bluuebunny/crossref_metadata_2025 huggingface dataset:
Metrology Benchmarking of 3D Scanning Sensors Using a Ceramic GD&T-Based Artefact
Authors: Cuesta Eduardo , Meana Víctor , Álvarez Braulio J. , Giganto Sara , Martínez-Pellitero Susana
Published: November 2022

The use of non-contact scanning equipment in metrology and in dimensional and geometric inspection applications is increasing due to its ease of use, the speed and density of scans, and the current costs. In fact, these technologies are becoming increasingly dominant in the industrial environment, thus moving from reverse engineering applications to metrological applications. However, this planned transfer requires actions to ensure the achievable accuracy by providing traceability of measurements. In the present study, a comparison between the devices is carried out and a specific standard artefact is designed, equipped with multiple ceramic optically friendly entities, and allowing a wide variety of geometric dimensioning and tolerancing (GD&T). Four different 3D scanning sensors are used in the experimentation. Three of them are based on laser triangulation, and the fourth is a structured blue light sensor (fringe pattern projection). The standard artefact is calibrated with a high accuracy, using a coordinate measuring machine (CMM) and probing sensors. With this CMM, reference values of multiple predefined GD&T are obtained. The evaluation methodology maximises the accuracy of each device in measuring the dimensions of the artefact due to the good dimensional (milling and turning), surface (control of machining variables), and the dimensional and spatial distribution characteristics. The procedure also includes the same treatment of the captured point clouds (trimming, filtering, and best-fit algorithm, etc.) in each of the four 3D scanning sensors considered. From this process, very reliable measurements of the maximum achievable accuracy of each device (deviations from the CMM measurements) are finally obtained, and a multi-characteristic comparison between the four sensors is performed, also with high reliability.
Determination of Fumonisin B1 by Aptamer-Based Fluorescence Resonance Energy Transfer
Authors: Zhao Xinyue , Gao Jiale , Song Yuzhu , Zhang Jinyang , Han Qinqin
Published: November 2022

Fumonisin FB is produced by Fusarium moniliforme Sheld, of which FB1 is the most common and the most toxic. The establishment of a rapid detection method is an important means to prevent and control FB1 pollution. A highly sensitive fluorescent sensor based on an aptamer for the rapid detection of fumonisin B1 (FB1) in corn was established. In this study, 5-carboxyfluorescein (FAM) was labeled on the aptamer of FB1 (F10). F10 was adsorbed on the surface of graphene oxide (GO) by π-π stacking. The FAM fluorescence signal could be quenched by fluorescence resonance energy transfer between fluorescent molecules and graphene oxide (GO). In the presence of FB1, the binding efficiency of the aptamer to GO was reduced. Therefore, the content of FB1 in corn samples was determined by fluorescence measurements of mixed FAM-labeled F10, GO and corn samples. This method had a good linear relationship in an FB1 concentration range of 0–3000 ng/mL. The equation was y = 0.2576x + 10.98, R2 = 0.9936. The limit of detection was 14.42 ng/mL, and the limit of quantification was 43.70 ng/mL. The recovery of a spiked standard in the corn sample was 89.13–102.08%, and the time of detection was 30 min.
Past, Present and Future of Research on Wearable Technologies for Healthcare: A Bibliometric Analysis Using Scopus
Authors: de-la-Fuente-Robles Yolanda-María , Ricoy-Cano Adrián-Jesús , Albín-Rodríguez Antonio-Pedro , López-Ruiz José Luis , Espinilla-Estévez Macarena
Published: November 2022

Currently, wearable technology is present in different fields that aim to satisfy our needs in daily life, including the improvement of our health in general, the monitoring of patient health, ensuring the safety of people in the workplace or supporting athlete training. The objective of this bibliometric analysis is to examine and map the scientific advances in wearable technologies in healthcare, as well as to identify future challenges within this field and put forward some proposals to address them. In order to achieve this objective, a search of the most recent related literature was carried out in the Scopus database. Our results show that the research can be divided into two periods: before 2013, it focused on design and development of sensors and wearable systems from an engineering perspective and, since 2013, it has focused on the application of this technology to monitoring health and well-being in general, and in alignment with the Sustainable Development Goals wherever feasible. Our results reveal that the United States has been the country with the highest publication rates, with 208 articles (34.7%). The University of California, Los Angeles, is the institution with the most studies on this topic, 19 (3.1%). Sensors journal (Switzerland) is the platform with the most studies on the subject, 51 (8.5%), and has one of the highest citation rates, 1461. We put forward an analysis of keywords and, more specifically, a pennant chart to illustrate the trends in this field of research, prioritizing the area of data collection through wearable sensors, smart clothing and other forms of discrete collection of physiological data.
Machine-Learning Classification of Pulse Waveform Quality
Authors: Ouyoung Te , Weng Wan-Ling , Hu Ting-Yu , Lee Chia-Chien , Wu Li-Wei , Hsiu Hsin
Published: November 2022

Pulse measurements made using wearable devices can aid the monitoring of human physiological condition. Accurate estimation of waveforms is often difficult for nonexperts; motion artifacts may occur during tonometry measurements when the skin–sensor contact pressure is insufficient. An alternative approach is to extract only high-quality pulses for use in index calculations. The present study aimed to determine the effectiveness of using machine-learning analysis in discriminating between high-quality and low-quality pulse waveforms induced by applying different contact pressures. Radial blood pressure waveform (BPW) signals were measured noninvasively in healthy young subjects using a strain-gauge transducer. One-minute-long trains of pulse data were measured when applying the appropriate contact pressure (67.80 ± 1.55 mmHg) and a higher contact pressure (151.80 ± 3.19 mmHg). Eight machine-learning algorithms were employed to evaluate the following 40 harmonic pulse indices: amplitude proportions and their coefficients of variation and phase angles and their standard deviations. Significant differences were noted in BPW indices between applying appropriate and higher skin–surface contact pressures. The present appropriate contact pressure could not only provide a suitable holding force for the wearable device but also helped to maintain the physiological stability of the underlying tissues. Machine-learning analysis provides an effective method for distinguishing between the high-quality and low-quality pulses with excellent discrimination performance (leave-one-subject-out test: random-forest AUC = 0.96). This approach will aid the development of an automatic screening method for waveform quality and thereby improve the noninvasive acquisition reliability. Other possible interfering factors in practical applications can also be systematically studied using a similar procedure.
WS2 Nanorod as a Remarkable Acetone Sensor for Monitoring Work/Public Places
Authors: Mishra Rajneesh Kumar , Kumar Vipin , Trung Le Gia , Choi Gyu Jin , Ryu Jeong Won , Mane Sagar M. , Shin Jae Cheol , Kumar Pushpendra , Lee Seung Hee , Gwag Jin Seog
Published: November 2022

Here, we report the synthesis of the WS2 nanorods (NRs) using an eco-friendly and facile hydrothermal method for an acetone-sensing application. This study explores the acetone gas-sensing characteristics of the WS2 nanorod sensor for 5, 10, and 15 ppm concentrations at 25 °C, 50 °C, 75 °C, and 100 °C. The WS2 nanorod sensor shows the highest sensitivity of 94.5% at 100 °C for the 15 ppm acetone concentration. The WS2 nanorod sensor also reveals the outstanding selectivity of acetone compared to other gases, such as ammonia, ethanol, acetaldehyde, methanol, and xylene at 100 °C with a 15 ppm concentration. The estimated selectivity coefficient indicates that the selectivity of the WS2 nanorod acetone sensor is 7.1, 4.5, 3.7, 2.9, and 2.0 times higher than xylene, acetaldehyde, ammonia, methanol, and ethanol, respectively. In addition, the WS2 nanorod sensor also divulges remarkable stability of 98.5% during the 20 days of study. Therefore, it is concluded that the WS2 nanorod can be an excellent nanomaterial for developing acetone sensors for monitoring work/public places.
Effects of the COVID-19 Pandemic Lockdown on the Quality and Pollution of Irrigation Water in the Dams of Jordan
Authors: Abualhaija Mahmoud , Shammout Maisa’a
Published: November 2022

While the world continues to tackle the COVID-19 pandemic and its impacts on public health and the economy, among other issues (e.g., the environment), water, as a major component of the environment, has been significantly affected. This research aims to examine the quality and pollution of irrigation water in six selected vital dams in Jordan, in terms of the Irrigation Water Quality Index (IWQI) and Irrigation Water Pollution Index (IWPI), respectively, in view of determining any changes in the water quality and pollution load between the COVID-19 lockdown and the pre-COVID-19 period. The results of this study revealed that all of the studied dams showed an improvement in the quality of irrigation water and a reduction in pollution levels during the COVID-19 lockdown. This was due to a decrease in industrial, anthropogenic, urban, and agricultural activities, and strict restrictions on mobility and transportation. The improvement percentage in the irrigation water quality during the lockdown based on the IWQI model was in the following order: King Talal dam > Al-Kafrein dam > Al-Wehdeh dam > Kufranja dam > Wadi Al-Arab dam > Zeqlab dam, which is similar to the order of the reduction percentage in pollution based on the IWPI model. Therefore, the results of the IWPI model are consistent with those of the IWQI model. The classification of irrigation water based on the IWQI values indicated that the irrigation water quality of Al-Wehdeh and King Talal dams changed to better categories during the lockdown. All values of physicochemical and biological parameters in the dams’ water were within the Jordanian and international (FAO) standard limits for irrigation, except for the Na concentrations in some dams that were above the FAO standards.
Natural Radioactivity Measurements and Radiological Hazards Evaluation for Some Egyptian Granites and Ceramic Tiles
Authors: Sidique Essam , Hassan Sedky H. A. , Dawoud Mohammad Mahmoud
Published: November 2022

All over the world, people widely use granites and ceramic tiles in their residential establishments. Information concerning the radiological properties of such materials reveals how to ensure the sustainability of their safe use in terms of these properties. In the present work, the distribution of the terrestrial radioisotopes U-238 (Ra-226), Th-232, and K-40 for 23 different brands of Egyptian commercial granites and ceramic tiles samples (widely used domestically and exported) was determined using gamma radiation spectroscopy. This process pinpoints the possible radiological health risks related to gamma ray exposure and radon gas resulting from the use of these materials indoors. The concentration values of the aforementioned radioisotopes in the examined samples were compared to the corresponding global average values (GAVs) of the UNSCEAR and to those available in other countries. The overall average concentrations for U-238, Th-232, and K-40 in the total samples were observed to be 46.17 ± 2.81 (less than its GAV), 51.65 ± 2.35 (slightly above its GAV), and 701.62 ± 40.60 Bq/kg (1.4 times greater than the GAV), respectively. The related radiological parameters and indices were calculated and compared to the prescribed limits set by commissions and organizations concerned with radiation protection (the WHO, ICRP, UNSCEAR, and EC) to ensure the safe use of the investigated granites and ceramic tiles. The assessed indices and parameters fall within the recommended values and safety limits. In conclusion, there is no risk from using the granites and ceramic tiles under investigation in residential facilities.
Experimental Investigation of Consolidated Undrained Shear Behavior on Peaty Soil in Dian-Chi, China
Authors: Li Xuemei , Yang Min , Li Weichao , Zhang Haowen
Published: November 2022

Peat deposits are extensively distributed worldwide, and the various common engineering-failure problems involving peat have attracted greater attention from researchers in regard to understanding the mechanical behavior of peat. This study investigated extensive index and consolidated undrained triaxial compression tests applied to the peaty soils of Dian-Chi Lake, located in Yunnan, China. A series of consolidated undrained triaxial compression tests on peat specimens were carried out in this study, and the influences of confining pressure, organic matter content and strength parameters were investigated. The test results indicate that, to obtain a B-value greater than 0.98, the average back pressure of the Dian-Chi Lake peaty soil should be at least 150 kPa, with organic matter contents N ranging from 30% to 85%. Compared with the existing oedometer consolidation results of the research team, the values of compressibility and permeability behavior under the oedometer and isotropic consolidation conditions are not significantly different. In addition, the average failure strain, εf, ranged from 9% to 15% for the investigated normally consolidated specimens, and the average effective angle of internal friction was 43.8°, which is greater than the maximum internal friction angle of 35° for typical soft clay and silt soils. A correlation was proposed by curve fitting the experimental data to better capture the nonlinear relationship between φ′ and N. Furthermore, the values of the normalized undrained strength, Su/σc′, varied from 0.56 to 0.84, which is larger than that of the inorganic soft clay and silt deposits.
The Role of Green Infrastructure in Pluvial Flood Management and the Legislation Surrounding It: A Case Study in Bristol, UK
Authors: Saunders Dudley , Martin John
Published: November 2022

Surface water flooding is an issue which has required an increased level of addressment over the past two decades, with the methods used to combat flood events seeing an evolution. This evolution has been influenced heavily by multi-scale legislations and policies, which has pushed for more holistic methods for pluvial flood management. This review will analyse how Bristol City Council have implemented these multi-scale pieces and what has been put in place to encourage sustainable flood management. This will be done through a purposive review of the literature and an extensive review of legislation and policies on a national, European, and regional scale. The findings of the review were able to show that international legislation and policies are not in place to support sustainable management. UK policies, however, were more supportive, with direct reference and guidance for how to move away from hard engineering solutions. The City of Bristol has embraced the concept of sustainable flood management, with the highest level of support witnessed through the multi-scale review. Overall, the City of Bristol has achieved a good understanding of how to use sustainable drainage, with many systems throughout the city, and schemes to support the use. However, further legislative pieces need to be passed on a national and European scale to encourage and promote the deployment of these systems, so the benefits can be acquired on a large scale.
An Integrated Approach to Map the Impact of Climate Change on the Distributions of Crataegus azarolus and Crataegus monogyna in Kurdistan Region, Iraq
Authors: Radha Kalthum O. , Khwarahm Nabaz R.
Published: November 2022

The hawthorns Crataegus azarolus L. and Crataegus monogyna Jacq are two ecologically and medicinally important endemic tree species that occur only in forests of oak in the mountain ranges of the Kurdistan region of Iraq (KRI). These species have been degrading across the mountain ranges at an alarming pace due to climate-related factors (e.g., wildfire events and drought) and anthropogenic drivers. Nevertheless, there is a gap in understanding their distributions today and in the future under a changing climate in Iraq. To address the species’ knowledge gap and thus establish a baseline for a future management and conservation strategy, this study used field observation records, species distribution modeling integrated with GIS techniques, and relevant environmental predictors to (i) estimate the species’ potential distributions and map their current known distributions across unsurveyed areas; (ii) model the species’ possible response under several scenarios for a weather change in the future; (iii) map the species’ overlap ranges and the direction of the distributions. Results suggest that under two global climatic models (GCMs), BCC-CSM2-MR and CNRM-CM6-1, the overall habitat expansion magnitude for the two species would be less than the overall habitat reduction magnitude. For C. azarolus, the habitat range would contract by 3714.64 km2 (7.20%) and 3550.47 km2 (6.89%), whereas it would expand by 2415.90 km2 (4.68%) and 1627.76 km2 (3.16%) for the GCMs, respectively. Modeling also demonstrated a similar pattern for C. monogyna. The species overlap by 7626.53 km2 (14.80%) and 7351.45 km2 (14.27%) for the two GCMs. The two species’ habitat ranges would contract significantly due to the changing climate. The direction of the species’ potential distribution would be mostly toward the KRI’s east and southeast mountain forests. Our results, for the first time, provide new data on the species’ present and future distributions and outline the advantages of distribution modeling combined with geospatial techniques in areas where species data are limited, such as Iraq.
Dynamic Key Extraction Technique Using Pulse Signal and Lightweight Cryptographic Authentication Scheme for WBAN
Authors: Ahmad Shafiq , Rehman Zia ur , Altaf Saud , Zaindin Mazen , Huda Shamsul , Haroon Muhammad , Iqbal Sofia
Published: November 2022

As a key component of ubiquitous computing, the wireless body area network (WBAN) can be used in a variety of disciplines, including health monitoring. Our everyday routines have been transformed by wearable technology, which has changed the medical industry and made our lives more convenient. However, the openness of the wireless network has raised concerns about the privacy and security of patient’s data because of the latent threat imposed by attackers. Patients’ sensitive data are safeguarded with authentication schemes against a variety of cyberattacks. Using pulse signals and a lightweight cryptographic approach, we propose a hybrid, anonymous, authentication scheme by extracting the binarized stream (bio-key) from pulse signal. We acquired 20 different sample signals to verify the unpredictability and randomness of keys, which were further utilized in an authentication algorithm. Formal proof of mutual authentication and key agreement was provided by the widely known BAN logic, and informal verification was provided by the Automated Validation of Internet Security Protocol and Applications (AVISPA) tool. The performance results depicted that storage cost on the sensor side was only 640 b, whereas communication cost was 512 b. Similarly, the computation time and energy consumption requirements were 0.005 ms and 0.55 µJ, respectively. Hence, it could be asserted that the proposed authentication scheme provided sustainable communication cost along with efficient computation, energy, and storage overheads as compared to peer work.
The Dynamic Impact of Renewable Energy and Economic Growth on CO2 Emissions in China: Do Remittances and Technological Innovations Matter?
Authors: Saliba Chafic Bassam , Hassanein Fida Ragheb , Athari Seyed Alireza , Dördüncü Hazar , Agyekum Ephraim Bonah , Adadi Parise
Published: November 2022

Several investigations show that remittances, renewable energy, and innovation promote the socioeconomic advancement of a nation. Nevertheless, the impacts of remittances and renewable energy on ecological quality are yet to be evaluated thoroughly. Therefore, the current investigation assesses the effects of remittances and renewable energy on CO2 emissions while taking into account the roles of technological innovation, globalization, and economic growth. Toward this end, this paper depends on yearly data between 1990 and 2019. The study employed bounds testing and its results disclosed long-term connections between CO2 and the regressors. Moreover, unlike prior studies that employ time-domain causality, we employed frequency domain causality, which considers causality at different frequencies. Furthermore, the ARDL long- and short-run results showed that economic growth amplified CO2 emissions, while green energy, remittances, and globalization lessened CO2 emissions. Lastly, the frequency domain causality approach revealed that globalization, renewable energy, economic growth, technological innovation, and remittances could predict CO2 emissions in the long-term. These findings’ sturdiness was established utilizing DOLS and FMOLS regression. Several policy recommendations are suggested in light of these ground-breaking discoveries.
Crisis Management in Restaurants: The Case of Polish Restaurants during the COVID-19 Pandemic
Authors: Nowacki Marek , Nurkowska Marianna
Published: November 2022

The article aims to identify effective actions taken by the catering industry as part of crisis management during the COVID-19 pandemic. The time scope of the research concerns the first wave of the COVID-19 pandemic in the period from 13 March to 18 May 2020. The research method used in the study was a questionnaire survey (CAWI). The survey results showed that the most frequent action taken by restaurants was applying for government assistance. On the other hand, most marketing activities were related to the assessment of the situation and the prospects for restaurants. Relationships were also found between restaurant management activities and restaurant characteristics (number of employees, number of years of operation and location). The developed research tool can help in assessing effective actions taken by restaurant managers during a crisis.
Why Return? Birdwatching Tourists’ Revisit Intentions Based on Structural Equation Modelling
Authors: Ren Jie , Su Kaiwen , Zhou Yihui , Hou Yilei , Wen Yali
Published: November 2022

Birdwatching tourism is a model of ecotourism that is beneficial to the sustainable development of developing regions and is growing rapidly in China. In order to explore the development path of birdwatching tourism, this study constructs and tests a model of revisit intention from the birdwatching tourists’ perspective and aims to understand the factors influencing tourists’ revisit intention for birdwatching destinations. The researchers collected 328 valid questionnaires from birdwatching tourists in Mingxi County, Fujian Province, China, and used structural equation modelling to validate the relationships among the study constructs. Cognitive image had the largest influence on revisit intent (0.219), followed by tourist satisfaction (0.172), and perceived image (0.155). Tourist motivation indirectly affects revisit intention through the full mediating effect of the cognitive image. This study provides some insights and references for policymakers and tourism project managers to design or adjust the construction plan of birdwatching-tourism sites.
Indoor Occupancy Detection Based on Environmental Data Using CNN-XGboost Model: Experimental Validation in a Residential Building
Authors: Mohammadabadi Abolfazl , Rahnama Samira , Afshari Alireza
Published: November 2022

Indoor occupancy prediction can play a vital role in the energy-efficient operation of building engineering systems and maintaining satisfactory indoor climate conditions at the lowest possible energy use by operating these systems on the basis of occupancy data. Many methods have been proposed to predict occupancy in residential buildings according to different data types, e.g., digital cameras, motion sensors, and indoor climate sensors. Among these proposed methods, those with indoor climate data as input have received significant interest due to their less intrusive and cost-effective approach. This paper proposes a deep learning method called CNN-XGBoost to predict occupancy using indoor climate data and compares the performance of the proposed method with a range of supervised and unsupervised machine learning algorithms plus artificial neural network algorithms. The comparison is performed using mean absolute error, confusion matrix, and F1 score. Indoor climate data used in this work are CO2, relative humidity, and temperature measured by sensors for 13 days in December 2021. We used inexpensive sensors in different rooms of a residential building with a balanced mechanical ventilation system located in northwest Copenhagen, Denmark. The proposed algorithm consists of two parts: a convolutional neural network that learns the features of the input data and a scalable end-to-end tree-boosting classifier. The result indicates that CNN-XGBoost outperforms other algorithms in predicting occupancy levels in all rooms of the test building. In this experiment, we achieved the highest accuracy in occupancy detection using inexpensive indoor climate sensors in a mechanically ventilated residential building with minimum privacy invasion.
Testing for Local Spatial Association Based on Geographically Weighted Interpolation of Geostatistical Data with Application to PM2.5 Concentration Analysis
Authors: Wang Fen-Jiao , Mei Chang-Lin , Zhang Zhi , Xu Qiu-Xia
Published: November 2022

Using local spatial statistics to explore local spatial association of geo-referenced data has attracted much attention. As is known, a local statistic is formulated at a particular sampling unit based on a prespecific proximity relationship and the observations in the neighborhood of this sampling unit. However, geostatistical data such as meteorological data and air pollution data are generally collected from meteorological or monitoring stations which are usually sparsely located or highly clustered over space. For such data, a local spatial statistic formulated at an isolate sampling point may be ineffective because of its distant neighbors, or the statistic is undefinable in the sub-regions where no observations are available, which limits the comprehensive exploration of local spatial association over the whole studied region. In order to overcome the predicament, a local-linear geographically weighted interpolation method is proposed in this paper to obtain the predictors of the underlying spatial process on a lattice spatial tessellation, on which a local spatial statistic can be well formulated at each interpolation point. Furthermore, the bootstrap test is suggested to identify the locations where local spatial association is significant using the interpolated-value-based local spatial statistics. Simulation with comparison to some existing interpolation and test methods is conducted to assess the performance of the proposed interpolation and the suggested test methods and a case study based on PM2.5 concentration data in Guangdong province, China, is used to demonstrate their applicability. The results show that the proposed interpolation method performs accurately in retrieving an underlying spatial process and the bootstrap test with the interpolated-value-based local statistics is powerful in identifying local patterns of spatial association.
The Influence of Paradoxical Leadership on Adaptive Performance of New-Generation Employees in the Post-Pandemic Era: The Role of Harmonious Work Passion and Core Self-Evaluation
Authors: Li Naiwen , Ding Mingming
Published: November 2022

The post-pandemic era is full of instability and uncertainty, which brings new challenges and opportunities to the development of organization. As a sustainable feature of enterprises, improving employees’ adaptive performance levels is a necessary condition for enterprises to achieve the sustainable development goal. This study is based on self-determination theory, which focuses on new-generation employees as the key force of enterprise and incorporates harmonious work passion and core self-evaluation into the research framework to explore the influence of paradoxical leadership on adaptive performance. The survey data obtained from 519 new-generation employees shows that: paradoxical leadership is significantly and positively correlated with adaptive performance of new-generation employees; the relationship between paradoxical leadership and adaptive performance is partially mediated by harmonious work passion; core self-evaluation positively adjusts the relationship between paradoxical leadership and harmonious work passion. In addition, core self-evaluation also regulates the intermediary role of harmonious work passion—that is to say, the higher core self-evaluation of new-generation employees is, the stronger the intermediary role of harmonious work passion. The research results explain the connotation of how paradoxical leadership improves adaptive performance of new-generation employees, reveal the medium of the relationship between the two, and find both the role boundary of paradoxical leadership and the strategy to improve adaptive performance.
Development of Sustainable Coastal Benchmarks for Local Wisdom in Pangandaran Village Communities
Authors: Rizal Achmad , Riyadi Agung , Haryanti  , Aliah Ratu Siti , Prayogo Teguh , Prayitno Joko , Purwanta Wahyu , Susanto Joko Prayitno , Sofiah Nida , Djayadihardja Yusuf Surachman , Ikhwanuddin Moch , Wahyono Sri , Yudo Satmoko , Sachoemar Suhendar I.
Published: November 2022

Local wisdom is frequently used by communities in managing their coastal resources without a precise measure of sustainability. As a result, the government must develop a standard for determining the wisdom of these practices. This study aimed to create such a standard, followed by a trial to evaluate management practices in Pangandaran coastal tourism. This qualitative case study included a literature review, direct observations, and in-depth interviews with fish farmers and fishers. They are standardizing instrument criteria for sustainable fishery resource management-defined wisdom. Such wisdom is divided into fundamental thinking (factual knowledge) and management practices (procedural knowledge). Each consists of five criteria: ecosystem and resource management, planning, governance, technology, and social and economic development. Each criterion has a specific rating indicator and parameter. The results show differences in the level of wisdom between the fish farmer and the fishers. Regarding basic thinking, fishers’ wisdom level is weak in three out of five criteria. Fishers reach a moderate wisdom level concerning fishing gear and technical criteria and a strong level on social and economic criteria. In contrast, the fish farmer is moderate to strong for four criteria and weak for the resources and ecosystems criterion. Regarding management practices, in general, fish farmers and fishers have the same level of wisdom. Both are weak in the ecosystem and resources, planning, and institutional criteria, while the fishing gear criteria reach moderate levels and the socio-economic criteria reach high levels.
Teacher-Assistant Knowledge Distillation Based Indoor Positioning System
Authors: Mazlan Aqilah Binti , Ng Yin Hoe , Tan Chee Keong
Published: November 2022

Indoor positioning systems have been of great importance, especially for applications that require the precise location of objects and users. Convolutional neural network-based indoor positioning systems (IPS) have garnered much interest in recent years due to their ability to achieve high positioning accuracy and low positioning error, regardless of signal fluctuation. Nevertheless, a powerful CNN framework comes with a high computational cost. Hence, there will be difficulty in deploying such a system on a computationally restricted device. Knowledge distillation has been an excellent solution which allows smaller networks to imitate the performance of larger networks. However, problems such as degradation in the student’s positioning performance, occur when a far more complex CNN is used to train a small CNN, because the small CNN does not have the ability to fully capture the knowledge that has been passed down. In this paper, we implemented the teacher-assistant framework to allow a simple CNN indoor positioning system to closely imitate a superior indoor positioning scheme. The framework involves transferring knowledge from a large pre-trained network to a small network by passing through an intermediate network. Based on our observation, the positioning error of a small network can be reduced to up to 38.79% by implementing the teacher-assistant knowledge distillation framework, while a typical knowledge distillation framework can only reduce the error to 30.18%.
Salinity Stress and the Influence of Bioinoculants on the Morphological and Biochemical Characteristics of Faba Bean (Vicia faba L.)
Authors: Kumar Anand , Yadav Alpa , Dhanda Parmdeep , Delta Anil , Sharma Meenakshi , Kaushik Prashant
Published: November 2022

Faba bean (Vicia faba L.) is an economically important crop cultivated globally for fulfilling human requirements. However, the productivity of the faba bean has declined due to poor management of soil, particularly under salt stress. Salt stress is a major constraint to crop productivity worldwide. Therefore, the objective of the present investigation is to check the behavior of faba bean genotypes on the basis of morphological and biochemical traits in response to salinity. In this study, we studied seven different treatments (including control) applied to faba bean under salt stress. Bioinoculants such as Trichoderma viride, Pseudomonas flourescens, Glomus mosseae, and Gigaspora gigantean, each separately and in combination, were tested for their efficacy under salinity stress. Data recorded on days to flowering (48.92 ± 1.15), days to maturity (144.56 ± 1.95), plant height (141.93 ± 4.81 cm), number of branches per plant (4.87 ± 0.09), number of clusters per plant (18.88 ± 0.24), number of pods per plant (48.33 ± 1.06), pod length (5.31 ± 0.02 cm), catalase (222.10 ± 2.76 mg), hydrogen peroxide (24 ± 4.58 mol/g), malondialdehyde (45 ± 1.00 mol/g), electrolyte leakage (54.67 ± 5.03), chlorophyll (51.67 ± 3.06 mg/g), proline content (2.96 ± 0.12 mg/g), and on other parameters indicated the combined inoculation of all the species (consortium) was taken to be highly effective even under salt stress. Overall, the consortium treatment comprising all of the bioinoculants was observed to be the most efficient treatment in improving all the morphological and biochemical traits of faba bean under salt stress. Although, other treatments also demonstrated considerable effects on faba bean as compared to one without bioinoculants under salt stress.
A Multistage Time-Delay Control Model for COVID-19 Transmission
Authors: Wu Zhuang , Wang Yuanyuan , Gao Jing , Song Jiayang , Zhang Yi
Published: November 2022

With the transmission of the COVID-19 epidemic at home and abroad, this paper considers the spread process in China, improves the classic epidemic SEIR model, and establishes a multistage time-delay control model (MTCM) for COVID-19 transmission. The MTCM divides the spread of COVID-19 into three periods: the outbreak period, the control period and the steady period. The classical SEIR model, the improved SEQIR model and the SEQIR Ⅱ model correspond to the three periods. The classical SEIR model was adopted for the outbreak period and yielded results that were consistent with the observed early propagation of COVID-19 transmission. In the control period, adding isolation measures and a time delay to the MTCM and adjusting the rates yielded a better simulation effect. In the steady period, the focus of consideration is the number of new patients, population movement (in-migration and out-migration of the population) and patient classification (symptomatic and asymptomatic patients). The MCTM was used for simulation, and the comparison results revealed that the simulated data of the MCTM (improved SEQIR model) and the actual data are similar in the control period. The control policy of isolation measures is effective. New infections, population flow and patients with symptomatic or asymptomatic symptoms are more consistent with the steady period characteristics. The multi-stage time-delay control model for COVID-19 transmission provides theoretical methods and good prevention and control measures for future epidemic policy formulation.
Gold and Bitcoin Optimal Portfolio Research and Analysis Based on Machine-Learning Methods
Authors: Li Jingjing , Rao Xinge , Li Xianyi , Guan Sihai
Published: November 2022

In recent years, the bitcoin market has developed rapidly and has been recognized as a new type of gold by many investors. It may replace gold as a hedge against inflation and become a new investment asset for financial management. The investment relationship with gold has increasingly important research value and practical significance. This paper modeled daily price flow data from 11 September 2016 to 10 September 2021 to help market traders determine whether they need to buy, hold, or sell assets in their portfolios daily. The model predicts price fluctuations through linear regression prediction of machine learning, K-Nearest Neighbor (KNN) algorithm. In the linear regression prediction, the goodness of fit of gold is 89.44%, and the goodness of fit of Bitcoin is 98.43%. In the test set prediction of KNN algorithm, the goodness of fit of gold is 97.25%, and the goodness of fit of Bitcoin is 95.06%. Based on this, the optimal investment strategy and the initial investment value are obtained. Empirical analysis shows that bitcoin price volatility and gold price volatility have a strong substitution effect; gold and currency used will be a suitable combination of hedging, which will bring momentum for the development of the market economy and become an important force in the sustainable development of a high-quality-driven economy.
Effects of Predictors on Power Consumption Estimation for IT Rack in a Data Center: An Experimental Analysis
Authors: Takcı Mehmet Türker , Gözel Tuba
Published: November 2022

The appropriate feature/predictor selection is as significant as building efficient estimation methods for the accurate estimation of power consumption, which is required for self-awareness and autonomous decision systems. Traditional methodologies define predictors by assessing whether there is a relationship between the predictors and the response variable. Contrarily, this study determines predictors based on their individual and group impacts on the estimation accuracy directly. To analyze the impact of predictors on the power-consumption estimation of an IT rack in a data center, estimations were carried out employing each prospective predictor separately using the measured data under the real-world workload. Then, the ratio of CPU usage was set as the default predictor, and the remaining variables were assigned as the second predictor one by one. By utilizing the same approach, the best combination of predictors was determined. As a result, it was discovered that some variables with a low correlation coefficient with power consumption improved the estimation accuracy, whereas some variables with high correlation coefficients worsened the estimation result. The CPU is the most power-consuming component in the server and one of the most used predictors in the literature. However, the estimation accuracy obtained using only the CPU is 10 times worse than the estimation result conducted by utilizing the predictor set determined at the end of the experiments. This study shows that instead of choosing predictors only from one point of view or one method, it is more convenient to select predictors by assessing their influence on estimation results. Examining the trend and characteristics of the estimated variable should also be considered.
A Green Approach—Cost Optimization for a Manufacturing Supply Chain with MFIFO Warehouse Dispatching Policy and Inspection Policy
Authors: Shekhawat Santosh , Alessa Nazek , Rathore Himanshu , Sharma Kalpna
Published: November 2022

The present paper considers a manufacturing supply chain of deteriorating type inventories. The problem addresses the extra rented warehouse (RW) to store extra inventories if the manufacturer is producing more inventories than their owned warehouse (OW) capacity. Now, the problem is which inventories should be used first with minimum cost and minimum deterioration. To solve this problem, we have assumed a MFIFO (mixed first in first out) dispatching policy and constant demand rate over a finite time horizon. Along with these we have also assumed an inspection policy during the supply chain to separate deteriorated items and a carbon tax policy is also considered to control carbon emissions. The rate of deterioration depends on the number of inspections. If the number of inspections increases, it minimizes the rate of the decaying process. Due to the adoption of the inspection policy, the supply chain moves toward a green supply chain as it removes deteriorated inventories that minimize further decay by contact, and simultaneously separated deteriorated products can be utilized for other purposes that solve the problem of the disposal of deteriorating inventories and reduce emission generation. We have also established the uniqueness of the established model. The motto of solving the mathematical model is to find the values of the optimum value of N, the number of cycles, and n, the number of inspections that helps to minimize total cost. At last, we illustrate the result with the help of a numerical example.
An Information-Theoretic Approach to Modeling the Major Drivers of Pro-Environmental Behavior
Authors: Walton Tobin N. , Jones Robert Emmet
Published: November 2022

Researchers attempting to identify and understand the so-called drivers of public concern for the environment and engagement in pro-environmental behaviors have conceptualized and modeled numerous theoretical constructs. This has made it hard to establish generalities across studies regarding the causal ordering of relationships between these constructs. This study reviews some of the major constructs employed within this area of research, and uses an information-theoretic approach to assess six models that position these constructs within different causal orderings to predict general pro-environmental behavior (G-PEB). The results indicate that environmental values and general environmental beliefs are distal drivers of engagement in G-PEB, and form the foundation—or primary motivational base—for environmental identity, concerns, attitudes, and norms that—directly and more proximally—influence this broad set of behaviors. This suggests that effective policies and interventions need to target both the proximal and distal influences on behavior, as well as the contexts and structures that support shared values and primary beliefs within a society or culture. Recommendations for ways that researchers could better understand individuals and their behaviors impacting the environment more deeply, both systematically and socio-ecologically, are suggested.
The Role of Architectural Skin Emissivity Influencing Outdoor Microclimatic Comfort: A Case Study in Bologna, Italy
Authors: Fabbri Kristian , Gaspari Jacopo , Costa Alessia , Principi Sofia
Published: November 2022

This article examines the influence of the emissivity of façade materials on outdoor microclimatic comfort. The developed methodology is based on the collection of input data regarding the site, the geometrical and technological characterization of the building envelope and the definition of the associated emissivity, the development of alternative emissivity-driven scenarios, the scenario simulation to obtain Outdoor Microclimate Maps (OMMs), and their interpretation and discussion. The operative steps of the proposed simplified method are applied to a specific case study in the city of Bologna made of a mix of buildings, including some towers overlooking an inner courtyard. The emissivity of the façade materials is assumed as the main variable. The results show how, by properly addressing the design choices, it is possible to achieve significant improvements in the outdoor microclimate for the space in-between the considered volumes.
Effect of Dimpled Rib with Arc Pattern on Hydrothermal Characteristics of Al2O3-H2O Nanofluid Flow in a Square Duct
Authors: Kumar Anil , Maithani Rajesh , Sharma Sachin , Kumar Sunil , Sharifpur Mohsen , Alam Tabish , Gupta Naveen Kumar , Eldin Sayed M.
Published: November 2022

The present work is concerned with the experimental analysis of the thermal and hydraulic performance of Al2O3−H2O nanofluid flow in dimpled rib with arc pattern in a square duct. The Alumina nanofluid consists of nanoparticles having a size of 30 nm. Reynolds number Renum studied in the square duct range from 5000 to 26,000. The nanoparticle volume fraction (ϕnp) ranges from 1.5% to 4.5%, the ratio of dimpled-arc-rib-height to print-diameter HAD/Pd ranges from 0.533 to 1.133, the ratio of the dimpled-rib-pitch to rib height PAD/HAD range from 3.71 to 6.71 and dimpled arc angle (αAD) range from 35° to 65°. The Al2O3−H2O-based nanofluid flow values of Nusselt number Nurs and friction factor frs are higher in comparison to pure water. The dimpled ribs in the arc pattern significantly improved the thermal-hydraulic performance of the investigated test section. The nanoparticle concentration of 4.5%, the ratio of dimpled arc rib height to print diameter of 0.933, the relative dimpled arc rib height of 4.64 and the dimpled arc angle of 55° deliver the maximum magnitude of the heat transfer rate. The maximum value of the thermal-hydraulic performance parameters was found to be 1.23 for Al2O3−H2O-based nanofluid flow in a dimpled rib with arc pattern square duct for the range of parameters investigated. Correlations of Nurs, frs and ηrs have been developed for the selected range of operating and geometric parameters.
Research on the Fault Diagnosis Method of a Synchronous Condenser Based on the Multi-Scale Zooming Learning Framework
Authors: Qian Baiyun , Huang Jinjun , Zhu Xiaoxun , Wang Ruijun , Lin Xiang , Gao Ning , Li Wei , Dong Lijiang , Liu Wei
Published: November 2022

Under the background of the “strong direct current and weak alternating current” large power grid, the synchronous condenser with dynamic reactive power support capability becomes more important. Due to factors such as manufacturing, installation, and changes in operating conditions, there are many faults associated with the synchronous condenser. This paper studies a fault diagnosis method based on multi-scale zooming learning framework. First, through the energy fully connected (energy FC) layer, the synchronous condenser feature components of the fault signal of the camera are learned, and the transient features of the signal are enhanced. At the same time, the data is adaptively compressed and the effective features are mapped in a distributed manner. The faults are effectively diagnosed and isolated in advance. Secondly, a multi-scale learning framework is constructed to learn the multi-frequency features in the vibration signal. Finally, experiments show that the proposed method has certain advantages over the existing excellent models. The accuracy rate of diagnosis is higher than 99%.