bluuebunny/crossref_metadata_2025 huggingface dataset:
A Machine Learning Approach for Air-Quality Forecast by Integrating GNSS Radio Occultation Observation and Weather Modeling
Authors: Li Wei , Kang Shengyu , Sun Yueqiang , Bai Weihua , Wang Yuhe , Song Hongqing
Published: December 2022

Air-quality monitoring and forecasting are crucial for atmosphere pollution control and management. We propose an innovative data-driven framework for air quality index (AQI) prediction by integrating GNSS radio occultation (GNSS-RO) observation and weather modeling. Empowered by the state-of-the-art machine learning approach, our method can effectively predict regional AQI with a comparable accuracy much more quickly than the traditional numerical modeling and simulation approach. In a real case study using a representative region of China, our data-driven approach achieves a 2000 times speedup; meanwhile, the prediction error measured by rRMSE is only 2.4%. We investigate further the effects of different models, hyperparameters, and meteorological factors on the performance of our AQI prediction framework, and reveal that wind field and atmospheric boundary-layer height are important influencing factors of AQI. This paper showcases a direct application of GNSS-RO observation in assisting in forecasting regional AQI. From a machine learning point of view, it provides a new way to leverage the unique merits of GNSS atmospheric remote sensing technology with the help of the more traditional weather forecasting modeling approach.
A Study on the Beech Wood Machining Parameters Optimization Using Response Surface Methodology
Authors: Pakzad Sajjad , Pedrammehr Siamak , Hejazian Mahsa
Published: December 2022

The surface quality of wooden products is of great importance to production industries. The best surface quality requires a thorough understanding of the cutting parameters’ effects on the wooden material. In this paper, response surface methodology, which is one of the conventional statistical methods in experiment design, has been used to design experiments and investigate the effect of different machining parameters as feed rate, spindle speed, step over, and depth of cut on surface quality of the beech wood. The mathematical model of the examined parameters and the surface roughness have also been obtained by the method. Finally, the optimal machining parameters have been obtained to achieve the best quality of the machined surface, which reduced the surface roughness up to 4.2 (µm). Each of the machining parameters has a considerable effect on surface quality, although it is noted that the feed rate has the greatest effect.
Conservative Management of Patent Ductus Arteriosus Is Feasible in the Peri-Viable Infants at 22–25 Gestational Weeks
Authors: Yang Misun , Chang Yun Sil , Ahn So Yoon , Sung Se In , Jo Heui Seung , Park Won Soon
Published: December 2022

The purpose of this study was to determine the natural course of hemodynamically significant (HS) patent ductus arteriosus (PDA) with conservative management and whether the presence or prolonged duration of HS PDA affected mortality/morbidities in infants at 22–25 weeks estimated gestational age (EGA). We retrospectively reviewed the medical records of 77 infants born at 22–25 weeks EGA, stratified into 22–23 weeks (n = 21) and 24–25 weeks EGA (n = 56). HS PDA was present in 77%, 76%, and 77%, and open ductus at discharge was 12%, 13%, and 12% in the total and at 22–23 and 24–25 weeks EGA infants, respectively. For backup rescue treatment, 7% and 5% of the infants received oral ibuprofen and device closure, respectively. A mortality rate of 9% was found in the HS PDA (+) infants, significantly lower than the 28% in HS PDA (−) infants. There are no significant differences in morbidities. In multivariate analyses, the presence and/or prolonged duration of HS PDA was not associated with increased mortality or morbidity. Spontaneous closure of HS PDA was achieved through conservative management in the peri-viable infants at 22–25 weeks EGA.
Ultrasensitive Optical Fingerprinting of Biorelevant Molecules by Means of SERS-Mapping on Nanostructured Metasurfaces
Authors: Kozhina Elizaveta , Bedin Sergey , Martynov Alexander , Andreev Stepan , Piryazev Alexey , Grigoriev Yuri , Gorbunova Yulia , Naumov Andrey
Published: December 2022

The most relevant technique for portable (on-chip) sensors is Surface Enhanced Raman Scattering (SERS). This strategy crashes in the case of large (biorelevant) molecules and nano-objects, whose SERS spectra are irreproducible for “homeopathic” concentrations. We suggested solving this problem by SERS-mapping. We analyzed the distributions of SERS parameters for relatively “small” (malachite green (MG)) and “large” (phthalocyanine, H2Pc*) molecules. While fluctuations of spectra for “small” MG were negligible, noticeable distribution of spectra was observed for “large” H2Pc*. We show that the latter is due to a random arrangement of molecules with respect to “hot spot” areas, which have limited sizes, thus amplifying the lines corresponding to vibrations of different molecule parts. We have developed a method for engineering low-cost SERS substrates optimized for the best enhancement efficiency and a measurement protocol to obtain a reliable Raman spectrum, even for a countable number of large molecules randomly distributed over the substrate.
Sustainable Building Optimization Model for Early-Stage Design
Authors: Elbeltagi Emad , Wefki Hossam , Khallaf Rana
Published: December 2022

Buildings represent the largest potential for carbon reduction worldwide. This highlights the need for a simulation and optimization method for energy management. The early design stage of buildings represents an important phase in which choices can be made to optimize design parameters. These parameters can focus on multiple areas, including energy and thermal comfort. This paper introduces the optimization of early-stage sustainable building design considering end-user energy consumption. It proposes an optimization model that integrates multiple layers, which consist of a parametric energy simulation, artificial neural network, and genetic algorithm. The proposed optimization model considers a single objective function to obtain the optimal design. The targeted goal is to obtain minimal energy consumption for residential buildings during the early design stages. Key design parameters of the building were identified for optimization and feasible ranges for them were obtained using genetic algorithms. Finally, the results of this paper include the identification of the optimal building design for the thermal comfort analysis and optimal energy performance. The model was applied to a case study in Egypt and the results showed that using the developed optimization model can lead to a 25% reduction in energy consumption.
Immunohistochemical Study of Bladder Cancer Molecular Subtypes and Their Association with PD-L1 Expression
Authors: Goutas Dimitrios , Palamaris Kostas , Stofas Anastasios , Politakis Nektarios , Despotidi Antonia , Giannopoulou Ioanna , Goutas Nikolaos , Vlachodimitropoulos Dimitrios , Kavantzas Nikolaos , Lazaris Andreas C. , Gakiopoulou Hariklia
Published: December 2022

The significant heterogeneity in clinical outcomes among patients with bladder cancer has highlighted the existence of different biological subtypes of muscle-invasive bladder cancer (MIBC) and non-muscle-invasive bladder cancer (NMIBC). Meanwhile, immune checkpoint proteins and their interference with tumor-related immune-evasive strategies has led to the development of several immunotherapeutic drugs targeting programmed death-1 (PD-1) or programmed death ligand-1 (PD-L1). However, the lack of any known biomarker that could predict responses to immunotherapy has led to a more agnostic therapeutic approach. Here, we present a study conducted in 77 bladder cancer (BC) patients (n = 77), ranging from stages pTa to pT2. Tumor specimens were resected via transurethral resection of bladder tumor (TURBT) and consistuted of 24 low-grade (LG) and 53 high-grade (HG) tumors. Patients’ tumors were then categorized into molecular subtypes, via immunohistochemistry (CK5/6 and GATA3). Furthermore, all tumor specimens were stained with anti-PD-L1 and demonstrated significant correlations with basal immunophenotype, stage pT2 and HG tumors. As such, we attempted to stratify patients into groups of likely-responders and likely-not-responders to immunotherapy with anti-PD-L1, based on their molecular phenotype. Finally, in acknowledging the fact that there is a universal lack of biomarkers associated with predicting BC response to immunotherapeutic drugs, we tested all tumors for deficiency of mismatch repair proteins (MMR).
Margin Free Resection Achieves Excellent Long Term Outcomes in Parathyroid Cancer
Authors: Schulte Klaus-Martin , Talat Nadia , Galatá Gabriele
Published: December 2022

Long-term outcomes of parathyroid cancer remain poorly documented and unsatisfactory. This cohort includes 25 consecutive parathyroid cancer patients with median follow-up of 10.7 years (range 4.1–26.5 years). Pre-operative work-up in the center identified a suspicion of parathyroid cancer in 17 patients. En bloc resection, including the recurrent laryngeal nerve in 4/17 (23.5%), achieved cancer-free resection margins (R0) in 82.4% and lasting loco-regional disease control in 94.1%. Including patients referred after initial surgery elsewhere, R0 resection was achieved in merely 17/25 (68.0%) of patients. Cancer-positive margins (R1) in 8 patients led to local recurrence in 50%. On multivariate analysis, only margin status prevailed as independent predictor of recurrence free survival (χ2 19.5, p < 0.001). Local excision alone carried a 3.5-fold higher risk of positive margins than en bloc resection (CI95: 1.1–11.3; p = 0.03), and a 6.4-fold higher risk of locoregional recurrence (CI95: 0.8–52.1; p = 0.08). R1-status was associated with an 18.0-fold higher risk of recurrence and redo surgery (CI95: 1.1–299.0; p = 0.04), and a 22.0-fold higher probability of radiation (CI95: 1.4–355.5; p = 0.03). In patients at risk, adjuvant radiation reduced the actuarial risk of locoregional recurrence (p = 0.05). When pre-operative scrutiny resulted in upfront oncological surgery achieving cancer free margins, it afforded 100% recurrence free survival at 5- and 10-year follow-up, whilst failure to achieve clear margins caused significant burden by outpatient admissions (176 vs. 4 days; χ2 980, p < 0.001) and exposure to causes for concern (1369 vs. 0 days; χ2 11.3, p = 0.003). Although limited by cohort size, our study emphasizes the paradigm of getting it right the first time as key to improve survivorship in a cancer with excellent long-term prognosis.
In Vitro Anticancer Properties of Novel Bis-Triazoles
Authors: Saleh Maysaa M. , Abuarqoub Duaa A. , Hammad Alaa M. , Hossan Md Shahadat , Ahmed Najneen , Aslam Nazneen , Naser Abdallah Y. , Moody Christopher J. , Laughton Charles A. , Bradshaw Tracey D.
Published: December 2022

Here, we describe the anticancer activity of our novel bis-triazoles MS47 and MS49, developed previously as G-quadruplex stabilizers, focusing specifically upon the human melanoma MDA-MB-435 cell line. At the National Cancer Institute (NCI), USA, bis-triazole MS47 (NCS 778438) was evaluated against a panel of sixty human cancer cell lines, and showed selective, distinct multi-log differential patterns of activity, with GI50 and LC50 values in the sub-micromolar range against human cancer cells. MS47 showed highly selective cytotoxicity towards human melanoma, ovarian, CNS and colon cancer cell lines; in contrast, the leukemia cell lines interestingly showed resistance to MS47 cytotoxic activity. Further studies revealed the potent cell growth inhibiting properties of MS47 and MS49 against the human melanoma MDA-MB-435 cell line, as verified by MTT assays; both ligands were more potent against cancer cells than MRC-5 fetal lung fibroblasts (SI > 9). Melanoma colony formation was significantly suppressed by MS47 and MS49, and time- and dose-dependent apoptosis induction was also observed. Furthermore, MS47 significantly arrested melanoma cells at the G0/G1 cell cycle phase. While the expression levels of Hsp90 protein in melanoma cells were significantly decreased by MS49, corroborating its binding to the G4-DNA promoter of the Hsp90 gene. Both ligands failed to induce senescence in the human melanoma cells after 72 h of treatment, corroborating their weak stabilization of the telomeric G4-DNA.
Generation of High-Frequency Ultrasound in a Liquid upon Excitation by Laser Radiation through a Light Guide with a Converter of Transparent Spheres
Authors: Bredikhin Vladimir I. , Kazakov Viacheslav V.
Published: December 2022

One of the important tasks in optoacoustics today is the development of methods and tools for generating high-frequency ultrasound (above 1 MHz) in liquids and other media. To expand the frequency range of ultrasound, it was proposed to use coatings consisting of focusing spheres on a fiber tip. The methodology of calculating the ultrasound spectra depending on the sphere size, index of refraction, and parameters of laser radiation was developed. Two cases of small and large spheres in strongly and weakly absorbing media were simulated. The experimental results were analyzed in the approximations allowing a fairly accurate estimation of the spectrum and indicatrix of the generated ultrasound upon laser excitation through a converter based on a coating of transparent spheres. A good agreement between the model and experimental result was obtained.
Special Issue: Behavior of Materials (Alloys, Coatings) in Conditions Specific to Gen IV Nuclear Reactors
Authors: Fulger Manuela , Khumsa-Ang Kittima , Šípová Monika , Ducu Catalin Marian , Sáez-Maderuelo Alberto
Published: December 2022

Today, countries all over the world, faced with a global energy crisis and the effects of climate change, are looking for alternatives to fossil fuels [...]
Casein-Hydrolysate-Loaded W/O Emulsion Preparation as the Primary Emulsion of Double Emulsions: Effects of Varied Phase Fractions, Emulsifier Types, and Concentrations
Authors: Salum Pelin , Ulubaş Çağla , Güven Onur , Aydemir Levent Yurdaer , Erbay Zafer
Published: December 2022

Stable primary emulsion formation in which different parameters such as viscosity and droplet size come into prominence for their characterization is a key factor in W/O/W emulsions. In this study, different emulsifiers (Crill™ 1, Crill™ 4, AMP, and PGPR) were studied to produce a casein-hydrolysate-loaded stable primary emulsion with lower viscosity and droplet size. Viscosity, electrical conductivity, particle size distribution, and emulsion stability were determined for three different dispersed phase ratios and three emulsifier concentrations. In 31 of the 36 examined emulsion systems, no electrical conductivity could be measured, indicating that appropriate emulsions were formed. While AMP-based emulsions showed non-Newtonian flow behaviors with high consistency coefficients, all PGPR-based emulsions and most of the Crill™-1- and -4-based ones were Newtonian fluids with relatively low viscosities (65.7–274.7 cP). The PGPR-based emulsions were stable for at least 5 days and had D(90) values lower than 2 µm, whereas Crill™-1- and -4-based emulsions had phase separation after 24 h and had minimum D(90) values of 6.8 µm. PGPR-based emulsions were found suitable and within PGPR-based emulsions, and the best formulation was determined by TOPSIS. Using 5% PGPR with a 25% dispersed phase ratio resulted in the highest relative closeness value. The results of this study showed that PGPR is a very effective emulsifier for stable casein-hydrolysate-loaded emulsion formations with low droplet size and viscosity.
Interplay of Ionic Species in Salts of Homoleptic Quaternary Phosphonium Cations Bearing Linear Biphenyl Moieties
Authors: Tan Monica Bernard , Sobolev Alexandre N. , Raston Colin L. , Dalgarno Scott J. , Ling Irene
Published: December 2022

Quaternary phosphonium salts are popular candidates used in many chemical transformations and synthetic chemistry, notably in catalysis. We have examined the single crystals of two bulky phosphonium compounds, tetra([1,1′-biphenyl]-4-yl) phosphonium dicyanamide (C48H36P+·N(CN)2−, compound 1), and tetra([1,1′-biphenyl]-4-yl) phosphonium bromide hydrate (C48H36P+·Br−, CH3CN, H2O, compound 2), and herein report the structural properties for the compounds with an emphasis on the influence of the ion-ion interaction towards self-assembly; the overall self-assembly for both structures is very similar, with subtle differences in the cell parameters. The symmetrical tetra ([1,1′-biphenyl]-4-yl) phosphonium cations in both compounds self-assembled to form robust stacked columns in the solid-state, with voids occupied by anions or solvent molecules. Quantitative examination of intermolecular interactions using Hirshfeld surface analysis found that classical and non-classical hydrogen bonding appears to be the dominant contributor in stabilizing the self-assembly in both cases. The present work can not only benefit in understanding the mutual interaction between the sterically encumbered tetra ([1,1′-biphenyl]-4-yl) phosphonium cations and between counterions, but also provide insights for the self-assembled arrays in the solid-state.
The Anti-Reflection Coating Design for the Very-Long-Wave Infrared Si-Based Blocked Impurity Band Detectors
Authors: Dong Zuoru , Zhou Yangzhou , Chen Yulu , Tao Jiajia , Liu Wenhui , Dai Xiaowan , Wang Bingbing , Wu Yifei , Wang Xiaodong
Published: December 2022

An anti-reflection coating on a back-illuminated 128 × 128 array Si-based blocked impurity band (BIB) detector in a very-long-wave infrared range was designed in this work. The reflectance and transmittance spectra of ZnS films with different thicknesses on intrinsic Si substrates were studied with a FDTD simulation and experiment. Compared to bare Si substrate, the reflectance of Si coated with 1.5, 2.0, 2.5, and 3.0 μm thick ZnS significantly decreased, while the transmittance increased in the range of 10.0~25.0 μm band. The transmittance enhancement ratio reached approximately 32%, 32%, 28%, and 29%, respectively. It was evidenced that the enhanced transmission at a specific wavelength was caused by the effective interference cancellation effect. Then, a 2.0 μm thick ZnS thin film was deposited on the backside of the 128 × 128 array Si-based BIB detector. The spectral responsivity of the detector increased significantly. Additionally, the blackbody responsivity increased by approximately 36%, suggesting that the ZnS film is an ideal anti-reflection material for VLWIR detectors in the range of 10.0~25.0 μm band.
Gastrointestinal Stromal Tumours (GIST) of the Rectum: A Systematic Review and Meta-Analysis
Authors: Khan Shaheer I. , O’Sullivan Niall J. , Temperley Hugo C. , Rausa Emanuele , Mehigan Brian J. , McCormick Paul , Larkin John O. , Kavanagh Dara O. , Kelly Michael E.
Published: December 2022

Background: Rectal gastrointestinal stromal tumours (GISTs) have many treatment options, but uncertainty remains regarding the best treatment regimen for this rare pathology. The aim of this review is to assess the optimal management approach including timing of chemotherapy. Methods: PubMed, EMBASE, and Cochrane databases were searched for relevant articles comparing the impact of radical vs. local excision, and neoadjuvant vs. adjuvant therapy had on outcomes in the management of rectal GISTs. We specifically evaluated the influence that the aforementioned factors had on margins, recurrence, overall survival, 5-year disease-free survival, and hospital length of stay. Results: Twenty-eight studies met our predefined criteria and were included in our study, twelve of which were included in the quantitative synthesis. When comparing neoadjuvant versus adjuvant chemotherapy, our meta-analysis noted no significance in terms of margin negativity (R0) (odds ratio [OR] 2.01, 95% confidence interval [CI], 0.7–5.79, p = 0.20) or recurrence rates (OR 0.22, 95% CI, 0.02–1.91, p = 0.17). However, there was a difference in overall 5-year survival in favour of neoadjuvant therapy (OR 3.19, 95% CI, 1.37–7.40, * p = 0.007). Comparing local excision versus radical excision, our meta-analysis observed no significance in terms of overall 5-year survival (OR1.31, 95% CI, 0.81–2.12, p = 0.26), recurrence (OR 0.67, 95% CI, 0.40–1.13, p = 0.12), or 5-year disease-free survival (OR 1.10, 95% CI, 0.55–2.19, p = 0.80). There was a difference in length of hospital stay with a reduced mean length of stay in local excision group (mean difference [MD] 6.74 days less in the LE group; 95% CI, −6.92–−6.56, * p =< 0.00001) as well as a difference in R0 rates in favour of radical resection (OR 0.68, 95% CI, 0.47–0.99, * p = 0.05). Conclusion: Neoadjuvant chemotherapy is associated with improved overall 5-year survival, while local excision is associated with reduced mean length of hospital stay. Further large-volume, prospective studies are required to further define the optimal treatment regimen in this complex pathology.
A Spatio-Temporal Fusion Framework of UAV and Satellite Imagery for Winter Wheat Growth Monitoring
Authors: Li Yan , Yan Wen , An Sai , Gao Wanlin , Jia Jingdun , Tao Sha , Wang Wei
Published: December 2022

Accurate and continuous monitoring of crop growth is vital for the development of precision agriculture. Unmanned aerial vehicle (UAV) and satellite platforms have considerable complementarity in high spatial resolution (centimeter-scale) and fixed revisit cycle. It is meaningful to optimize the cross-platform synergy for agricultural applications. Considering the characteristics of UAV and satellite platforms, a spatio-temporal fusion (STF) framework of UAV and satellite imagery is developed. It includes registration, radiometric normalization, preliminary fusion, and reflectance reconstruction. The proposed STF framework significantly improves the fusion accuracy with both better quantitative metrics and visualized results compared with four existing STF methods with different fusion strategies. Especially for the prediction of object boundary and spatial texture, the absolute values of Robert’s edge (EDGE) and local binary pattern (LBP) decreased by a maximum of more than 0.25 and 0.10, respectively, compared with the spatial and temporal adaptive reflectance fusion model (STARFM). Moreover, the STF framework enhances the temporal resolution to daily, although the satellite imagery is discontinuous. Further, its application potential for winter wheat growth monitoring is explored. The daily synthetic imagery with UAV spatial resolution describes the seasonal dynamics of winter wheat well. The synthetic Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index 2 (EVI2) are consistent with the observations. However, the error in NDVI and EVI2 at boundary changes is relatively large, which needs further exploration. This research provides an STF framework to generate very dense and high-spatial-resolution remote sensing data at a low cost. It not only contributes to precision agriculture applications, but also is valuable for land-surface dynamic monitoring.
Attack–Defense Game Model with Multi-Type Attackers Considering Information Dilemma
Authors: Qi Gaoxin , Li Jichao , Xu Chi , Chen Gang , Yang Kewei
Published: December 2022

Today, people rely heavily on infrastructure networks. Attacks on infrastructure networks can lead to significant property damage and production stagnation. The game theory provides a suitable theoretical framework for solving the problem of infrastructure protection. Existing models consider only the beneficial effects that the defender obtains from information gaps. If the attacker’s countermeasures are ignored, the defender will become passive. Herein, we consider that a proficient attacker with a probability in the game can fill information gaps in the network. First, we introduce the link-hiding rule and the information dilemma. Second, based on the Bayesian static game model, we establish an attack–defense game model with multiple types of attackers. In the game model, we consider resource-consistent and different types of distributions of the attacker. Then, we introduce the solution method of our model by combining the Harsanyi transformation and the bi-matrix game. Finally, we conduct experiments using a scale-free network. The result shows that the defender can be benefited by hiding some links when facing a normal attacker or by estimating the distribution of the attacker correctly. The defender will experience a loss if it ignores the proficient attacker or misestimates the distribution.
Multiple Working Condition Bearing Fault Diagnosis Method Based on Channel Segmentation Improved Residual Network
Authors: Jiang Yuanyuan , Xie Jinyang , Meng Linghui , Jia Hanguang
Published: December 2022

To address the problems of poor model diagnosis and poor noise immunity caused by inconsistent distribution of bearing fault features and difficulty in feature extraction in multi-condition environments, a multi-condition bearing fault diagnosis method based on a channel segmentation improved residual network is proposed. A channel segmentation mechanism is designed for channel information highlighting, by selecting one channel of the three-channel feature image as the main operation channel, stacking it with the secondary operation channel after convolution, and then inputting the stacked feature map into the convolutional neural network to realize the extraction and classification of bearing fault features. Four different network models were selected to verify the diagnostic performance of the channel segmentation mechanism on the Case Western Reserve University bearing dataset and the Jiangnan University bearing dataset, and noise immunity experiments were conducted on the Jiangnan University bearing dataset. The experiments show that the proposed diagnostic model on the Case Western Reserve bearing dataset has a minimum improvement of 6.8% compared to the comparison method for multi-case bearing fault diagnosis experiments. In terms of noise immunity, the diagnostic accuracy of the fault diagnosis model with the addition of the channel cut-off mechanism improves the diagnostic accuracy of the noisy data by an average of 4.3% compared to that without the addition. The proposed model still has excellent diagnostic performance when diagnosing variable speed bearing faults.
Data Processing Unit for Energy Saving in Computer Vision: Weapon Detection Use Case
Authors: Perea-Trigo Marina , López-Ortiz Enrique J. , Salazar-González Jose L. , Álvarez-García Juan A. , Vegas Olmos J. J.
Published: December 2022

The growth of the Internet has led to the emergence of servers that perform increasingly heavy tasks. Some servers must remain active 24 h a day, but the evolution of network cards has facilitated the use of Data Processing Units (DPUs) to reduce network traffic and alleviate server workloads. This capability makes DPUs good candidates for load alleviation in systems that perform continuous data processing when the data can be pre-filtered. Computer vision systems that use some form of artificial intelligence, such as facial recognition or weapon detection, tend to have high workloads and high power consumption, which is becoming increasingly costly. Reducing the workload is therefore desirable and possible in some scenarios. The main contributions of this study are threefold: (1) to explore the potential benefits of using a DPU to alleviate the workload of a 24-h active server; (2) to present a study that measures the workload reduction of a CCTV weapon detection system and evaluate its performance under different conditions. We observed a 43,123% reduction in workload over the 24 h of video used in the experimentation, reaching more than 98% savings during night hours, which significantly reduces system stress and has a direct impact on electrical energy expenditure; and (3) to provide a framework that can be adapted to other computer vision-based detection systems.
A Modified Particle Swarm Optimization Algorithm for Power Sharing and Transient Response Improvement of a Grid-Tied Solar PV Based A.C. Microgrid
Authors: Abbas Ghulam , Bhutto Aqeel Ahmed , Jumani Touqeer Ahmed , Mirsaeidi Sohrab , Tunio Mohsin Ali , Alnuman Hammad , Alshahir Ahmed
Published: December 2022

The increasing penetration of Distributed Generators (D.G.) into the existing power system has brought some real challenges regarding the transient response of electrical systems. The injection of D.G.s and abrupt load changes may cause massive power, current, and voltage overshoots/undershoots, which consequently affects the equilibrium of the existing power system and deteriorate the performance of the connected electrical appliances. A robust and intelligent control strategy is of utmost importance to cope with these issues and enhance the penetration level of D.G.s into the existing power system. This paper presents a Modified Particle Swarm Optimization (MPSO) algorithm-based intelligent controller for attaining a desired power-sharing ratio between the M.G. and the main grid with an optimal transient response in a grid-tied Microgrid (M.G.) system. The proposed MPSO algorithm includes an additional parameter named best neighbor particles (rbest) in the velocity updating equation to convey additional information to every individual particle about all its neighbor particles, consequently leading to the increased exploration capability of the algorithm. The MPSO algorithm optimizes P.I. parameters for transient and steady-state response improvement of the studied M.G. system. The main dynamic response evaluation parameters are the overshoot and settling time for active and reactive power during the D.G. connection and load change. Furthermore, the performance of the proposed controller is compared with the PI-PSO-based MG controller, which validates the effectiveness of the proposed M.G. control scheme in maintaining the required active and reactive power under different operating conditions with minimum possible overshoot and settling time.
A Review on Crosswind Airborne Wind Energy Systems: Key Factors for a Design Choice
Authors: Pereira André F. C. , Sousa João M. M.
Published: December 2022

Airborne wind energy (AWE) has received increasing attention during the last decade, with the goal of achieving electricity generation solutions that may be used as a complement or even an alternative to conventional wind turbines. Despite that several concepts have already been proposed and investigated by several companies and research institutions, no mature technology exists as yet. The mode of energy generation, the type of wing, the take-off and landing approaches, and the control mechanisms, to name a few, may vary among AWE crosswind systems. Given the diversity of possibilities, it is necessary to determine the most relevant factors that drive AWE exploration. This paper presents a review on the characteristics of currently existing AWE technological solutions, focusing on the hardware architecture of crosswind systems, with the purpose of providing the information required to identify and assess key factors to be considered in the choice of such systems. The identified factors are categorized into four distinct classes: technical design factors (aerodynamic performance, mass-to-area ratio, durability, survivability); operational factors (continuity of power production, controllability, take-off and landing feasibility); fabrication and logistical factors (manufacturability, logistics); and social acceptability factors (visual impact, noise impact, ecological impact, safety).
Intelligent Control for Voltage Regulation in the Distribution Network Equipped with PV Farm
Authors: Ghaeb Jasim , Salah Samer , Obeidat Firas
Published: December 2022

A combined PSO-ANN control is proposed in this work to achieve the best voltage regulation in a distribution network, based on quick response and minimum average voltage deviation. The Jordanian Sabha Distribution Network (JSDN) with PV Farms is used as a real case study to examine a voltage variation issue. Two STATCOMs are used to solve the voltage fluctuation problem on the network’s three buses. The required reactive powers of STATCOMs for voltage regulation during load variation are calculated in offline mode using a particle swarm optimization (PSO) algorithm. Despite its high performance in solving voltage issue in the JSDN network, the PSO controller is unable to react promptly to dynamic changes in the network. An artificial neural network (ANN) is therefore suggested as an online mode controller for quick and efficient voltage regulation. The offline dataset is used to train the ANN for online voltage regulation utilizing the MATLAB-Tool Box. At an average voltage deviation (AVD) of 1.168%; (whereas an acceptable one is 6%), the results revealed the proposed ANN controller’s competence for voltage regulation in the distribution network. Moreover, to find the best position based on an efficient voltage regulation, many sites for STATCOMs are taken into consideration.
Profitability of Batteries in Photovoltaic Systems for Small Industrial Consumers in Spain under Current Regulatory Framework and Energy Prices
Authors: Echevarría Camarero Fernando , Ogando-Martínez Ana , Durán Gómez Pablo , Carrasco Ortega Pablo
Published: December 2022

In recent years, important regulatory changes have been introduced in Spain in the fields of self-consumption and energy tariffs. In addition, electricity prices have risen sharply, reaching record highs in the last year. This evidences the need to conduct new research studies in order to provide an accurate picture of the profitability of battery energy storage systems and photovoltaic systems. This paper proposes a complex simulation tool developed to assist in the optimal design of these kinds of facilities. The tool is used in this study to analyze the benefits of including batteries in PV systems under different self-consumption models, different consumer profiles and different locations across the country. The research results indicate that at current electricity prices, the use of batteries is less profitable than selling excess energy to the grid, unless the price of batteries drops drastically by more than 50% in all the cases analyzed. However, at current battery prices, they become a valuable resource in facilities that do not feed energy surplus into the grid.
An Empirical Analysis of the Effects of Energy Price Shocks for Sustainable Energy on the Macro-Economy of South Asian Countries
Authors: Lal Karn Arodh , Kondamudi Bhavana , Gupta Ravi , Pustokhin Denis , Pustokhina Irina , Alharbi Meshal , Vairavasundaram Subramaniyaswamy , Varadarajan Vijayakumar , Sengan Sudhakar
Published: December 2022

Energy prices (EPs) play an imperative role in South Asian Country (SAC) Gross Domestic Product (GDP). This research empirically examines the influence of sustainable energy price shocks (EPSs) on macroeconomic indicators. The study is to forecast the impact of EPS on macroeconomic indicators from 1980 to 2020. The analysis is carried out by employing the Vector Auto-Regression (VAR) approach. Impulse Response Functions (IRFs) results indicate that EPS decreases Gross Domestic Product (GDP). They exist in the short run and the long run. This research study’s overall findings suggest that high EPSs have a negative impact on GDP. The study implies that policymakers should develop, adopt, and initiate some imperatives to control the unanticipated volatility and movements in EP. The study highlights that policy should be designed to prevent fluctuations in sustainable EP and plan conservative energy policies that motivate discovering alternative energy sources to meet increasing energy demand and improve economic growth.
Innovative Experimental Design for the Evaluation of Nanofluid-Based Solvent as a Hybrid Technology for Optimizing Cyclic Steam Stimulation Applications
Authors: García-Duarte Hugo Alejandro , Ruiz-Cañas María Carolina , Pérez-Romero Romel Antonio
Published: December 2022

Worldwide gas emissions are being strictly regulated, therefore processes to reduce steam injection for enhanced oil recovery (EOR) require a deeper analysis to identify the means to contribute to environmental impact reduction. Lately the usage of additives such as a solvent for steam injection processes has taken a new interest due to its positive impact on improving oil recovery and energy efficiency and reducing greenhouse gas emissions. In that sense, the use of nanoparticles in thermal EOR has been explored due to its impact on avoiding the volatilization of the solvent, offering greater contact with the oil in the reservoir. Nanoparticles have well-known effects on asphaltenes adsorption, aquathermolysis reactions, oil upgrading, and improving energy efficiencies. This article presents a summary and ranking of the nanoparticles evaluated in nanofluid-based solvent for steam processes, specifically in the catalysis of aquathermolysis reactions. A novel experimental design is proposed for the characterization, formulation (based on catalytic activity and dispersion), and evaluation of solvent improved with nanoparticles. This new approach will be used as a guideline for the evaluation of nanoparticles dispersed in hydrocarbon-type solvents as a hybrid technology to improve steam injection processes.
Embodied vs. Operational Energy and Carbon in Retail Building Shells: A Case Study in Portugal
Authors: Ferreira Ana , Pinheiro Manuel Duarte , de Brito Jorge , Mateus Ricardo
Published: December 2022

(1) Background: The embodied energy of building materials is a significant contributor to climate change, in tandem with the energy use intensity (EUI). Yet, studies on the material impacts of European retail buildings, namely with relation to EUI, are missing. Hence, this study set out to: (i) evaluate the embodied energy and carbon emissions for a European retail building; (ii) quantify the material flow in terms of mass; (iii) compare the embodied aspects to the operational EUI and carbon use intensity (CUI); (iv) assess building materials with higher impacts; and (v) investigate strategies to mitigate materials’ impacts. (2) Methods: A Portuguese retail building was selected as a case study. A simplified LCA method was followed (cradle-to-gate), analysing the shell building materials in terms of primary energy demand and global warming potential. (3) Results: the embodied energy represented 32% of total lifecycle energy while the embodied carbon represented 94%. EUI was 1×kWh/m2/y while CUI was 21 kg CO2eq/m2/y. The embodied energy was 4248 kWh/m2, and the embodied carbon was 1689 kg CO2eq/m2. Cement mortar, steel, concrete, and extruded polystyrene were the most intensive materials. (4) Conclusions: The embodied impacts of the analysed store could decrease by choosing stone wool sandwich panels for the facades instead of extruded polystyrene panels and roof systems with metal sheet coverings instead of bitumen materials.
Possibilities for the Development of an Electric Hybrid Skidder Based on Energy Consumption Measurement in Real Terrain Conditions
Authors: Šušnjar Marijan , Pandur Zdravko , Bačić Marin , Lepoglavec Kruno , Nevečerel Hrvoje , Kopseak Hrvoje
Published: December 2022

Growing demand for forest machines that cost less to operate than current compared to traditional hydraulic and mechanical ones, along with regulatory pressures for lower emissions, is increasing manufacturers’ interest in developing electric and hybrid drives. While purely electric drives of forest machines meet a lot of bottlenecks (costs of the electric components, battery durability, duration of charging, access to the electrical grid, size of batteries that can ensure enough energy for 8 h working time), electric hybrid drives offer a favorable solution for the propulsion of forestry machinery in terms of lower fuel consumption and improved efficiency. Among all forest vehicles, specialized forest tractors (skidders), so far, have not been considered for forest vehicles with hybrid drive capabilities. A skidder is a forest-articulated self-propelled vehicle for pulling trees or parts of trees. In most countries in southern Europe, the use of skidders equipped with forest winches is the most common technique for timber extraction. The first goal of the research is to develop methods for measuring the energy consumption of skidders at different operating tasks and under different field conditions. Research was performed on the skidder Ecotrac 140V (from Croatian producer Hittner Ltd.) during timber extraction in mountainous terrains in Lika–Senj County. The skidder was equipped with a measuring device WIGO-E (Telematic Data collector) gateway with an integrated GPS system, which ensured data were collected from sensors and motor and stored in a computer via CANBUS and data transfer with GSM to Web platforms. Additionally, a fuel-flow meter was installed on the skidder. Data on fuel consumption (mL), position (traveling route), detection of winch work, engine rpm (min−1), engine torque (% of max), throttle position (%), and engine temperature were measured with a sampling frequency of 5 s. Furthermore, skidder load volumes per cycles and slopes of tractor paths were constantly measured. The paper shows the skidder’s energy consumption per day, work cycle, and individual work procedure with regard to the size of the load, the slope of the tractor path, and the direction of movement based on overlapping and merging all measurement data. Using mathematical and simulation models of the drive with defined operating cycles obtained by measurement, the possibilities of the hybrid drive and the dimensions of the elements of the hybrid drive (internal combustion engine, electric motor, batteries, control unit) were determined and are presented in this paper.
Drifting Streaming Peaks-Over-Threshold-Enhanced Self-Evolving Neural Networks for Short-Term Wind Farm Generation Forecast
Authors: Liu Yunchuan , Ghasemkhani Amir , Yang Lei
Published: December 2022

This paper investigates the short-term wind farm generation forecast. It is observed from the real wind farm generation measurements that wind farm generation exhibits distinct features, such as the non-stationarity and the heterogeneous dynamics of ramp and non-ramp events across different classes of wind turbines. To account for the distinct features of wind farm generation, we propose a Drifting Streaming Peaks-over-Threshold (DSPOT)-enhanced self-evolving neural networks-based short-term wind farm generation forecast. Using DSPOT, the proposed method first classifies the wind farm generation data into ramp and non-ramp datasets, where time-varying dynamics are taken into account by utilizing dynamic ramp thresholds to separate the ramp and non-ramp events. We then train different neural networks based on each dataset to learn the different dynamics of wind farm generation by the NeuroEvolution of Augmenting Topologies (NEAT), which can obtain the best network topology and weighting parameters. As the efficacy of the neural networks relies on the quality of the training datasets (i.e., the classification accuracy of the ramp and non-ramp events), a Bayesian optimization-based approach is developed to optimize the parameters of DSPOT to enhance the quality of the training datasets and the corresponding performance of the neural networks. Based on the developed self-evolving neural networks, both distributional and point forecasts are developed. The experimental results show that compared with other forecast approaches, the proposed forecast approach can substantially improve the forecast accuracy, especially for ramp events. The experiment results indicate that the accuracy improvement in a 60 min horizon forecast in terms of the mean absolute error (MAE) is at least 33.6% for the whole year data and at least 37% for the ramp events. Moreover, the distributional forecast in terms of the continuous rank probability score (CRPS) is improved by at least 35.8% for the whole year data and at least 35.2% for the ramp events.
Logically-Centralized SDN-Based NDN Strategies for Wireless Mesh Smart-City Networks
Authors: Kalafatidis Sarantis , Skaperas Sotiris , Demiroglou Vassilis , Mamatas Lefteris , Tsaoussidis Vassilis
Published: December 2022

The Internet of Things (IoT) is a key technology for smart community networks, such as smart-city environments, and its evolution calls for stringent performance requirements (e.g., low delay) to support efficient communication among a wide range of objects, including people, sensors, vehicles, etc. At the same time, these ecosystems usually adopt wireless mesh technology to extend their communication range in large-scale IoT deployments. However, due to the high range of coverage, the smart-city WMNs may face different network challenges according to the network characteristic, for example, (i) areas that include a significant number of wireless nodes or (ii) areas with frequent dynamic changes such as link failures due to unstable topologies. Named-Data Networking (NDN) can enhance WMNs to meet such IoT requirements, thanks to the content naming scheme and in-network caching, but it necessitates adaptability to the challenging conditions of WMNs. In this work, we aim at efficient end-to-end NDN communication in terms of performance (i.e., delay), performing extended experimentation over a real WMN, evaluating and discussing the benefits provided by two SDN-based NDN strategies: (1) a dynamic SDN-based solution that integrates the NDN operation with the routing decisions of a WMN routing protocol; (2) a static one which based on SDN-based clustering and real WMN performance measurements. Our key contributions include (i) the implementation of two types of NDN path selection strategies; (ii) experimentation and data collection over the w-iLab.t Fed4FIRE+ testbed with real WMN conditions; (ii) real measurements released as open-data, related to the performance of the wireless links in terms of RSSI, delay, and packet loss among the wireless nodes of the corresponding testbed.
Relationship between Undernutrition and Anemia in Patients with Ulcerative Colitis
Authors: Uspenskiy Yury P. , Ivanov Sergei V. , Krasichkov Alexander S. , Galagudza Michael M. , Fominykh Yulia A.
Published: December 2022

This study aimed to assess the relationship between malnutrition and anemia in patients with ulcerative colitis (UC). The cross-sectional retrospective study included 80 patients with UC. Body mass index and total body fat mass were derived retrospectively from bioimpedance measurements. Anemia was diagnosed retrospectively according to WHO criteria. A binary logistic regression was performed to study the relationship between nutritional status parameters and anemia, and adjusted for demographic and disease-associated characteristics. The prevalence of anemia in the study population was 40.0%. Among all included patients, 86.3% had acute disease corresponding to S1–S3 disease behavior. In the adjusted binary logistic model, total serum protein level below 64 g/L and low body fat percentage were associated with high odds for the of development of anemia, with odds ratios of 5.1 (95% CI 1.5; 17.8; p = 0.01) and 8.5 (95% CI 1.1; 63.6; p = 0.037), respectively. The adjusted model included sex, age, disease activity, extent of gut involvement, quantity of relapses from disease onset, and treatment with immunosuppressive drugs as confounders. Hypoproteinemia and low body fat percentage were associated with anemia in patients with UC. These results suggested that undernutrition may be involved as one of the causative factors of anemia in UC.