bluuebunny/crossref_metadata_2025 huggingface dataset:
Demonstration of a Modular Prototype End-to-End Simulator for Aquatic Remote Sensing Applications
Authors: Matthews Mark W. , Dekker Arnold , Price Ian , Drayson Nathan , Pease Joshua , Antoine David , Anstee Janet , Sharp Robert , Woodgate William , Phinn Stuart , Gensemer Stephen
Published: September 2023

This study introduces a prototype end-to-end Simulator software tool for simulating two-dimensional satellite multispectral imagery for a variety of satellite instrument models in aquatic environments. Using case studies, the impact of variable sensor configurations on the performance of value-added products for challenging applications, such as coral reefs and cyanobacterial algal blooms, is assessed. This demonstrates how decisions regarding satellite sensor design, driven by cost constraints, directly influence the quality of value-added remote sensing products. Furthermore, the Simulator is used to identify situations where retrieval algorithms require further parameterization before application to unsimulated satellite data, where error sources cannot always be identified or isolated. The application of the Simulator can verify whether a given instrument design meets the performance requirements of end-users before build and launch, critically allowing for the justification of the cost and specifications for planned and future sensors. It is hoped that the Simulator will enable engineers and scientists to understand important design trade-offs in phase 0/A studies easily, quickly, reliably, and accurately in future Earth observation satellites and systems.
Design and Implementation of a Highly Efficient Quasi-Cyclic Low-Density Parity-Check Transceiving System Using an Overlapping Decoder
Authors: Sun Yuxuan , Zhao Liangbin , Li Jianguo , Zhang Ziyi , Yang Xiao , Bu Xiangyuan
Published: September 2023

The traditional LDPC encoding and decoding system is characterized by low throughput and high resource consumption, making it unsuitable for use in cost-efficient, energy-saving sensor networks. Aiming to optimize coding complexity and throughput, this paper proposes a combined design of a novel LDPC code structure and the corresponding overlapping decoding strategies. With regard to structure of LDPC code, a CCSDS-like quasi-cyclic parity check matrix (PCM) with uniform distribution of submatrices is constructed to maximize overlap depth and adapt the parallel decoding. In terms of reception decoding strategies, we use a modified 2-bit Min-Sum algorithm (MSA) that achieves a coding gain of 5 dB at a bit error rate of 10−6 compared to an uncoded BPSK, further mitigating resource consumption, and which only incurs a slight loss compared to the standard MSA. Moreover, a shift-register-based memory scheduling strategy is presented to fully utilize the quasi-cyclic characteristic and shorten the read/write latency. With proper overlap scheduling, the time consumption can be reduced by one third per iteration compared to the non-overlap algorithm. Simulation and implementation results demonstrate that our decoder can achieve a throughput up to 7.76 Gbps at a frequency of 156.25 MHz operating eight iterations, with a two-thirds resource consumption saving.
Traffic Agents Trajectory Prediction Based on Spatial–Temporal Interaction Attention
Authors: Xie Jincan , Li Shuang , Liu Chunsheng
Published: September 2023

Trajectory prediction aims to predict the movement intention of traffic participants in the future based on the historical observation trajectories. For traffic scenarios, pedestrians, vehicles and other traffic participants have social interaction of surrounding traffic participants in both time and spatial dimensions. Most previous studies only use pooling methods to simulate the interaction process between participants and cannot fully capture the spatio-temporal dependence, possibly accumulating errors with the increase in prediction time. To overcome these problems, we propose the Spatial–Temporal Interaction Attention-based Trajectory Prediction Network (STIA-TPNet), which can effectively model the spatial–temporal interaction information. Based on trajectory feature extraction, the novel Spatial–Temporal Interaction Attention Module (STIA Module) is proposed to extract the interaction relationships between traffic participants, including temporal interaction attention, spatial interaction attention, and spatio-temporal attention fusion. By adaptive allocation of attention weights, temporal interaction attention is a temporal attention mechanism used to capture the movement pattern of each traffic participant in the scene, which can learn the importance of historical trajectories at different moments to future behaviors. Since the participants number in recent traffic scenes dynamically changes, the spatial interaction attention is designed to abstract the traffic participants in the scene into graph nodes, and abstract the social interaction between participants into graph edges. Coupling the temporal and spatial interaction attentions can adaptively model the temporal–spatial information and achieve accurate trajectory prediction. By performing experiments on the INTERACTION dataset and the UTP (Unmanned Aerial Vehicle-based Trajectory Prediction) dataset, the experimental results show that the proposed method significantly improves the accuracy of trajectory prediction and outperforms the representative methods in comparison.
TIVC: An Efficient Local Search Algorithm for Minimum Vertex Cover in Large Graphs
Authors: Zhang Yu , Wang Shengzhi , Liu Chanjuan , Zhu Enqiang
Published: September 2023

The minimum vertex cover (MVC) problem is a canonical NP-hard combinatorial optimization problem aiming to find the smallest set of vertices such that every edge has at least one endpoint in the set. This problem has extensive applications in cybersecurity, scheduling, and monitoring link failures in wireless sensor networks (WSNs). Numerous local search algorithms have been proposed to obtain “good” vertex coverage. However, due to the NP-hard nature, it is challenging to efficiently solve the MVC problem, especially on large graphs. In this paper, we propose an efficient local search algorithm for MVC called TIVC, which is based on two main ideas: a 3-improvements (TI) framework with a tiny perturbation and edge selection strategy. We conducted experiments on real-world large instances of a massive graph benchmark. Compared with three state-of-the-art MVC algorithms, TIVC shows superior performance in accuracy and possesses a remarkable ability to identify significantly smaller vertex covers on many graphs.
Gas Sensor Array Fault Diagnosis Based on Multi-Dimensional Fusion, an Attention Mechanism, and Multi-Task Learning
Authors: Huang Pengyu , Wang Qingfeng , Chen Haotian , Lu Geyu
Published: September 2023

With the development of gas sensor arrays and computational technology, machine olfactory systems have been widely used in environmental monitoring, medical diagnosis, and other fields. The reliable and stable operation of gas sensing systems depends heavily on the accuracy of the sensors outputs. Therefore, the realization of accurate gas sensor array fault diagnosis is essential to monitor the working status of sensor arrays and ensure the normal operation of the whole system. The existing methods extract features from a single dimension and require the separate training of models for multiple diagnosis tasks, which limits diagnostic accuracy and efficiency. To address these limitations, for this study, a novel fault diagnosis network based on multi-dimensional feature fusion, an attention mechanism, and multi-task learning, MAM-Net, was developed and applied to gas sensor arrays. First, feature fusion models were applied to extract deep and comprehensive features from the original data in multiple dimensions. A residual network equipped with convolutional block attention modules and a Bi-LSTM network were designed for two-dimensional and one-dimensional signals to capture spatial and temporal features simultaneously. Subsequently, a concatenation layer was constructed using feature stitching to integrate the fault details of different dimensions and avoid ignoring useful information. Finally, a multi-task learning module was designed for the parallel learning of the sensor fault diagnosis to effectively improve the diagnosis capability. The experimental results derived from using the proposed framework on gas sensor datasets across different amounts of data, balanced and unbalanced datasets, and different experimental settings show that the proposed framework outperforms the other available methods and demonstrates good recognition accuracy and robustness.
IMU Networks for Trajectory Reconstruction in Logistics Applications
Authors: Sequeira João Silva
Published: September 2023

This paper discusses the use of networks of Inertial Measurement Units (IMUs) for the reconstruction of trajectories from sensor data. Logistics is a natural application domain to verify the quality of the handling of goods. This is a mass application and the economics of logistics impose that the IMUs to be used must be low-cost and use basic computational devices. The approach in this paper converts a strategy from the literature, used in the multi-target following problem, to reach a consensus in a network of IMUs. This paper presents results on how to achieve the consensus in trajectory reconstruction, along with covariance intersection data fusion of the information obtained by all the nodes in the network.
Low-Latency Collaborative Predictive Maintenance: Over-the-Air Federated Learning in Noisy Industrial Environments
Authors: Bemani Ali , Björsell Niclas
Published: September 2023

The emergence of Industry 4.0 has revolutionized the industrial sector, enabling the development of compact, precise, and interconnected assets. This transformation has not only generated vast amounts of data but also facilitated the migration of learning and optimization processes to edge devices. Consequently, modern industries can effectively leverage this paradigm through distributed learning to define product quality and implement predictive maintenance (PM) strategies. While computing speeds continue to advance rapidly, the latency in communication has emerged as a bottleneck for fast edge learning, particularly in time-sensitive applications such as PM. To address this issue, we explore Federated Learning (FL), a privacy-preserving framework. FL entails updating a global AI model on a parameter server (PS) through aggregation of locally trained models from edge devices. We propose an innovative approach: analog aggregation over-the-air of updates transmitted concurrently over wireless channels. This leverages the waveform-superposition property in multi-access channels, significantly reducing communication latency compared to conventional methods. However, it is vulnerable to performance degradation due to channel properties like noise and fading. In this study, we introduce a method to mitigate the impact of channel noise in FL over-the-air communication and computation (FLOACC). We integrate a novel tracking-based stochastic approximation scheme into a standard federated stochastic variance reduced gradient (FSVRG). This effectively averages out channel noise’s influence, ensuring robust FLOACC performance without increasing transmission power gain. Numerical results confirm our approach’s superior communication efficiency and scalability in various FL scenarios, especially when dealing with noisy channels. Simulation experiments also highlight significant enhancements in prediction accuracy and loss function reduction for analog aggregation in over-the-air FL scenarios.
Automatic Multiview Alignment of RGB-D Range Maps of Upper Limb Anatomy
Authors: Di Angelo Luca , Di Stefano Paolo , Guardiani Emanuele , Neri Paolo , Paoli Alessandro , Razionale Armando Viviano
Published: September 2023

Digital representations of anatomical parts are crucial for various biomedical applications. This paper presents an automatic alignment procedure for creating accurate 3D models of upper limb anatomy using a low-cost handheld 3D scanner. The goal is to overcome the challenges associated with forearm 3D scanning, such as needing multiple views, stability requirements, and optical undercuts. While bulky and expensive multi-camera systems have been used in previous research, this study explores the feasibility of using multiple consumer RGB-D sensors for scanning human anatomies. The proposed scanner comprises three Intel® RealSenseTM D415 depth cameras assembled on a lightweight circular jig, enabling simultaneous acquisition from three viewpoints. To achieve automatic alignment, the paper introduces a procedure that extracts common key points between acquisitions deriving from different scanner poses. Relevant hand key points are detected using a neural network, which works on the RGB images captured by the depth cameras. A set of forearm key points is meanwhile identified by processing the acquired data through a specifically developed algorithm that seeks the forearm’s skeleton line. The alignment process involves automatic, rough 3D alignment and fine registration using an iterative-closest-point (ICP) algorithm expressly developed for this application. The proposed method was tested on forearm scans and compared the results obtained by a manual coarse alignment followed by an ICP algorithm for fine registration using commercial software. Deviations below 5 mm, with a mean value of 1.5 mm, were found. The obtained results are critically discussed and compared with the available implementations of published methods. The results demonstrate significant improvements to the state of the art and the potential of the proposed approach to accelerate the acquisition process and automatically register point clouds from different scanner poses without the intervention of skilled operators. This study contributes to developing effective upper limb rehabilitation frameworks and personalized biomedical applications by addressing these critical challenges.
Full-Field Vibration Response Estimation from Sparse Multi-Agent Automatic Mobile Sensors Using Formation Control Algorithm
Authors: Jana Debasish , Nagarajaiah Satish
Published: September 2023

In structural vibration response sensing, mobile sensors offer outstanding benefits as they are not dedicated to a certain structure; they also possess the ability to acquire dense spatial information. Currently, most of the existing literature concerning mobile sensing involves human drivers manually driving through the bridges multiple times. While self-driving automated vehicles could serve for such studies, they might entail substantial costs when applied to structural health monitoring tasks. Therefore, in order to tackle this challenge, we introduce a formation control framework that facilitates automatic multi-agent mobile sensing. Notably, our findings demonstrate that the proposed formation control algorithm can effectively control the behavior of the multi-agent systems for structural response sensing purposes based on user choice. We leverage vibration data collected by these mobile sensors to estimate the full-field vibration response of the structure, utilizing a compressive sensing algorithm in the spatial domain. The task of estimating the full-field response can be represented as a spatiotemporal response matrix completion task, wherein the suite of multi-agent mobile sensors sparsely populates some of the matrix’s elements. Subsequently, we deploy the compressive sensing technique to obtain the dense full-field vibration complete response of the structure and estimate the reconstruction accuracy. Results obtained from two different formations on a simply supported bridge are presented in this paper, and the high level of accuracy in reconstruction underscores the efficacy of our proposed framework. This multi-agent mobile sensing approach showcases the significant potential for automated structural response measurement, directly applicable to health monitoring and resilience assessment objectives.
A Sub-Aperture Overlapping Imaging Method for Circular Synthetic Aperture Radar Carried by a Small Rotor Unmanned Aerial Vehicle
Authors: Chu Lina , Ma Yanheng , Li Bingxuan , Hou Xiaoze , Shi Yuanping , Li Wei
Published: September 2023

Circular synthetic aperture radar (CSAR) can obtain higher image resolution and more target information using 360° observation of the target. Due to the anisotropy of target scattering characteristics in the actual scene, the sub-aperture imaging method is usually used for CSAR imaging. However, the uniformly divided overlapping sub-aperture CSAR imaging algorithm only considers phase compensation, ignoring the effect of target scattering characteristics on echo amplitude. In CSAR imaging scenarios carried by small rotor unmanned aerial vehicles (SRUAVs), the size of the observed scene cannot be ignored compared to the distance between the target and the antenna and the effect of the anisotropy of the target scattered energy on the echo amplitude should be considered. In this paper, a sub-aperture CSAR imaging method based on adaptive overlapping sub-aperture is proposed. First, the boundary points of the sub-aperture are determined by analyzing the correlation coefficient and the variation coefficient of the energy function. Next, the overlapping sub-aperture division schemes are automatically generated by screening and combining the boundary points. The sub-aperture images are then generated by a Back Projection (BP) algorithm. Finally, sub-aperture image registration and incoherent superposition are used to generate the final CSAR image. Verified by the CSAR field echo data, the proposed method can realize imaging of the original echo data without the Inertial Navigation System (INS) and Global Positioning System (GPS) observation data. Compared with the CSAR full-aperture BP imaging algorithm, the entropy of the image generated by the proposed method increased by 66.77%. Compared with the sub-aperture CSAR imaging algorithm, the entropy of the image generated by the proposed method was improved by 11.12%, retaining more details of the target, improving the target contour features, and enhancing the focusing effect.
On Hens, Eggs, Temperatures and CO2: Causal Links in Earth’s Atmosphere
Authors: Koutsoyiannis Demetris , Onof Christian , Kundzewicz Zbigniew W. , Christofides Antonis
Published: September 2023

The scientific and wider interest in the relationship between atmospheric temperature (T) and concentration of carbon dioxide ([CO2]) has been enormous. According to the commonly assumed causality link, increased [CO2] causes a rise in T. However, recent developments cast doubts on this assumption by showing that this relationship is of the hen-or-egg type, or even unidirectional but opposite in direction to the commonly assumed one. These developments include an advanced theoretical framework for testing causality based on the stochastic evaluation of a potentially causal link between two processes via the notion of the impulse response function. Using, on the one hand, this framework and further expanding it and, on the other hand, the longest available modern time series of globally averaged T and [CO2], we shed light on the potential causality between these two processes. All evidence resulting from the analyses suggests a unidirectional, potentially causal link with T as the cause and [CO2] as the effect. That link is not represented in climate models, whose outputs are also examined using the same framework, resulting in a link opposite the one found when the real measurements are used.
Separation and Isolation of a New Hydroxylated Resveratrol Trimer Together with Other Stilbenoid Compounds from the Lianas of Gnetum microcarpum Blume and Their Inhibitory Effects of Prostaglandin E2
Authors: Azmin Nik Fatini Nik , Ahmat Norizan , Jalil Juriyati , Sabandar Carla Wulandari , Zawawi Nik Khairunissa’ Nik Abdullah , Sazali Siti Norafiqah Mohd , Yusof Mohd Izwan Mohamad , Kamarozaman Aisyah Salihah , Tanjung Mulyadi
Published: September 2023

A new oligostilbene trimer, malaysianol F (1), together with ten known stilbenes (2–11), were successfully separated and purified from the acetone extract of the lianas of Gnetum microcarpum. Malaysianol D (2) was isolated for the first time in Gnetum plants. The tanninless crude extract (52.5 g) was fractionated using a vacuum liquid chromatography (VLC) technique to give five major fractions. Fraction 2 (4.68 g), 3 (4.79 g) and 4 (9.29 g) were all subjected to further isolation and purification using VLC, column chromatography (CC) and repetitive radial chromatography (RC) techniques with the best solvent system to yield malaysianol F (1) (6.2 mg), malaysianol D (2) (62.5 mg), malaysianol E (3) (2.4 mg), ε-viniferin (4) (10 mg), resveratrol (5) (6.5 mg), gnetol (6) (3.5 mg), gnetucleistol C (7) (12.2 mg), isorhapontigenin (8) (8 mg), cuspidan B (9) (3.2 mg), parvifolol D (10) (4.8 mg) and gnetifolin M (11) (2.5 mg). Their structures were determined on the basis of the analysis of spectral evidence by extensive NMR data analyses and comparison with the related published data. Several compounds were tested for anti-inflammatory activity. Their inhibitory effect on Prostaglandin E2 (PGE2) was tested using radioimmunoassay techniques. Compound 6 exhibited significant concentration-dependent inhibitory effects on PGE2 production with IC50 values of 1.84 µM comparable with the positive control, indomethacin (IC50 1.29 µM).
Properties of Glass-Ceramics Prepared from Industrial Multi-Wastes
Authors: Zhang Zuoliang , Ma Heli , Wu Chunlei , Sun Ye , Chen Ren , Guo Xiaoying
Published: September 2023

Because of their excellent properties, glass-ceramics have been widely developed and applied in many fields, and there are many potential application values to be disseminated. The preparation of glass-ceramics from industrial slag and metallurgical waste provides a new way for the comprehensive utilization of solid waste. Coal gangue is the largest of all kinds of industrial waste slag, while iron tailings and high-carbon ferrochrome slag also occupy a large proportion of China’s industrial solid waste. With cheap industrial solid waste as the main raw material, the production of high-value-added glass-ceramics can reduce pollution, protect the ecological environment, and have good economic and social benefits. Cordierite glass-ceramics were prepared using the sintering method with coal gangue, iron tailings, and high-carbon ferrochrome slag as the main raw materials. Meanwhile, an iron silicon alloy containing chromium was obtained. The heat treatment system of basic glass was determined by differential scanning calorimetry (DSC), and the sintered product was analyzed by X-ray diffraction (XRD) and scanning electron microscopy (SEM). An orthogonal test was used to analyze the effects of the mass of basic glass powder, molding pressure, and holding time on the grain size and crystallinity of the samples. The hardness, acid and alkali resistance, density, and water absorption of the sintered products were determined. The results show that the main crystal phase of the prepared glass-ceramics is cordierite. The optimal combination for the green body is “basic glass powder mass 6 g, molding pressure 35 MPa, holding time 10 min”. The properties of glass-ceramics are good. At the crystallization temperature of 970 °C, the Vickers hardness is up to 866 HV, and the bulk density is up to 2.99 g/cm3. This study may provide a useful reference for the treatment of industrial solid waste.
Effect of Alkaline Salts on Pyrolyzed Solid Wastes in Used Edible Oils: An Attenuated Total Reflectance Analysis of Surface Compounds as a Function of the Temperature
Authors: Romero-Sarria Francisca , Real Concepción , Córdoba José Manuel , Hidalgo María Carmen , Alcalá María Dolores
Published: September 2023

Biochars obtained via the pyrolysis of biomass are very attractive materials from the point of view of their applications and play key roles in the current energy context. The characterization of these carbonaceous materials is crucial to determine their field of application. In this work, the pyrolysis of a non-conventional biomass (solid wastes in used edible oils) was investigated. The obtained biochars were characterized using conventional techniques (TG, XRD, and SEM-EDX), and a deep analysis via ATR-FTIR was performed. This spectroscopic technique, which is a rapid and powerful tool that is well adapted to study carbon-based materials, was employed to determine the effect of temperature on the nature of functional groups on the surface. Moreover, the water washing of the raw sample (containing important quantities of inorganic salts) before pyrolysis evidenced that the inorganic salts act as catalysts in the biomass degradation and influence the degree of condensation (DOC) of PAH. Moreover, it was observed that these salts contribute to the retention of oxygenated compounds on the surface of the solid.
Questions of Identity in Sport Psychology Scholar–Practitioners
Authors: Howells Karen
Published: September 2023

As with other academic disciplines, sport psychology academics working in higher education (HE) in the United Kingdom (UK) in lecturer and senior lecturer positions are typically required to hold a PhD in sport psychology or a related discipline. To work in applied practice with athletes, coaches, National Governing Bodies (NGBs), and sporting organisations, practitioners are required to acquire a qualification that affords registration with the Health and Care Professions Council (HCPC) through either the British Psychology Society (BPS) or the British Association of Sport and Exercise Sciences (BASES). Accordingly, scholar–practitioners, who have “a foot in both worlds” (Tenkasi and Hay, 2008), are required to have two related but distinct qualifications, each of which requires considerable resources (i.e., time, finances, and commitment) to achieve. This paper addresses some of the dilemmas and conflicts that these individuals may encounter in their primary workplace, which typically does not provide for applied practice (either in time or financial incentives). Specifically, issues around the knowledge-transfer gap will be addressed. Real-world examples will be in the form of reflections from the author’s own experiences. I am a senior lecturer in sport and exercise psychology at Cardiff Metropolitan University and the programme director of the MSc Sport Psychology. The role requires me to be HCPC registered, as well as have a PhD in sport psychology. I am also an HCPC Practitioner Psychologist, registered following completion of the BPS Qualification in Sport and Exercise Psychology (QSEP). My practice is limited to minimal private work and the supervision of trainee sport psychologists (BPS). At the end of the paper, I leave the reader with three questions to prompt reflection on what being a sport psychologist means and what contributions scholar–practitioners may offer to academic institutions and the clients we work with.
The Impact of COVID-19 Lockdown on West Romanian Crop Production in 2020
Authors: Gherasimescu Loredana , Imbrea Florin , Imbrea Ilinca , Șmuleac Laura , Pașcalău Raul , Popoescu Cosmin Alin , Prus Piotr , Sălășan Cosmin
Published: September 2023

The COVID-19 pandemic and the resulting public health crisis had an enormous impact on the global economy and its sectors. Most components were adversely affected, especially the tertiary industry (the part of a country’s economy that provides services), with different types and sizes of businesses suffering to varying degrees. Reports on the impact on agriculture are not lacking, and the crisis was perceived and responded to differently from the supply chain to the household level. The research question proposed in this paper concerns the impact of the early restrictions induced by COVID-19, namely the lockdown period from March to June 2020, on Romanian agriculture and more precisely Western Romanian crop production. Two counties in the West were selected: the county Timis for its highly favourable agricultural production and the county Caras Severin for its integrative agricultural production centred on mixed farms, where crop production is integrated with animal production towards a higher added value. Using secondary data from the National Agency for Payments and Interventions in Agriculture allowed the disaggregation of data at the level of each municipality for 2019 and 2020. The choice of this dataset was related to the level of precision, as the beneficiaries of direct payments in the respective areas are verified each year for both areas and crops. The paired two-tailed t-test was used to test the data for each LAU 2 municipality in each of the selected counties; as the crops sown in 2019 could not be affected by the COVID lockdown period in spring 2020 (March to June), the most important spring crops in terms of area in 2020 were selected and tested against the 2019 datasets. The results show that there is not enough evidence to conclude a significant statistical difference between the two years, and therefore, we cannot reject the null hypothesis and conclude that the pandemic lockdown did not affect the spring crops during their most restrictive period in spring 2020; thus, the overall influence of the COVID-19 lockdown on crop production in Western Romania was insignificant, observed only at the level of primary production. Spring crop production could have been severely affected by the restrictions imposed by the health crisis on access to certain inputs, freedom of movement for field work, and other activities closely linked to agricultural production, leading to early disruptions along the food chain.
Efficiency Analysis of Oil Refineries Using DEA Window Analysis, Cluster Analysis, and Malmquist Productivity Index
Authors: Oliveira Maiquiel Schmidt de , Lizot Mauro , Siqueira Hugo , Afonso Paulo , Trojan Flavio
Published: September 2023

Oil and gas refineries play a key role in the economies of countries by providing energy to various industrial sectors. A lack of an integrated efficiency analysis procedure, in many industries, could significantly impact the planning of sustainable industrial structures and operations. It also can influence company performance and competitiveness, and, eventually, negatively compromise the fuel supply process. All these problems taken together might negatively impact the environment and sustainable practices. Studies of efficiency in the oil industry can help to reduce its environmental and social impact and to achieve long-term green transition goals. In this work, the data envelopment analysis (DEA) method was used to present improvement goals for production units, based on efficiency indexes. Furthermore, the DEA window analysis model, integrated with the Malmquist index and cluster analysis, was used to evaluate efficiency and the factors that explain the differences between refineries in a number of timeframes. A numerical analysis was carried out with data collected from 12 Brazilian oil refineries between 2012 and 2020, using DEA window analysis, cluster analysis, and the Malmquist index.
Peer-to-Peer Lending as a Determinant of Federal Housing Administration-Insured Mortgages to Meet Sustainable Development Goals
Authors: Avgeri Evangelia , Psillaki Maria , Zervoudi Evanthia
Published: September 2023

In this paper, we investigate the influential factors of Federal Housing Administration (FHA) mortgage loans, focusing our research interest on peer-to-peer (P2P) lending, the most successful FinTech lending model. We consider P2P lending an alternative source of financing that marginal borrowers use to pay the increased mortgage down payment, making them eligible to receive a mortgage from conventional banks. In other words, we examine whether and to what extent P2P lending has a positive impact on the FHA loans volume by providing the ability to circumvent the loan-to-value (LTV) cap policy. As a result, P2P lending can be seen as a means for ”rationed” borrowers to have access to the market by reducing inequalities and promoting financial inclusion, thus achieving Sustainable Development Goals (SDGs). We employ hand-collected data from FHA mortgages, P2P loans, and other economic factors from all 50 U.S. states during 2007–2017 and use panel data techniques for this purpose. Research shows that P2P lending, GDP per capita, population growth, broad money growth rate, interest rate, unemployment rate, new housing units, and consumer confidence Index produce effects on FHA loans. We show that P2P lending, a nonconventional determinant, is causally associated with a significant increase in the count and volume of FHA loans, implying that P2P lending has a positive impact on them. The ability of P2P to bypass mortgage supply constraints (tightened LTV caps) by providing small loans to borrowers to meet the increased down payment requirements is very important to policy-makers, as it shows that constraining the volume of mortgage loans may be not achieved. Macroprudential tools designed to control credit growth may prove ineffective, as the use of alternative forms of lending helps circumvent them and ultimately leads to excessive household leverage with all the risks that it poses to the financial system.
Machine Learning Models for the Prediction of the Compressive Strength of Self-Compacting Concrete Incorporating Incinerated Bio-Medical Waste Ash
Authors: Chakravarthy H G Nahushananda , Seenappa Karthik M , Naganna Sujay Raghavendra , Pruthviraja Dayananda
Published: September 2023

Self-compacting concrete (SCC) is a special form of high-performance concrete that is highly efficient in its filling, flowing, and passing abilities. In this study, an attempt has been made to model the compressive strength (CS) of SCC mixes using machine-learning approaches. The SCC mixes were designed considering lightweight expandable clay aggregate (LECA) as a partial replacement for coarse aggregate; ground granulated blast-furnace slag (GGBS) as a partial replacement for binding material (cement); and incinerated bio-medical waste ash (IBMWA) as a partial replacement for fine aggregate. LECA, GGBS, and IBMWA were replaced with coarse aggregate, cement, and fine aggregate, respectively at different substitution levels of 10%, 20%, and 30%. M30-grade SCC mixes were designed for two different water/binder ratios—0.40 and 0.45—and the CS of the SCC mixes was experimentally determined along with the fresh state properties assessed by slump-flow, L-box, J-ring, and V-funnel tests. The CS of the SCC mixes obtained from the experimental analysis was considered for machine learning (ML)-based modeling using paradigms such as artificial neural networks (ANN), gradient tree boosting (GTB), and CatBoost Regressor (CBR). The ML models were developed considering the compressive strength of SCC as the target parameter. The quantities of materials (in terms of %), water-to-binder ratio, and density of the SCC specimens were used as input variables to simulate the ML models. The results from the experimental analysis show that the optimum replacement percentages for cement, coarse, and fine aggregates were 30%, 10%, and 20%, respectively. The ML models were successful in modeling the compressive strength of SCC mixes with higher accuracy and the least errors. The CBR model performed relatively better than the other two ML models, with relatively higher efficiency (KGE = 0.9671) and the least error (mean absolute error = 0.52 MPa) during the testing phase.
A Study on Female Consumers’ Perceptions of the Health Value of Visual Elements of Weight Loss Health Product Packaging
Authors: Zhang Fan , Zhang Lu , Guo Yanlong , Zhang Han
Published: September 2023

The obesity epidemic has evolved into a significant problem globally and poses a serious threat to the health of society. Despite increasing international research on obesity food packaging, the health value of visual elements in weight loss supplement packaging varies according to regional cultures and consumer groups. The scope of this study is the health perceptions of obese urban Chinese women regarding the design of visual elements of weight loss health product packaging. This study constructed a visual element index system for weight loss health product packaging in order to design a related questionnaire. The research team administered the online questionnaire to 305 obese women (aged 18–45+) in urban China from 22 June to 2 July 2022. The factors influencing the perceived health value of weight loss supplement packaging in the female obese group were examined using SPSS 26.0. A t-test analysis was conducted to derive the difference in the correlation between purchase status and brand identity. Linear regression analysis showed that age groups differed in package color and package shape, respectively. The study concluded that, first, there were differences in the perceptions of weight loss health product packaging among respondents with different purchase statuses; second, the older the respondents were, the more they perceived white packaging as having health value (p < 0.05); third, the health marker’s shape had little or no statistical significance on the individuals (p > 0.05); fourth, respondents who were older were more likely to think a cylindrical design has a benefit for their health than a square box shape (p < 0.05) and an anthropomorphic shape was more likely to produce a healthy weight loss effect than cylindrical packaging (p < 0.05); and younger people thought that an anthropomorphic shape was more likely to make them recognize the health effect than square box-shaped packaging (p < 0.05).
How Do Global Uncertainties Spillovers Affect Leading Renewable Energy Indices? Evidence from the Network Connectedness Approach
Authors: Rehman Mohd Ziaur , Khan Shabeer , Khan Uzair Abdullah , Alonazi Wadi B. , Noman Abul Ala
Published: September 2023

By using data from 2018 to 2022 and employing quantile VAR time-frequency and quantile VAR spillover models, this study investigates the spillover connectedness between global uncertainties, namely, geopolitical risk, economic policy uncertainty, and climate policy uncertainty, and seven leading global renewable energy indices. The results show strong total connectedness (82.87%) between renewable energy and uncertainty indices. DJRE, R&CE, MSCIEE, WRE_cpu, GEPU_C, and GEPU_P are found to be net receivers, and WRE to be net transmitters of spillovers. Additionally, the MSCIEE sector is the least connected, i.e., 2.51%, followed by the R&CE sector at 4.55%, while the ERE sector is the most connected one, i.e., 65.8%. We discover that the two market-based uncertainties have less impact than economic policy uncertainty (EPU), which has a significant impact. The conclusions have ramifications for decision-makers and investors in the renewable energy markets from the standpoint of sustainable development. The study reveals diversification avenues and recommends that investors consider MSCIEE and R&CE sectors for parking their funds because of lower risk, i.e., less connectivity and greater diversification.
Theoretical Evaluation of Photovoltaic Thermal Water Source Heat Pump, Application Potential and Policy Implications: Evidence from Yangtze River Economic Belt, China
Authors: Wu Qiuyi
Published: September 2023

In the context of carbon neutrality, making full use of renewable energy is the key to further improve China’s development index. Within China’s Yangtze River Economic Belt, solar energy and river water, as clean and abundant sources of renewable energy, have garnered increasing. In this paper, a solar energy and surface water driven cogeneration system model is developed by TRNSYS to provide users with heat, cold and electricity. Six representative cities located along the upper, middle, and lower reaches of the Yangtze River Economic Belt were selected to evaluate and analyze the energy-saving, emission reducing, and economic and environmental benefits of solar energy and river water heat utilization in the aspects of energy, economy, and environment. The results shows that the annual power output of PV/T-GSHP system, from the west to the east of the Yangtze River, shows a phase growth trend, which is related to the light intensity. The annual heat output of PV/T plate gradually decreases from the lower reaches of the Yangtze River to the upper reaches. The research findings confirm the application potential of new energy sources in the Yangtze River Economic Belt and quantify the emission reduction effects of new environmental protection actions such as solar energy and river water heat sources. It provides valuable guidance for the utilization of new energy sources, including solar energy and surface water heat energy in the Yangtze River Economic Belt, as well as for optimizing energy policies.
Peer-to-Peer Power Energy Trading in Blockchain Using Efficient Machine Learning Model
Authors: Rahman Mahfuzur , Chowdhury Solaiman , Shorfuzzaman Mohammad , Hossain Mohammad Kamal , Hammoudeh Mohammad
Published: September 2023

The advancement of mircogrids and the adoption of blockchain technology in the energy-trading sector can build a robust and sustainable energy infrastructure. The decentralization and transparency of blockchain technology have several advantages for data management, security, and trust. In particular, the uses of smart contracts can provide automated transaction in energy trading. Individual entities (household, industries, institutes, etc.) have shown increasing interest in producing power from potential renewable energy sources for their own usage and also in distributing this power to the energy market if possible. The key success in energy trading significantly depends on understanding one’s own energy demand and production capability. For example, the production from a solar panel is highly correlated with the weather condition, and an efficient machine learning model can characterize the relationship to estimate the production at any time. In this article, we propose an architecture for energy trading that uses smart contracts in conjunction with an efficient machine learning algorithm to determine participants’ appropriate energy productions and streamline the auction process. We conducted an analysis on various machine learning models to identify the best suited model to be used with the smart contract in energy trading.
Examining the Interplay of Climate Change, Cultural Dynamics, and Sustainable Development: A Global Perspective
Authors: Kostis Pantelis C. , Kafka Kyriaki I.
Published: September 2023

This study undertakes a comprehensive multi-country analysis to investigate the intricate relationships among climate change, cultural dynamics, and sustainable development. Leveraging a robust, unbalanced panel dataset that encompasses one hundred and eight countries or regions over nearly four decades (1981–2019), this study employs fixed-effects estimation techniques to mitigate the impact of time-invariant heterogeneity across observational units. Structural equation modeling (SEM) is also employed as an advanced analytical tool to explore complex causal pathways and latent variables. Conducted in Stata, this multifaceted approach allows us to delve into the causal interconnections between climate change indicators, various cultural attributes, and indices of sustainable development. The findings reveal a negative influence of climate change on cultural background formation, which in turn impacts sustainable development. On the other hand, it is found that cultural background contributes positively to sustainable development. This suggests integrating cultural considerations into climate change adaptation, mitigation strategies, and sustainable development interventions. These strategies account for diverse societal values and behaviors, facilitating more effective climate change mitigation and adaptation. This study contributes to the growing research on the interplay between climate change and sustainable development by emphasizing a culturally informed policy framework. Its findings stand to inform national and international policymaking and enrich the discourse surrounding the creative economy’s role in promoting sustainable development in the face of climate change.
Golf Club Management Challenges towards Sustainability: Opportunities and Innovations during and after the COVID-19 Pandemic: A Qualitative Perspective
Authors: Macías Ricardo , Bonal José , León-Quismondo Jairo , Iván-Baragaño Iyán , del Arco Javier , Burillo Pablo , Fernández-Luna Álvaro
Published: September 2023

Golf is among the top five sports practiced in Spain because sport tourism is one of their main lines of income. During the COVID-19 pandemic, all golf clubs had to shut down and thus enter a situation of economic uncertainty. The main objective of this study was to assess the sport management challenges that general managers of golf clubs have had to face before, during, and after the COVID-19 pandemic, and how they dealt with these issues. In this qualitative case study, management challenges and strategies were explored by subjecting five golf club managers with at least 5 years of experience to semi-structured interviews. Managers described that they went from a pre-pandemic situation of economic growth to having to deal with different challenges such as a lack of tourism, increased costs, and a need to install new safety and maintenance protocols. All participants highlighted the importance of constant communication with and transparency among stakeholders (clubs, government bodies, federations) during and after the lockdown period. Management during the pandemic expedited the digitization of golf clubs, and this was considered one of the main upsides of this period. Exploring the strategies used could help managers of this and other sports facilities to design sustainable management protocols to deal with possible future health or economic crises.
Estimation of Layered Ground Thermal Properties for Deep Coaxial Ground Heat Exchanger
Authors: Wang Changlong , Fu Qiang , Sun Wanyu , Lu Jinli , Sun Yanhong , Li Wanwan
Published: September 2023

A ground heat exchanger (GHE) can efficiently exploit geothermal energy, and a ground source heat pump (GSHP) is an important type of geothermal application. The distributed thermal response test (DTRT) is widely used to measure layered ground thermal properties for shallow GHEs, but nowadays, there is a lack of studies applying the DTRT to deep coaxial GHEs (DCGHEs). This study proposes a new parameter estimation method (PEM) by adopting the DTRT data of a DCGHE to estimate layered ground thermal properties and applies the proposed PEM to simulated DTRTs under different boundary conditions, and the estimated values of the layered ground thermal properties are compared with the true values. Under heat output rate or inlet temperature boundary conditions, the relative errors of the thermal conductivities and heat capacities of ground estimated using the proposed PEM are basically within 2% and 4%, respectively, except for shallower layers with a depth range of 0–800 m. The larger errors for shallower layers may be caused by weaker heat transfer between the fluid and ground, and the errors are basically lower for higher heat output rates. The predicted fluid temperature distributions during 120 d using the estimated values of the layered ground thermal properties match well with those using the true values. The results show that the proposed PEM is viable for DCGHE DTRT interpretation under heat output rate and inlet temperature boundary conditions, is a cost-effective way to establish key parameters for GSHP design, and would promote geothermal development.