bluuebunny/crossref_metadata_2025 huggingface dataset:
Energy Performance Evaluation of Historical Building
Authors: Ameen Arman , Bahrami Alireza , El Tayara Khaled
Published: October 2022

Retrofitting measures in old buildings aimed at reducing energy usage have become important procedures meant to counteract the effects of climate change and greenhouse gas emissions. The aim of this study is to evaluate energy usage, thermal comfort, and CO2 emissions of an old building by changing parameters such as building orientation, shading systems, location, low energy film application, and alternative energy supply in the form of a geothermal heat pump. When evaluating the buildings in terms of geographical location with or without applying the low energy film, the results show that the city of Gävle in Sweden requires the most heating energy, 150.3 kWh/m2∙year (B0) compared to Jakarta (L0), which requires 23.8 kWh/m2∙year. When examining the thermal comfort, cases B4 and L4 demonstrate the best results in their respective categories (B0–B4 are cases without low energy film and L0–L4 are cases with applied low energy film). The results for the CO2 emissions levels for B0–B4 and L0–L4 indicate that B4 has the highest value, 400 kg CO2 eq/year higher than B0, and L1 has the lowest value, 731 kg CO2 eq/year lower than B0. The economic feasibility study illustrates that the installation of a geothermal heat pump with at least a coefficient of performance of 4.0 leads to a shorter payback period than solely applying LEF.
Buildings’ Heating and Cooling Load Prediction for Hot Arid Climates: A Novel Intelligent Data-Driven Approach
Authors: Irshad Kashif , Zahir Md. Hasan , Shaik Mahaboob Sharief , Ali Amjad
Published: October 2022

An important aspect in improving the energy efficiency of buildings is the effective use of building heating and cooling load prediction models. A lot of studies have been undertaken in recent years to anticipate cooling and heating loads. Choosing the most effective input parameters as well as developing a high-accuracy forecasting model are the most difficult and important aspects of prediction. The goal of this research is to create an intelligent data-driven load forecast model for residential construction heating and cooling load intensities. In this paper, the shuffled shepherd red deer optimization linked self-systematized intelligent fuzzy reasoning-based neural network (SSRD-SsIF-NN) is introduced as a novel intelligent data-driven load prediction method. To test the suggested approaches, a simulated dataset based on the climate of Dhahran, Saudi Arabia will be employed, with building system parameters as input factors and heating and cooling loads as output results for each system. The simulation of this research is executed using MATLAB software. Finally, the theoretical and experimental results demonstrate the efficacy of the presented techniques. In terms of Mean Square Error (MSE), Root Mean Square Error (RMSE), Regression (R) values, Mean Absolute Error (MAE), coefficient of determination (R2), and other metrics, their prediction performance is compared to that of other conventional methods. It shows that the proposed method has achieved the finest performance of load prediction compared with the conventional methods.
The Effect of GFRP Wrapping on Lateral Performance of Double Shear Lap Joints in Cross-Laminated Timber as a Part of Timber Bridges
Authors: Rostampour Haftkhani Akbar , Rashidi Maria , Abdoli Farshid , Gerami Masood
Published: October 2022

Timber elements, such as timber bridges, are exposed to heavy loads. Therefore, reinforcement might be useful. Due to a lack of wood supplies, poplar, a fast-growing tree, could be used to construct CLT (cross-laminated timber). The low density of fast-growing wood species directly impacts the mechanical properties of CLT. Therefore, in this study, a CLT panel was reinforced with GFRP (glass-fiber-reinforced polymer), and the lateral resistance of double shear lap joints in reinforced CLTs with 0-90-0° arrangements in two strength directions was investigated. Lag screws (Ø = 8 mm) at the end distances of 1 and 3 cm were employed for making the lateral test specimens. First, the effect of the number of GFRP layers on lateral resistance of the joints was investigated. The results revealed that, as the number of GFRP layers changed from one to three, the lateral resistance increased by 45.47%, and then, by four layers, it decreased by 1.3%. Since the joints with three layers of FRP had the highest strength, the effects of the end distance and the CLT panel strength directions on the lateral performance of the reinforced and non-reinforced specimens were investigated. The results indicated that the lateral resistance of reinforced CLTs with GFRP was about 26.5% more than the unreinforced ones. Moreover, CLTs in the major strength direction showed 4.2% more lateral resistance than those in the minor strength direction. Moreover, lag screws at the end distance of 3 cm had 60% more lateral resistance than those at the end distance of 1 cm. In terms of failure modes, bearing, shear, and net-tension modes were observed in the CLTs, while Is, IIIs, and IV modes were observed in the lag screws.
Dissecting the Immunological Profiles in NSD3-Amplified LUSC through Integrative Multi-Scale Analyses
Authors: Xu Duo , Liu Shengchen , Wu Xi , Marti Thomas M. , Dorn Patrick , Schmid Ralph A. , Peng Ren-Wang , Shu Yongqian
Published: October 2022

The histone H3 lysine 36 (H3K36) methyltransferase NSD3, a neighboring gene of FGFR1, has been identified as a critical genetic driver of lung squamous cell carcinoma (LUSC). However, the molecular characteristics, especially the immunological roles of NSD3 in driving carcinogenesis, are poorly understood. In this study, we systematically integrated multi-omics data (e.g., genome, transcriptome, proteome, and TMA array) to dissect the immunological profiles in NSD3-amplified LUSC. Next, pharmaco-transcriptomic correlation analysis was implemented to identify the molecular underpinnings and therapeutic vulnerabilities in LUSC. We revealed that NSD3-amplified LUSC presents a non-inflamed tumor immune microenvironment (TIME) state in multiple independent LUSC patient cohorts. Predictably, elevated NSD3 expression was correlated with a worse immunotherapy outcome. Further molecular characterizations revealed that the high activity of unfolded protein response (UPR) signaling might be a pivotal mediator for the non-immunogenic phenotype of NSD3-amplified LUSC. Concordantly, we showed that NSD3-amplified LUSCs exhibited a more sensitive phenotype to compounds targeting UPR branches than the wild-type group. In brief, our multi-level analyses point to a previously unappreciated immunological role for NSD3 and provide therapeutic rationales for NSD3-amplified squamous lung cancer.
Photocatalytic Bacterial Inactivation of Acinetobacter baumannli on Cu/TiO2/Diatomite
Authors: Xu Xiaolin , Yang Yacong , Miao Yingchun , Liu Kaiquan , Lv Fujian , Zhou Liping , Tang Xuqi , Liu Yanmi , Guo Xinchun
Published: October 2022

Cu4Ti2O/TiO2/diatomite with double interface Cu4Ti2O/TiO2 and rutile/anatase heterojunction were fabricated, which demonstrated good antibacterial activity (100%) against Acinetobacter baumannii. Cu/TiO2/diatomite prepared under optimum preparation conditions (added diatomite, 0.005 g; Cu, 0.005 g; reaction temperature, 180 °C; reaction time, 8 h) exhibited high antibacterial activity (100%) against A. baumannii. For the Cu/TiO2/diatomite powders, their structural, compositional, optical and morphological traits were characterized by XRD, SEM, TEM, XPS, BET, FTIR, Mapping, and DRS. It was shown that Cu/TiO2/diatomite under optimum conditions consisted of the double interface Cu4Ti2O/TiO2 and rutile/anatase heterojunction with the narrowest band gap and largest BET surface area, pore size, and pore volume. Then, it could exhibit the best photocatalytic activity.
Multidisciplinary Management of Children with Occult Spinal Dysraphism: A Comprehensive Journey from Birth to Adulthood
Authors: Vetrano Ignazio G. , Barbotti Arianna , Erbetta Alessandra , Mariani Sabrina , Bova Stefania M. , Colombo Luca , Caretti Valentina , Marinoni Federica , Vestri Elettra , Selvaggio Giorgio G. O. , Valentini Laura G.
Published: October 2022

Occult spinal dysraphism (OSD) comprises different forms of failure in embryogenic development that can lead to genitourinary, spinal, or lower limb alterations, thus determining progressive neurological deterioration. The correct management of children harboring OSD represents a significant issue during their life up to adulthood. However, patients often have to entertain individual consultations with each specialist. We settled on a multidisciplinary team comprising pediatric neurosurgeons, urologists, neurologists, orthopedists, and other supporting physicians. We present the results of such actions by analyzing a series of 141 children with OSD subjected to neurosurgical procedures, evaluating the impact of multidisciplinary management on outcomes. We also evaluated the specific actions according to the different ages of OSD patients from birth to adulthood to provide a schematic plan that could represent a basis for establishing and disseminating the need for a multidisciplinary approach in OSD management. The multidisciplinary team allows all consultants to see the patient together, covering specific aspects of history and examination pertinent to their management. Offering a one-stop service prevents coordination issues between the different medical teams, avoids delays or cancellations of the various appointments, optimizes cost-effectiveness, and improves efficiency and parents’ satisfaction.
Climate Shocks and Social Networks: Understanding Adaptation among Rural Indian Households
Authors: Ramsawak Richard Anthony
Published: October 2022

This paper seeks to uncover the impact of negative rainfall shocks on household social network relationships. I leverage the uncertainty generated from fluctuating long-term rainfall patterns across India, to estimate the impact of heightened climate risks on investments in social network relationships. In so doing, I attempt to disentangle the “direct” and “adaptive” impacts of climate shocks on social network relationships. I found that households that experience higher than average negative rainfall shocks (lower than average rainfall levels over the long term) tend to invest more in family–caste and vertical or linked network relationships. These network relationships were also found to be associated with greater access to financial credit, credit sourced specifically from family members, higher reported collaboration, more diversified businesses, and the use of private irrigation technologies, all of which are key to mitigating the negative impacts of climate shocks. Unlike past research, these results suggest that households’ decisions to invest in social networks may be an adaptive response to higher climate risk. In terms of policy implications, these results highlight the importance of strengthening and supporting family-based and linked networks (such as links to local governmental agencies and extension services) in the face of higher climate risks.
Paired Patterns in Logical Analysis of Data for Decision Support in Recognition
Authors: Masich Igor S. , Tyncheko Vadim S. , Nelyub Vladimir A. , Bukhtoyarov Vladimir V. , Kurashkin Sergei O. , Borodulin Aleksey S.
Published: October 2022

Logical analysis of data (LAD), an approach to data analysis based on Boolean functions, combinatorics, and optimization, can be considered one of the methods of interpretable machine learning. A feature of LAD is that, among many patterns, different types of patterns can be identified, for example, prime, strong, spanned, and maximum. This paper proposes a decision-support approach to recognition by sharing different types of patterns to improve the quality of recognition in terms of accuracy, interpretability, and validity. An algorithm was developed to search for pairs of strong patterns (prime and spanned) with the same coverage as the training sample, having the smallest (for the prime pattern) and the largest (for the spanned pattern) number of conditions. The proposed approach leads to a decrease in the number of unrecognized observations (compared with the use of spanned patterns only) by 1.5–2 times (experimental results), to some reduction in recognition errors (compared with the use of prime patterns only) of approximately 1% (depending on the dataset) and makes it possible to assess in more detail the level of confidence of the recognition result due to a refined decision-making scheme that uses the information about the number and type of patterns covering the observation.
Study on the Electromagnetic Characteristics of Ring-Shaped Superconducting Permanent Magnets for Medical Applications
Authors: Zhao Chen , Shi Jinhong , Sheng Jie , Chen Wanli
Published: October 2022

The ring-shaped superconducting permanent magnet, with its great advantages in flexible sizing and trapped field, has become a potential candidate for portable medical applications. However, due to the complex geometry involved, it is difficult to predict its electromagnetic performance by traditional numerical methods. This paper presents a field-circuit coupling method to study the entire magnetization process of the ring-shaped magnet. Firstly, the principle of the numerical method is introduced and it is proved to be sufficient for a ring-shaped magnet with a large turn number. Then, the numerical model is used to discuss the relationship between pulse waveform and magnitude of trapped field. Next, the accumulation effect under multi-pulse magnetization is theoretically analyzed and proved by both experiments and simulation. Finally, based on the numerical model, a study on the decay process of ring-shaped magnets is also presented. Conclusions from this paper will be helpful for obtaining the optimization strategy of magnetization of ring-shaped magnets for practical medical applications.
Assessment of the Physical Properties of an Experimental Adhesive Dentin Bonding Agent with Carbon Nanoparticles
Authors: Binhasan Mashael , Al-Habeeb Khaled M , Almuqbil Abdullah S. , Alhaidary Tarik A. , Alfawaz Yasser F. , Farooq Imran , Vohra Fahim , Abduljabbar Tariq
Published: October 2022

The present study was aimed at reinforcing the control adhesive (CA) with two concentrations (2.5% & 5%) of carbon nanoparticles (CNPs) and evaluating the impact of these additions on the adhesive’s properties. Scanning electron microscopy (SEM) and energy dispersive X-Ray (EDX) spectroscopy were utilized to examine the morphological characteristics and elemental mapping of the filler CNPs. To investigate the adhesive’s properties, rheological assessment, shear bond strength (SBS) testing, analysis of the adhesive–dentin interface, degree of conversion (DC) analysis, and failure mode investigations were carried out. The SEM micrographs of CNPs verified roughly hexagonal-shaped cylindrical particles. The EDX plotting established the presence of carbon (C), oxygen (O), and zirconia (Zr). Upon rheological assessment, a gradual reduction in the viscosity was observed for all the adhesives at higher angular frequencies. The SBS testing revealed the highest values for 2.5% CNP adhesive group (25.15 ± 3.08 MPa) followed by 5% CNP adhesive group (24.25 ± 3.05 MPa). Adhesive type interfacial failures were most commonly found in this study. The 5% CNP containing adhesive revealed thicker resin tags and a uniform hybrid layer without any gaps (compared with 2.5% CNP adhesive and CA). The reinforcement of the CA with 2.5% and 5% CNPs augmented the adhesive’s bond strength. Nevertheless, a diminished viscosity (at higher angular frequencies) and reduced DC were observed for the two CNP reinforced adhesives. CNP reinforced dentin adhesives are effective in enhancing the adhesive bond integrity of resin to dentin.
Automatic Malignant and Benign Skin Cancer Classification Using a Hybrid Deep Learning Approach
Authors: Bassel Atheer , Abdulkareem Amjed Basil , Alyasseri Zaid Abdi Alkareem , Sani Nor Samsiah , Mohammed Husam Jasim
Published: October 2022

Skin cancer is one of the major types of cancer with an increasing incidence in recent decades. The source of skin cancer arises in various dermatologic disorders. Skin cancer is classified into various types based on texture, color, morphological features, and structure. The conventional approach for skin cancer identification needs time and money for the predicted results. Currently, medical science is utilizing various tools based on digital technology for the classification of skin cancer. The machine learning-based classification approach is the robust and dominant approach for automatic methods of classifying skin cancer. The various existing and proposed methods of deep neural network, support vector machine (SVM), neural network (NN), random forest (RF), and K-nearest neighbor are used for malignant and benign skin cancer identification. In this study, a method was proposed based on the stacking of classifiers with three folds towards the classification of melanoma and benign skin cancers. The system was trained with 1000 skin images with the categories of melanoma and benign. The training and testing were performed using 70 and 30 percent of the overall data set, respectively. The primary feature extraction was conducted using the Resnet50, Xception, and VGG16 methods. The accuracy, F1 scores, AUC, and sensitivity metrics were used for the overall performance evaluation. In the proposed Stacked CV method, the system was trained in three levels by deep learning, SVM, RF, NN, KNN, and logistic regression methods. The proposed method for Xception techniques of feature extraction achieved 90.9% accuracy and was stronger compared to ResNet50 and VGG 16 methods. The improvement and optimization of the proposed method with a large training dataset could provide a reliable and robust skin cancer classification system.
A Virtual Point-Oriented Control for Distance-Based Directed Formation and Its Application to Small Fixed-Wing UAVs
Authors: Yan Jiarun , Yu Yangguang , Xu Yinbo , Wang Xiangke
Published: October 2022

This paper proposes a new algorithm to solve the control problem for a special class of distance-based directed formations, namely directed-triangulated Laman graphs. The central idea of the algorithm is to construct a virtual point for the agents who have more than two neighbors by employing the information of the desired formation. Compared with the existing methods, the proposed algorithm can make the distance error between the agents converge faster and the path consumption is less. Furthermore, the proposed algorithm is modified to be operable for the small fixed-wing UAV model with nonholonomic and input constraints. Finally, the effectiveness of the proposed method is verified by a series of simulation experiments.
Simulation and Characterization of Nanostructured Electromagnetic Scatterers for Information Encoding
Authors: Drexler Petr , Nešpor Dušan , Kadlec Radim , Kříž Tomáš , Nebojsa Alois
Published: October 2022

Nanostructured scattering arrays for the optical spectral domain can be used as passive tags for information encoding, similarly to the manner in which RFID technology does. Setting up their specific spectral response depends on their geometry and the properties of the building materials. The primary design can be provided by using an analytical calculation procedure that is more straightforward and simpler than a numerical simulation. However, the question arises as to the validity of the results. Both approaches are examined in this article. Complementary scatterer arrays were designed using simplified analytical calculation and by means of numerical modeling. The experimental samples were fabricated by the focused ion beam milling of a gold film on a glass substrate and characterized by a spectroscopic system. The results of the analytical calculations, the numerical simulations, and the experimental measurements were compared. On the basis of the comparison, it was observed that for quick array design, both approaches can be used with satisfactory accuracy. Moreover, the simple numerical model also proved the possibility of the identification of the basic dipole mode splitting. Focused ion beam milling was shown to be suitable for the rapid production of complementary scatterer arrays.
Stochastic-Metaheuristic Model for Multi-Criteria Allocation of Wind Energy Resources in Distribution Network Using Improved Equilibrium Optimization Algorithm
Authors: Alanazi Abdulaziz , Alanazi Mohana , Nowdeh Saber Arabi , Abdelaziz Almoataz Y. , Abu-Siada Ahmed
Published: October 2022

In this paper, a stochastic-meta-heuristic model (SMM) for multi-criteria allocation of wind turbines (WT) in a distribution network is performed for minimizing the power losses, enhancing voltage profile and stability, and enhancing network reliability defined as energy not-supplied cost (ENSC) incorporating uncertainty of resource production and network demand. The proposed methodology has been implemented using the SMM, considering the uncertainty modeling of WT generation with Weibull probability distribution function (PDF) and load demand based on the normal PDF and using a new meta-heuristic method named the improved equilibrium optimization algorithm (IEOA). The traditional equilibrium optimization algorithm (EOA) is modeled by the simple dynamic equilibrium of the mass with proper composition in a control volume in which the nonlinear inertia weight reduction strategy is applied to improve the global search capability of the algorithm and prevent premature convergence. First, the problem is implemented without considering the uncertainty as a deterministic meta-heuristic model (DMM), and then the SMM is implemented considering the uncertainties. The results of DMM reveal the better capability of the IEOA method in achieving the lowest losses and the better voltage profile and stability and the higher level of the reliability in comparison with conventional EOA, particle swarm optimization (PSO), manta ray foraging optimization (MRFO) and spotted hyena optimization (SHO). The results show that in the DMM solving using the IEOA, traditional EOA, PSO, MRFO, and SHO, the ENSC is reduced from $3223.5 for the base network to $632.05, $636.90, $638.14, $635.67, and $636.18, respectively, and the losses decreased from 202.68 kW to 79.54 kW, 80.32 kW, 80.60 kW, 80.05 kW and 80.22 kW, respectively, while the network minimum voltage increased from 0.91308 p.u to 0.9588 p.u, 0.9585 p.u, 0.9584 p.u, 0.9586 p.u, and 0.9586 p.u, respectively, and the VSI improved from 26.28 p.u to 30.05 p.u, 30.03 p.u, 30.03 p.u, 30.04 p.u and 30.04 p.u; respectively. The results of the SMM showed that incorporating uncertainties increases the losses, weakens the voltage profile and stability and also reduces the network reliability. Compared to the DMM, the SMM-based problem is robust to prediction errors caused by uncertainties. Therefore, SMM based on existing uncertainties can lead to correct decision-making in the conditions of inherent-probabilistic changes in resource generation and load demand by the network operator.
Towards Robotic Marble Resin Application: Crack Detection on Marble Using Deep Learning
Authors: Vrochidou Eleni , Sidiropoulos George K. , Ouzounis Athanasios G. , Lampoglou Anastasia , Tsimperidis Ioannis , Papakostas George A. , Sarafis Ilias T. , Kalpakis Vassilis , Stamkos Andreas
Published: October 2022

Cracks can occur on different surfaces such as buildings, roads, aircrafts, etc. The manual inspection of cracks is time-consuming and prone to human error. Machine vision has been used for decades to detect defects in materials in production lines. However, the detection or segmentation of cracks on a randomly textured surface, such as marble, has not been sufficiently investigated. This work provides an up-to-date systematic and exhaustive study on marble crack segmentation with color images based on deep learning (DL) techniques. The authors conducted a performance evaluation of 112 DL segmentation models with red–green–blue (RGB) marble slab images using five-fold cross-validation, providing consistent evaluation metrics in terms of Intersection over Union (IoU), precision, recall and F1 score to identify the segmentation challenges related to marble cracks’ physiology. Comparative results reveal the FPN model as the most efficient architecture, scoring 71.35% mean IoU, and SE-ResNet as the most effective feature extraction network family. The results indicate the importance of selecting the appropriate Loss function and backbone network, underline the challenges related to the marble crack segmentation problem, and pose an important step towards the robotic automation of crack segmentation and simultaneous resin application to heal cracks in marble-processing plants.
Measuring Institutions’ Adoption of Artificial Intelligence Applications in Online Learning Environments: Integrating the Innovation Diffusion Theory with Technology Adoption Rate
Authors: Almaiah Mohammed Amin , Alfaisal Raghad , Salloum Said A. , Hajjej Fahima , Shishakly Rima , Lutfi Abdalwali , Alrawad Mahmaod , Al Mulhem Ahmed , Alkhdour Tayseer , Al-Maroof Rana Saeed
Published: October 2022

Artificial intelligence applications (AIA) increase innovative interaction, allowing for a more interactive environment in governmental institutions. Artificial intelligence is user-friendly and embraces an effective number of features among the different services it offers. This study aims to investigate users’ experiences with AIA for governmental purposes in the Gulf area. The conceptual model comprises the adoption properties (namely trialability, observability, compatibility, and complexity), relative advantage, ease of doing business, and technology export. The novelty of the paper lies in its conceptual model that correlates with both personal characteristics and technology-based features. The results show that the variables of diffusion theory have a positive impact on the two variables of ease of doing business and technology export. The practical implications of the current study are significant. We urge the concerned authorities in the governmental sector to understand the significance of each factor and encourage them to make plans, according to the order of significance of the factors. The managerial implications provide insights into the implementation of AIA in governmental systems to enhance the development of the services they offer and to facilitate their use by all users.
Electricity Demand Elasticity, Mobility, and COVID-19 Contagion Nexus in the Italian Day-Ahead Electricity Market
Authors: Bollino Carlo Andrea , D’Errico Maria Chiara
Published: October 2022

The magnitude of the impact of the pandemic on key variables, such as electricity demand, mobility of people and number of COVID-19 hospitalization cases, has been unprecedented. Existing economic models have not estimated the impact of sucokh events. This paper fills this gap, investigating the nexus among electricity demand elasticity, shifting behaviors of mobility and COVID-19 contagion with econometric estimation techniques. Firstly, using the single bids to purchase recorded in the Italian day-ahead wholesale electricity market in 2020, we estimate hourly electricity demand and price elasticity directly from short-run consumer behavior. Then, we analyze the effects of the main aspects of the pandemic, the health situation and the mobility contraction at the national level, on the estimated price elasticities. The period of heavy lockdown between 10 March and 3 June recorded a reduction in the price elasticity of electricity demand. However, when the pandemic broke out again at the beginning of October, elasticity increased, highlighting how companies and economic activities had adopted countermeasures to avoid the arrest of the economy and, consequently, the sharp contraction in electricity demand.
Flow Boiling Heat Transfer of R134a in a Horizontal Smooth Tube: Experimental Results, Flow Patterns, and Assessment of Correlations
Authors: Bediako Ernest Gyan , Dančová Petra , Vít Tomáš
Published: October 2022

This study presents an extensive evaluation of heat transfer characteristics, flow patterns, and pressure drop for saturation pressures ranging from 460–660 kPa in a horizontal smooth tube of 5 mm internal diameter using R134a as the working fluid. The effect of saturation pressures for mass fluxes of 150–300 kg/m2s and heat fluxes of 8.26–23.3 kW/m2 which are typical of refrigeration and air conditioning applications are also investigated. Flow patterns observed during the study are predicted with a well-known flow pattern map of Wojtan et al. The experimental results are compared with seven (7) correlations developed based on different theories to find which correlation best predicts the experimental data. The results show that, at low mass flux, increasing saturation pressure results in an increased heat transfer coefficient. This effect is more pronounced in the low vapor quality region and the dominant mechanism is nucleate boiling. At high mass flux, increasing saturation pressure leads to an insignificant increase in the heat transfer coefficient. At this high mass flux but low heat flux, the heat transfer coefficient increases with vapor quality, indicating convective boiling dominance. However, for high heat flux, the heat transfer coefficient is linear over vapor quality, indicating nucleate boiling dominance. Pressure drop is observed to decrease with increasing saturation pressure. Increasing saturation pressure increases the vapor quality at which the flow pattern transitions from intermittent flow to annular flow. The flow patterns predicted are a mixture of slug and stratified wavy and purely stratified wavy for low mass fluxes. For increased mass fluxes, the flow patterns predicted are slug, intermittent, annular, and dryout. Cooper’s model was the best predictor of the experimental data and the trend of heat transfer coefficient.
The Anatomy of Unaffordable Electricity in Northern Europe in 2021
Authors: Jääskeläinen Jaakko , Huhta Kaisa , Syri Sanna
Published: October 2022

European electricity prices soared to unusually high levels during 2021, which exposed vulnerabilities in the economy and led to concerns about affordability. The concerns were further exacerbated in 2022, as Europe strove to cut its dependence on the Russian fossil fuel supply, as a result of the Russian invasion of Ukraine. This article explores the causes of the price increases in 2021 and assesses their likely future development by using Finland as a case example. We address a gap in the existing energy literature by elucidating the origins and future outlooks of price spikes in highly interconnected electricity markets. Based on an interdisciplinary combination of legal and qualitative data analysis, this study approaches its key objective in three stages. First, we describe the European market and its regulatory design to demonstrate the legislative framework and preconditions within which the market is expected to operate and how these rules connect to guaranteeing the affordability of electricity. Second, we explore how these preconditions functioned in practice in 2021 by analysing the wider macro-level trends that resulted in the elevated prices throughout Europe, particularly in Finland. Third, we assess the impacts of these trends on Finnish electricity price development. Based on these descriptive and predictive analyses, we show that the European market design fundamentally necessitates price variation to ensure market-based investment and energy security in the long-term. Our analysis demonstrates that the high energy prices in 2021 were, in general, the result of various weather-related, economic, and political factors. Moreover, our findings indicate that the dynamics of price formation within a Member State are complex, and in the case of Finland, strongly impacted by neighbouring markets.
Microstructure, Deformation Characteristics and Energy Analysis of Mudstone under Water Absorption Process
Authors: Feng Zheyuan , Xu Qi , Luo Xinyu , Huang Ruyu , Liao Xin , Tang Qiang
Published: October 2022

In geological engineering, a series of safety problems caused by expansive mudstone are common, such as slope instability and roadbed up-arch. In this paper, the mineral composition of mudstones in the Xining area was analyzed by X-ray diffraction (XRD), and the microstructural and morphological changes of mudstones after water absorption were observed by scanning electron microscopy (SEM) test to analyze the internal factors and microstructural evolution patterns of water absorption and swelling of mudstones. Based on the microstructural units, the mudstones were defined into two categories, one is N-type mudstone with flat sheet-like stromatolite units, and the other is SN-type mudstone with more clastic particle units. Water absorption experiments were conducted on the rock samples to study the microstructure of these two types of mudstones under different water absorption conditions. The pore characteristics of the mudstones were analyzed by using Image-Pro Plus to reveal the water absorption mechanism. The results show that the pore area of N-type mudstone is smaller, as well as the distribution of pore diameter. The pore area of N-type mudstone develops rapidly, in the early stage of water absorption, lots of pores are produced, and the pore area of SN-type mudstone shows an overall decreasing trend. The pore area and the number of SN-type mudstones are at a low level after full water absorption. Under the condition of full immersion, water enters the pores rapidly and soluble salts are dissolved in large quantities. The change of water absorption rate of mudstone with time can be divided into the stage of sudden increase, decrease and stability of water absorption rate. Then, based on the stress theory, the relationship between the macroscopic expansion process and the microstructure of mudstone was analyzed. Finally, the energy basis of mudstone water absorption is discussed. In the swelling of mudstone, the energy gradually turns into swelling strain energy.
Shades of Green: Life Cycle Assessment of a Novel Small-Scale Vertical Axis Wind Turbine Tree
Authors: Ngoc Duong Minh , Luengchavanon Montri , Anh Pham Thi , Humphreys Kim , Techato Kuaanan
Published: October 2022

Are small-scale wind turbines green? In this study, we perform a ‘cradle to grave’ life cycle assessment of a novel domestic-scale 10 kW vertical axis wind turbine tree which uses combined Savonius and H-Darrieus blades. Situated at a test site in Surat Thani, Thailand, SimaPro software was used to evaluate the environmental impact profile of the tree. Comparisons to the Thai grid mix were made, using both with and without end-of-life treatments. Impact profiles were calculated using wind data collected over two years at Surat Thani, and from wind data from a higher capacity factor (CF) site at Chiang Mai, Thailand. Energy and greenhouse gas payback times were estimated for both locations. The relative magnitudes of impacts were compared with environmental prices protocol, and we investigated reductions in impacts using three mitigative scenarios: changes to design, transportation and materials. The results showed that Chiang Mai had a CF = 7.58% and Surat Thani had a CF = 1.68%. A total of 9 out of 11 impacts were less than the grid values at Chiang Mai, but at Surat Thani, 9 of 11 impacts were more than the grid values. End-of-life treatments reduced impacts by an average of 11%. The tower and generator were majority contributors to impacts (average 69%). Greenhouse gas and energy payback times were 28.61 and 54.77 years, and 6.50 and 12.50 years for Surat Thani and Chiang Mai, respectively, with only the Chiang Mai times being less than the turbine’s estimated lifetime. Location changes mitigated impacts most, followed by design, transportation, and then materials. We make recommendations to further improve the environmental impact profile of this turbine tree.
Intended and Unintended Consequences of Two Paradigms of Urban Planning, and Their Social Justice and Human Health Impacts, in Portland, Oregon
Authors: Kolmes Steven A.
Published: October 2022

This article describes two contesting paradigms of urban planning employed successively in Portland, Oregon; (1) urban planning typical of the US in the first half of the 20th Century that was focused on traffic and infrastructure, and (2) progressive urban planning focused on neighborhood livability and connections. It gives a history of their implementation in Portland, focusing on issues of racial and socioeconomic justice in the Albina neighborhood. Recent knowledge about air pollution’s impacts on human health, and infant and childhood development, are integrated into the discussion of urban planning. It describes racially and socioeconomically disproportionate access to urban green spaces, with the corresponding health implications. It also describes attempts to mitigate such health implications, sometimes resulting in “green gentrification” and displacement. The article asks if the results of the two paradigms of urban planning were objectively different from one another in terms of impacts on minority and disadvantaged communities. Future urban planning, and the need for human health concerns becoming central, are discussed.
An Integrated Similarity Analysis of Anatomical and Physical Wood Properties of Tropical Species from India, Mozambique, and East Timor
Authors: Bessa Fernanda , Sousa Vicelina , Quilhó Teresa , Pereira Helena
Published: October 2022

Tropical species are highly valued timber sources showing a large diversity of wood characteristics. Since there are major concerns regarding the sustainability of these tropical species in many tropical regions, knowledge of the variability in wood properties is therefore a valuable tool to design targeted exploitation and to enlarge the wood resources base, namely by identifying alternatives for CITES-listed species. In this study, 98 tropical wood species belonging to 73 genera from India, Mozambique, and East Timor were investigated regarding wood anatomy and physical properties. Numerical taxonomy, by means of cluster analysis and principal component analysis grouped species with anatomical and physical similarities from different geographical origins. In addition to wood density, ray and vessel characteristics as well as wood moisture and wood shrinkage properties explained the main variability of these species. The contribution of wood color patterns was highlighted as consistently separating the Mozambique woods. A distinct geographical pattern was not observed, reinforcing that species from India, Mozambique, and East Timor show similar anatomical and physical wood properties, which could be useful to increase timber trade diversity. The multivariate analysis showed that species from Mozambique, such as Morus mesozygia, and Millettia stuhlmannii and Swartzia madagascariensis, could be alternatives for the CITES-listed species Cedrela odorata and Dalbergia melanoxylon, respectively.
Woodfuel Consumption in Refugee Hosting Areas and Its Impact on the Surrounding Forests—The Case of Uganda
Authors: Gianvenuti Arturo , Bedijo Nelly Grace , Jalal Rashed , Hitimana Leonidas , Walter Sven , Linhares-Juvenal Thais , Xia Zuzhang
Published: October 2022

The high dependency of households on woodfuels in Uganda is a renowned driver of forest degradation. Refugee settlements might aggravate the pressure on the environment caused by woodfuel consumption in the absence of impact assessments and measures to improve environmental management and build sustainable livelihoods. In collaboration with the Government of Uganda, UNHCR, and the World Bank, FAO conducted assessments on woodfuel consumption at the household level in displacement settings in Uganda, as well as its impact on the surrounding forests, through field forest inventories, household surveys, and geospatial analysis. The results of these assessments highlight the impact of woodfuel consumption and other drivers on forest degradation, and provide guidance for the development of comprehensive interventions on landscape management and improvement of household cooking fuels and technologies, targeting both refugee and host communities.
Stem Growth of Horse Chestnut (Aesculus hippocastanum L.) under a Warming Climate—Tree Age Matters
Authors: Plichta Roman , Úradníček Luboš , Gebauer Roman
Published: October 2022

This research provides new information about the effect of drought on horse chestnut growth (Aesculus hippocastanum L.) in different ages. Global climatic scenarios predict a higher frequency of heatwaves and drought periods; however, investigations into the growth reaction of horse chestnut to drought are completely lacking. Approximately 50-year-old solitary, 100-year-old solitary, and 100-year-old canopy horse chestnut trees in a floodplain area were investigated. Growth reactions measured using automated dendrometers with respect to meteorological variables and water table depth were investigated during the years 2019–2021. Cambial activity was shown to be driven by tree age, as younger trees had higher stem radial increment rates. Both mature tree groups suffered from a low depth of water level and from higher sensitivity to meteorological variables, as growth was limited when mean daily vapor pressure deficit (VPD) exceeded 600 Pa. Together with a lower probability of growing days and a shorter growing season (GS) with earlier cessation of growth resulted in a lower total year radial increment (GRO) and basal area increment (BAI) when compared to younger trees. The young trees also exhibited lower tree-water-deficit-induced stem shrinkage (TWD) across all the studied years. Overall, horse chestnut trees in this floodplain area could be endangered by the decreasing level of soil water, with a greater age exacerbating the effects of drought. The year water deficit exceeded −340 mm in this locality every year, which has to be compensated for by regular flooding.
Flavor and Functional Analysis of Lactobacillus plantarum Fermented Apricot Juice
Authors: Sun Jinkui , Zhao Cai , Pu Xilei , Li Tian , Shi Xuewei , Wang Bin , Cheng Weidong
Published: October 2022

The small white apricot is a juicy, delicious fruit with a short shelf life. Slight fermentation can significantly promote the flavors and nutrient value of apricot juice. This study used high-performance liquid chromatography (HPLC) and headspace solid-phase microextraction combined with gas chromatography–mass spectrometry (HS-SPME-GC-MS) to examine the physicochemical properties, nutritive value and flavor substances of apricot juice fermented by Lactobacillus plantarum LP56. Fermentation significantly increased lactic acid bacteria (LAB) and their product lactic acid, adding probiotic benefits to fermented apricot juice. In addition, the total phenolic compounds and antioxidant capacity increased, while the levels of soluble solids and organic acids decreased. Gallic acid, 3-caffeoylquinic acid and rutin mainly contributed to the antioxidant activity of fermented apricot juice. Alcohols, aldehyde, acid, ester, etc., were the main volatile compounds. Among the flavors, 12 substances with high odor activity values (OAV > 1) were the key aroma-producing compounds with fruit, pine and citrus flavors. In conclusion, this study shows that L. plantarum LP56 fermentation can improve the nutritional value and aroma characteristics of apricot juice.
In Vitro Characterisation of Potential Probiotic Bacteria Isolated from a Naturally Fermented Carrot and Ginger Brine
Authors: Cele Nontobeko , Nyide Babalwa , Khoza Thandeka
Published: October 2022

Unhealthy dietary patterns have been associated with an increase in mortality rate as well as the high occurrence of nontransmissible chronic degenerative diseases. Subsequently, the development of new functional foods has been proposed to reduce the incidence of nontransmissible chronic degenerative diseases. Probiotics represent a group of functional foods, defined as live microbial feeds, which provide the host with intestinal health benefits. The present study focused on the identification and characterisation of the probiotic potential of lactic acid bacteria isolated from a fermented carrot and ginger brine. Sixteen isolates were identified as Leuconostoc mesenteroides subsp. mesenteroides species, following preliminary screening based on 16S rDNA gene sequencing, and were further characterised for probiotic candidature. The probiotic properties tested included resistance towards gastrointestinal conditions (bile, acid, lysozyme tolerance), cell surface hydrophobicity, antioxidant activity, and antagonistic activity against intestinal pathogens. In general, all the isolated Leuconostoc mesenteroides subsp. mesenteroides strains exhibited high acid, bile, and lysozyme tolerance. They also showed strong antibacterial activity against common intestinal pathogens, i.e., Staphylococcus aureus and Escherichia coli, as well as antioxidant activity such as hydroxyl radical-scavenging ability and hydrogen peroxide resistance. Overall, Leuconostoc mesenteroides subsp. mesenteroides possesses a great potential as a beneficial strain for functional food.
Unreachable Peers Communication Scheme in Decentralized Networks Based on Peer-to-Peer Overlay Approaches
Authors: Li Gengxian , Wang Chundong , Wang Huaibin
Published: October 2022

Decentralized networks bring us many benefits, but as networks evolve, many nodes either actively or passively become unreachable behind an NAT or a firewall. This has become a hindrance to the development of decentralized networks, where peer-to-peer communication data transfer between unreachable nodes cannot be accomplished, whether in decentralized file systems, decentralized social, or decentralized IoT. The existing scheme requires a series of centralized servers or requires network-wide flooding for consensus data, which can lead to the loss of decentralized nature of the network and cause flooding bottlenecks, contrary to the design concept of decentralization. In this paper, our proposed scheme uses a structured P2P overlay network to store the indexes of unreachable nodes in the whole network, so that the characteristics of a decentralized network are still maintained while ensuring the efficiency of lookup. When nodes communicate, the transmission channel is established so that both nodes continuously transmit data streams peer-to-peer without relying on the central server. Moreover, the scheme guarantees the security and privacy of nodes’ data transmission and the P2P overlay network without relying on centralized trusted institutions. Finally, we deploy a real cluster environment to verify the effectiveness of each module at different network sizes and prove the overall feasibility of the scheme. The scheme has certain advantages over existing solutions in terms of security, privacy, communication efficiency, device democracy, etc.
Models for Point-of-Sale (POS) Market Entry
Authors: Pattamatta Pritham , Dabadghao Shaunak S.
Published: October 2022

Point of Sale (PoS) or Buy-Now-Pay-Later (BNPL) type financing has observed a strong rise in the last decade, and accounts for about 10% of the unsecured lending market in the USA. This market is largely covered by FinTechs, and traditional financial institutions are beginning to demonstrate interest in entering this market. In this short communication, we identify why the POS market is attractive and what drives its growth, examine the market participants, and illustrate the various ways in which financial institutions can enter this market.