulsating velocity operation mode does not necessarily lead to drag reduction; it may even result in increased resistance. Inserting rest periods within the pulsation cycle can achieve drag reduction effects, with the maximum drag reduction rate reaching 21.8\%. Comparisons with experimental data and Direct Numerical Simulation (DNS) from the literature validate the feasibility of using the LES method for pipe pulsating operation modes.

This study aims to quantify greenhouse gas (GHG) emissions from China's sludge treatment industry and explore the potential of novel technologies to mitigate these emissions, promoting sustainable sludge management practices. Using a life cycle assessment approach, the total GHG emissions from sludge treatment in 2017 were estimated at 18.54 Mt CO2-eq, with significant regional variations. Incineration contributed the highest emissions (1,011.53 kg CO2-eq/t dry sludge), followed by landfill (717.51 kg CO2-eq/t) and land application (276.41 kg CO2-eq/t). The uneven distribution of emissions across cities highlights the need for targeted mitigation strategies that consider local sustainability challenges. Principal component analysis revealed that economic factors and treatment scale significantly influence emissions, emphasizing the importance of integrating sustainability goals with economic development. To reduce the carbon footprint of sludge treatment and enhance sustainability, the mitigation potential and costs of various technologies were evaluated, including thermal conversion, anaerobic digestion, hydrothermal processes, and wet oxidation. These technologies offer GHG emission reductions ranging from 0.09 to 0.46 t CO2-eq/t dry solids compared to conventional methods, while also promoting resource recovery and circular economy principles. However, further optimization is necessary to enhance their environmental and economic performance, ensuring long-term sustainability. By implementing these technologies and adopting optimization strategies, China's sludge treatment industry can significantly contribute to achieving the country's carbon neutrality goal and fostering sustainable development.

As the development of low-altitude economies and aerial countermeasures continues, the safety of unmanned aerial vehicles becomes increasingly critical, making the emitter identification in remote sensing practices more essential. Effective recognition of Radio Frequency (RF) signal attributes is a prerequisite for identifying emitters. However, due to the diverse wireless communication environments, RF signals often face challenges from complex and time-varying wireless channel conditions. These challenges lead to difficulties in data collection and annotation, as well as disparities in data distribution across different communication scenarios. To address this issue, this paper proposes a Progressive Maximum Similarity-based Unsupervised Domain Adaptation (PMS-UDA) method for RF signal attribute recognition. Initially, we introduce a noise perturbation consistency optimization method to enhance the robustness of the PMS-UDA method under low signal-to-noise conditions. Subsequently, a progressive label alignment training method is proposed, combining sample-level maximum correlation with distribution-level maximum similarity optimization techniques to enhance the similarity of cross-domain features. Finally, domain adversarial optimization method is employed to extract domain-independent features, reducing the impact of channel scenario. Experimental results demonstrate that the PMS-UDA method achieves superior recognition performance in automatic modulation recognition and RF fingerprint identification tasks, as well as cross both ground-to-ground and air-to-ground scenarios, compared to baseline methods.

Dental scaling has high importance in oral health due to its effect on preventing the teeth root destruction. Moreover, it can influence other health aspects such as reducing the risk factor of infective endocarditis. There are various devices applicable to dental scaling. It is essential to select the most appropriate type of instrument to achieve more efficient scaling. Multi-Attribute Decision Making (MADM) is a mathematical approach widely applied in for selecting the best alternative based on essential criteria. In the present research, several criteria including cost, ease of use and sterilization, durability and effectiveness considered as criteria for device selection. The experts in the related field were asked to score the criteria and corresponded alternative. According to the results, obtained by applying the TOPSIS method, Piezon Master is the most option which is mainly due to its durability and effectiveness. The second rank belongs to Cavitron due to its ease of use and sterilization in addition to affordable cost.

Soil carbon (C) cycling under anoxic conditions is mechanistically linked to dissimilatory iron (Fe) reduction, which could be regulated by exogenous dissolved organic matter (DOM). However, the impact of complex exogenous DOM on soil microbial activity and C-Fe coupling in paddy soils remains underexplored. With a 100-day microcosm experiment, we determined that the molecular composition of DOM derived from organic fertilizers and biochar on paddy soil affects soil microbial community, Fe reduction, and C emissions. Our results indicated that biochar-DOM significantly promoted Fe reduction and accelerated CH₄ and CO₂ emissions, and organic manure-DOM increased soil CO₂ emissions, which was closely related to the molecular composition of exogenous DOM. DOM molecules with high aromaticity (AI_mod) and high DBE, including lignins-vascular plant-derived polyphenols, lignins-polycyclic aromatics (PA), and condensed aromatics-PA, promoted soil Fe reduction and CH₄ emissions by enriching soil Fe-reducing bacteria (FeRB), reducing methanotrophs, and facilitating r-strategists at the early stage of incubation. Conversely, DOMs with low AI_mod, low DBE and high H/C, such as lignins-highly unsaturated and phenolic compounds and proteins-aliphatic compounds, enhanced CO₂ emissions by facilitating recalcitrant C degradation and CH₄ oxidation at the late stage of incubation. In conclusion, our study highlights the importance of the molecular composition of DOMs derived from organic amendments in regulating soil Fe reduction and C emissions. The findings offer novel insights into the effective utilization of agricultural resources and the mitigation of soil C emissions.

The key question for the understanding of corrosion phenomena of Si3N4-SiC material in Al reduction cell – whether corrosion by gases (and oxidation in particular) opens way for the corrosion by molten cryolite, or corrosion of Si3N4-SiC by cryolite plays its own role in degradation of the material in service. More probably the reactions of silicon carbide and silicon nitride with molten cryolite go after the stage of pre oxidation to silicon dioxide. The corrosion of Si3N4-SiC material in the reduction cell may take place by “gas-solid” reaction and by “liquid-solid” reaction. There are several variants of lab corrosion test for the evaluation of corrosion resistance of Si3N4-SiC material to cryolite. The results of investigation of Si3N4-SiC lab corrosion test give no direct evidence of selective dissolution of specific phase (Si3N4 or SiC, α-Si3N4 or β-Si3N4) in cryolite. The existing variants of lab corrosion testing require clarification.

Unmanned Aerial Vehicles (UAVs) have become increasingly important in various applications, including environmental monitoring, disaster response, and surveillance, due to their flexibility, efficiency, and ability to access hard-to-reach areas. Effective path planning for multiple UAVs exploring a target area is crucial for maximizing coverage and operational efficiency. This study presents a novel approach to optimizing collaborative navigation for UAVs using Multi-Agent Reinforcement Learning (MARL). To enhance the efficiency of this process, we introduce the Adaptive Dimensionality Reduction (ADR) framework, which includes Autoencoders (AEs) and Principal Component Analysis (PCA) for dimensionality reduction and feature extraction. The ADR framework significantly reduces computational complexity by simplifying high-dimensional state spaces while preserving crucial information. Additionally, we incorporate communication modules to facilitate inter-UAV coordination, further improving path planning efficiency. Our experimental results demonstrate that the proposed approach significantly enhances exploration performance and reduces computational complexity, showcasing the potential of combining MARL with ADR techniques for advanced UAV navigation in complex environments.

Background: Despite the tremendous effort in hamstring injury prevention, injury rates in soccer remain elevated. Hypothesis: Hamstring and hip/pelvic isometric strength imbalances may increase the likelihood of hamstring strain injury (HSI) occurrence amongst soccer players.Study design: Case-control study.Methods: In 120 male soccer players (mean age: 20.0 +/- 6.96 years) pre-season maximum isometric strength of the hip adductors, abductors, flexors, and knee flexors were measured, and strength ratios were calculated. Previous injury and anthropometric data were registered. Injury data were collected following the FIFA/UEFA consensus. A multivariate and Machine learning-based Logistic Regression model was employed to estimate the likelihood of players sustaining a hamstring injury.Results: 21 players sustained 32 hamstring injuries. Players with higher abduction strength in the dominant leg had an 18.5% lower risk of sustaining a hamstring injury (OR = 0.818, CI 95% = [(0.695-0.964]). No other significant contributor variables were found to be significant predictors of hamstring injury in the player group (p> 0.05).Conclusion: Isometric strength of the abductor muscles in the dominant limb had a significant association with hamstring injury.Clinical relevance: Our findings indicate the utilization of hip strength measurements as an integral component of the pre-season screening assessment, thereby affirming prior research on the correlation between lumbo-pelvic stability and incidence of hamstring injuries. Consequently, clinicians should contemplate incorporating hip strengthening exercises into regular training sessions, rehabilitation programs, and injury prevention protocols especially indicated when lower hip strength levels are observed.

Materials based on platinum metals with very good catalytic activity were tested in the reduction of p–nitrophenol (pNP) to p–aminophenol (pAP). The majority of presented catalytic materials are based on the adsorption of p–nitrophenol on the surface of the catalyst simultaneously with molecular hydrogen, coming from sodium borohydride in an aqueous medium, which reduces it. In the present paper, a catalytic material based on osmium dispersed in n–decanol or n–dodecanol is presented, which also works as an emulsion membrane, for the absorption of p–nitrophenol and molecular hydrogen at the n–alcohol aqueous solution interface. The hydrogenation of p–nitrophenol is carried out in a reaction and separation column in which the acid receiving phase emulsion is dispersed in the osmium nanodispersion, in n–alcohols. This emulsion circulates from the base of the column to its upper part in co-current or counter-current with the source phase consisting of p–nitrophenol and sodium borohydride. Experiments show that the circulation of counter-current phases is superior to that in co-current, and the emulsion based on n–decanol is more efficient than the one based on n–dodecanol. The increase in the pH difference between the source and receiving phases leads to the increase in the conversion of p–nitrophenol to p–aminophenol. The efficiency of separation of the reaction product in the receiving phase is lower than the conversion at the same operating time. The apparent catalytic rate constant (kapp) of the new catalytic material based on the emulsion membrane with nanodispersion of osmium nanoparticles (0.1×10−3 for n–dodecanol and 0.9×10−3 for n-decanol) is lower by an order of magnitude compared to those based on adsorption on catalysts from platinum metal group. The advantage of the tested membrane catalytic material is that it extracts p–aminophenol in the acid receiving phase. The paper proposes a mechanism of the studied reduction system.

We study a two-echelon supply chain made up of a supplier and a manufacturer, both of which can reduce their component/product carbon emissions. With the vertical technology spillovers, we explore the optimal decisions of centralized and decentralized supply chains with price dependent demand and propose coordination strategy for the decentralized supply chain. Considering the cost contraction effectiveness of the technology spillovers, the centralized and decentralized game theoretic models of a two-echelon supply chain are developed to investigate optimal decisions of pricing and carbon emissions reduction. Through a systematic comparison and numerical analysis, we show that the profits of both players and the entire supply chain improve with the effect of technology spillovers increasing. Carbon emissions reduction will be taken by various protective measures so that the supplier and the manufacturer who do not innovate can hardly share the results of innovating via the “free-riding” methods when the technology spillover is relatively small. We also propose a revenue-cost sharing contract through bargaining to enhance the performance of the decentralized supply chain.

Bipolar neutrosophic set is an important extension of bipolar fuzzy set. This set is a hybridization of bipolar fuzzy set and neutrosophic set. Every element of a bipolar neutrosophic set consists of three independent positive membership functions and three independent negative membership functions. In this paper, we develop cross entropy measures of bipolar neutrosophic sets and prove its properties. We also define cross entropy measures of interval bipolar neutrosophic sets and prove its properties. Thereafter, we develop two novel multi-attribute decision making methods based on the proposed cross entropy measures. In the decision making framework, we calculate the weighted cross entropy measures between each alternative and the ideal alternative to rank the alternatives and choose the best one. We solve two illustrative examples of multi-attribute decision making problems and compare the obtained result with the results of other existing methods to show the applicability and effectiveness of the developed method. In the end, the main conclusion and future scope of research are summarized.

Decomposed fuzzy sets are the resent extension of intuitionistic fuzzy sets by incorporating functional and dysfunctional points of views to the definition of membership functions. This paper extends the Technique of Order Preference Similarity to the Ideal Solution (TOPSIS) method to the Decomposed Fuzzy TOPSIS (DF TOPSIS) method and applies it to a multi-criteria risk-based supplier selection problem under fuzziness. DF TOPSIS involves finding a positive ideal solution and a negative ideal solution and measuring the distance of each alternative to these solutions. The final ranking is obtained based on the proportion of distances to the positive and negative ideal solutions. The developed DF TOPSIS method incorporates the accuracy and consistency of expert judgments, enhancing the decision-making process. A sensitivity analysis is also presented in order to show the robustness of the obtained rankings by DF TOPSIS.

Fractal array antennas are multiband arrays having ultra wide band and space filling facility. But Side lobe levels and large number of antenna elements are the major designing challenges of these arrays. In this paper, design and analysis of octagonal fractal array antenna is investigated with Morse-Thue fractal density tapering technique (MTFDT). Due to the proposed technique, a remarkable improvement has observed in Side lobe levels and thinning of the elements can also be attained at the various iterations of octagonal fractal array antenna. These arrays are analyzed and simulated by MATLAB-15 programming.

The uncertainty in the secondary path of an Active Noise Control (ANC) headphone affects the waterbed effect and stability of the feedback system. This study focuses on the uncertainty of the secondary path when real users wear headphones, and proposes a new uncertainty constraint based on the measured results of the secondary path transfer function under different wearing conditions of dummy head and limited subjects. This constraint and a cascaded second-order infinite impulse response filter with fixed coefficients are used to formulate a control strategic function, which is optimized by the Improved Grey Wolf Optimizer (IGWO) algorithm to obtain the optimal controller with better noise reduction performance. The proposed method and simulation model are validated based on the experimental test results. The results demonstrate that the safety factor and waterbed suppressing factor contained in the proposed uncertainty constraint ensures more stable noise reduction and effective suppression of the waterbed effect for new subjects without a priori data.

Replacing carbon by hydrogen is a huge step towards reducing CO₂ emissions in the iron- and steel-making industry. The reduction of iron oxides using hydrogen plasma smelting reduction as an alternative to conventional steel-making routes has been studied at Montanuniversitaet Leoben, Austria. The aim of this work was to study the slag formation during the reduction process and the reduction behaviour of iron oxides. Furthermore, the reduction behaviour of iron ore during continuous feeding was assessed. Mixtures of iron ore and calcined lime with a basicity of 0, 0.8, 1.6, 2.3, and 2.9 were melted and reduced by hydrogen. The off-gas composition was measured during the operations to calculate the process parameters. The reduction parameters, namely the degree of reduction, degree of hydrogen utilisation, produced iron, and slag, are presented. The results of the batch-charged experiments showed that at the beginning of the reduction process, the degree of hydrogen utilisation was high, and then, it decreased over the operation time. In contrast, during the continuous-feeding experiment, the degree of hydrogen utilisation could be kept approximately constant. The highest degrees of reduction and hydrogen utilisation were obtained upon the application of a slag with a basicity of 2.3. The experiment showed that upon the continuous feeding of iron ore, the best conditions for the reduction process using hydrogen could be applied.

In this paper, the mechanism of combined process of ion beam sputtering (IBS) and HF acid etching on the chemical structure defects of fused silica and its laser damage resistance perfor-mance were investigated. During the removal process of surface material, the sputtering effect causes lattice atoms to detach from their original positions, forming a large number of unsaturated structures on silica surface, which improves the chemical activity of Si and O atoms, accelerates the chemical reaction process between surface atoms and water molecules, increases the content of hydroxyl (OH-) groups in the shallow layer, and enhances the photothermal weak absorption intensity. However, the presence of OH- groups reduces the bonding strength of Si-O, destroys the spatial network structure of silica bulk, and reduces the mechanical strength of silica surface, resulting in a decrease in its laser damage resistance performance. The paper reveals for the first time the mechanism of IBS to change the structure characteristics of silica material, accelerate the surface hydroxylation process, and thereby reduces the laser damage resistance performance. This work provides technical guidance for effectively suppressing chemical structure defects on silica surface and improving laser damage resistance performance of optical components under high-flux laser irradiation.

Modern search engines encounter a significant challenge when it comes to handling duplicate and nearly identical web pages, particularly during the indexing process for vast amounts of web content. This issue can lead to slow search results and increased costs due to the accumulation of storage space necessary for storing indexes. To tackle this issue, different techniques have been proposed to find similar websites. However, it has long been a challenge in research to distinguish between web pages. In the current study, sentence-level features i.e., hashcode and thresholding are used to determine the nearly identical web pages. We employ an adaptive threshold that enables the application of our model in both large- and small-scale settings. The benchmark datasets consisting of Shakespeare’s collections, free text, job descriptions, and Reuters-21578 are used to test the proposed approach. With an accuracy of 0.99 and an F1-score of 0.97, the proposed technique outperforms existing methods.

Background: The surgical approach to treating calcaneal fractures, specifically Sanders types II, III, and IV, is still a subject of controversy. The decision to opt for open reduction and internal fix-ation (ORIF) or minimally invasive osteosynthesis (MIOS) remains uncertain owing to varying complication rates. This study intends to examine each treatment’s pros and cons in relation to the incidence of complications. Methods: In this retrospective study, we examined 70 patients treated between January 2018 and September 2022: 50 received ORIF, and 20 underwent MIOS. We evaluated functional outcomes with the Maryland Foot Score and the Creighton-Nebraska Health Foundation Assessment Scale (CNHFAS). We also assessed the amount of anatomical restoration using Böhler’s and Gissane’s angles, determined by radiographic checks, and the frequency of postoperative complications. Results: A significant reduction in the risk of reinter-vention was observed in patients with high anesthetic risks, polytraumatized individuals, and those with comorbidities who underwent MIOS. The chi-square test analysis of postoperative complications highlighted no substantial differences between the ORIF and MIOS groups in rela-tion to tendinitis, chronic pain and swelling, algodystrophy, and surgical wound infections (p > 0.20 for all). Conclusion: ORIF is more suitable for patients who have high motor function re-quirements, a long-term activity schedule and can endure potential additional procedures. This preference is because ORIF provides better anatomical restoration and features a lower osteoar-thritis occurrence rate.

Batch and transport experiments were used to investigate the remediation of a loamy sand soil contaminated with Cr(VI) using zero valent iron nanoparticles (nZVI) stabilized by the carboxy methyl cellulose (CMC-nZVI). The effect of pH, ionic strength (IS), and flow rate on the removal efficiency of Cr(VI) were investigated under equilibrium (uniform transport) and non-equilibrium (Two-site sorption) transport using the Hydrus-1D model. The overall removal efficiency ranged from 70 to over 90% based on the chemical characteristics of the CMC-nZVI suspension and the transport conditions. The concentration and pH of the CMC-nZVI suspension had the most significant effect on the removal efficiency and transport of Cr(VI) in the soil. The average removal efficiency of Cr(VI) was increased from 24.1 to 75.5% when the concentration of CMC-nZVI nanoparticles was increased from 10 to 250 mg L-1, mainly because of the increased total surface area at larger particle concentration. Batch experiments showed that removal efficiency of Cr(VI) was much larger under acidic conditions. The average removal efficiency of Cr(VI) reached 90.1 and 60.5 % at pH 5 and 7, respectively. The Two-site sorption model described quite well (r2 = 0.96-0.98) the transport of Cr(VI) in soil as compared to the uniform transport model (r2 = 0.81-0.98). The average retardation of Cr(VI) was 3.51 and 1.61 at pH 5 and 7, respectively, indicating earlier arrival for the breakthrough curves and shorter time to reach maximum relative concentration at lower pH. The Hydrus-1D model has proven to be an efficient tool for assessing the removal of Cr(VI) pollutants, offering innovative, cost-effective, and environmentally friendly remediation methodologies.

As prominent oil producers, the Gulf Cooperation Council (GCC) countries have played a significant role in the global energy market. However, as the world's attention increasingly shifts towards environmental sustainability, understanding the implications of the GCC's economic activities on CO2 emissions becomes indispensable. This research paper investigates the relationship between specific economic indicators and their impact on CO2 emissions in the GCC from 2001 to 2021. The study employs quantile regression, a powerful statistical method that estimates the conditional quantiles of a response variable given a set of predictor variables. The findings reveal several important insights: Financial institution efficiency is significant and negative at a 1% level at the lower (10th) -83537.3 and the higher quantile (90th) -549002.3. The relationship between GDP per capita and CO2 emissions varies across quantiles, highlighting the complexity of the growth-environment nexus. Total patents exhibit a positive and significant relationship with emissions, underscoring the importance of directing innovation towards environmentally sustainable solutions. Renewable energy consumption displays a nuanced relationship with CO2 emissions, with a stronger negative impact observed at higher consumption levels. This underscores the potential of renewable energy to mitigate emissions when integrated at scale. The study's outcomes hold crucial policy implications for the GCC countries as they seek to align economic growth with environmental sustainability. The findings emphasize the importance of fostering financial institution efficiency, promoting green innovation, and expanding renewable energy sources to reduce emissions

Background/Objectives: The 28-day mortality rate for septic shock is high, necessitating rapid and effective empiric antimicrobial therapy. This study investigates whether the rate of catecholamine dose reduction in septic shock can indicate bacterial susceptibility to initial antimicrobial therapy. Methods: This retrospective observational study involved 108 adult patients with bacteraemia and septic shock admitted at the intensive care unit of Nihon University Itabashi Hospital between January 2017 and December 2023. They were classified into the Susceptible or Resistant group based on the bacteria’s susceptibility to the initial empiric antimicrobial therapy. Catecholamine dosages were converted to norepinephrine equivalent (NEE) scores, with the time course from peak to end of administration measured at NEE reductions. Results: Of the 108 patients, 94 were in the Susceptible group and 14 in the Resistant group. The Susceptible group showed faster reductions in catecholamine doses: time to reduce the dose from the maximum NEE to 25% was 19 vs. 49.5 hours (p = 0.0057); and 0%, 29 vs. 54 hours (p = 0.0475). Conclusions: In septic shock with bacteraemia, faster catecholamine dose reduction indicates bacterial susceptibility to initial empiric antibiotics. This reduction rate can serve as an early clinical indicator of appropriate initial empiric therapy.

Fermatean fuzzy linguistic (FFL) set theory provides an efficient tool for modeling a higher level of uncertain and imprecise information, which cannot be represented using intuitionistic fuzzy linguistic (IFL)/Pythagorean fuzzy linguistic (PFL) sets. On the other hand, the linguistic scale function is the better way to consider the semantics of the linguistic terms during the evaluation process. In the present paper, we first define some new modified operational laws for Fermatean fuzzy linguistic numbers (FFLNs) based on linguistic scale function (LSF) to overcome the shortcomings of the existing operational laws and prove some important mathematical properties of them. Based on it, the work defines several new aggregation operators (AOs), namely, the FFL-weighted averaging (FFLWA) operator, the FFL-weighted geometric (FFLWG) operator, the FFL-ordered weighted averaging (FFLOWA) operator, the FFL-ordered weighted geometric (FFLOWG) operator, the FFL-hybrid averaging (FFLHA) operator and the FFL-hybrid geometric (FFLHG) operator under FFL environment. Several properties of these AOs are investigated in detail. Further, based on these operators, a multiple attribute group decision-making (MAGDM) approach with FFL information is developed. Finally, to illustrate the effectiveness of the present approach, a real-life supplier selection problem is presented where the evaluation information of the alternatives is given in terms of FFLNs.

Bare titania and metal promoted TiO2 catalysts were employed in the treatment of nitrates, which are ubiquitous pollutants of wastewater. The results show that the process can be carried out under visible light (from white light LED lamp) and, in the best case, 23.5% conversion of nitrate was obtained over 4 hours with full selectivity towards N2 by employing 0.1 mol% Ag/TiO2 prepared by flame spray pyrolysis. Moreover, the performance was worse when testing the same catalysts with tap water (11.3% conversion), due to the more complex composition of the matrix. At last, it was found that the photoreduction of nitrate can be effectively performed in combina-tion with the photo-oxidation of ammonium without loss in the activity, opening to the possi-bility to treat highly polluted wastewater with a single process. The latter treatment employs the two contaminants simultaneously as electron and holes scavengers, with very good selectiv-ity, in a completely new process that we may call Photo-Selective Catalytic Reduction (Pho-to-SCR).

The exploring comprehensive review of cutting-edge techniques for securing cloud-stored data and managing sensitive information in the context of smart cities through the application of Ciphertext-Policy Attribute-Based Encryption (CP-ABE). It highlights the innovative integration of blockchain technology with CP-ABE, which introduces a decentralized and tamper-resistant key management system, thereby enhancing the overall security framework in cloud environments where data sharing is prevalent. Introducing an online/offline multi-authority CP-ABE scheme, characterized by hidden policies, offers significant advancements in protecting user attributes and access structures, ensuring that sensitive information remains confidential even during encryption and decryption processes. This dual approach not only fortifies security but also optimizes the efficiency of data-sharing mechanisms. Furthermore, the paper delves into implementing hidden sensitive policies and keyword search techniques within smart city infrastructures, which are designed to facilitate secure and efficient data retrieval. These techniques ensure that while data remains accessible to authorized users, privacy is rigorously maintained. Collectively, these approaches represent significant strides in bolstering the security and confidentiality of data in both cloud-based and smart city applications, addressing the growing demand for robust and efficient data management solutions in increasingly interconnected environments.

Exploring the effect of the sample size on the estimation accuracy of airborne LiDAR forest attributes in a large-scale area can help in optimizing the technical application scheme of operational ALS-based large-scale forest stand inventories. In our study, sample datasets composed of different sample plots were constructed by repeated sampling from 1003 sample plots in a subtropical study area covering 2376 × 103 km2. Sixteen multiplicative power models were built in each forest type consisting of four forest attributes. Through these models, the variations of standard deviation (SD) and coefficient of variation (CV) of R2 and rRMSE of forest attribute estimation models for different quantity levels of sample plots were also analyzed. The results showed that, first, when the sample size increased from 30 to the top limit, the SD of the forest attributes and LiDAR variables showed a decreasing trend. Second, as the sample size increased, the rRMSE of the 16 forest attribute estimation models gradually decreased, while the R2 gradually increased. Third, when the sample size was small, both the SD of R2 and rRMSE of the models were large, and the SD of R2 and rRMSE gradually decreased as the sample size increased. In 50 models conducted for each attribute at the same sample size, for the mean standard deviations of forest attributes, the ten best performing models were lower than those of the total 50 models, and the worst ten models were the opposite. When the sample size increased, the accuracy of each forest attribute estimation model for each forest type gradually improved. The variation of forest attributes and the LiDAR variable of the construction model are critical factors that affect the model’s accuracy. To efficiently apply airborne LiDAR in order to survey large-scale subtropical forest resources, the sample size of the Chinese fir forest, pine forest, eucalyptus forest, and broad-leaved forest should be 110, 80, 85, and 70, respectively.

Expansive soil shows dual swell-shrink which is not suitable for the construction. Several mitigating techniques exist to counteract the problem promulgate by expansive clayey soils. This paper explored the penitential mecho-chemical reinforcement of expansive clayey soil to mitigate the effect of upward swelling pressure and heave. The polypropylene fiber is randomly distributed in the soil for mechanical stabilization, and the industrial residual silica fume is used as a chemical stabilizer. The experimental analysis is made in three phases which involved the tests on mechanical reinforced expansive soil using randomly distributed polypropylene fibers with different percentages (0.25%, 0.50%, and 1.00%), and 12mm length. The second phase of experiments carried out on chemical stabilized expansive soil with different percentages (2%, 4% and 8%) of silica and next phase of the experimental focused in the combination of mecho-chemical stabilization of the expansive soil with different combination of silica (i.e., 2%, 4% and 8%) and polypropylene fibers (i.e., 0.25%, 0.50% and 1.00%). Maximum dry density (MDD), optimum moisture content (OMC), liquid limit (LL), plastic limit (PL), plastic index (PI) grain size, and constant volume swelling pressure test were performed on unreinforced and reinforced expansive soil to investigate the effect of polypropylene fiber and silica fume on the engineering properties of expansive clayey soil. The experimental results illustrate that the inclusion of polypropylene fiber has a significant effect on the upward swelling pressure and expansion property of expansive soil. The reduction in the upward swelling pressure and expansion is a function of fiber content. These results also indicated that the use of silica fume caused a reduction in upward swelling potential, and its effect was considerably more than the influence of fiber.

The data from social networks like Twitter is a valuable source for research but full of redundancy, making it hard to provide large-scale, self-contained, and small datasets. The data recording is a common problem in social media-based studies and could be standardized. Sadly, this is hardly done. This paper reports on lessons learned from a long-term evaluation study recording the complete public sample of the German and English Twitter stream. It presents a recording solution proposal that merely chunks a linear stream of events to reduce redundancy. If events are observed multiple times within the time-span of a chunk, only the latest observation is written to the chunk. A 10 Gigabyte Twitter raw dataset covering 1,2 Million Tweets of 120.000 users recorded between June and September 2017 was used to analyze expectable compression rates. It turned out that resulting datasets need only between 10\% and 20\% of the original data size without losing any event, metadata or the relationships between single events. This kind of redundancy reduction recording makes it possible to curate large-scale (even nation-wide), self-contained, and small datasets of social networks for research in a standardized and reproducible manner.

Biochar has a carbon-based porous structure that is advantageous for adsorbing air pollutants. The pore structure of biochar ranges from several tens to hundreds of m²/g depending on the production temperature. We compared the pore characteristics of biochar with its NOx and SOx adsorption performances to evaluate its adsorbing potential. Biochar was produced from cypress biomass via slow pyrolysis in a fixed-bed reactor and CO2 partial gasification. Slow pyrolysis experiments at 500–700°C were conducted to produce biochar, followed by yield, proximate, elemental, and surface area analyses. CO2 partial gasification activated the biochar produced by slow pyrolysis at the same temperature, and similar analyses were performed. NO and SO2 adsorption experiments were conducted separately and together for biochar produced under each condition. Biochar produced at 600°C showed the highest specific surface area. CO2 partial gasification increased the surface area at each temperature, reaching 680.3 m²/g at 700°C. In single adsorption experiments, N2 carbonized biochar showed NO and SO2 adsorption efficiencies of 84–90% and 88.2–91.4%, respectively, with similar trends in simultaneous adsorption experiments. CO2 activated biochar exhibited higher adsorption efficiencies due to its increased surface area, with NO and SO2 adsorption at 89.8–93.9% and 92–98.9%, respectively. Biochar activated at 700°C simultaneously adsorbed approximately 99% NO and SO2. The NO adsorption efficiency increased with increasing surface area of the small pores, whereas the SO2 adsorption efficiency improved with the development of both small and large pores.

This paper explores the application of the generalized double reduction method to the (1+1)-dimensional Kaup-Boussinesq (K-B) system, which models nonlinear wave propagation. Double reduction method is a structured and systematic approach in the analysis of partial differential equations (PDEs). We first identify the Lie point symmetries of the K-B system and construct four non-trivial conservation laws using the multiplier method. The association between the Lie point symmetries and the conservation laws is established, and the generalized double reduction method is then applied to transform the B-K system into second-order differential equations or algebraic equations. The reduction process allowed us to derive two exact solutions for the K-B system, illustrating the method’s effectiveness in handling nonlinear systems. This work highlights the effectiveness of the generalized double reduction method in simplifying and solving nonlinear nonlinear systems of PDEs, contributing to a deeper understanding of the systems.

Reduction of the density operator for quantum subsystems is derived without the use of the Born Rule.

The Universe at last scattering is locally treated as an unbound gas. The internal kinetic energy of the gas effectively constitutes a scalar energy field. The gas’s adiabatic expansion is entropic, giving repulsive entropic pressure. Gas kinetic energy is converted into entropic energy gain (63%) and isoentropic work against gravity (37%) at a constant 63:37 ratio. A three-term expression of the gas’s Hubble parameter is derived and found to be exclusively dependent on its mass density. At last scattering, this model gives a Hubble constant that is 125% of the value found from the ΛCDM model. After partition of Universal mass into the cosmic web of galaxies and the intergalactic medium (IGM), expansion came mostly from the IGM, presently comprising about 84% of total Universal mass and 90% of its volume. The onset of star formation within the cosmic web increased the IGM’s kinetic energy through the action of starlight, giving free electrons as an additional repository. Many of these free electrons are suprathermal. Suprathermal energy from both electrons and protons comprises about half of the IGM’s total kinetic energy and is expressed in the ΛCDM model as “dark energy” Λ. Entropic pressure derives from thermodynamic laws not found within general relativity.

This paper proposes an improvement to a recent Peak-to-Average-Power-Ratio (PAPR) reduction technique for Orthogonal Frequency Division Multiplexing (OFDM), the GreenOFDM. This technique, which is inspired by SeLected Mapping (SLM), generates several waveform candidates using a given number of Inverse Fast Fourier Transforms (IFFT), and selects the one with the lowest PAPR for the transmission of the OFDM symbol. In GreenOFDM, we show that for (U) IFFTs, it provides better PAPR reduction capabilities than SLM-OFDM as it increases the number of waveform candidates from U (available for SLM-OFDM) to U^2/4. In this work, we propose a simple and clever extension of the GreenOFDM that further increases the number of waveform candidates by a factor 4 (from U^2/4 to U^2), or equivalently reduces by a factor of 2 the number of IFFTs for a same PAPR performance. Compared to SLM-OFDM, the improved GreenOFDM technique reduces the complexity by requiring only the square-root of the number of IFFTs for a same PAPR reduction performance.

Multidisciplinary neurotechnology holds the promise of understanding and non-invasively treating neurodegenerative diseases. In this preclinical trial on Parkinson's disease (PD), we combined neuroscience together with the nascent field of medical virtual reality and generated several important observations. First, we established the Oculus Rift virtual reality system as a potent measurement device for parkinsonian involuntary hand tremors (IHT). Interestingly, we determined changes in rotation were the most sensitive marker of PD IHT. Secondly, we determined parkinsonian tremors can be abolished in VR with algorithms that remove tremors from patients' digital hands. We also found that PD patients were interested in and were readily able to use VR hardware and software. Together these data suggest PD patients can enter VR and be asymptotic of PD IHT. Importantly, VR is an open-medium where patients can perform actions, activities, and functions that positively impact their real lives - for instance, one can sign tax return documents in VR and have them printed on real paper or directly e-sign via internet to government tax agencies. Lastly, we generated a technical framework wherein movements in the real world can be measured side-by-side with those in virtual reality. With this framework, we observed anecdotal evidence of parkinsonian tremors being reduced in real life when our algorithms abolished digital hand tremors in VR.

The development of hydrogen plasma smelting reduction as a CO₂ emission-free steel-making process is a promising approach. This study presents a concept of the reduction of hematite using hydrogen thermal plasma. A laboratory scale and pilot scale hydrogen plasma smelting reduction (HPSR) process are introduced. To assess the reduction behavior of hematite, a series of experiments has been conducted and the main parameters of the reduction behavior, namely the degree of hydrogen utilization, degree of reduction and the reduction rate are discussed. The thermodynamic aspect of the hematite reduction is considered and the pertinent calculations have been carried out using FactSage^TM 7.2. The degree of hydrogen utilization and the degree of reduction were calculated using the off-gas chemical composition. The contribution of carbon, introduced from the graphite electrode, ignition pin and steel crucible, to the reduction reactions was studied. The degree of reduction of hematite, regarding H₂O, CO and CO₂ as the gaseous reduction products, is determined. It is shown that the degree of hydrogen utilization and the reduction rate were high at the beginning of the experiments, then decreased during the reduction process owing to the diminishing of iron oxide. Conducting experiments with the high basicity of slag B₂=2 led to a decrease of the phosphorus concentration in the produced iron.

One of the key areas of the International Renewable Energy Agency’s (IRENA) programme of work is the analysis of renewable technology costs and performance and the dissemination of these results to as wide an audience as possible. In addition to analysis, IRENA seeks to engage a broad range of stakeholders in the context of this work through the Renewable Costing Alliance. The Costing Alliance brings together companies, industry association, governments and researchers to share, confidentially, data for real-world renewable energy project cost and performance, helping to build further on IRENA’s costing work to date. In this context the authors analyse the current cost differentials and cost reduction potentials for solar PV in Japan compared to best practice levels and identify the reasons for higher costs in Japan and how to reduce them. This study analyses the current installed cost differentials for utility-scale, commercial and residential rooftop solar PV systems. In addition to identifying the reasons for the cost differentials, potential policy recommendations to reduce the cost differential are identified.

Oxytocin is well known for its role in relationships and social cognition and has more recently been implicated in pain relief and pain perception. Connections between prosocial feelings and pain relief are also well documented, however, effects of exogenous oxytocin on social cognition and pain have not been explored. The current study tested whether intranasally-delivered oxytocin affects pain perception through prosocial behaviors. Additionally, moderation of the effects of oxytocin by life history or genetic polymorphisms are examined. Young adults (n=43; 65% female) were administered intranasal oxytocin (24 IU) or placebo in a crossover design on two visits separated by a one-week washout period. Pain was delivered via cold-pressor. Baseline measures for decision-making and social cognition were collected, as well as pain sensitivity and medication history. Saliva samples were collected for analysis of genetic markers, and urine samples were collected to assess oxytocin saturation. Following oxytocin administration, participants reported increased prosocial cognition and decision-making. Pain perception appeared to be adaptive, with procedural order and expectation affecting perception. Finally, behavioral trust and cooperation responses were significantly predicted by genetic markers. Oxytocin may increase a patient’s trust and cooperation and reduce pain sensitivity while having fewer physiological side effects than current pharmaceutical options.

Symmetry concepts in parametrized dynamical systems may reduce the number of external parameters by a suitable normalization prescription. If, under the action of a symmetry group G, parameter space A becomes a (locally) trivial principal bundle, A ~ A/G x G, then the normalized dynamics only depends on the quotient A/G. In this way, the dynamics of fractional variables in homogeneous epidemic SI(R)S models, with standard incidence, absence of R-susceptibility and compartment independent birth and death rates, turns out to be isomorphic to (a marginally extended version of) Hethcote's classic endemic model, first presented in 1973. The paper studies a 10-parameter master model with constant and I -linear vaccination rates, vertical transmission and a vaccination rate for susceptible newborns. As recently shown by the author, all demographic parameters are redundant. After adjusting time scale, the remaining 5-parameter model admits a 3-dimensional abelian scaling symmetry. By normalization we end up with Hethcote's extended 2-parameter model. Thus, in view of symmetry concepts, reproving theorems on endemic bifurcation and stability in such models becomes needless.

Introduction: Various studies have widely documented how weight (being overweight, underweight or obese) plays a significant role in increasing health problems and adversely affects the quality of life. Out of all the weight-related issues, obesity is the number one contributing factor for causing health problems. To overcome this factor, a trial was conducted where a drug 'Lite plus’ (Garcinia Cambogia and Green Coffee bean) was given to a select number of people for a period of three months. Materials and Methods: 'Lite Plus' is a drug which contains Garcinia cambogia 500 mg + Green Coffee Bean Extract 400 mg, with a recommended dosage of 2 tablets/day with or before meals for 3 months. Most of the patients included in our study were females suffering from obesity-related problems such as PCOs and Infertility. During the trial, all patients were given the recommended dosage of 'Lite plus', while those with diabetes had the dose amended accordingly. Results: As a result of this trial, it was found that the use of 'Lite Plus' was very effective in decreasing weight/BMI with continuous use. After 6 weeks, out of the 250 respondents participating in the trial, 232 got a reduction in their BMI, and after 12 weeks (end of the trial), 29 respondents (11.6%) reached the desired level of BMI. Conclusion: Garcinia cambogia and Green Coffee bean are effective and safe in weight reduction.

Breast Cancer Associated Susceptibility Proteins Type 1/2 (BRCA1/2) promote cellular functioning by modulating NRF2-mediated antioxidant signaling. Redox failure in women with BRCA1/2 insufficiency increases the risk for breast/ovarian/uterine cancers. Risk‐reducing salpingo‐oophorectomy (RRSO) is a prophylactic surgery of the reproductive organs, which is frequently conducted by the age of 40 to lower the occurrence of cancer in women with BRCA1/2 mutations. However, abrupt estrogen decline following RRSO causes ovarian failure, which implicates various cellular physiological processes, resulting in increased release of free radicals and subsequent severe onset of menopausal symptoms. Comfort measures (e.g., hormonal replacement therapy (HRT) and mindfulness-based stress reduction (MBSR)) may improve chronological menopause-related quality of life, but their specific effects are not clear in women with gene mutations. Aiming to fill the gap, this study used path analysis to examine the effects of HRT and MBSR on menopausal symptoms among RRSO patients (N = 199, mean age = 50.5 ± 6.7 years). HRT alleviated menopausal symptoms in BRCA2 carriers and women who were physically active, premenopausal at the time of RRSO, had a high BMI, and had no history of breast cancer. It increased the severity of urogenital symptoms in those with a history of cancer. MBSR, on the other hand, was associated with increased intensity of most menopausal symptoms. The results suggest that young women undergoing recent RRSO may benefit from HRT at an individual level while their need for extensive measures to optimize their psychological wellbeing is ongoing. Adverse effects of MBSR, which are captured in the present study imply that MBSR may interfere with redox sensitivity associated with estradiol fluctuations in BRCA1/2 carriers. Investigations are needed to test this hypothesis and elaborate on the underlying mechanisms in those women.

Ethanolic extracts of propolis collected from Apis mellifera hives have been shown to reduce Nosema ceranae infection in honey bee workers. This study aimed to determine the propolis concentration that optimizes the control of N. ceranae infection in honey bee workers. Newly emerged worker bees were individually fed 2 μL of 50% sucrose solution (w/v) containing 105 N. ceranae spores per bee, and treated with 0%, 25%, 50%, and 75% ethanolic extracted propolis in 50% sucrose solution (v/v). The treated bees were maintained in cages at 34 ± 2˚C and 55 ± 5% RH throughout the study. The number of dead bees was counted and removed from the cages daily for 30 days to generate survival curves. The number of N. ceranae spores per bee (infectivity) was quantified using a hemocytometer, and the number of infected bees per hundred bees (infection rate) was determined. The survival rates of N. ceranae infected bees fed 25%, 50%, and 75% propolis (NP1, NP2, NP3, respectively) were significantly higher than those of infected bees not fed propolis (NO). Furthermore, the infectivity and infection rates of NP2 and NP3 groups were significantly lower than those of the NO group. The infectivity and infection rates of the NP1 group were not different than those of the NO group. Our data suggest that 50% and 75% propolis extracts are appropriate concentrations for treating N. ceranae infection in honey bees. Ethanolic extracted propolis can be an alternative treatment for N. ceranae infection in Western honey bees.

Parametric models have been widely used in pertinent literature for reconstructing, modifying and representing a wide range of airfoil and/or hydrofoil profile geometries. Design spaces corresponding to these models can be exploited for modeling and profile-shape optimization under various performance criteria. Accuracy requirements, along with the need for modeling local features, often lead to high-dimensional design spaces that hinder the process of shape optimization and design through analysis. In this work, we propose a shape-informed dimensional reduction approach that attempts to tackle this deficiency by producing low-dimensional latent design spaces that can be efficiently used in shape representation and optimization. Furthermore, geometric moments are introduced in an attempt to cost-effectively capture analysis-relevant information that is generally expensive to produce. Specifically, geometric integral properties, although intrinsic features of the shape, are quite commonly related to performance indicators employed in performance optimization and therefore provide a cost-effective physics-informed component in the description of the design in the latent space. To this end, we employ the generalized Karhunen-Loeve expansion to produce a shape- and physics-informed subspace retaining the highest possible geometric variance and robustness, that is, lack of invalid designs. At the same time, a series of shape discretizations, encoding the foil's shape profile, are examined with regards to their effect on the resulting latent space's quality and efficiency. Our results demonstrate a significant reduction in the dimensionality of the original design space while maintaining a high representational capacity and a large percentage of valid geometries that facilitate fast convergence to optimal solutions in performance-based optimization.

We present a class of efficient parametric closure models for 1D stochastic Burgers equations. Casting it as statistical learning of the flow map, we derive the parametric form by representing the unresolved high wavenumber Fourier modes as functionals of the resolved variables’ trajectory. The reduced models are nonlinear autoregression (NAR) time series models, with coefficients estimated from data by least squares. The NAR models can accurately reproduce the energy spectrum, the invariant densities, and the autocorrelations. Taking advantage of the simplicity of the NAR models, we investigate maximal and optimal space-time reduction. Reduction in space dimension is unlimited, and NAR models with two Fourier modes can perform well. The NAR model’s stability limits time reduction, with a maximal time step smaller than that of the K-mode Galerkin system. We report a potential criterion for optimal space-time reduction: the NAR models achieve minimal relative error in the energy spectrum at the time step where the K-mode Galerkin system’s mean CFL number agrees with the full model’s.

Intelligent Transportation Systems (ITS) encompasses technologies, services, and applications facilitating communication between vehicles (V2V) and between vehicles and fixed infrastructure (V2I and I2V). This mutual interaction constitutes a Vehicular Ad-Hoc Network (VANET) which supports a plethora of applications targeting critical transportation aspects, such as safety, mo-bility, and environmental considerations. Dedicated Short Range Communications (DSRC), op-erating on the 5.9 GHz band, is pivotal for such exchanges. We introduced an innovative algorithm designed to identify jamming attacks and transition the Safety Application to a secure fail-safe mode. This algorithm leverages a dual-metric strategy, incorporating both distance and PDR measurements. Field tests confirm that our algorithm adeptly recognizes the activities of deceptive jammers, ensuring a prompt shift of the safety application into its fail-safe state. This paper delves into these countermeasures, evaluating their efficiency via mathematical modelling, simulations, and on-ground testing. Findings acknowledge that these strategies bolster the reliability of safety applications in jamming scenarios. Furthermore, the approaches propounded align with ongoing standardization endeavours by relevant authorities, ensuring communication mediums remain unhindered.

When disposing of waste metal resources in landfills, environmental issues such as soil contamination may arise. Recycling these resources not only recovers valuable metals but also mitigates environmental pollution. Platinum (Pt), a valuable metal used in fuel cells for its high water production activity, will see increased future demand as a fossil fuel alternative. This study analyzes the environmental and resource reduction effects of recycled Pt, considering the growing emphasis on its recycling for stable supply and demand of Pt. The environmental impact and resource consumption of recycled Pt with primary Pt (from natural mines) were compared and analyzed using the Life Cycle Assessment (LCA) technique. Results revealed that resource consumption for primary Pt was 8.25E+01 kg Sb-eq./kg, significantly more than the 5.45E+00 kg Sb-eq./kg for recycled Pt. This represents an environmental reduction effect of approximately 93%. In the case of greenhouse gas emissions, primary Pt emitted 1.35E+04 kg CO2-eq./kg, while recycled Pt emitted 6.94E+02 kg CO2-eq./kg, resulting in an environmental reduction effect of approximately 95%. In conclusion, recycling Pt, compared to primary extraction, offers substantial environmental and resource reduction benefits. This study underscores the significance of recycling and highlights the potential environmental improvements achievable through sustainable practices.

The processes of hydrogen reduction of silicon and germanium chlorides under the conditions of high-frequency (40.68 MHz) counteracted arc discharge stabilized between two rod electrodes are investigated. The main gas-phase and solid products of plasma-chemical transformations are determined. Thermodynamic analysis of SiCl4 + H2 and GeCl4 + H2 systems for optimal process parameters was carried out. Using the example of hydrogen reduction of SiCl4 by the method of numerical modeling, gas-dynamic and thermal processes for this type of discharge are investigated. The impurity composition of gas-phase and solid reaction products is investigated. The possibility of single-stage production of high-purity Si and Ge mainly in the form of compact ingots, as well as high-purity chlorosilanes and trichlorogermane, is shown.

This work presents a new algorithm to resolve the problem of distribution network reconfiguration using the Tabu Search metaheuristic and voltage drop. The proposed method presents a different way to obtain the initial solution using the voltage drop, as well as a new way to generate a neighborhood of candidate solutions, based on randomness and the voltage drop, to perform a smooth and steady descent execution of the algorithm. The proposed algorithm has the main objective to reduce the active power losses in distribution networks, but it also achieves an refinement in the voltage profile. The proposed method was tested in two distribution systems, the 33-Bus and 94-Bus systems, commonly found and tested in the literature. The results of the tests show that the proposed method presents a low computational cost, achieves good solutions, and is better than the other methods found in the literature.

Zero-rate output (ZRO) drift induces deteriorated micro-electromechanical system (MEMS) gy-roscope performances, severely limiting its practical applications. Hence, it is vital to explore an effective method toward ZRO drift reduction. In this work, we conduct an elaborate investigation on the impacts of the internal and packaging stresses on the ZRO drift at the thermal start-up stage, and propose a temperature-induced stress release method to reduce the duration and magnitude of ZRO drift. Self-developed high-Q dual mass tuning fork gyroscopes (TFGs) are adopted to study the correlations between temperature, frequency and ZRO drift. Furthermore, a rigorous finite element simulation model is built based on the actual device and packaging structure, revealing the temperature and stresses distribution inside TFGs. Meanwhile, the relationship between temperature and stresses are deeply explored. Moreover, we introduce a temperature-induced stress release process to generate thermal stresses and reduce the temperature-related device sensitivity. By this way, the ZRO drift duration is drastically reduced from ~2000 s to ~890 s, and the drift magnitude decreases from ~0.4 °/s to ~0.23 °/s. This stress release method achieves a small bias instability (BI) of 7.903 °/h and a low angle random walk (ARW) of 0.792 °/√h, and the long-term bias performance is significantly improved.

Molecular mechanisms for N2 fixation (solar NH3) and CO2 conversion to C2+ products in enzymatic conversion (Nitrogenase), electrocatalysis, metal-complexes and plasma-catalysis are analysed and compared. It is evidenced that differently from what present in thermal and plasma-catalysis, the electrocatalysis path requires not only the direct coordination and hydrogenation of undissociated N2 molecule, but to realize a series of features present in the Nitrogenase mechanism. There is the need of i) a multi-electron and -proton simultaneous transfer, not as sequential steps, ii) forming bridging metal hydride species, iii) generate intermediates stabilized by bridging multiple metal atoms, iv) have the capability of the same sites to be effective both in N2 fixation and in COx reduction to C2+ products. Only iron oxide/hydroxide stabilized at defective sites of nanocarbons was found to have these features. This comparison of the molecular mechanisms in solar NH3 production and relations with CO2 reduction is proposed to be a source of inspiration to develop the next generation electrocatalysts to address the challenging transition to a future sustainable energy and chemistry beyond fossil fuels.

Though the share of the world population living in extreme poverty declined to 10 percent in 2015, from 16 percent in 2010 and 36 percent in 1990, data shows that the world is not on track in achieving the target of less than 3 percent of people living in extreme poverty by 2030. Hence the study sought to investigate the influence of AI on poverty reduction. Using content analysis one of the unobtrusive research techniques, the study found out that, the availability of relevant data is making AI be able to deliver value to humanity and AI has a strong influence on poverty in areas of relevant data collection through poverty maps, its ability to revolutionize agriculture, education, and the financial sector through digital financial inclusion. The study also discovered that many countries especially developing nations are not collecting as much data to identify the number of poor people and the regions where these people are located. However, the existence of AI is assisting to change this, or instance the study discovered that the research team at Stanford University is using satellite images to provide an alternative to map poverty, to identify the regions where poverty is more concentrated. Also, various robotics and AI programs such as Google and Stanford University’s Sustainability and Artificial Intelligence Lab, are coming forth with AI programs in agriculture which are doing a lot to improve farming, through the identification of diseases, prediction of crop yields, and location of areas prone to a scarcity among several other notable signs of progress in education. Therefore, the study recommends that governments, development institutions and other organizations that are striving to fight poverty to invest more in AI as well as adopting and scaling up its use as it presents benefits in the quest to ensure that poverty is reduced.

Jakarta belongs to the cities with the highest flood risk in the world. Its flood hazard is driven by land subsidence, soil sealing, changes in river discharge and increasingly sea level rise. As all of these trends are set to continue, Jakarta’s flood hazard is expected to intensify in the future. Designing and implementing risk reduction and adaption measures is therefore of utmost importance. Against the background, the paper draws on a discourse analysis and original empirical household survey data to review and evaluate current adaptation measures and to analyze in how far they describe a path that is transformative from previous risk reduction approaches. The results show that the focus is clearly on engineering solutions, foremost in the Giant Sea Wall project. The project is likely to transform the city’s flood hydrology. However, it cements rather than transforms the current risk management paradigm which gravitates around the goal of controlling flood symptoms, rather than addressing their anthropogenic root causes. The results also show that the planned measures are heavily contested due to concerns about ecological impacts, social costs, distributional justice, public participation and long-term effectiveness. On the outlook, the results therefore suggest that the more the flood hazard will intensify in the future, the deeper a societal debate will be needed about the desired pathway in flood risk reduction and overall development planning – particularly with regards to the accepted level of transformation, such as partial retreat from the most flood-affected areas.

Containing hundreds of spectral bands (features), hyperspectral images (HSIs) have high ability in discrimination of land cover classes. Traditional HSIs data processing methods consider the same importance for all bands in the original feature space (OFS), while different spectral bands play different roles in identification of samples of different classes. In order to explore the relative importance of each feature, we learn a weighting matrix and obtain the relative weighted feature space (RWFS) as an enriched feature space for HSIs data analysis in this paper. To overcome the difficulty of limited labeled samples which is common case in HSIs data analysis, we extend our method to semisupervised framework. To transfer available knowledge to unlabeled samples, we employ graph based clustering where low rank representation (LRR) is used to define the similarity function for graph. After construction the RWFS, any arbitrary dimension reduction method and classification algorithm can be employed in RWFS. The experimental results on two well-known HSIs data set show that some dimension reduction algorithms have better performance in the new weighted feature space.

This article considers the concepts of sustainability and sustainable development in relation to disaster risk reduction and climate change adaptation. We conceptualize sustainability from a social systemic perspective, that is, from a perspective that encompasses the multiple functionalities of a social system and their interrelationships in particular environmental contexts. The systems perspective is applied in our consideration and analysis of disaster risk reduction (DRR), climate change adaptation (CCA), and sustainable development (SD). Section 1 introduces briefly sustainability and sustainable development, followed by a brief presentation of the theory of complex social systems (Section 2). The theory conceptualizes interdependent subsystems, their multiple functionalities, and the agential and systemic responses to internal and external stressors on a social system. Section 3 considers disaster risk reduction (DRR) and climate change adaptation (CCA), emerging in response to one or more systemic stressors. It illustrates these with disaster risk reduction in the cases of food and chemical security regulation in the EU. CCA is illustrated by initiatives and developments on the island of Gotland, Sweden and in the Gothenburg Metropolitan area, which go beyond a limited CCA perspective, taking into account long-term sustainability issues. Section 4 discusses the limitations of DRR and CCA, not only their technical limitations but economic, socio-cultural, and political limitations, as informed from a sustainability perspective. It is argued that DRRs are only partial subsystems and must be considered and assessed in the context of a more encompassing systemic perspective. Part of the discussion is focused on the distinction between sustainable and non-sustainable DRRs and CCAs. Section 5 presents a few concluding remarks about the importance of a systemic perspective in analyzing DRR and CCA as well as other similar subsystems in terms of sustainable development.

Cardiovascular diseases (CVDs) a leading cause of mortality worldwide. Therefore, there is in-creasing interest in dietary interventions to reduce risk factors associated with these conditions. Cherries and berries are rich sources of bioactive compounds and have attracted attention for their potential cardiovascular benefits. This review summarizes the current research on the effects of cherries and berries consumption on cardiovascular health, including in vivo studies and clinical trials. These red fruits are rich in phenolic compounds, such as anthocyanins and flavonoids, which have multiple bioactive properties. These properties include antioxidant, anti-inflammatory, and vasodilatory effects. Studies suggest that regular consumption of these fruits may reducing in-flammation and oxidative stress, leading to lower blood pressure, improved lipid profiles, and enhanced endothelial function. However, interpreting findings and establishing optimal dosages is challenge due to the variability in fruit composition, processing methods, and study design. Despite these limitations, the evidence highlights the potential of cherries and berries as components of preventive strategies against CVD. Further research is needed to maximize their health benefits and improve clinical practice.

Post-industrial recycling (PIR) and post-consumer recycling (PCR) are measures to sustain re-sources by improving materials’ circularity and sustainability. Currently, circularity is mostly measured as the degree of reutilization of a material from 0 to 100 % at product or company lev-el. This lacks in assessing the resources’ usage over multiple product life cycles. Therefore, we propose to assess circularity as (i) the frequency resources are used in products (effective circu-larity eC) and (ii) a vehicle to reduce virgin resource use and environmental impacts (environ-mentally efficient circularity eeC). Besides, to compare the environmental impacts of using recy-cled materials from PIR or PCR, we analyse their impact reduction potential (IRP) indicating the environmental benefits of recycling in relation to virgin material put onto the market. We demonstrate the suggested indicators for a case study material: polypropylene. For this polymer type, the eC ranges between 0.93 and 9.08 uses of the resource on average depending on collec-tion, sorting, and recycling rates. Likewise, the eeC ranges between 0.31 and 1.50 uses per kg of CO2 equivalents emitted. PCR has a higher IRP regarding climate change impacts than PIR in all analysed scenarios. The results reveal the relevance of PCR and PIR beyond the product life cycle.

With the rapid advancements in remote sensing technology, the spectral information from hyperspectral remote sensing images has become increasingly rich, facilitating detailed spectral analysis of the Earth's surface objects. However, this abundance of spectral data poses significant challenges in data processing, such as the curse of dimensionality leading to the “Hughes” phenomenon, "strong correlation" due to high resolution, and "non-linear characteristics" caused by varied surface reflectance rates. Therefore, dimensionality reduction of hyperspectral data has become a crucial task. This paper, grounded in manifold theory and manifold learning techniques, and considering the non-linear structures and features in hyperspectral remote sensing data, elucidates the principles and processes of dimensionality reduction in hyperspectral remote sensing images using manifold learning, with a formalized expression of the process. This article introduces spectral information divergence (SID) into the nearest neighbor graph computation of manifold learning algorithms. The principles and computational processes of nearest neighbor graph algorithms based on Euclidean distance (ED), spectral angle mapping (SAM), and SID are studied, and a comparative analysis of the dimensionality reduction effects under these three metrics in hyperspectral data is conducted. Experiments on feature extraction under different metrics were performed using the publicly available Indian Pines hyperspectral dataset. The intrinsic features obtained post-dimensionality reduction were used as inputs for classification algorithms in ground objects classification experiments, with algorithm runtime, overall accuracy, and Kappa coefficient as evaluation metrics for dimensionality reduction quality. The results demonstrate that nearest neighbor graph computation based on SAM and SID outperforms traditional ED methods; SAM-based computation has the lowest time complexity, while SID-based manifold learning yields the highest accuracy in ground objects classification. Thus, manifold learning based on SAM and SID metrics proves to be an effective method for feature extraction in hyperspectral remote sensing data, underscoring the potential of manifold learning techniques in the dimensionality reduction of hyperspectral remote sensing images.

Greenhouses are instrumental in the advancement of regions globally. The geometric arrangement of these structures plays a pivotal role in governing sunlight distribution, facilitating ventilation, and managing condensation. The roof's shape significantly affects energy efficiency and the accumulation of condensation water, which, when dripping onto crops, can induce diseases and diminish production. This study introduces a Matlab program designed for defining and analyzing greenhouse roof geometry, adaptable to both single-span and multi-span structures. Various roof shapes are examined, determining angles along their length to facilitate condensation droplet runoff. Furthermore, the greenhouse's energy efficiency is evaluated by analyzing diverse roof models, accounting for surface area and internal air volume. The results aim to aid in selecting the optimal greenhouse type based on climate and latitude. This study offers a valuable decision-making tool for the planning and design of agricultural structures, providing insights to enhance overall sustainability and performance in diverse environmental contexts.

Mosquitoes transmit pathogens that cause human diseases such as malaria, Dengue fever, Chikungunya, yellow fever, Zika fever, and filariasis. Chemical pesticides are effective instruments for reducing disease transmission and managing mosquito populations. However, these chemicals have drawbacks such as high production costs and negative effects on the environment and non-target organisms. Furthermore, as mosquitoes develop resistance to chemical insecticides, they become less effective. As a result, researchers are investigating novel insecticides. Advances in microbial biotechnology have resulted in a wide range of value-added products, including biopesticides for agricultural and public health purposes. In this review, we highlight the mainly untapped biotechnological potential of microorganisms for vector mosquito control and disease transmission reduction. Also, we underline the importance of more basic research and improved translational research methodologies, encouraging efforts to bridge the gap between academic research on biopesticides and public health interventions.

Emerging infectious disease threats are becoming more frequent due to various social, political and geographical pressures including increased human-animal contact, global trade, transportation and changing climate conditions. As a result, the threat that emerging agents can be spread by blood contact or transfusion of blood products also becomes increasingly problematic. Blood transfusion is essential in treating patients with anemia, blood loss, and other medical conditions. However, these lifesaving components can become a vector for spreading diseases, particularly to vulnerable populations. New methods have been implemented on a global basis for prevention of transfusion transmission via plasma, platelet, and whole blood products. Implementing proactive pathogen reduction methods can significantly reduce the likelihood of disease transmission via blood transfusion, even for newly emerging agents whose transmissibility and susceptibility are still being evaluated as they emerge. In this review, we consider the Mirasol PRT system for blood safety which is based on a photochemical method involving Riboflavin and UV light. We provide examples of how emerging threats such as Ebola, SARS-CoV-2, Hepatitis E, monkeypox and other agents have been evaluated in real time regarding effectiveness of this method for reducing the likelihood of disease transmission via transfusion. Keywords: transfusion; blood; pathogen reduction; emerging infectious diseases.

The COVID-19 pandemic has contributed to mental health problems worldwide. Nurses are particularly prone to stress because they directly care for individuals with suspected or confirmed cases of COVID-19. The aim of this study is to explore the association between the mental health promotion strategies used by nurses during the coronavirus disease (COVID-19) outbreak and their symptoms of depression, anxiety, and stress, and to compare the strategies and symptoms of mental health nurses and non-mental health nurses. Cross-sectional study was conducted with a sample of 821 nurses. Portuguese nurses demonstrated high symptoms of depressive symptoms, stress, and anxiety. Healthy eating, physical activity, rest between shifts, maintaining social contacts, verbalizing feelings/emotions, and spending less time searching for information about COVID-19 are associated with better mental health. Mental health nurses have less depression, anxiety, and stress, and use more strategies to promote mental health than other nurses. We consider it important to promote nurses’ mental health literacy by encouraging them to develop skills and strategies aimed at improving their resilience and ability to deal with difficult situations while caring for the population.

This paper presents an investigation into the influence of drag-reducing polymer (DRP) on the flow of water inside and around U-bends. The DRPs tested were partially hydrolysed polyacrylamide (HPAM) and two different molecular weight polyethylene oxides (PEO). The Reynolds number and in-situ concentration ranges of water and DRP were and respectively. The test section was made up of clear polyvinyl chloride (CPVC) pipes that contained two U-bends with radii of curvature (R) of 100 and 200 mm as well as straight sections of 19-mm ID. Higher pressure gradient was recorded in the under-developed flow section after the bend. Drag reduction (DR) increased with in-situ concentration of the DRP and the flowrate up to some threshold values. The concentration and flowrate thresholds were about 20 ppm and 1.07 m/s respectively. DR differed among the different sections considered with the highest values recorded for developed flows upstream of the bend and the least values recorded in the bend. DR at station 1 (immediately after the bend) was generally lower than that at station 2 (further downstream). P-K Plots showed that, the DR envelop for developed flow in straight pipes did not apply to the bend and the under-developed flow section. DR increased with polymer molecular weight and ionic strength in straight sections. However, molecular weight (and polymer shearing) had limited effect on DR in the bends. DR characteristics at the bend suggested that there was a different or additional mechanism. The effect of bend curvature ratio on DR was predominant after the U-bend. The larger the ratio of pipe diameter to bend diameter, the lower the DR in the redeveloping sections after the bend. For R = 100 mm, the highest DR recorded for fully developed flows before bend, in the bend, at stations 1 and 2 were 57, 31, 36 and 33% respectively while that for R = 200 mm were 64, 28, 42 and 49% respectively.

Behavior of supported alloyed and de-alloyed platinum-copper catalysts, which contained 14% - 27% wt. of Pt, was studied in the reactions of methanol electrooxidation (MOR) and oxygen electroreduction (ORR) in 0.1 M HClO4 solutions. Alloyed PtCux/C catalysts were prepared by a multistage sequential deposition of copper and platinum onto a Vulcan XC72 dispersed carbon support. De-alloyed PtCux-y/C catalysts were prepared by PtCux/C materials pretreatment in acid solutions. The effects of the catalysts initial composition and the acid treatment condition on their composition, structure, and catalytic activity in MOR and ORR were studied. Functional characteristics of platinum-copper catalysts were compared with those of commercial Pt/C catalysts when tested, both in an electrochemical cell and in H2/Air membrane-electrode assembly (MEA). It was shown that the acid pretreatment of platinum-copper catalysts practically does not have negative effect on their catalytic activity, but it reduces the amount of copper passing into the solution during the subsequent electrochemical study. The activity of platinum-copper catalysts in the MOR and the current-voltage characteristics of the H2/Air PEMFC MEAs measured in the process of their life tests were much higher than those of the Pt/C catalysts.

The increasing spatial and spectral resolution of hyperspectral imagers yields detailed spectroscopy measurements from both space-based and airborne platforms. Machine learning algorithms have achieved state-of-the-art material classification performance on benchmark hyperspectral data sets; however, these techniques often do not consider varying atmospheric conditions experienced in a real-world detection scenario. To reduce the impact of atmospheric effects in the at-sensor signal, atmospheric compensation must be performed. Radiative Transfer (RT) modeling can generate high-fidelity atmospheric estimates at detailed spectral resolutions, but is often too time-consuming for real-time detection scenarios. This research utilizes machine learning methods to perform dimension reduction on the transmittance, upwelling radiance, and downwelling radiance (TUD) data to create high accuracy atmospheric estimates with lower computational cost than RT modeling. The utility of this approach is investigated using the instrument line shape for the Mako long-wave infrared hyperspectral sensor. This study employs physics-based metrics and loss functions to identify promising dimension reduction techniques. As a result, TUD vectors can be produced in real-time allowing for atmospheric compensation across diverse remote sensing scenarios.

This review describes how resonance in amides is greatly affected upon substitution at nitrogen by two electronegative atoms. Nitrogen becomes strongly pyramidal and resonance stabilisation, evaluated computationally, can be reduced to as little as 50% that of N,N-dimethylacetamide. However, this occurs without significant twisting about the amide bond, which is borne out both experimentally and theoretically. In certain configurations, reduced resonance and pronounced anomeric effects between heteroatom substituents are instrumental in driving the HERON (Heteroatom Rearrangement On Nitrogen)^† reaction, in which the more electronegative atom migrates from nitrogen to the carbonyl carbon in concert with heterolysis of the amide bond, to generate acyl derivatives and heteroatom-substituted nitrenes. In other cases the anomeric effect facilitates S­_N1 and S_N2 reactivity at the amide nitrogen.

While texturing of workpiece surfaces to promote lubrication in metal forming has been applied for several decades tool surface texturing is rather new. In the present paper tool texturing is studied as a method to prevent galling. Adopting a strip reduction test longitudinal pocket geometries oriented perpendicular to the sliding direction, with shallow pocket depth, small pocket angle to the workpiece surface and varying distance between pockets are investigated. The experiments reveal that the distance between pockets should be larger than the pocket width thereby creating a topography similar to flat table mountains to avoid mechanical interlocking in the valleys; otherwise an increase in drawing load and pick-up on the tools is observed. The textured tool surface lowers friction and improves lubrication performance provided that the distance between pockets is 2-4 times larger than the pocket width. Larger drawing speed facilitates escape of the entrapped lubricant in the pockets. Testing with low to medium viscosity oils leads to a low sheet roughness on the plateaus but also local workpiece material pick-up on the tool plateaus. Large lubricant viscosity results in higher sheet plateau roughness but also prevents pick-up and galling.

Background: Colorectal cancer is a major global health burden, with surgical resection being the standard treatment aimed at curative tumor removal. Oxidative stress plays a crucial role in colorectal cancer progression and prognosis. This study hypothesized that physical removal of colorectal cancer, a primary source of oxidative stress, would reduce blood levels of reactive oxygen metabolite derivatives (d-ROMs), a marker of oxidative stress, and biologic antioxidant potential (BAP) levels, a marker of antioxidant potential. Methods: The study included 123 patients who underwent radical resection for colorectal cancer. d-ROM and BAP levels were measured before and one month after surgery. Results: The clinicopathological analysis showed a correlation between preoperative d-ROM levels and tumor size (p<0.001). The study confirmed a significant reduction in d-ROM levels following tumor resection, indicating reduced systemic oxidative stress. The reduction was significant in stages II and III but not in stage I. The d-ROM ratio before and after tumor resection was significantly higher in cases with positive lymph node metastasis and larger tumor size. BAP levels showed no significant changes post-surgery. Conclusion: These results suggest that d-ROMs could serve as a valuable biomarker for monitoring tumor burden and surgical efficacy in patients with colorectal cancer.

This paper presents a study on the idle vibration reduction of a diesel sport utility vehicle (SUV). To reduce idle vibration, the transmission paths of vibration from the engine to the driver seat floor were investigated with the vehicle components related to idle vibration. Furthermore, operational deflection shape (ODS) tests were conducted to visualize the vibration shapes during engine idling. Experimental modal analyses were performed to obtain the natural frequencies and mode shapes. Through the ODS and modal tests, the vibration characteristics of the diesel SUV during idling were identified. Considering these vibration characteristics, a multi-body dynamic model for the diesel SUV described by differential equations of motion was established to evaluate the idle vibration. To implement the dynamic model effectively, the equivalent stiffnesses and damping coefficients included in the model were determined experimentally or analytically. The established dynamic model was verified by comparing the natural frequencies and idle vibration levels between simulations. Using this dynamic model, we analyzed the effects of various design variables on idle vibration and obtained an optimal design for reducing the idle vibration level. Finally, we present a design guide to reduce the idle vibration for diesel SUVs.

Accuracy and computational cost are the main challenges of deep neural networks in image recognition. This paper proposes an efficient ranking reduction to binary classification approach using a new feed-forward network and feature selection based on ranking the image pixels. Preference net (PN) is a novel deep ranking learning approach based on Preference Neural Network (PNN), which uses new ranking objective function and positive smooth staircase (PSS) activation function to accelerate the image pixels’ ranking. PN has a new type of weighted kernel based on spearman ranking correlation instead of convolution to build the features matrix. The PN employs multiple kernels that have different sizes to partial rank image pixels’ in order to find the best features sequence. PN consists of multiple PNNs’ have shared output layer. Each ranker kernel has a separate PNN. The output results are converted to classification accuracy using the score function. PN has promising results comparing to the latest deep learning (DL) networks using the weighted average ensemble of each PN models for each kernel on CFAR-10 and Mnist-Fashion datasets in terms of accuracy and less computational cost.

Water scarcity is a key challenge to global development. In Brazil, the Sao Francisco River Basin (SFB) has experienced water scarcity problems because of decreasing streamflow and increasing demands from multiple sectors. However, the drivers of decreased streamflow, particularly the potential role of surface-groundwater interaction, have not been yet investigated. Here, we assess long-term trends in baseflow, quickflow, and streamflow of the SFB during 1980–2015 and constrain the most likely drivers of observed decreases through trend analysis of precipitation (P), evapotranspiration (ET), and terrestrial water storage change (TWS). We found that over 82% of the observed decrease in streamflow can be attributed to a significant decreasing baseflow trend (< -20 m3 s-1 y-1) along the SFR with spatial agreement between decreased baseflow, increased ET, and irrigated agricultural land. We also noted a decrease in TWS across the SFB with trends exceeding -20 mm y-1. Overall, our findings indicate that decreasing groundwater contributions (i.e., baseflow) is providing the observed reduction in total SFR flow. A lack of significant P trends indicates that only P variability likely has not caused the observed baseflow reduction, mainly in the Middle and Sub-middle SFB. Therefore, groundwater and surface withdrawals may be likely a driver of water scarcity over the SFB.

A solution of general relativity is presented that describes an Alcubierre propulsion system in which it is possible to travel at superluminal speed while reducing the energy, by an arbitrary value.

Two methods for Pd nanoparticle synthesis were explored with three different carbide-derived carbon (CDC) support materials of which one was nitrogen-doped. These materials were studied for oxygen reduction reaction (ORR) in 0.1 M KOH solution. The prepared CDC/Pd catalysts were characterized using TEM, XRD and XPS. Using these methods, we can observe the influence of support materials for one-pot synthesis methods as we use the standard citrate method and polyol method with polyvinylpyrrolidone (PVP) as a capping agent. Comparing the two methods N-doping of carbon material resulted in smaller nanoparticles only in the case of citrate synthesis, suggesting that the influence of support is weaker using polyol method. Citrate method on CDC1, which was predominantly microporous led to a higher degree of agglomeration and larger particle formation in comparison to CDC2 and CDC3 supports, which possessed higher degree of mesoporosity. Smaller Pd particle sizes were achieved using citrate and NaBH4 in comparison to the ethylene glycol-PVP method. CDC2 and CDC3 did not show significant difference suggesting that the N-doping did not have much of an influence on the ORR. Highest SA value was observed on CDC1/Pd_Cit, which could be attributed to formation of larger particles and agglomerates.

All over the world energy is used for different purposes and hence its continuous high demand which has brought about an increase in crisis and prices of energy. Ghana has faced a lot of supply and high electricity consumption challenges over a period of time. The Energy Commission of Ghana has developed regulations and guidelines to help reduce high consumption challenges among users, these included the replacement of incandescent bulbs with fluorescent bulbs, ban of importation of low energy efficient appliances. In spite of the effort to reduce electricity wastage, there is still a high increase in electricity consumption. The research investigated what contributed to electricity consumption in Kwame Nkrumah University of Science and Technology with the lecture halls as the main focus, the research also analyzed the current occupant behavior characterized by the electrical energy consumption practices. And investigated how the contemporary theories for reducing energy consumption was used in the lecture halls. A questionnaire survey was conducted to investigate occupants on their energy use practices in lecture halls that causes wastages, observation was made to establish relevant data on the use of contemporary theories for energy reduction in lecture halls. In a total of 110 occupants that responded to the questionnaire, 79 occupants almost always turn off electrical fitting and fixtures when not in use. From the responses, a majority of the occupants claimed to comply to best practices of energy use. The research concluded that some contemporary theories to reduce energy consumptions was not used and considered in the lecture halls.

Rare earths are classified as most important and critical material for US economy and defense by Congress and a mandate has been set to increase their in-house production, domestic resource utilization and decrease reliance on foreign resources and reserves. They are widely available in earth crust as ore (bastnaesite (La, Ce)FCO₃, monazite, (Ce, La, Y, Th)PO₄, and xenotime, YPO₄), but their so-called economic reserves are sparsely located geographically. They may be produced by various means such as beneficiation (physical, chemical, mechanical, or electrical), reduction (direct or indirect), electrolysis (of aqueous or molten / fused single or mixed salt systems) at high temperature or hydrometallurgy. Out of these, direct reduction also known as metallothermic reduction (La and Ca reduction) is mostly utilized. Its variant, high temperature electrowinning of fused salts is also practiced widely. These processes are material and application specific. In this study, author will employ thermodynamics (Ellingham diagrams, free energy of formation, reduction potential, Nernst equation, Pourbaix (E_h-pH) diagrams, E-pO^-2 diagrams), kinetics and energetic of a chemical reaction (chemical metallurgy) to reduce rare earth oxide / salt to rare earth metals (REO/RES – REM). It is shown that materials and energy requirement vary greatly depending on type of mineral ore, production facility, and beneficiation / mineral processing method selected. Aim is to reduce dependence on coal deposits. It is anticipated this route will be able to produce rare earths with > 35% yield and > 98% purity which be described in subsequent studies and patents.

Oil and gas refineries are highly water-intensive industrial settings, with effluent containing a significant level of pollution stemming from diverse organic and inorganic compounds. Besides adhering to discharge standards for industrial effluent, incorporating treated oil refinery effluent (ORE) into the production cycle can play a pivotal role in curbing water consumption. In recent years, there has been research into different approaches to reclaiming ORE. Yet, selecting treatment methods that are technically, economically, and environmentally effective is crucial to preventing resource waste. Therefore, this study aimed to examine the last two decades of literature on methods and technologies used for ORE treatment. Based on the inclusion criteria, the final screening included 82 studies, with acceptable agreement assessed using Cohen's inter-examiner kappa equal to 0.86. The included studies were of biological treatment (n = 27), physicochemical processes (n = 12), advanced purification processes (n = 16), membrane-based technologies (n = 15), and green technologies (n = 13). This comprehensive review showed that the advanced membrane-based techniques are effective in the removal of pollutants from ORE for several reasons, such as reducing the consumption of chemicals, high efficiency, and ease of setup and maintenance. However, combined methods with a focus on membrane-based processes (e.g. UF-RO) are the most promising options for the reclamation of ORE. Since some effluent treatment methods require the use of chemicals and energy to run, future research should focus on environmentally friendly methods and the use of renewable energy.

Abstract Big datasets are becoming increasingly common and can be challenging to understand and apply in public health. One method for lowering the dimensionality of these datasets and improving interpretability while minimizing information loss is data reduction using principal component analysis (PCA). It achieves this by successively maximizing variance through the creation of new, uncorrelated variables. PCA is an adaptive data analysis technique because it simplifies the process of finding new variables, or principal components, by solving an eigenvalue or eigenvector problem. These new variables are determined by the dataset being used, rather than by the analyst starting from scratch. It is also adaptable in another way because varieties of the method have been designed to adjust to various data structures and types. However, there are serious problems in the theoretical understanding and practical application of PCA in public health researchers, whereas its application is becoming popular in developing countries. Therefore, this article, which concentrated on using PCA to reduce data, began by outlining the fundamental concepts of PCA and going over what it can and cannot do, as well as when and how to use it. We also discussed the fundamental assumptions, benefits, and drawbacks of PCA. Furthermore, this article demonstrated and resolved PCA practical application problems in public health that most scholars are unaware of, such as variable preparation, variable inclusion and exclusion criteria for PCA, iteration steps, wealth index analysis, interpretation, and ranking.

The cycling of soil phosphorus (P) is inherently linked with soil organic carbon-iron (C-Fe) cycling, yet empirical integration of these processes within paddy soils remains scarce. In this study, we conducted a microcosm experiment using paddy soils subjected to six distinct fertilization regimes involving varying P inputs for five years. In addition to evaluating P activation under reflooding conditions, we assessed the Fe reduction process and characterized the properties of dissolved organic matter (DOM) at the molecular level using Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR MS), alongside profiling the composition of soil microbial communities with high-throughput sequencing. Our findings revealed that after 25 days of reflooding, soil Olsen-P content increased by an average of 73% compared to its initial state, showing a strong correlation with the Fe reduction process. Specifically, treatments involving pig manure application exhibited higher Fe reduction rates and enhanced P activation, highlighting the role of organic matter in facilitating Fe reduction. Investigations on the relative abundance of typical iron-reducing microbes further supported their importance in P activation, but the rate of iron reduction is limited by soil organic matter content. Delving deeper into DOM properties, soil DOM composition profiling and network analysis suggested that high-molecular-weight DOM, particularly lignins, served as the primary resources driving Fe reduction by iron-reducing microbes, consequently promoting Fe reduction and P release. Taken together, our study assembled the C-Fe-P cycling dynamics in paddy soils, emphasizing the pivotal role of microbial-driven Fe reduction facilitated by soil DOM in P availability and subsequently sustainable agricultural practices.