bluuebunny/crossref_metadata_2025 huggingface dataset:
Experimental and Mathematical Models for Real-Time Monitoring and Auto Watering Using IoT Architecture
Authors: Yousif Jabar H. , Abdalgader Khaled
Published: January 2022

Manufacturing industries based on Internet of Things (IoT) technologies play an important role in the economic development of intelligent agriculture and watering. Water availability has become a global problem that afflicts many countries, especially in remote and desert areas. An efficient irrigation system is needed for optimizing the amount of water consumption, agriculture monitoring, and reducing energy costs. This paper proposes a real-time monitoring and auto-watering system based on predicting mathematical models that efficiently control the water rate needed. It gives the plant the optimal amount of required water level, which helps to save water. It also ensures interoperability among heterogeneous sensing data streams to support large-scale agricultural analytics. The mathematical model is embedded in the Arduino Integrated Development Environment (IDE) for sensing the soil moisture level and checking whether it is less than the pre-defined threshold value, then plant watering is performed automatically. The proposed system enhances the watering system’s efficiency by reducing the water consumption by more than 70% and increasing production due to irrigation optimization. It also reduces the water and energy consumption amount and decreases the maintenance costs.
Hoeing as a Possibility for Mechanical Weed Control in Winter Oilseed Rape (Brassica napus L.)
Authors: Schwabe Sebastian , Gruber Sabine , Claupein Wilhelm
Published: January 2022

The framework conditions for chemical weed control in oilseed rape (OSR) are becoming increasingly unfavorable in Central Europe. On the one hand, weed resistance is spreading and, on the other, there is a growing social desire to reduce or eliminate the use of chemical crop protection products. In a field experiment, hoeing, as a weed control measure performed two times per growing season (one time in autumn and one time in spring) in oilseed rape (Brassica napus; two varieties), was compared to chemical control by herbicides and a combination of hoeing and herbicide application (five treatments altogether). The chemical control by herbicides consisted of a broad-spectrum pre-emergence treatment and a post-emergence graminicide application. The trial was set up in each of three periods (years 2014/2015, 2015/2016, and 2016/2017) at the experimental station Ihinger Hof, University of Hohenheim, Stuttgart, Germany. The effect of the treatments on weed plant density, weed biomass at the time of harvesting, and on OSR grain yield was investigated. Weed plant density was measured four times per trial year, each time before and after hoeing. In 2015/2016 after spring hoeing, and in 2016/2017 at all data collection times, weed plant density was significantly higher in hoeing without herbicide application than in the other variants. No significant differences occurred at the other data collection times. The weed plant density ranged from 0.5 to 57.8 plants m−2. Regardless of the trial year, pure hoeing always resulted in a significantly higher weed biomass at the time of harvesting than the herbicide applications or the combinations. The weed biomass at the time of harvesting ranged between 0.1 and 54.7 g m−2. No significant differences in grain yield between hoeing and herbicide application occurred in all three trial years. According to the results, hoeing is a suitable extension of existing integrated weed control strategies in OSR.
Functional Encryption for Pattern Matching with a Hidden String
Authors: Kim Jongkil , Chow Yang-Wai , Susilo Willy , Baek Joonsang , Kim Intae
Published: January 2022

We propose a new functional encryption for pattern matching scheme with a hidden string. In functional encryption for pattern matching (FEPM), access to a message is controlled by its description and a private key that is used to evaluate the description for decryption. In particular, the description with which the ciphertext is associated is an arbitrary string w and the ciphertext can only be decrypted if its description matches the predicate of a private key which is also a string. Therefore, it provides fine-grained access control through pattern matching alone. Unlike related schemes in the literature, our scheme hides the description that the ciphertext is associated with. In many practical scenarios, the description of the ciphertext cannot be public information as an attacker may abuse the message description to identify the data owner or classify the target ciphertext before decrypting it. Moreover, some data owners may not agree to reveal any ciphertext information since it simply gives greater advantage to the adversary. In this paper, we introduce the first FEPM scheme with a hidden string, such that the adversary cannot get any information about the ciphertext from its description. The security of our scheme is formally analyzed. The proposed scheme provides both confidentiality and anonymity while maintaining its expressiveness. We prove these security properties under the interactive general Diffie–Hellman assumption (i-GDH) and a static assumption introduced in this paper.
Structural Diversities of a Series of Cd(II) Coordination Complexes Based on a Flexible Tripodal N-donor Ligand
Authors: Wang Huarui , Qin Jianhua
Published: January 2022

Three Cd(II) coordination complexes with unique structures and topologies, namely, {[Cd(tttmb)(Hbtc)]·5H2O}n (1), {[Cd(tttmb)(m-phda)(H2O)]·2H2O}n (2), and {[Cd(tttmb)(o-cpla)]·(CH3CN)·(H2O)1.5}n (3), have been successfully synthesized under hydro(solvo)thermally condition based on a flexible tripodal N-contained ligand 1,3,5-tris(1,2,4-triazol-1-ylmethyl)-2,4,6-trimethylbenzene (tttmb) and aromatic polycarboxylate acids (H3btc = 1,2,4-benzenetricarboxylic acid, m-H2phda = 1,3-phenylenediacetic acid and o-H2cpla = Homophthalic acid). Complexes 1–3 were characterized by elemental analysis, IR spectroscopy, X-ray single-crystal diffraction and thermogravimetric analyses. 1 crystallize in the orthorhombic chiral space group P212121 and feature 3D coordination networks. 2 reveals a 2D ladder-like structure with (4,4) topology containing alternating Cd(II)/m-phda2− left- and right-handed helical motifs. 3 exhibits a 3D net with (63)(66)(7·82) topology. The structural and dimensional diversity of these complexes not only indicates that the flexible ligand tttmb exhibits strong coordination ability and diverse coordination modes, but also shows that aromatic polycarboxylates play important roles in constructing the frameworks of complexes. Moreover, the different photoluminescence behaviors of 1–3 have been studied in the solid state.
Severe Plastic Deformation and Phase Transformations in High Entropy Alloys: A Review
Authors: Straumal Boris B. , Kulagin Roman , Baretzky Brigitte , Anisimova Natalia Yu. , Kiselevskiy Mikhail V. , Klinger Leonid , Straumal Petr B. , Kogtenkova Olga A. , Valiev Ruslan Z.
Published: January 2022

This review discusses an area of expertise that is at the intersection of three large parts of materials science. These are phase transformations, severe plastic deformation (SPD), and high-entropy alloys (HEA). First, SPD makes it possible to determine the borders of single-phase regions of existence of a multicomponent solid solution in HEAs. An important feature of SPD is that using these technologies, it is possible to obtain second-phase nanoparticles included in a matrix with a grain size of several tens of nanometers. Such materials have a very high specific density of internal boundaries. These boundaries serve as pathways for accelerated diffusion. As a result of the annealing of HEAs subjected to SPD, it is possible to accurately determine the border temperature of a single-phase solid solution area on the multicomponent phase diagram of the HEA. Secondly, SPD itself induces phase transformations in HEAs. Among these transformations is the decomposition of a single-phase solid solution with the formation of nanoparticles of the second phase, the formation of high-pressure phases, amorphization, as well as spinodal decomposition. Thirdly, during SPD, a large number of new grain boundaries (GBs) are formed due to the crystallites refinement. Segregation layers exist at these new GBs. The concentration of the components in GBs differs from that in the bulk solid solution. As a result of the formation of a large number of new GBs, atoms leave the bulk solution and form segregation layers. Thus, the composition of the solid solution in the volume also changes. All these processes make it possible to purposefully influence the composition, structure and useful properties of HEAs, especially for medical applications.
The Design and Optimization of an Anti-Reflection Coating and an Intermediate Reflective Layer to Enhance Tandem Solar Cell Photons Capture
Authors: Sayed Hassan , Matar Z. S. , Al-Dossari M. , Amin A. F. , El-Gawaad N. S. Abd , Aly Arafa H.
Published: January 2022

We have theoretically demonstrated an efficient way to improve the optical properties of an anti-reflection coating (ARC) and an intermediate reflective layer (IRL) to enhance tandem solar cell efficiency by localizing the incident photons’ energy on a suitable sub-cell. The optimum designed ARC from a one-dimensional ternary photonic crystal, consisting of a layer of silicon oxynitride (SiON), was immersed between two layers of (SiO2); thicknesses were chosen to be 98 nm, 48 nm, and 8 nm, respectively. The numerical results show the interesting transmission properties of the anti-reflection coating on the viable and near IR spectrum. The IRL was designed from one-dimensional binary photonic crystals and the constituent materials are Bi4Ge3O12 and μc-SiOx: H with refractive indexes was 2.05, and 2.8, respectively. The numbers of periods were set to 10. Thicknesses: d1 = 62 nm and d2 = 40 nm created a photonic bandgap (PBG) in the range of [420 nm: 540 nm]. By increasing the second material thickness to 55 nm, and 73 nm, the PBG shifted to longer wavelengths: [520 nm: 630 nm], and [620 nm: 730 nm], respectively. Thus, by stacking the three remaining structures, the PBG widened and extended from 400 nm to 730 nm. The current theoretical and simulation methods are based on the fundamentals of the transfer matrix method and finite difference time domain method.
View VULMA: Data Set for Training a Machine-Learning Tool for a Fast Vulnerability Analysis of Existing Buildings
Authors: Cardellicchio Angelo , Ruggieri Sergio , Leggieri Valeria , Uva Giuseppina
Published: January 2022

The paper presents View VULMA, a data set specifically designed for training machine-learning tools for elaborating fast vulnerability analysis of existing buildings. Such tools require supervised training via an extensive set of building imagery, for which several typological parameters should be defined, with a proper label assigned to each sample on a per-parameter basis. Thus, it is clear how defining an adequate training data set plays a key role, and several aspects should be considered, such as data availability, preprocessing, augmentation and balancing according to the selected labels. In this paper, we highlight all these issues, describing the pursued strategies to elaborate a reliable data set. In particular, a detailed description of both requirements (e.g., scale and resolution of images, evaluation parameters and data heterogeneity) and the steps followed to define View VULMA are provided, starting from the data assessment (which allowed to reduce the initial sample of about 20.000 images to a subset of about 3.000 pictures), to achieve the goal of training a transfer-learning-based automated tool for fast estimation of the vulnerability of existing buildings from single pictures.
Sleep Treatments in Disorders of Consciousness: A Systematic Review
Authors: Cacciatore Martina , Magnani Francesca G. , Leonardi Matilde , Rossi Sebastiano Davide , Sattin Davide
Published: January 2022

Sleep disorders are among the main comorbidities in patients with a Disorder of Consciousness (DOC). Given the key role of sleep in neural and cognitive functioning, detecting and treating sleep disorders in DOCs might be an effective therapeutic strategy to boost consciousness recovery and levels of awareness. To date, no systematic reviews have been conducted that explore the effect of sleep treatments in DOCs; thus, we systematically reviewed the existing studies on both pharmacological and non-pharmacological treatments for sleep disorders in DOCs. Among 2267 assessed articles, only 7 were included in the systematic review. The studies focused on two sleep disorder categories (sleep-related breathing disorders and circadian rhythm dysregulation) treated with both pharmacological (Modafinil and Intrathecal Baclofen) and non-pharmacological (positive airway pressure, bright light stimulation, and central thalamic deep brain stimulation) interventions. Although the limited number of studies and their heterogeneity do not allow generalized conclusions, all the studies highlighted the effectiveness of treatments on both sleep disorders and levels of awareness. For this reason, clinical and diagnostic evaluations able to detect sleep disorders in DOC patients should be adopted in the clinical routine for the purpose of intervening promptly with the most appropriate treatment.
COVID-19 Pulmonary Pathology: The Experience of European Pulmonary Pathologists throughout the First Two Waves of the Pandemic
Authors: Fortarezza Francesco , Pezzuto Federica , Hofman Paul , Kern Izidor , Panizo Angel , von der Thüsen Jan , Timofeev Sergei , Gorkiewicz Gregor , Berezowska Sabina , de Leval Laurence , Ortiz-Villalón Cristian , Lunardi Francesca , Calabrese Fiorella
Published: January 2022

Autoptic studies of patients who died from COVID-19 constitute an important step forward in improving our knowledge in the pathophysiology of SARS-CoV-2 infection. Systematic analyses of lung tissue, the organ primarily targeted by the disease, were mostly performed during the first wave of the pandemic. Analyses of pathological lesions at different times offer a good opportunity to better understand the disease and how its evolution has been influenced mostly by new SARS-CoV-2 variants or the different therapeutic approaches. In this short report we summarize responses collected from a questionnaire survey that investigated important pathological data during the first two pandemic waves (spring-summer 2020; autumn-winter 2020–2021). The survey was submitted to expert lung pathologists from nine European countries involved in autoptic procedures in both pandemic waves. The frequency of each lung lesion was quite heterogeneous among the participants. However, a higher frequency of pulmonary superinfections, both bacterial and especially fungal, was observed in the second wave compared to the first. Obtaining a deeper knowledge of the pathological lesions at the basis of this complex and severe disease, which change over time, is crucial for correct patient management and treatment. Autoptic examination is a useful tool to achieve this goal.
A New Angle Measurement in Translabial Ultrasound as an Adjunct for the Diagnosis of Pelvic Organ Prolapse
Authors: Nam Gina , Song Jae-Yen , Lee Sa-Ra
Published: January 2022

The aim of this study was to compare the data obtained by a pelvic organ prolapse quantification (POP-Q) examination with the translabial ultrasound (TLUS) quantification of prolapse, using a new method of angle measurement. We analyzed the TLUS and POP-Q exam findings of 452 patients with symptoms of POP. The POP-Q system was used for clinical staging. TLUS was performed both at rest, and during the Valsalva maneuver after proper preparation. A horizontal reference line was drawn through the inferior margin of the symphysis pubis and the levator plate connected to the rectal ampulla, and the difference was calculated between the rest and the Valsalva maneuver. The Spearman’s correlation coefficient of agreement between the TLUS and the clinical POP-Q staging was used for statistical analysis. There was a weak degree of correlation between the POP-Q findings for the Ap parameter and our new angle measurement (rho = 0.17, p < 0.001). Thus, POP staging in conjunction with TLUS with this new angle measurement shows better agreement for the diagnosis of POP than POP-Q staging alone.
Diagnostic Value of Endoscopic Ultrasound after Neoadjuvant Chemotherapy for Gastric Cancer Restaging: A Meta-Analysis of Diagnostic Test
Authors: Sacerdotianu Victor Mihai , Ungureanu Bogdan Silviu , Iordache Sevastita , Turcu-Stiolica Adina , Facciorusso Antonio , Crinò Stefano Francesco , Saftoiu Adrian
Published: January 2022

This study aimed to evaluate the diagnostic value of endoscopic ultrasound (EUS) after neoadjuvant therapy (NT) for gastric cancer restaging by meta-analysis. We conducted a systematic search of studies published on PubMed and Web of Science up to 30th August 2021. Assessing the risk of bias in the included studies was done with the QUADAS-2 tool. We used R and Review Manager 5.4.1 for calculations and statistical analysis. To evaluate the diagnostic value of EUS after NT for gastric cancer restaging, we performed a meta-analysis on six studies, with a total of 283 patients, including true-positive, true-negative, false-positive, and false-negative results for T1-T4, N0. EUS as a diagnostic test for GC patients after chemotherapy has a relatively low DOR for the T2 (3.96) and T4 stages (4.79) and a relatively high partial AUC for the T2 (0.85) and T4 (0.71) stages. Our results reveal that the pooled sensitivity for T stages after chemotherapy is rather low (29–56%), except for the T3 stage (71%). A potential limitation of our study was the small number of included studies, but no significant heterogeneity was found between them. Our meta-analysis concludes that EUS is not recommended or is still under debate for GC restaging after NT.
Structural Entropy of the Stochastic Block Models
Authors: Han Jie , Guo Tao , Zhou Qiaoqiao , Han Wei , Bai Bo , Zhang Gong
Published: January 2022

With the rapid expansion of graphs and networks and the growing magnitude of data from all areas of science, effective treatment and compression schemes of context-dependent data is extremely desirable. A particularly interesting direction is to compress the data while keeping the “structural information” only and ignoring the concrete labelings. Under this direction, Choi and Szpankowski introduced the structures (unlabeled graphs) which allowed them to compute the structural entropy of the Erdős–Rényi random graph model. Moreover, they also provided an asymptotically optimal compression algorithm that (asymptotically) achieves this entropy limit and runs in expectation in linear time. In this paper, we consider the stochastic block models with an arbitrary number of parts. Indeed, we define a partitioned structural entropy for stochastic block models, which generalizes the structural entropy for unlabeled graphs and encodes the partition information as well. We then compute the partitioned structural entropy of the stochastic block models, and provide a compression scheme that asymptotically achieves this entropy limit.
The Effect of COVID-19 Pandemic on Corporate Dividend Policy in Indonesia: The Static and Dynamic Panel Data Approaches
Authors: Tinungki Georgina Maria , Robiyanto Robiyanto , Hartono Powell Gian
Published: January 2022

This research examines the effect of the crisis due to the COVID-19 pandemic on dividend policy in Indonesia. The purposive sampling method was used to collect data from corporates listed on the IDX from 2014 to 2020 and analyzed using static and dynamic panel data approaches. The fixed-effect models (FEM) were selected for the static panel data regression. Meanwhile, the first difference-generalized method of moments (FD-GMM) and system-generalized method of moments (SYS-GMM) were used for determine the robustness of the estimated dynamic panel data. The results showed that the crisis due to the pandemic led to higher dividend distribution on SYS-GMM. Furthermore, companies maintained the dividend level as a positive signal for investors which lifted the sluggish trade condition in the capital market. Profitability and previous year dividends positively affect dividend policy robustly. Furthermore, the results showed that age affects dividend policy on FD-GMM. Financial leverage has a robust effect, and firm size has an effect on FD-GMM in different directions, while investment opportunity does not affect dividend policy. Statistically, the FEM selected that violates the best linear unbiased estimation was proven to form parameters that were not much different from the estimates produced by the dynamic model, both from the coefficient of influence direction and significance, and the omitted variable bias occurs as evidenced in the robust test with dynamic model was solved. This research is also used as a reference for considering investors’ investment decisions in the new normal condition. Therefore, dividend policy can be considered as a positive signal to investors with the ability to stock trading activities in the capital market.
Forward-Reverse Orthogonal Matching Pursuit-Union-Subspace Pursuit-Based Multiuser Detector for Uplink Grant-Free NOMA Networks
Authors: Oyerinde Olutayo Oyeyemi
Published: January 2022

Multiuser Detection (MUD) is quite challenging in uplink grant-free non-orthogonal multiple access wireless communication networks in which users sporadically transmit data. The reason for this is that the base station (BS) must perform detection of both multiuser activity and user signals concurrently, because knowledge of user activity status is not available at the BS. In this paper, a new multiuser detector, named the Forward-Reverse Orthogonal Matching Pursuit–Union–Subspace pursuit (FROMPUS)-based MUD, is proposed. The detector takes advantage of the concept of an initial support set. This serves as initial knowledge that is then employed in the reconstruction of active users’ signals. In addition, the detector uses the “serial-include” technique of incorporating a likely support set element candidates and a reliability testing procedure in which the most prominent elements of the support set are selected. To assess the performance of the proposed detector, computer simulations are performed. The results obtained for various parameter settings show that the FROMPUS performs better than any of the other five detectors considered in this paper. However, this excellent performance comes with a slightly higher computational complexity cost. Nonetheless, the cost is inconsequential, since the detector operates at the BS where complexity is of low priority in comparison to performance.
A Simple Model to Compute the Characteristic Parameters of a Slotted Rectangular Microstrip Patch Antenna
Authors: Ossa-Molina Oscar , López-Giraldo Francisco
Published: January 2022

In this study, we developed an analytical model of slot-loaded rectangular microstrip patch antennas based on the simulation results by varying slot parameters. The dominant resonant frequency predicted by such a model is in strong agreement with the experimental results measured at several locations of slot-loaded rectangular microstrip patch antennas. The model enables a reliable and quick computation of the resonant frequency, which generally follows a harmonic behavior that nearly resembles the resonant frequency of a microstrip antenna without a slot, which can be related to a small change in the impedance caused by the slot position. Results showed a good agreement between simulations and measurements for all the slot positions. Mathematical analytic functions to describe the changes in specific characteristic parameters of the slot-loaded rectangular microstrip patch antennas are also included.
Big Data Handling Approach for Unauthorized Cloud Computing Access
Authors: Razaque Abdul , Shaldanbayeva Nazerke , Alotaibi Bandar , Alotaibi Munif , Murat Akhmetov , Alotaibi Aziz
Published: January 2022

Nowadays, cloud computing is one of the important and rapidly growing services; its capabilities and applications have been extended to various areas of life. Cloud computing systems face many security issues, such as scalability, integrity, confidentiality, unauthorized access, etc. An illegitimate intruder may gain access to a sensitive cloud computing system and use the data for inappropriate purposes, which may lead to losses in business or system damage. This paper proposes a hybrid unauthorized data handling (HUDH) scheme for big data in cloud computing. The HUDH scheme aims to restrict illegitimate users from accessing the cloud and to provide data security provisions. The proposed HUDH consists of three steps: data encryption, data access, and intrusion detection. The HUDH scheme involves three algorithms: advanced encryption standards (AES) for encryption, attribute-based access control (ABAC) for data access control, and hybrid intrusion detection (HID) for unauthorized access detection. The proposed scheme is implemented using the Python and Java languages. The testing results demonstrated that the HUDH scheme can delegate computation overhead to powerful cloud servers. User confidentiality, access privilege, and user secret key accountability can be attained with more than 97% accuracy.
Persistent Postural-Perceptual Dizziness Interventions—An Embodied Insight on the Use Virtual Reality for Technologists
Authors: Zaidi Syed Fawad M. , Shafiabady Niusha , Beilby Justin
Published: January 2022

Persistent and inconsistent unsteadiness with nonvertiginous dizziness (persistent postural-perceptual dizziness (PPPD)) could negatively impact quality of life. This study highlights that the use of virtual reality (VR) systems offers bimodal benefits to PPPD, such as understanding symptoms and providing a basis for treatment. The aim is to develop an understanding of PPPD and its interventions, including current trends of VR involvement to extrapolate and re-evaluate VR design strategies. Therefore, recent virtual-reality-based research work that progressed in understanding PPPD is identified, collected, and analysed. This study proposes a novel approach to the understanding of PPPD, specifically for VR technologists, and examines the principles of effectively aligning VR development for PPPD interventions.
Conversion of Carbohydrates in Lignocellulosic Biomass after Chemical Pretreatment
Authors: Zborowska Magdalena , Waliszewska Hanna , Waliszewska Boguslawa , Borysiak Slawomir , Brozdowski Jakub , Stachowiak-Wencek Agata
Published: January 2022

The aim of the study was to determine the quantitative and qualitative changes taking place in biomass components actively participating in methane fermentation, i.e., in carbohydrates, as a result of chemical pretreatment. Analyses were conducted on agricultural waste (corn stover, also called corn straw, and corncobs) as materials most commonly used in methane fermentation, as well as poplar wood, a material relatively rarely used in biogas production. Pretreatment with the aim of increasing efficiency of methane fermentation was carried out with the use of acid and alkaline solutions of different concentrations. The effect of pretreatment on carbohydrates was analyzed based on the quantitative and qualitative changes in this component. Due to the structural heterogeneity of carbohydrates, their varied reactivity and fermentability were determined in terms of holocellulose, cellulose, and pentosans. The chemical structure of cellulose was also analyzed. It is shown in this study that chemical pretreatment causes transformations of carbohydrate components, which differ quantitatively and qualitatively in the compared raw materials. It was found that the alkaline treatment caused smaller changes in the percentage shares of the carbohydrate biomass components as compared to the acid treatment. Moreover, it was observed that the compared materials differ in terms of quantitative changes in their chemical composition depending on the composition of the raw material prior to pretreatment. In the case of corn waste subjected to the action of 1 and 3% NaOH, the share of pentosans in the biomass increased. It was established that this is a change with a positive effect on fermentation efficiency. The action of acids and alkalis on the biomass led to similar structural changes in cellulose, which are adverse for the fermentation process.
The Role of Pro-Innovative HR Practices and Psychological Contract in Shaping Employee Commitment and Satisfaction: A Case from the Energy Industry
Authors: Gadomska-Lila Katarzyna , Rogozińska-Pawełczyk Anna
Published: January 2022

Innovation is a very important attribute of energy companies. Its level largely depends on employees’ attitudes and behaviours, which are determined on the one hand by individual factors (e.g., psychological contract) and on the other hand by organisational factors (e.g., human resources (HR) practices). The aim of this article is to identify the relationship between pro-innovative HR practices, psychological contract and employee attitudes—commitment and job satisfaction. The research was conducted in a company which is one of the leading Polish electricity suppliers. Data were collected using a proprietary survey questionnaire. The research involved 402 HR professionals responsible for shaping and implementing pro-innovative HR practices in the company. Hypotheses were tested using the partial least squares structural equation modelling technique (PLS-SEM). The results indicate the existence of a positive relationship between pro-innovative HR practices, psychological contract and employee commitment and satisfaction. Furthermore, they indicate that organisations wishing to increase employee commitment and job satisfaction should strengthen and implement existing psychological contracts. One way to do this is to invest in pro-innovative HR practices, such as competence development, knowledge sharing or creativity-based candidate selection, as they have been shown to be good moderators of these relationships. The obtained results may be of particular importance for HR management specialists and managers responsible for shaping desired attitudes and behaviours of employees.
Simulation Model for the Estimation of Energy Consumption of the Baggage Handling System in the Landside Area of the Airport
Authors: Kierzkowski Artur , Kisiel Tomasz
Published: January 2022

The purpose of this paper was to develop a simulation model to perform a sensitivity analysis of the energy consumption of an airport baggage handling system to a change in resource allocation strategy. This is a novel approach as this aspect has not been considered until now. This aspect, in turn is very important in terms of sustainability. The paper presents the detailed structure of the model and the data on which it operates. It is universal and can be the basis for analyzing any structure of the baggage handling system in the landside of any airport. An example analysis has shown that even up to 35% benefits can be gained by using the model. Three scenarios were analyzed in the model (dedicated check-in desks scenario, common desks scenario and mixed strategy scenario). However, the model is not limited to these strategies and any resource allocation is possible. The model is useful both for planning a new system as well as optimizing an existing system during its operation.
Priorities for Research on Sustainable Agriculture: The Case of Poland
Authors: Wieliczko Barbara , Floriańczyk Zbigniew
Published: January 2022

The need for sustainable agricultural sector is growing rapidly due to climate changes. As there are still knowledge gaps and the need for innovations that support farmers in the sustainability transition, there is a need for determining priority research areas that are vital for the sustainable development of agriculture. The aim of our study was to derive a long-term vision of the desirable agricultural sector in Poland and prioritize research areas required to make Polish agriculture sustainable. We applied the living lab approach and, by conducting a backcasting exercise with the lab members, we identified a desirable vision of agriculture in Poland and the research areas needed to realize this vision. Using Analytic Hierarchy Process (AHP) and Cumulative Voting (CV), we prioritized these research areas. Our results show that adaptation to climate changes is the most important area of research, having 38.6% of the total possible number of points using AHP and 29.7% in the case of CV. The analysis of the Polish strategic documents related to agriculture and agricultural research shows that, to some extent, these key research areas are already part of the national policy, but there is not sufficient funding and coordination to tackle all aspects of sustainability in agriculture.
Optimisation of Propane Production from Hydrothermal Decarboxylation of Butyric Acid Using Pt/C Catalyst: Influence of Gaseous Reaction Atmospheres
Authors: Onwudili Jude A. , Razaq Iram , Simons Keith E.
Published: January 2022

The displacement and eventual replacement of fossil-derived fuel gases with biomass-derived alternatives can help the energy sector to achieve net zero by 2050. Decarboxylation of butyric acid, which can be obtained from biomass, can produce high yields of propane, a component of liquefied petroleum gases. The use of different gaseous reaction atmospheres of nitrogen, hydrogen, and compressed air during the catalytic hydrothermal conversion of butyric acid to propane have been investigated in a batch reactor within a temperature range of 200–350 °C. The experimental results were statistically evaluated to find the optimum conditions to produce propane via decarboxylation while minimizing other potential side reactions. The results revealed that nitrogen gas was the most appropriate atmosphere to control propane production under the test conditions between 250 °C and 300 °C, during which the highest hydrocarbon selectivity for propane of up to 97% was achieved. Below this temperature range, butyric acid conversion remained low under the three reaction atmospheres. Above 300 °C, competing reactions became more significant. Under compressed air atmosphere, oxidation to CO2 became dominant, and under nitrogen, thermal cracking of propane became significant, producing both ethane and methane as side products. Interestingly, under a hydrogen atmosphere, hydrogenolytic cracking propane became dominant, leading to multiple C–C bond cleavages to produce methane as the main side product at 350 °C.
Multi-Criteria Analysis in the Decision-Making Approach for the Linear Ordering of Urban Transport Based on TOPSIS Technique
Authors: Hajduk Sławomira
Published: January 2022

The effects of urban transport are highly concerning. The rapid urbanization and motorization in smart cities have a huge impact on sustainability. The goal of the paper is to analyse the smart cities selected, in terms of the urban transport. This paper presents an overview of research works published between 1991 and 2020 concerning urban transport and MCDM (multi-criteria decision making). The author highlights the importance of decision-making criteria and their weight, as well as techniques. Seven criteria and forty-four objects were used as the input of the approach. The entropy weight method was used to compute the weight of each criterion. The TOPSIS (Technique for Order Performance by Similarity to Ideal Solution) was applied to calculate the assessment and ranking of transport performance for each smart city. Portland was found to be the best location for transport enterprises and projects; Tbilisi was ranked last. The values of the relative closeness coefficient ranged from 0.03504 to 0.921402. Finally, some suggestions for future research are discussed.
Research on the Power Capture and Wake Characteristics of a Wind Turbine Based on a Modified Actuator Line Model
Authors: Xue Feifei , Duan Heping , Xu Chang , Han Xingxing , Shangguan Yanqing , Li Tongtong , Fen Zhefei
Published: January 2022

On a wind farm, the wake has an important impact on the performance of the wind turbines. For example, the wake of an upstream wind turbine affects the blade load and output power of the downstream wind turbine. In this paper, a modified actuator line model with blade tips, root loss, and an airfoil three-dimensional delayed stall was revised. This full-scale modified actuator line model with blades, nacelles, and towers, was combined with a Large Eddy Simulation, and then applied and validated based on an analysis of wind turbine wakes in wind farms. The modified actuator line model was verified using an experimental wind turbine. Subsequently, numerical simulations were conducted on two NREL 5 MW wind turbines with different staggered spacing to study the effect of the staggered spacing on the characteristics of wind turbines. The results show that the output power of the upstream turbine stabilized at 5.9 MW, and the output power of the downstream turbine increased. When the staggered spacing is R and 1.5R, both the power and thrust of the downstream turbine are severely reduced. However, the length of the peaks was significantly longer, which resulted in a long-term unstable power output. As the staggered spacing increased, the velocity in the central near wake of the downstream turbine also increased, and the recovery speed at the threshold of the wake slowed down. The modified actuator line model described herein can be used for the numerical simulation of wakes in wind farms.
Locational Marginal Price Forecasting Using SVR-Based Multi-Output Regression in Electricity Markets
Authors: Cantillo-Luna Sergio , Moreno-Chuquen Ricardo , Chamorro Harold R. , Riquelme-Dominguez Jose Miguel , Gonzalez-Longatt Francisco
Published: January 2022

Electricity markets provide valuable data for regulators, operators, and investors. The use of machine learning methods for electricity market data could provide new insights about the market, and this information could be used for decision-making. This paper proposes a tool based on multi-output regression method using support vector machines (SVR) for LMP forecasting. The input corresponds to the active power load of each bus, in this case obtained through Monte Carlo simulations, in order to forecast LMPs. The LMPs provide market signals for investors and regulators. The results showed the high performance of the proposed model, since the average prediction error for fitting and testing datasets of the proposed method on the dataset was less than 1%. This provides insights into the application of machine learning method for electricity markets given the context of uncertainty and volatility for either real-time and ahead markets.
Impact of Hydraulic System Stiffness on Its Energy Losses and Its Efficiency in Positioning Mechanical Systems
Authors: Dudziński Piotr , Skurjat Aleksander
Published: January 2022

Hydraulic steering systems for mechanical devices, for example, manipulators or vehicle steering systems, should be able to achieve high positioning precision with high energy efficiency. However, this condition is very often not met in practical applications. This is usually due to the stiffness of the hydraulic system being too low. As a result, additional corrections are required to achieve the required positioning precision. Unfortunately, this means additional energy losses in the hydraulic control system. In this study, this problem is presented using the example of a hydraulic steering system for an articulated frame steer vehicle. This hydraulic steering system should provide the required directional stability for road traffic safety reasons. So far, this issue, connected mainly with the harmful phenomenon of so-called vehicle snaking behaviour, has not been solved sufficiently practically. To meet the needs of industrial practice, taking into account the current global state of knowledge and technology, Wrocław University of Science and Technology is performing comprehensive experimental and computational studies on the snaking behaviour of an articulated frame steer wheeled commercial vehicle. The results of these tests and analyses showed that the main cause of problems that lead to the snaking behaviour of this vehicle class is the effective torsional stiffness of the hydraulic steering system. For this reason, a novel mathematical model of the effective torsional stiffness was developed and validated. This model comprehensively took into account all important mechanical and hydraulic factors that affect the stiffness of a hydraulic system, resulting in the examined snaking behaviour. Because of this, it is possible at the design stage to select the optimal parameters of the hydraulic steering system to minimise any adverse influence on the snaking behaviour of articulated frame steer wheeled vehicles. This leads to minimising the number of required corrections and minimising energy losses in this hydraulic steering system. The innovative model presented in the article can be used to optimise positioning accuracy, for example, in manipulators and any mechanical system with hydraulic steering of any system of any mechanical parts.
Misfire Detection Using Crank Speed and Long Short-Term Memory Recurrent Neural Network
Authors: Wang Xinwei , Zhang Pan , Gao Wenzhi , Li Yong , Wang Yanjun , Pang Haoqian
Published: January 2022

In this work, a new approach was developed for the detection of engine misfire based on the long short-term memory recurrent neural network (LSTM RNN) using crank speed signal. The datasets are acquired from a six-cylinder-inline, turbo-charged diesel engine. Previous works investigated misfire detection in a limited range of engine running speed, running load or misfire types. In this work, the misfire patterns consist of normal condition, six types of one-cylinder misfire faults and fifteen types of two-cylinder misfire faults. All the misfire patterns are tested under wide range of running conditions of the tested engine. The traditional misfire detection method is tested on the datasets first, and the result show its limitation on high-speed low-load conditions. The LSTM RNN is a type of artificial neural network which has the ability of considering both the current input in-formation and the previous input information; hence it is helpful in extracting features of crank speed in which the misfire-induced speed fluctuation will last one or a few cycles. In order to select the engine operating conditions for network training properly, five data division strategies are attempted. For the sake of acquiring high performance of designed network, four types of network structure are tested. The results show that, utilizing the datasets in this work, the LSTM RNN based algorithm can overcome the limitation at high-speed low-load conditions of traditional misfire detection method. Moreover, the network which takes fixed segment of raw speed signal as input and takes misfire or fault-free labels as output achieves the best performance with the misfire diagnosis accuracy not less than 99.90%.
Climate Change and Renewable Energy Generation in Europe—Long-Term Impact Assessment on Solar and Wind Energy Using High-Resolution Future Climate Data and Considering Climate Uncertainties
Authors: Yang Yuchen , Javanroodi Kavan , Nik Vahid M.
Published: January 2022

Climate change can strongly affect renewable energy production. The state of the art in projecting future renewable energy generation has focused on using regional climate prediction. However, regional climate prediction is characterized by inherent uncertainty due to the complexity of climate models. This work provides a comprehensive study to quantify the impact of climate uncertainties in projecting future renewable energy potential over five climate zones of Europe. Thirteen future climate scenarios, including five global climate models (GCMs) and three representative concentration pathways (RCPs), are downscaled by the RCA4 regional climate model (RCM) over 90 years (2010–2099), divided into three 30-year periods. Solar and wind energy production is projected considering short-/long-term climate variations and uncertainties in seven representative cities (Narvik, Gothenburg, Munich, Antwerp, Salzburg, Valencia, and Athens). The results showed that the uncertainty caused by GCMs has the most substantial impact on projecting renewable energy generation. The variations due to GCM selection can become even larger than long-term climate change variations over time. Climate change uncertainties lead to over 23% and 45% projection differences for solar PV and wind energy potential, respectively. While the signal of climate change in solar radiation is weak between scenarios and over time, wind energy generation is affected by 25%.
A Multi-Scale Numerical Model for Investigation of Flame Dynamics in a Thermal Flow Reversal Reactor
Authors: Li Jia , Mao Ming-Ming , Gao Min , Chen Qiang , Shi Jun-Rui , Liu Yong-Qi
Published: January 2022

In this paper, the flame dynamics in a thermal flow reversal reactor are studied using a multi-scale model. The challenges of the multi-scale models lie in the data exchanges between different scale models and the capture of the flame movement of the filtered combustion by the pore-scale model. Through the multi-scale method, the computational region of the porous media is divided into the inlet preheating zone, reaction zone, and outlet exhaust zone. The three models corresponding to the three zones are calculated by volume average method, pore-scale method, and volume average method respectively. Temperature distribution is used as data for real-time exchange. The results show that the multi-scale model can save computation time when compared with the pore-scale model. Compared with the volumetric average model, the multi-scale model can capture the flame front and predict the flame propagation more accurately. The flame propagation velocity increases and the flame thickness decreases with the increase of inlet flow rates and mixture concentration. In addition, the peak value of the initial temperature field and the width of the high-temperature zone also affect the flame propagation velocity and flame thickness.
Remote Sensing Assessment of the Expansion of Ips typographus Attacks in the Chuprene Reserve, Western Balkan Range
Authors: Georgiev Georgi , Georgieva Margarita , Dimitrov Stelian , Iliev Martin , Trenkin Vladislav , Mirchev Plamen , Belilov Sevdalin
Published: January 2022

The Chuprene Reserve was created in 1973 to preserve the natural coniferous forests in the Western Balkan Range in Bulgaria. The first infestations by European spruce bark beetle (Ips typographus) were registered in Norway spruce (Picea abies) stands in the mid-1980s. The aim of this study is to assess the damages caused by I. typographus in the Chuprene Reserve using remote sensing techniques – unmanned aerial vehicle (UAV) images, airborne images, and satellite images of European Space Imaging (EUSI), combined with terrestrial verification. High-resolution images in four bands of the electromagnetic spectrum and in a standard RGB channel were taken in 2017 via a multispectral camera ‘Parrot Sequoia’, integrated with a specialized professional UAV system eBee ‘Flying Wing’. The health status of Norway spruce stands in the reserve was assessed with the normalized difference vegetation index, based on the digital mixing of imagery captured in the red and near infrared range. The dynamic of bark beetle attacks was studied in GIS on the basis of maps generated from photographic surveys, airborne images taken in 2011 and 2015, and satellite images from 2020. In the UAV-captured area (314.0 ha), the size of Norway spruce stands attacked by I. typographus increased from 7.6 ha (2.4%) in 2011 to 44.9 ha (14.3%) in 2020. The satellite images showed that on the entire territory of the Chuprene Reserve (1451.9 ha), I. typographus killed spruce trees on 137.4 ha, which is 9.6% of the total area.