bluuebunny/crossref_metadata_2025 huggingface dataset:
Studying the Effects of Varied Dosages and Grinding Times on the Mechanical Properties of Mortar
Authors: Zhang Wenwen , Li Shujin , Song Luguang , Sheng Yanmin , Xiao Junwen , Zhang Tianxiang
Published: March 2023

With the rapid development of construction and the construction industry, the demand for mortar as a building material is also increasing. With the development of economic society, glass products have been widely used, and glass manufacturing enterprises have produced hundreds of tons of glass fragments and slag. The main component of glass is silica, which has the potential to be used as an auxiliary cementing material. Therefore, waste glass is expected to be recycled in buildings to achieve sustainability. However, due to the chemical properties of the silica tetrahedral structure stabilized by the waste glass, its pozzolanic activity is potent and needs to be stimulated. Glass powders with different degrees of fineness were obtained by physical grinding of waste glass powder (WGP). The standard consistency water consumption, compressive strength, and flexural strength of waste glass powder cement mortar were studied. The grinding times of waste glass powder are 5 min, 10 min, and 15 min, respectively, and the dosages are 5%, 10%, 15%, and 20%, respectively. The experimental results show that the average particle sizes of the grinding times of 5 min, 10 min, and 15 min are 1670.0 μm, 243.0 μm, and 13.2 μm, respectively. The waste glass powder with a grinding time of 15 min has a specific surface area of 670 m2/kg, which has high pozzolanic activity. The compressive and flexural strength of cement mortar specimens with 5% waste glass powder is the best, and the later strength is improved to a certain extent. The consistency of the cement mortar increased after adding waste glass powder. Compared with the 28 d compressive strength activity index of pure cement mortar specimens, the waste glass powder with 5–10% content reached more than 70%.
Aligning the Goals Hybrid Model for the Diagnosis of Mental Health Quality
Authors: Costa Wagner Silva , Pinheiro Plácido R. , dos Santos Nádia M. , Cabral Lucídio dos A. F.
Published: March 2023

The social distancing imposed by the COVID-19 pandemic has been described as the “greatest psychological experiment in the world”. It has tested the human capacity to extract meaning from suffering and challenged individuals and society in Brazil and abroad to promote cohesion that cushions the impact of borderline experiences on mental life. In this context, a survey was conducted with teachers, administrative technicians, and outsourced employees at the Federal Institute of Piauí (IFPI). This educational institution offers professional and technological education in Piauí, Brazil. This study proposes a system for the early diagnosis of health quality during social distancing in the years 2020 and 2021, over the COVID-19 pandemic, combining multi-criteria decision support methodology, the Analytic Hierarchy Process (AHP) with machine learning algorithms (Random Forest, logistic regression, and Naïve Bayes). The hybrid approach of the machine learning algorithm with the AHP multi-criteria decision method with geometric mean accurately obtained a classification that stood out the most in the characteristics’ performance concerning emotions and feelings. In 2020, the situation was reported as the SAME AS BEFORE, in which the hybrid AHP with Geographical Average with the machine learning Random Forest algorithm stands out, highlighting the atypical situation in the quality of life of the interviewees and the timely manner in which they realized that their mental health remained unchanged. After that, in 2021, the situation was reported as WORSE THAN BEFORE, in which the hybrid AHP with geometric mean with the machine learning Random Forest algorithm provided an absolute result.
Data-Driven Analysis of Privacy Policies Using LexRank and KL Summarizer for Environmental Sustainability
Authors: Md Abdul Quadir , Anand Raghav V. , Mohan Senthilkumar , Joshua Christy Jackson , Girish Sabhari S. , Devarajan Anthra , Iwendi Celestine
Published: March 2023

Natural language processing (NLP) is a field in machine learning that analyses and manipulate huge amounts of data and generates human language. There are a variety of applications of NLP such as sentiment analysis, text summarization, spam filtering, language translation, etc. Since privacy documents are important and legal, they play a vital part in any agreement. These documents are very long, but the important points still have to be read thoroughly. Customers might not have the necessary time or the knowledge to understand all the complexities of a privacy policy document. In this context, this paper proposes an optimal model to summarize the privacy policy in the best possible way. The methodology of text summarization is the process where the summaries from the original huge text are extracted without losing any vital information. Using the proposed idea of a common word reduction process combined with natural language processing algorithms, this paper extracts the sentences in the privacy policy document that hold high weightage and displays them to the customer, and it can save the customer’s time from reading through the entire policy while also providing the customers with only the important lines that they need to know before signing the document. The proposed method uses two different extractive text summarization algorithms, namely LexRank and Kullback Leibler (KL) Summarizer, to summarize the obtained text. According to the results, the summarized sentences obtained via the common word reduction process and text summarization algorithms were more significant than the raw privacy policy text. The introduction of this novel methodology helps to find certain important common words used in a particular sector to a greater depth, thus allowing more in-depth study of a privacy policy. Using the common word reduction process, the sentences were reduced by 14.63%, and by applying extractive NLP algorithms, significant sentences were obtained. The results after applying NLP algorithms showed a 191.52% increase in the repetition of common words in each sentence using the KL summarizer algorithm, while the LexRank algorithm showed a 361.01% increase in the repetition of common words. This implies that common words play a large role in determining a sector’s privacy policies, making our proposed method a real-world solution for environmental sustainability.
Design and Development of a Fog-Assisted Elephant Corridor over a Railway Track
Authors: Mondal Manash Kumar , Mandal Riman , Banerjee Sourav , Biswas Utpal , Lin Jerry Chun-Wei , Alfarraj Osama , Tolba Amr
Published: March 2023

Elephants are one of the largest animals on earth and are found in forests, grasslands and savannahs in the tropical and subtropical regions of Asia and Africa. A country like India, especially the northeastern region, is covered by deep forests and is home to many elephants. Railroads are an effective and inexpensive means of transporting goods and passengers in this region. Due to poor visibility in the forests, collisions between trains and elephants are increasing day by day. In the last ten years, more than 190 elephants died due to train accidents. The most effective solution to this collision problem is to stop the train immediately. To address this sensitive issue, a solution is needed to detect and monitor elephants near railroad tracks and analyze data from the camera trap near the intersection of elephant corridors and railroad tracks. In this paper, we have developed a fog computing-based framework that not only detects and monitors the elephants but also improves the latency, network utilization and execution time. The fog-enabled elephant monitoring system informs the train control system of the existence of elephants in the corridor and a warning light LED flashes near the train tracks. This system is deployed and simulated in the iFogSim simulator and shows improvements in latency, network utilization, and execution time compared to cloud-based infrastructures.
Food Waste Management: A Case of Taiwanese High School Food Catering Service
Authors: Chu Chung-Min , Chih Chueh , Teng Chih-Ching
Published: March 2023

This study aims to understand the current state of food waste in Taiwanese school food catering services and the causal configurations that make school food waste possible, as food waste management has generated considerable concern. Combining document analysis, direct weighing, observation, and semi-structured interviews, a mixed methodology was employed to collect data. In order to comprehend and quantify food waste, the amount of school lunch provided and food waste during a 35-day period were measured, as well as the inefficiency index of lunch food at the two schools. According to this study, the inefficiency index of all dishes offered at Y Senior High School by the same lunch caterer is lower than at X Girls High School. In addition, this study identifies seven factors that contribute to school food waste, comprising meal quality, rigid budget limitation, tracking and feedback system, unforeseen factors, partial eating behavior, environmental awareness, and lack of initiatives for reducing food waste. This research also proposes five strategies to improve the management of contracted catering companies in schools, thereby reducing school lunch waste from supply sources. Taiwan’s experience can serve as a model for countries in comparable situations and academically fills the gaps in the experiences of varied societies.
Hybrid Optimization of Green Supply Chain Network and Scheduling in Distributed 3D Printing Intelligent Factory
Authors: Jin Yuran , Gao Cheng
Published: March 2023

Considering the advantages of 3D printing, intelligent factories and distributed manufacturing, the 3D printing distributed intelligent factory has begun to rise in recent years. However, because the supply chain network of this kind of factory is very complex, coupled with the impact of customized scheduling and environmental constraints on the enterprise, the 3D printing distributed intelligent factory is facing the great challenge of realizing green supply chain networks and optimizing production scheduling at the same time, and thus a theoretical gap appears. This paper studies the hybrid optimization of green supply chain networks and scheduling of the distributed 3D printing intelligent factory. Firstly, according to the green supply chain network architecture of the distributed 3D printing intelligent factory, the cost minimization model is constructed. Secondly, mathematical software is used to solve the model, and the scheduling plan can be worked out. Finally, through the simulation analysis, it is concluded that the influencing factors such as demand, factory size and production capacity complicate the production distribution, and it can be observed that the carbon emission cost has gradually become the main factor affecting the total cost. The study has a reference value for the management decision making of the distributed 3D printing intelligent factory under the background of carbon emissions.
Optimizing Traffic Flow in Smart Cities: Soft GRU-Based Recurrent Neural Networks for Enhanced Congestion Prediction Using Deep Learning
Authors: Abdullah Sura Mahmood , Periyasamy Muthusamy , Kamaludeen Nafees Ahmed , Towfek S. K. , Marappan Raja , Kidambi Raju Sekar , Alharbi Amal H. , Khafaga Doaa Sami
Published: March 2023

Recently, different techniques have been applied to detect, predict, and reduce traffic congestion to improve the quality of transportation system services. Deep learning (DL) is becoming increasingly valuable for solving critiques. DL applications in transportation have been collected in several recently published surveys over the last few years. The existing research has discussed the cloud environment, which does not provide timely traffic forecasts, which is the cause of frequent traffic accidents. Thus, a solid understanding of the difficulties in predicting congestion is required because the transportation system varies widely between non-congested and congested states. This research develops a bi-directional recurrent neural network (BRNN) using Gated Recurrent Units (GRUs) to extract and classify traffic into congested and non-congested. This research uses a bidirectional recurrent neural network to simulate and forecast traffic congestion in smart cities (BRNN). Urban regions worldwide struggle with traffic congestion, and conventional traffic control techniques have failed miserably. This research suggests a data-driven approach employing BRNN for traffic management in smart cities, which uses real-time data from sensors and linked devices to control traffic more efficiently. The primary measures include predicting traffic metrics such as speed, weather, current, and accident probability. Congestion prediction performance has also been improved by extracting more features such as traffic, road, and weather conditions. The proposed model achieved better measures than the existing state-of-the-art methods. This research also explores an overview and analysis of several early initiatives that have shown promising results; moreover, it explores two potential future research approaches to increase the accuracy and efficiency of large-scale motion prediction.
A Sustainable Iterative Product Design Method Based on Considering User Needs from Online Reviews
Authors: Wang Qi , Wang Shuo , Fu Si
Published: March 2023

Small and medium-sized manufacturing industries can use online reviews to add valuable user requirements, enabling them to iteratively and precisely upgrade their products based on user needs. However, a sustainable, iterative approach to product design requires the integration of a large amount of information about user requirements for accurate selection. Currently, product iterations are primarily focused on developing new solutions or upgrading a few components with little screening to see if the product iterations meet user needs. This leads to a large number of wasted resources and a shortened product lifecycle. To address these challenges, this paper proposes a sustainable iterative research method that mines user needs and provides comprehensive decision making for product design based on online reviews, using probabilistic semantic term sets (PLTS). The proposed method considers the hesitation and uncertainty among evaluating experts regarding indicators, and uses the decision-making trial and evaluation laboratory (DEMATEL) method to analyze the correlations between demand indicators. The DEMATEL correlation function is improved by reconstructing the PLTS acquisition score function and deviance into a DEMATEL correlation function, in the form of exact values using an improved binary semantic approach. This iterative design approach provides accurate feedback on how users feel about the use of product components and ensures that most product components are sustainably recycled. A drone case study is presented to demonstrate the feasibility of this approach. In-depth interviews with experts confirm that this approach is more sustainable and provides a new research methodology for sustainable iterative product design.
Energy Sufficiency in the Passenger Transport of Lithuania
Authors: Bobinaite Viktorija , Konstantinaviciute Inga , Galinis Arvydas , Pazeraite Ausra , Miskinis Vaclovas , Cesnavicius Mindaugas
Published: March 2023

This paper aims to understand the significance of energy sufficiency (ES) in passenger transport for the long-term resolution of energy, climate, and sustainable development issues in Lithuania. It computes related indicators, by fixing the passenger-kilometres (pkm) travelled by various modes of transportation and applying a scenario analysis with the MESSAGE model. The findings indicated that the country’s final energy consumption (FEC) in transportation could be reduced by 21.8% by 2050 due to slowing growth rate of distances travelled by passenger car but increasing use of public transport and bicycles. This would result in a decrease in the growth rate of primary energy consumption (PEC) by half (to 0.3% a year), an increase in the use of renewable energy sources (RES) to 67.2% in the PEC structure, savings of oil products by 6.4 TWh, and savings of new electricity generation capacity by 550 MW. Furthermore, 20 MtCO2eq. in greenhouse gas (GHG) emission reductions could be realised between 2021 and 2050. To take advantage of the potential of ES, the policy measures of passenger car demand containment and a shift to non-motorised and less polluting modes of transportation should be implemented. Furthermore, priority should be given to policy measures that encourage use of public transportation.
Customer Experience in Sports Centres: Adaptation and Validation of a Measurement Scale
Authors: García-Pascual Fernando , Parra-Camacho David , Martínez Rico Gabriel
Published: March 2023

The service experience in fitness centres is important for understanding how users perceive and value the quality of the service. The service experience in fitness centres is constructed from the expectations and needs of the users and the capacity of the centre to satisfy them. This paper aims to adapt and validate the consumer experience quality (EXQ) scale (Klaus and Maklan, 2012) that analyses consumer experience in the context of fitness centres. This research was carried out in a sports centre in Spain with a sample of 413 users (52% male, 48% female) and an average age of 36.5 years. A CFA was carried out to check the fit of the model and then to check the reliability and validity of the scale, as well as the correlations with other factors. It can be seen that after different steps, the model shows good fitting as well as good reliability and validity values. The research also shows that this scale significantly predicts the satisfaction and future intentions of the service users. Therefore, managers of sports centres should consider the perceptions and positive experiences of their users in order to improve the viability of their service.
Coupled and Coordinated Analysis of Urban Green Development and Ecological Civilization Construction in the Yangtze River Delta Region
Authors: Hu Xinyu , Dong Chun , Wang Yihan
Published: March 2023

Managing the human–nature relationship is key to facilitating the sustainable development of cities. The coupled coordination relationship between ecological civilization construction and urban green development and influence of spatio-temporal heterogeneity has been insufficiently studied. We used the coupled coordination degree model (CCDM) and spatio-temporal weighted model (GTWR) to analyze the relationship and heterogeneity between ecological civilization construction and UGD and ECC in each city in the Yangtze River Delta region from 2010 to 2019. The results show that: (1) UGD and ECC coordination levels fluctuated more from 2010 until 2019. There was a transition from lagging UGD and ECC to lagging ecological civilization construction and a decreasing degree of coupling coordination in the Yangtze River Delta region from east to west from near imbalance to primary coordination. (2) The Yangtze River Delta’s negative UGD and ECC effect was concentrated in northwest inland cities; the positive UGD and ECC effect was concentrated in southeast coastal cities. Thus, UGD and ECC and ecological civilization construction complement each other. This study provides a scientific basis for analyzing the coordination between ecological civilization construction and UGD and ECC and provides practical guidance for formulating and implementing urban high-quality development countermeasures.
Introducing the Power of Plant Growth Promoting Microorganisms in Soilless Systems: A Promising Alternative for Sustainable Agriculture
Authors: Mourouzidou Snezhana , Ntinas Georgios K. , Tsaballa Aphrodite , Monokrousos Nikolaos
Published: March 2023

Soilless systems, such as hydroponics and aquaponics, are gaining popularity as a sustainable alternative to traditional soil-based agriculture, aiming at maximizing plant productivity while minimizing resource use. Nonetheless, the absence of a soil matrix poses challenges that require precise management of nutrients, effective control of salinity stress, and proactive strategies to master disease management. Plant growth-promoting microorganisms (PGPM) have emerged as a promising solution to overcome these issues. Research demonstrated that Bacillus, Pseudomonas, and Azospirillum are the most extensively studied genera for their effectiveness as growth promoters, inducing changes in root architecture morphology. Furthermore, PGPM inoculation, either alone or in synergy, can reverse the effects of nutrient deficiency and salt stress. The genera Pseudomonas and Trichoderma were recognized for their solid antagonistic traits, which make them highly effective biocontrol agents in hydroponic systems. The latest findings indicate their ability to significantly reduce disease severity index (DSI) through mycoparasitism, antibiosis, and induced systemic resistance. In aquaponic systems, the inoculation with Bacillus subtilis and Azospirillum brasilense demonstrated increased dissolved oxygen, improving water quality parameters and benefiting plant and fish growth and metabolism. This review also establishes the interaction variability between PGPM and growing media, implying the specificity for determining inoculation strategies to maximize the productivity of soilless cultivation systems. These findings suggest that using PGPM in soil-free settings could significantly contribute to sustainable crop production, addressing the challenges of nutrient management, disease control, and salinity issues.
A Smart Decision Support Framework for Sustainable and Resilient Supplier Selection and Order Allocation in the Pharmaceutical Industry
Authors: Kayani Saheeb Ahmed , Warsi Salman Sagheer , Liaqait Raja Awais
Published: March 2023

Supplier selection and order allocation (SS-OA) are two of the most important strategic decisions for supply chain network design and operation. If sustainability and resilience criteria are taken into consideration together and a holistic sustainable and resilient SS-OA is carried out, it may enable the supply chain network to perform better when subjected to disruption scenarios. In this research paper, a novel comprehensive multi-phase, multi-period sustainable, and resilient SS-OA decision support framework has been proposed. This framework integrates fuzzy multi-criteria decision-making (MCDM) techniques with a fuzzy multi-objective mixed integer nonlinear programming (MOMINLP) mathematical model to optimize triple bottom line (TBL) sustainability and resilience criteria simultaneously for a multi-modal, multi-echelon supply chain network. The proposed framework has been implemented using real-time data collected from the pharmaceutical industry. The results show that among the TBL sustainability criteria, product price, past business, innovative capability, and information disclosure rank as the most significant sub-criteria for the DMs in the pharmaceutical industry, while robustness and flexibility are considered the most valued attributes for potential suppliers as far as the resilience criterion is concerned. An assessment of the results has revealed that transfer cost and custom clearance cost together comprise more than two-thirds of the overall cost of the supply chain network. It has also been noted that inland transportation of goods is dominated by rail as the most preferred mode of transport. Transportation by rail is particularly preferred by suppliers located in geographically contiguous countries.
A Study on Project Prioritisation and Operations Performance Measurements by the Analysis of Local Financial Investment Projects in Korea
Authors: Shim Heecheol , Kim Jaehwan
Published: March 2023

The study aims to prioritise investment projects of local Korean governments through priority analysis and provide basic data to secure sustainability by measuring the operational performances of these projects. We utilised the AHP technique to draw relative weight, followed by their correction, thereby deriving priorities across all 14 fields; the most important ones being social welfare (0.110), healthcare (0.108), fire prevention (0.097), environmental hygiene (0.092), and cultural tourism (0.088). Important areas prioritise balance and equity and their operational efficiency and productivity were based on statistical annual reports of 17 cities and provinces in Korea and Kosis data obtained from the National Statistical Office. For the measurement, we used six years of time series data from 2015 to 2020 and single point-in-time and annual trend analyses. The input factors were the amount of revenue and the number of public officials, and the output factors included the water and sewage supply rate, the number of tourists travelling for free, and the number of hospitals, welfare facilities, and rescue cases. These selections were appropriate to draw conclusions by analysing operational performance with detailed items in the priority field. Results revealed that the operating performance was excellent. However, to secure additional operational efficiency and productivity, technological innovation is needed.
Technical Assistance Providers Identify Climate Change Adaptation Practices and Barriers to Adoption among California Agricultural Producers
Authors: Johnson Devon , Parker Lauren E. , Pathak Tapan B. , Crothers Laura , Ostoja Steven M.
Published: March 2023

Climate change will challenge California agriculture, requiring producers (i.e., farmers and ranchers) to adopt climate-adaptive management practices to sustain production. Agricultural technical assistance providers (TAPs) play a significant role in supporting producers’ efforts to adopt climate-smart management practices. It is therefore important to understand current TAP perceptions of climate change, TAP recommendations for climate adaptation, and the barriers to adopting climate-smart practices. To understand these issues, we held four focus group discussions with small groups of TAPs from across the state and evaluated transcripts from the discussions to identify common themes and concepts. The TAPs that participated in the focus groups understood climate change and its impacts on California agriculture, with climate extremes and water-related issues being the most frequently cited climate-related challenges. Focus group discussions and subsequent evaluation revealed that while TAPs recommend science-backed practices for adapting California agriculture to climate change, producers may not be accepting of some recommendations. Critically, the TAP focus groups cited insufficient monetary support—both for themselves and for producers—and insufficient information and messaging around climate-adaptive practices as key barriers to practice adoption. This improved understanding of the intersection of TAPs’ work on climate change and climate adaptation in California agriculture is useful for the development of information and resources that can bridge these identified barriers.
Changing Perception of Nurses during COVID-19: A Comparative Study on Leadership Behaviors, Meaning of Job and Meaningful Work
Authors: Gökkaya Öznur , Gökkaya Hilal , Cantürk Nuh Zafer , Özkan Arif
Published: March 2023

This study’s primary intent was to investigate the effect of extreme conditions, specifically the COVID-19 pandemic, by examining nurses’ perceptions of authentic leadership, meaningful work, and job meaning, and to compare this with the nurses’ perceptions from before the outbreak. In the study, 458 responses for both periods were analyzed and compared statistically by using the Mann–Whitney U test. The findings showed that nurses’ perception of line managers’ authenticity decreased during the outbreak. Therefore, in extreme conditions, leadership behaviors can be affected negatively by the context. During the outbreak, nurses attributed more meaning to their work. They felt more self-worth because of working for the greater good, and found greater meaning through the work during the COVID-19 outbreak compared to before the pandemic. The findings suggest that extreme conditions in a challenging environment can help nurses to find more meaning at work. For nurses, during the COVID-19 outbreak the purpose and meaning of their jobs remained the same as before the pandemic. Nursing requires different skills, talents, and opportunities for self-development, and it is challenging in nature.
Technologies to Optimize the Water Consumption in Agriculture: A Systematic Review
Authors: Preite Luca , Solari Federico , Vignali Giuseppe
Published: March 2023

Agriculture is facing several very important challenges. Population growth means that more food must be produced to meet nutritional needs. However, this is putting excessive pressure on natural resources, such as water and land, which are heavily used for agricultural practices. For these reasons, we carried out a systematic review of existing studies in the scientific literature to better understand how innovative strategies can decrease water consumption in agriculture. It was performed following PRISMA guidelines, using the Scopus database to select papers that have investigated the link between water consumption and the main agriculture strategies, such as controlled-environment agriculture, hydroponics, and precision farming for field crops, in the last ten years. Data relating to the water requirements of the selected crops were estimated to provide a framework for evaluating possible solutions. The results showed that these innovative strategies have the potential to decrease water consumption, but more research is needed to fully understand their effectiveness and potential trade-offs. Therefore, both exogenous and endogenous crop factors should be considered to maximize water savings. The results will form the basis for a framework for assessing the sustainability of agricultural strategies and how they can be applied in a real-life case study.
Habitat Modification Alters Food Web Interactions with Focus on Biological Control of Aphids in Apple Orchards
Authors: Alhmedi Ammar , Belien Tim , Bylemans Dany
Published: March 2023

To date, direct interactions between pests and natural enemies are often considered in biocontrol programs. Recently there has been an increase of evidence for the importance of third-party mediated indirect interactions in determining the population dynamics of insects. Predicting the strength of such interactions remains a central challenge in biocontrol assessments. Here, two field experiments were performed in two years to investigate to which extent Dysaphis plantaginea Passerini, Aphis pomi De Geer, and Myzus cerasi Fabricius might indirectly interact through shared natural enemies and ants. We first studied the population dynamics of target insects in isolated orchards of apples and cherries. Secondly, we investigated how the spatial coexistence of aphid-infested cherries can indirectly affect the population dynamics of apple aphids via natural enemies and ants. In the first experiment, nine parasitoid species were recorded on apple and cherry aphids, among them were three species in common. Six predatory families were found on cherry and apple aphids, while only one ant species, Lasius niger L., was found associating with these aphids. In the second experiment, temporal variation in the natural enemy-mediated apparent competition between M. cerasi and apple aphids was found. The cherry aphid is likely to be an important source of natural enemies that attack apple aphids early in the season. Significantly reduced numbers of ants associating with apple aphids in the intercropping habitat were found. Our results emphasize the importance of considering indirect interactions in the designing of pest management strategies.
The Integration of WoT and Edge Computing: Issues and Challenges
Authors: Anees Tayyaba , Habib Qaiser , Al-Shamayleh Ahmad Sami , Khalil Wajeeha , Obaidat Muath A. , Akhunzada Adnan
Published: March 2023

The Web of Things is an improvement on the Internet of Things (IoT) that incorporates smart objects into both the web architecture (application) and the internet (network). WoT applications are inescapable in residential homes and communities. The intent behind WoT applications is to increase sustainable development for reducing resource consumption. The Web of Things (WoT) aims to create a decentralized Internet of Things. Edge computing addresses IoT computing demands by reducing the escalation in resource congestion situations. In edge computing data is placed closed to the end users which diverts computation load from the centralized data centers. Furthermore, the dispersed structure balances network traffic and minimizes traffic peaks in IoT networks. Therefore, resulting in reducing transmission delays between edge servers and end users which improves response times for real-time WoT applications. Low battery supply to nodes with enough power resources can increase the lifespan of the individual nodes by moving processing and communication overhead from the nodes. This paper integrates WoT and edge computing and compares their functionalities. In addition, it demonstrates how edge computing enhances WoT performance and concentrates on transmission, storage, and computation aspects. Furthermore, for performance evaluation it categorizes edge computing based on different architectures. Moreover, the challenges of Web of Things and edge computing have been discussed in terms of bandwidth, latency, energy, and cost. Finally, advantages of the Web of Things and edge computing have been discussed.
Construction and Optimization of an Ecological Network in the Comprehensive Land Consolidation Project of a Small Rural Town in Southeast China
Authors: Su Mengyuan , Fang Xiaoqian , Sun Kaiying , Bao Jiahao , Cao Yu
Published: March 2023

In recent years, China has put forward comprehensive land consolidation projects to solve problems in rural areas, such as cultivated land fragmentation, scattered spatial pattern of construction land and ecological environment pollution, and boost the rural revitalization strategy. Constructing ecological networks is important for maintaining ecological security. This study built an ecological network using morphological spatial pattern analysis (MSPA), spatial principal component analysis (SPCA) method and minimum cumulative resistance model (MCR) models to analyze the spatial and temporal characteristics and ecological security pattern. Finally, it was optimized by analyzing ecological network indices and using two methods of adding additional ecological sources and stepping stones. The results show that ecological sources and ecological corridors for three phases are located in the central and northern parts with an uneven distribution. In fact, adding new ecological sources is more efficient in balancing the ecological pattern of a study area. The ecological network indices α, β, γ and C values increased by 15.3%, 8.4%, 8.5% and 3.3%, respectively. Constructing and optimizing an ecological network is expected to provide scientific basis for small-scale landscape design, provide theoretical reference for spatial pattern optimization of comprehensive land consolidation projects and coordination of regional development and ecological protection.
Enhancing Mesophilic Anaerobic Digestion of Waste-Activated Sludge through Heat Pretreatment and Kinetic Modeling
Authors: Wang Jing , Liu Bing , Chen Feiyong , Li Yifan , Xu Baojian , Zhang Ruina , Goel Rajeev , Terashima Mitsuharu , Yasui Hidenari
Published: March 2023

Sewage sludge is a useful raw material for the production of renewable energy due to its stable annual output. In this study, the enhancement of mesophilic anaerobic digestion of sewage sludge through heat pretreatment at 95 °C for 30 min was tested in an anaerobic moving bed biofilm reactor (hAMBBR). The sludge retention time was set at 20, 15, 10, and 5 days during 300 days of operation and compared to a traditional anaerobic continuous stirred tank reactor (AnCSTR) without pretreatment. Results of this research indicate that the digestion ratio of volatile soluble solids in the hAMBBR process could be improved by 50%, and the average conversion ratio of methane could be increased by 45%. When the sludge retention time (SRT) was shortened to 5 days, the methane production approached twice that of the contrast reactor. The expanded anaerobic digestion model, including activated sludge models, was utilized for operation simulation. The effect of sludge retention time (SRT) shortening on volatile suspended solids (VSS) digestibility and methane production was well reproduced with simulations. The research conclusion reveals the impact of pretreatment and reactor types on anaerobic digestion and provides the scientific basis for improving methane production and process efficiency in anaerobic digestion.
Investigation on Thermal and Electrical Performance of Late-Model Plate-and-Tube in Water-Based PVT-PCM Collectors
Authors: Li Manfeng , Yang Zongshuai , Lu Lanjing , Yin Kui , Lu Yiji
Published: March 2023

A large amount of redundant energy gained from incident solar energy is dissipated into the environment in the form of low-grade heat, which significantly reduces and limits the performance of photovoltaic cells, so removing or storing redundant heat and converting it back into available thermal energy is a promising way to improve the utilization of solar energy. A new combined water-based solar photovoltaic-thermophotovoltaic system embedded in the phase change material (PCM) mainly is proposed and designed. The effects of the water flow rate, cell operating temperature, the presence of PCM, and the thickness of the PCM factor on the overall module performance are explored comprehensively. The maximum thermal power output and the corresponding efficiency of the combined-system-embedded PCM are calculated numerically, The results obtained are compared with those of the PV (photovoltaic) and PVT(photovoltaic-thermal) cells with the same solar operating conditions. In addition, the PVT-PCM system possesses a higher power output and overall efficiency in comparison with the PVT and PV system, and the maximum cell temperature reduction of 12.54 °C and 42.28 °C is observed compared with PVT and PV systems. Moreover, an increased average power of 1.13 W and 4.59 in PVT-PCM systems is obtained compared with the PVT system and the PV system. Numerical calculation results illustrate that the maximum power output density and efficiency of the PVT-PCM are 3.06% and 16.15% greater than those of a single PVT system and PV system in the working time range, respectively. The obtained findings show the effectiveness of using PCM to improve power output and overall efficiency.
Multifunctional and Agroecological Agriculture as Pathways of Generational Renewal in Italian Rural Areas
Authors: Sivini Silvia , Vitale Annamaria
Published: March 2023

Rural regeneration is a transformative process that can be based on multiple possible pathways that are not always clearly distinct and that can be combined or co-exist. We argue that regeneration can be understood as a new framework for socio-natural relations. This paper aims at demonstrating that the combination of agroecology and strong multifunctional agriculture play a central role in re-producing the conditions of socio-ecological wellbeing. The study uses data collected through a qualitative methodology. Two case studies were executed in two Southern Italian regions: the first on farm successors in Sicily and the second on new entrants to farming in Apulia. In total, 41 in depth-interviews, two focus groups, and two restitution meetings were conducted. Findings suggest that agroecology and strong multifunctionality are innovative approaches to farming, allowing new farmers to deal with social, environmental, and economic production challenges, spreading benefits to the rural areas where they operate. The conclusion highlights that policies need to be more effective to support these processes.
Research on Topic Evolution Path Recognition Based on LDA2vec Symmetry Model
Authors: Zhang Tao , Cui Wenbo , Liu Xiaoli , Jiang Lei , Li Jinling
Published: March 2023

Topic extraction and evolution analysis became a research hotspot in the academic community due to its ability to reveal the development trend of a certain field and discover the evolution law of topic content in different development stages of the field. However, current research methods still face challenges, such as inaccurate topic recognition and unclear evolution paths, which can seriously compromise the comprehensiveness and accuracy of the analysis. To address the problem, the paper proposes a topic evolution path recognition method based on the LDA2vec symmetry model. Under given conditions, both the LDA and Word2vec used in the model conform to the structural symmetry of their datasets in high-dimensional space, and the fused LDA2vec method improves the accuracy of the analysis results. Firstly, we recognize the topics based on the LDA model, which uses Gibbs symmetric sampling and obeys the symmetric Dirichlet distribution to ensure data convergence. Secondly, Word2vec is used to learn the contextual information of the topic words in the document collection, and the words in the corpus are projected as vectors in the high-dimensional space so that the computed pairs of words with similar semantics have symmetry in the hyperplane of the high-dimensional space. Subsequently, the word vector is used as a weight, and the LDA topic word probability value is weighted to generate a new topic vector. Thirdly, the vector similarity index is employed to calculate the semantic similarity among topics at adjacent stages, and evolution paths that directly reflect the topic relationships are constructed. Finally, an empirical study is conducted in the field of data security to demonstrate the effectiveness of the proposed approach for topic evolution analysis. The results show that the proposed approach can accurately recognize the topic content and construct clear evolution paths, which contribute to the comprehensive and accurate analysis of topic evolution in a specific research field.
A Class of Rough Generalized Marcinkiewicz Integrals on Product Domains
Authors: Ali Mohammed , Al-Qassem Hussain
Published: March 2023

In this article, suitable estimates for a class of rough generalized Marcinkiewicz integrals on product spaces are established. By these estimates, together with employing Yano’s extrapolation technique, we obtain the boundedness of the aforementioned integral operators under weak conditions on singular kernels. A number of known previous results on Marcinkiewicz as well as generalized Marcinkiewicz operators over a symmetric space are essentially improved or extended.
Certain Properties and Applications of Convoluted Δh Multi-Variate Hermite and Appell Sequences
Authors: Wani Shahid Ahmad , Alazman Ibtehal , Alkahtani Badr Saad T.
Published: March 2023

This study follows the line of research that by employing the monomiality principle, new outcomes are produced. Thus, in line with prior facts, our aim is to introduce the Δh multi-variate Hermite Appell polynomials ΔhHAm[r](q1,q2,⋯,qr;h). Further, we obtain their recurrence sort of relations by using difference operators. Furthermore, symmetric identities satisfied by these polynomials are established. The operational rules are helpful in demonstrating the novel characteristics of the polynomial families and thus operational principle satisfied by these polynomials is derived and will prove beneficial for future observations. Further, a few members of the Δh Appell polynomial family are considered and their corresponding results are derived accordingly.
Deformation Characteristics Analysis of Temporary Support in Unsymmetrical Loading Tunnel Excavation under Composite Support
Authors: Wang Kezhong , Xiong Yu , Li Sheng , Zhou Xin , Li Zhikuan
Published: March 2023

Based on practical engineering, considering the characteristics of unsymmetrical loading, shallow burying, and weak surrounding rock of the tunnel, MIDAS finite element software is adopted to analyze the influence effect and deformation characteristics of a temporary steel support when the tunnel is excavated by a two-step center diaphragm method (CDM). The simulation results are compared with the field monitoring results. It can be seen that: (1) Affected by the unsymmetrical loading, the settlement of the right spandrel of the tunnel is obvious. The existence of a temporary steel support reduces the settlement of the surrounding rock at the spandrel greatly, making the distribution of principal stress at the spandrel more reasonable. (2) The deformation of the temporary steel support at the upper bench undergoes four stages: convergence, expansion, convergence, and stabilization; and the deformation at the lower bench undergoes five stages: convergence, expansion, convergence, expansion, and stabilization. (3) There is an obvious “bench-type” phenomenon in the principal stress change of the temporary steel support. The analysis results provide a scientific basis and technical guidance for the construction optimization of unsymmetrical loading tunnels using the same support technology.
A Novel Strong S-Box Design Using Quantum Crossover and Chaotic Boolean Functions for Symmetric Cryptosystems
Authors: Alsaif Haitham , Guesmi Ramzi , Kalghoum Anwar , Alshammari Badr M. , Guesmi Tawfik
Published: March 2023

In this paper, we propose a new method for drawing a cryptographically strong substitution box using the Lorenz system and quantum genetic algorithm techniques. We used the chaotic function to generate an initial random sequence of bits and the quantum crossover to provide a new and improved substitution box with increased non-linearity. The aim of the proposed algorithm was to generate a strong and secure substitution box that can be utilized in symmetric cryptosystems. The use of chaotic Boolean functions, genetic algorithm techniques, and the quantum crossover helped achieve this goal, and statistical tests further confirmed the randomness and efficiency of the generated substitution box. The results of the statistical test suite showed that the substitution box produced by the proposed algorithm is a “pass” in terms of randomness and has strong cryptographic properties. The tests include a frequency (monobit) test, a frequency test within a block, a linear complexity test, an approximate entropy test, and a cumulative sums test among others. The p-values obtained in the tests indicate that the randomness of the generated substitution box meets the requirements of a cryptographically secure substitution box.
A Dual-Population Genetic Algorithm with Q-Learning for Multi-Objective Distributed Hybrid Flow Shop Scheduling Problem
Authors: Zhang Jidong , Cai Jingcao
Published: March 2023

In real-world production processes, the same enterprise often has multiple factories or one factory has multiple production lines, and multiple objectives need to be considered in the production process. A dual-population genetic algorithm with Q-learning is proposed to minimize the maximum completion time and the number of tardy jobs for distributed hybrid flow shop scheduling problems, which have some symmetries in machines. Multiple crossover and mutation operators are proposed, and only one search strategy combination, including one crossover operator and one mutation operator, is selected in each iteration. A population assessment method is provided to evaluate the evolutionary state of the population at the initial state and after each iteration. Two populations adopt different search strategies, in which the best search strategy is selected for the first population and the search strategy of the second population is selected under the guidance of Q-learning. Experimental results show that the dual-population genetic algorithm with Q-learning is competitive for solving multi-objective distributed hybrid flow shop scheduling problems.