bluuebunny/crossref_metadata_2025 huggingface dataset:
Mit Kindern über Mehrsprachigkeit sprechen – Methodische (Selbst-)Reflexion einer Befragung von Viertklässler*innen
Authors: Rühlmann Liesa
Published: December 2021

Forschung mit Kindern stellt zwar die Perspektive von Kindern in den Fokus, jedoch ist auch hier der Blick Forschender zentral. In der Erhebung von Interviews nehmen Forschende durch ihre Anwesenheit Einfluss auf den Verlauf des Gesprächs und entscheiden in der Auswertung, welche Aussagen sie als bedeutsam einordnen und wie sie diese interpretieren. Im Artikel blicke ich (selbst-)reflexiv auf eine von mir durchgeführte Studie, in welcher Kinder zum Erleben ihrer Mehrsprachigkeit in der Schule befragt wurden. Zunächst gehe ich auf die zentralen Ergebnisse der Studie ein. Der Fokus liegt anschließend auf zwei Reflexionsfolien: Erst reflektiere ich anhand meines Vorgehens übergreifend die Forschung mit mehrsprachigen Kindern. Anschließend werden sich daraus ergebende ethische Aspekte beleuchtet.
Ministerialeliten in Deutschland
Authors: Veit Sylvia , Vedder Stefanie , Manschwetus Anika , Scholz Simon , Strobel Bastian
Published: December 2021

In diesem Bericht wird ein Datensatz zu den Ministerialeliten in Deutschland seit Beginn des 20. Jahrhunderts vorgestellt. Der Datensatz umfasst sowohl Politiker*innen als auch Spitzenbeamt*innen in zentralstaatlichen Ministerien. Für diese Personen wurden sozialstrukturelle Informationen, die Karriereverläufe sowie Daten zum politischen Engagement und zur Systemnähe – mit einem besonderen Fokus auf der NS-Zeit – erhoben. Der Datensatz basiert auf der Auswertung einer Vielzahl öffentlich zugänglicher Quellen sowie auf umfangreichen Aktenauswertungen im Bundesarchiv.
Politische Raumkultur als Verstärker der Corona-Pandemie? Einflussfaktoren auf die regionale Inzidenzentwicklung in Deutschland in der ersten und zweiten Pandemiewelle 2020
Authors: Richter Christoph , Wächter Maximilian , Reinecke Jost , Salheiser Axel , Quent Matthias , Wjst Matthias
Published: December 2021

In diesem Beitrag untersuchen wir, welchen spezifischen Einfluss Faktoren der politischen Raumkultur auf die regionalen Unterschiede in den Corona Inzidenzverläufen in den beiden Expansionsphasen der Pandemie im Frühjahr 2020 sowie im Herbst/Winter 2020 hatten. Ausgehend von der Annahme, dass in Regionen mit hohem Zuspruch zu Parteien der radikalen Rechten eine höhere Skepsis bzw. Distanz zu demokratischen Institutionen und damit einhergehend eine geringere Akzeptanz der Schutzmaßnahmen vorherrscht, prüfen wir, inwiefern sich diese Unterschiede in der politischen Raumkultur auf die Inzidenzentwicklungen auswirken. Wir nutzen neben Daten zum Infektionsgeschehen die Wahlergebnisse und Nichtwählendenanteile vergangener Bundestagswahlen und zahlreiche soziostrukturelle Variablen für die 401 Kreise und kreisfreien Städte. Zur Modellierung der dynamischen An- und Abstiegsphasen greifen wir auf latente Wachstumsmodelle zurück. Die Ergebnisse zeigen hohe signifikante Effekte der AfD-Zweitstimmenanteile auf die Anstiege der Infektionszahlen in beiden Wellen – sowohl in Ost- als auch in Westdeutschland. Für andere im Bundestag vertretene Parteien lassen sich keine systematischen Effekte auf die Anstiegsphase beider Wellen finden. Dazu kann gezeigt werden, dass auch die Zweitstimmenanteile rechtsextremer Kleinparteien und die Nichtwählendenanteile zu den Bundestagswahlen 2005 und 2013 positive Effekte auf die Inzidenzentwicklungen haben; das weist auf eine hohe Persistenz politischer und demokratischer Distanz in Teilen dieser Regionen hin.
Mutterschaft als modernisiertes Inklusionsversprechen für Frauen. Extrem rechte Thematisierungen von Mutterschaft vor dem Hintergrund der Krise der sozialen Reproduktion
Authors: Reusch Max
Published: December 2021

Untersucht wird, wie die extreme Rechte Widersprüche in der liberaldemokratischen Konstruktion von Zugehörigkeit zu einem politischen Gemeinwesen für sich nutzbar macht. Dies arbeitet der Beitrag am Beispiel der Verschiebungen heraus, die Mutterschaft als Marker der Zugehörigkeit von Frauen im Zuge der Ökonomisierung der Sozialpolitik erfahren hat. Eine inhaltsanalytische Auswertung von Debattenbeiträgen extrem rechter Protagonistinnen zeigt, dass die Thematisierung von Mutterschaft der extremen Rechten als Vehikel dient, um Frauen ein modernisiertes Inklusionsversprechen zu machen. Vor dem Hintergrund der Krise der sozialen Reproduktion und des Einzugs neoliberaler Rationalitäten in den gesellschaftlichen Diskurs um Mutterschaft entfaltet dieses Angebot seine Wirksamkeit.
Ausgestiegene in der Bildungsarbeit – eine kritische Betrachtung
Authors: Besche Julia
Published: December 2021

In den letzten Jahren ist eine Zunahme der Nachfrage nach Darbietungen von Ausgestiegenen aus der extremen Rechten in der schulischen und außerschulischen Bildungsarbeit zu verzeichnen. Diese Anfragen werden auch an professionelle Ausstiegshilfen im Bereich Rechtsextremismus herangetragen (Lehmeier 2020: 67). Im Kontext dieser Anfragen hat sich mittlerweile ein Diskurs zur Sinnhaftigkeit sowie Professionalität in diesem unübersichtlichen Feld von Angeboten ergeben. Der Diskurs soll an dieser Stelle in einigen Punkten nachvollzogen werden und umfasst verschiedene Aspekte, wie die unterschiedliche Verwendung des Ausstiegsbegriffs sowie die Heterogenität von Angeboten mit Ausgestiegenen in der Bildungsarbeit. Ein Schwerpunkt liegt auf dem Vergleich von Qualitätsstandards und Darstellungen zur Bildungsarbeit verschiedener Angebote sowie kritischen Positionen aus Wissenschaft und Praxis. Berücksichtigung findet dabei u. a. der Aspekt der Authentizität, welcher häufig als Argument für die Durchführung derartiger Veranstaltung angeführt wird. Im Anschluss werden kritische Hinweise und Alternativen zur Umsetzung von Bildungsveranstaltungen mit Ausgestiegenen im Kontext von Bildungsveranstaltungen vorgestellt.
Extrem rechte Zugriffe auf die Arbeitswelt: eine kritischhermeneutische Untersuchung von Propagandamaterial von Zentrum Automobil und der Kampagne „Werde Betriebsrat“
Authors: Aderholz David
Published: December 2021

Im Artikel werden die Ergebnisse einer kritisch-hermeneutischen Dokumentenanalyse des zwischen Ende 2017 und Anfang 2021 veröffentlichten Propagandamaterials von Zentrum Automobil und der Kampagne Werde Betriebsrat präsentiert. Ziel der Untersuchung ist, herauszuarbeiten, wie Zentrum Automobil versucht, die Arbeiter_innenschaft für sich zu gewinnen. Über den Untersuchungszeitraum lässt sich ein Wandel feststellen: Wurde zunächst versucht, über eine klassisch extrem rechte Zielgruppenansprache Arbeiter_innen für sich zu gewinnen, versuchte ZA nach der Betriebsratswahl 2018, mittels einer diverseren Selbstdarstellung eine breitere Öffentlichkeit anzusprechen. Außerdem wird im Artikel dargestellt, dass ZA keine eigenen betrieblichen Positionen entwickelt, weil alle im Betrieb auftretenden Konflikte auf eine Verschwörung von Gewerkschaften und internationalem Management reduziert werden.
Assessment of Information Literacy Competency of Management Science Students at the University of Ilorin, Kwara State, Nigeria
Authors: TELLA Adeyinka , AMADU Rukayat , OLANİYİ Oluwakemi , BAMİDELE Sunday
Published: December 2021

The study examined the information literacy (IL) competencies of management science students at the University of Ilorin, Nigeria. A survey research design was adopted. Data was collected through a questionnaire developed by the authors. A total of 337 randomly selected undergraduate students from six departments represent the sample for the study. Six research questions were developed and answered. The findings demonstrate that respondents generally perceived information literacy IL as an insight on how to effectively and efficiently access and select needed information and as a tool for long-life learning. Respondents have high IL skills in the use of the internet to access information needed in their field and can assess such information from various sources. Competence in the circulation of information, identifying and locating information, and accessing sources and evaluating information are the areas of strength revealed in the study; while the weaknesses identified are inability to determine when information is needed, none awareness of specific search engines. The results also reveal that respondents can use IL skills to retrieve information, search the library, and use specific search terms. Prominent information literacy courses offered are the use of libraries, introduction to computers, and IL training on the use of popular search engines such as Google and Ask Me. The common factors confronting the teaching of IL are power supply, high cost of computers, and limited time available for IL practical and hands-on.
Base-Frequency Negative Sequence Impedance Modeling and Sensitivity Analysis of DFIG
Authors: Pang Simin , Jia Ruiyuan , Liu Qihui , Wu Linlin , Liu Hui
Published: December 2021

The phenomenon of three phase voltage imbalance frequently occurs in large-scale new energy grid connected areas in China; in severe cases, a large number of wind turbines will be disconnected from the grid. To solve the problem of the voltage imbalance at the point of common coupling (PCC), analyze the influence of generator parameters change on negative sequence voltage under the background of unbalanced power grid, a modeling method of base-frequency negative sequence impedance of doubly fed induction generator (DFIG) which including phase locked loop (PLL), rotor side converter (RSC) and grid side converter (GSC) is proposed. By establishing the negative sequence equivalent circuit of grid-connected system of DFIG, the relationship between the negative sequence voltage of PCC and the negative sequence impedance of DFIG is listed, and analyzing the sensitivity of control parameters link to base-frequency impedance, the parameter that has great influence on base-frequency negative sequence impedance of PCC is found out. Finally, the accuracy of impedance modeling and sensitivity analysis is verified by simulation studies.
Influence of Environmental Regulation and Resource Utilization on Research and Development Towards Sustainability: A Perspective of China’s Coal Mining Industry
Authors: Du Jianguo , Dabuo Francis Tang , Madzikanda Beverlley , Boamah Kofi Baah , Coulibaly Pierrette Tiefigue , Anfom Kingsley
Published: December 2021

The multi-directional variation of results among environmental regulation, resource utilization, and Research and Development (R&D) investments continue to gain more popularity but few studies have examined the nexus among these variables in an open economy. To analyze the individual and combined effects of environmental regulation and resource utilization on industrial R&D investments, a panel data of 30 provinces in China from 2008 to 2018 was used. We also examined the mechanism underlying the magnitude of pollution inducement on R&D investment. The results show that environmental regulation significantly and positively promotes R&D investment while resource utilization has an inverse effect. Environmental regulation does not interfere with efficiency and neither does the quest for efficiency in resource utilization weaken the impact of environmental regulation on R&D investment. Only a few provinces are within the rising stages of the inverted n-shaped curve indicating environmental regulation level is still low. Even though there are tighter laws, government needs to continue to put in more stringent environmental policies towards achieving sustainable development.
Natural Fractures Characteristics of the Carboniferous Volcanic Reservoir in Northwestern Margin of Junggar Basin
Authors: Lv Bei , Yao Luo , Wang Bo , Wang Jian , Wang Lizhi
Published: December 2021

Research on the regional fracture’s development is important for reservoir fracturing. This paper takes the Carboniferous volcanic reservoir in the northwestern margin of Junggar Basin as the research object. Based on understanding the regional tectonic faults and geological characteristics, the parameter characteristics of natural fractures are analyzed using imaging logging data, and natural fractures distribution characteristics are compared with regional faults and in-situ stresses, as well as the pattern of natural fractures formation is revealed. The results indicated that: (1) The Carboniferous in the northwestern margin of Junggar Basin area mainly develops 3 NE-trending reverse faults. The reservoir type is pore-fracture dual media type, with an average porosity of 7.64% and an average permeability of 1.16mD, which belongs to the medium-porosity and ultra-low permeability reservoir; (2) Reservoir fractures are generally well developed. High-conductivity fractures and high-resistance fractures coexist, but high-conductivity fractures are the main ones. The fracture width is between 0.053 and 0.23 mm, and the fracture density is between 0.5 and 1.68 strips/m. The length is between 0.54-1.88m, the fracture porosity is between 3.4×10-5-41×10-5, and the dominant fracture trend is mainly NE50°-NE80°; (3) The direction of the maximum horizontal in-situ stress of the reservoir is mainly NE30°-NE60°, in the direction of NEE, it differs from fracture strike by 10°-50°, and roughly the same as the strike of the three reverse faults.
Development of Photovoltaic Agriculture in China Based on SWOT Analysis
Authors: Wang Jieqiong , Zhang Chenchi , Zhang Lingxian
Published: December 2021

As the coupling of photovoltaic (PV) and agriculture, PV agriculture can effectively promote the development of the PV industry and modern agriculture. PV agriculture has attracted numerous countries, prompting the emergence of a growing number of PV farms. As the largest polysilicon producer with large agricultural production area and abundant solar energy resources, China is selected as a case study. This paper identifies indicate that the weakness-threat (WT) strategy should be adopted to promote the development of PV agriculture in China by establishing a unified support policy, encouraging the participation of market capital, and promoting the development of related technology. Similarly, the Chinese scenario might provide a useful reference for other developing countries.
Research on Solar Power Generation Control System Based on 360 Degree Rotate Double-Motor
Authors: Wang Zheng , Jia Xuezeng
Published: December 2021

Aiming at the problems of low utilization rate of solar energy and poor anti-interference ability of tracking structure solar energy control system in fixed structure solar energy device, this paper designs a dual axis high-precision solar tracking system based on four quadrant rule. The system adopts two ways: automatic tracking and manual correction. The system uses four photoresistors as detection elements, uses the four quadrant principle to judge the tracking offset angle, and drives two-dimensional two axis stepper motor through STC89C52 processor to achieve the purpose of vertical angle, so as to ensure that the solar panel is always in the state of maximum light receiving surface; When the system is disturbed, it can be judged according to the change of the photosensitive resistance in the energy monitoring system, and the artificial correction can be realized by modulating the size of the divider resistance, which can basically achieve 360° Automatic rotation tracking. In addition, the energy monitoring system based on LabView is designed. Through the real system analysis, it can be concluded that the photoelectric energy conversion rate of the fixed solar device is increased by 32.4%.
Research on Parameter Optimization Based on Genetic Algorithm to Suppress Transient Overvoltage of LCC-HVDC System
Authors: Liu Qihui , Wu Yong
Published: December 2021

Since the 1980s, the direct current (DC) transmission technology has developed rapidly in China. However, with the large-scale application of DC transmission technology, new problems are brought to the power system. Commutation failure of the DC system will cause overvoltage in the alternating current (AC) power network, which will cause great harm to the weak AC system with wind power access. Therefore, it is urgent to carry out research on overvoltage suppression at the sending end of AC system. This paper reveals the formation mechanism of transient overvoltage and confirms that the control parameters of DC system influence the voltage at the sending end directly. And the appropriate control parameters were selected to suppress the overvoltage by the parameter optimization based on genetic algorithm (GA).
Pressure Drop Measurements and Simulations for the Protective Mesh Screen Before the Gas Turbine Compressor
Authors: Nie L X , Yin Y , Yan L Y , Zhou S W
Published: December 2021

This paper characterizes the pressure drop of incompressible airflow when passing by a metal mesh screen which acts as a protection from sucking foreign solid matters before the gas turbine compressor. The wire diameter is 1.2mm and the mesh number is 3. Two experiments are conducted in different time period of a day to guarantee the experimental repeatability. The experimental data are used in regression analysis to obtain a quadratic correlation between the pressure drop across the screen and the fluid velocity. Numerical simulations are utilized to investigate detailed velocity and pressure fields around the wires and the Standard k-ε turbulence model is used. The results show that the fluid suffers from around 140Pa and 250Pa total pressure drop at the velocity of 20m/s and 30m/s respectively. The pressure closely upstream of the wires is as high as 4 times of the inlet flow level, while wide negative pressure regions are observed downstream of the wires resulting from fluid stagnation, reverse flow and recirculation. The empirical correlation obtained in the paper has a high confidence level and can be used in calculating the overall pressure drop of the gas turbine air intake system.
Systematic Analysis of Power Dispatching: Using Machine Learning
Authors: Tang Yongjin , Tang Xuehua , Yang Yang , Zhou Zhiyong , Deng Yaochao
Published: December 2021

With the proposal of the concept of carbon neutrality in China, the market will continue to pay more attention to sustainable energy and energy conversion. The management of the power system is a very important part in this field due to the discontinuity of wind energy and solar energy, which is also called power dispatching. It is particularly important in the process of power system conversion and management, so it is very helpful to understand the development and bottleneck of power system to understand the main research content of power dispatching through literature. Based on machine learning and text mining, this paper analyses the related research of power dispatching. The results show that the related papers of power dispatching mainly include safe operation, power dispatching strategy, power dispatching automation, power dispatching technology analysis and so on. This paper also shows the results by a clear visualization method.
Study on Surface and Internal Humidity of Dunes in the Southwestern Margin of the Taklimakan Desert
Authors: Zhang Wei , Cao Liping , Yu Shengqing , Zhou Xiangling
Published: December 2021

Greening on the edge of desert is an effective way to prevent and control desertification. This paper studies the humidity characteristics of sand samples collected from the surface and interior of dunes in the southwestern edge of the Taklimakan Desert, whose geographic coordinates are 39°43’37” – 39°43’41” N, 78°43’02” – 78°43’09” E. The humidity of sand samples at vertical depths under five different surface positions of windward slope bottom, windward slope middle, windward slope top, leeward slope middle and leeward slope bottom was studied. On the surface of the sand at different locations and at the same depth under the surface, the sand humidity is greater closer to the bottom of the slope, for both windward slope and leeward slope. The humidity gradient values on the surface and at different depths on different positions of the dunes are obtained. Finally, recommendations are provided for effective ways to prevent desertification in the desert edge of Kashgar.
Farmland and Crop Quality Improvement for Heavy Metals Based on Six Sigma
Authors: Xue Shengping , Pan Baohai
Published: December 2021

Scientific methodology of soil and farmland health management and crop quality improvement is urgent to set up. Our objective was to recognize and sweep away the choke point and improve the farmland health course by applying the philosophy and method of six sigma. Farmland and crop quality improvement for heavy metals belongs to Systems Engineering. SIPOC flux of heavy metals indicates the key process by causal factor analysis through quantitative way. Multi-source and multi-scale data suggest that key process of performance is source control. Measurement processes analyse data and eliminate root causes to improve sigma levels. The study was conducted on farmland and crop health management with concern at the inputs and mechanism that had choke point and high defect rate. DMAIC of sources and exports of cd in the soil will improve and control the Quality of crop and farmland. The premise of implementing six sigma is that the chosen metrics are measurable and effective, but we find a lack of good correlation between terminal index and intermediate index in heavy metal pollution. Suggestion is as follows: Big data could be shared and processed intelligently; policy and culture to pollution-free is more important to farmland and crop health by six sigma.
Quantitative Risk Assessment and Application of Landslide Disaster in Chongqing
Authors: Li Ke , Liu Dongsheng , Wang Yanlei , She Na
Published: December 2021

Chongqing is located in southwestern China, and geological disasters occur frequently. The amount of potential landslide disasters is far greater than the number of landslides that can be managed by government funds, so the risk assessment for potential landslide disasters is critical. In practical applications, risk assessment methods based on landslide stability and loss are restricted by various factors. These methods can be simplified to semi-empirical assessment methods, which are influenced by the discrimination factors near the limit values of the determined conditions, possibly leading to sudden changes in the evaluation results and distort the conclusions. To solve this problem, we propose a full quantitative risk assessment method according to the probability of landslide damage. The mathematical probability model is used to quantitatively describe the risk assessment impacting factors, weaken the boundary influence, and improve the accuracy of landslide risk assessment. Correspondingly, the software is developed to conduct quantitative risk assessment on six landslides in Feng jie County, Chongqing, which verifies the accuracy and reliability of the full quantitative risk assessment method, and provides an important reference for judging urban landslide geological disasters.
Thermogravimetric Analysis and Kinetic Calculation on the Combustion Characteristics of Two Typical Shenhua Chars
Authors: Xiang Baixiang , Huang Jun , Tan Houzhang , Xiong Xiaohe , Li Yan , Han Binqiao
Published: December 2021

Recently, a new ultra-low nitrogen combustion technology, pyrolysis and gasification coupling combustion, was proposed. The dependence on SCR or SNCR was reduced measurably with this technology. However, given the lower content of volatile matter in semi-chars, the burn-up ratio and combustion efficiency seemed to become lower. Thus, in this study, the combustion characteristics of the Shenhun and Carboniferous char were investigated under combustion conditions with the thermogravimetric method; meantime, kinetic calculation on the combustion characteristics were evaluated with Coats–Redfern method. Experiments indicated that Shenhun char showed good ignition and burnout characteristics when the pyrolysis temperature ranged from 973.15 K to1073.15 K; meanwhile, Carboniferous char showed good ignition and burnout characteristics when the pyrolysis temperature ranged from 873.15 K to 973.15 K. Besides, both the calculations and experiments indicated that Shenhun char showed better combustion characteristics than Carboniferous char.
Stress-Responses of Performance and Microbial Community in Anaerobic Digestion System Under Long-Term Enrichment of Phenanthrene
Authors: Shi Yongsen , Xu Chunli , Li Jingyi , Yao Yilin , Niu Qigui
Published: December 2021

The expanded granular sludge blanket reactor (EGSB) was operated for 198 days to study the long-term effects of phenanthrene (PHE) enrichment on system performance and microbial community. The results showed that the PHE was significantly enriched in the reactor. The final PHE concentration in effluent and sludge reached to 1.764±0.05 mg/L and 12.52±0.42 mg/gTS, respectively. While the average daily methane production was decreased by 5.0%-9.8% under long-term PHE exposure. The 3D-EEM of effluent indicated that PHE stimulated the microbial metabolism with the higher intensity of soluble microbial byproduct-like materials (SMP) and proteins. Moreover, the removal efficiency of soluble chemical oxygen demand (SCOD) and NH4+-N gradually diminished with the enrichment of PHE. PHE shaped the microbial community, and the predominant fermentative bacteria (Mesotoga) was severely inhibited. Contrarily, the bacteria (Syntrophorhabdus, Acinetobacter, Desulfovibrio, Desulfomicrobium) involved in PHE-degradation was enriched at end of Phase V. In addition, the relative abundance (RA) of hydrotrophic methanogens (Methanofastidiosum, Methanolinea, Methanobacterium, Methanomassiliicoccus) increased by 0.96-fold with the long-term enrichment of PHE, while the RA of acetoclastic Methanosaeta obviously decreased.
Construction of Big Database in the Field of Public Security: A Case Study in Shenzhen
Authors: Yin Jixiao , Yuan Diping , Wu Guohua , Wang Zuoxin , Zeng Yiping , Ling Jun , Li Qinliang
Published: December 2021

In terms of the actual needs of the emergency management department, at the beginning of the establishment of Shenzhen Emergency Management Bureau, all kinds of information resources have not been connected, and the business of the Department is still supported by the information assets of the original unit of the transferred department. However, there are significant differences in the construction years, construction standards and system architecture of the existing information systems, which lead to the “one number, multiple sources” and multiple management of all kinds of business data, and the systems are basically in the chimney construction pattern of mutual separation. At the same time, the exchange and sharing of internal and external data of the Emergency Management Bureau lacks a unified data standard, and a unified data platform has not been built for centralized management. Therefore, it is difficult for the newly established emergency management bureau to effectively support the business process reengineering, collaborative command, big data intelligent decision-making and other needs. In addition, considering the construction of smart city at the municipal and district levels, as well as the complex information situation such as the information collaboration within the security committee office and emergency committee system, it is urgent to prepare the top-level design of emergency management information, and plan the future development with a higher position, a wider vision and a new idea.
Simulation and Prediction of the Groundwater Pollution Caused by the Leakage from a Sewage Plant in an Aluminum Factory Under the Action of Ocean Tide Support
Authors: Zhang Yu , Lan Junkang
Published: December 2021

In order to predict the impact of wastewater from an aluminum plant treatment station on the groundwater environment under abnormal conditions (i.e., sewage leakage accident). Through the investigation of hydrogeological conditions, and then the permeability coefficient of the aquifer was measured through borehole injection tests. Finally, the groundwater pollution transport halo was obtained by numerical simulation based GMS software. The simulation results showed that the groundwater aquifer will be seriously polluted by COD and fluoride (F-) after the sudden sewage seepage accident. What’s more, the simulation results showed that the pollution concentration is getting higher and higher with time, which is analyzed to be caused by the small permeability of the water-bearing medium in the aquifer and the groundwater flow field was supported by seawater tide.
Study on the Bonding Mechanism and Bonding Performance of Double Layer Fine-Surfacing Technology
Authors: Wu Xiangyan , Chen Xiangling , Yang Jianping , Hu Xin , Yang Guang
Published: December 2021

Double layer fine-surfacing technology is a thin layer maintenance technology of pavement positive texture, which plays an important role in preventing road safety accidents. Bonding performance is the key quality control index of the double layer fine-surfacing, which directly affects its service performance and durability. In this paper, the bonding mechanism of the double layer fine-surfacing is analyzed, and the bonding strength between the double layer fine-surfacing and the old asphalt pavement surface is studied by using the pullout test method under the factors of different cement dosage and different temperature environment, and the bonding index of the double layer fine-surfacing is put forward. Experiments show that the double layer fine-surfacing technology has the characteristics of super bonding and wear resistance. It is an economical and practical maintenance technology and has broad development prospects.
Key Feature Selecting in the Clean Oil Refinery Process Based on a Two-Stage Data Mining Framework
Authors: Lin X , Ma J Y , Lin F X , Wang D Q , Xiao X S
Published: December 2021

Maintaining the ratio of octane number and reducing the proportion of harmful substances in the heavy oil fluid catalytic cracking process meets both environmental and economic benefits. Through collecting tremendous processing data by digital hardware, gasoline refiners are still hard to do well data analytical work in production process control due to the large scale of ambiguous intermediate operating variables. This paper proposes a two-stage data mining framework integrates the strengths of Ridge regression and Person correlation analysis to extract a scale limited group of key features. Different with traditional recursive feature elimination methods, we pay more attention to the correlation analysis between every couple of features in the result. Two stop criterions guarantee to fulfil refining standards and limit the computational work in finite steps. A real word case study contains 325 samples, 13 quality indicators and 354 operating variables which testifies the validity and practicality of our algorithm. The result shows only 13 features (operating variables) are significant to the rationality of process design and the improvement of process control.
A Deep Learning Framework in FCC Process Control
Authors: Yan Q Y , Lin X , Qin Z Q , Luo G L , Wang D Q , Xiao X S
Published: December 2021

The controlling of exhaust gas from gasoline is crucial for atmospheric environment protection. Research Octane Number (RON) loss and restricted sulphur (S) content matter the quality of gasoline. To obtain gasoline with high quality, the paper proposes a novel data-driven optimization model integrating deep neural network (DNN) and genetic algorithm (GA) to model for Fluid Catalytic Cracking (FCC) process then optimize. To begin with, the DNN is used to fit the relations between 13 related input variables and output variables in FCC. Subsequently, the FCC process is modelled and GA is proposed to solve the optimization model. Ultimately, 305 samples from real datasets have been analysed to testify feasibility and effectiveness of the method. This paper provides a guideline for the production process of FCC gasoline.
Automatically Identifying Eviction Cases and Outcomes Within Case Law of Dutch Courts of First Instance
Authors: Medvedeva Masha , Dam Thijmen , Wieling Martijn , Vols Michel
Published: December 2021

In this paper we attempt to identify eviction judgements within all case law published by Dutch courts in order to automate data collection, previously conducted manually. To do so we performed two experiments. The first focused on identifying judgements related to eviction, while the second focused on identifying the outcome of the cases in the judgements (eviction vs. dismissal of the landlord’s claim). In the process of conducting the experiments for this study, we have created a manually annotated dataset with eviction-related judgements and their outcomes.
A Kelsenian Deontic Logic
Authors: Ciabattoni Agata , Parent Xavier , Sartor Giovanni
Published: December 2021

Inspired by Kelsen’s view that norms establish causal-like connections between facts and sanctions, we develop a deontic logic in which a proposition is obligatory iff its complement causes a violation. We provide a logic for normative causality, define non-contextual and contextual notions of illicit and duty, and show that the logic of such duties is well-behaved and solves the main deontic paradoxes.
Explaining Factor Ascription
Authors: Mumford Jack , Atkinson Katie , Bench-Capon Trevor
Published: December 2021

Explanation and justification of legal decisions has become a highly relevant topic in light of the explosion of interest in the use of machine learning (ML) approaches to predict legal decisions. Current suggestions are to use the established factor based explanations developed in AI and Law as the basis for explaining such programs. We, however, identify factor ascription as an important aspect of explanation of case outcomes not currently covered, and argue that explanations must also include this aspect. Finally, we outline our proposal for a hybrid system approach that combines ML and Abstract Dialectical Framework (ADF) layers to engender an explainable process.
Timed Dyadic Deontic Logic
Authors: Kharraz Karam Younes , Leucker Martin , Schneider Gerardo
Published: December 2021

In this paper, we introduce TDDL, a timed dyadic deontic logic. Our starting point is a version of a dyadic deontic logic with conditional obligations, permissions, and obligations, and with a “reparation” operator for representing contrary-to-duties and contrary-to-prohibitions. We also consider a sequence operator allowing us to define norms as sequences of individual norms and most importantly with timed intervals, allowing us to express deadlines of norms. We provide a trace semantics capturing both satisfaction and violation of norms and discuss fulfillment of TDDL specifications.
Can Predictive Justice Improve the Predictability and Consistency of Judicial Decision-Making?
Authors: Bex Floris , Prakken Henry
Published: December 2021

There has recently been talk of algorithms that predict decisions in legal cases being used by the judiciary to improve the predictability and consistency of judicial decision making. We argue that their use may minimise the error rate of decisions in the long run, but that this would require not only major technical advances but also major changes in legal thinking about what is the most important objective of judicial decision-making: optimising individual justice in a particular case or reducing errors in the long run. We further argue that if algorithmic decision predictors give any useful information in individual cases to judges at all, this is not in its predictions but in its explanations.
Rationale Discovery and Explainable AI
Authors: Steging Cor , Renooij Silja , Verheij Bart
Published: December 2021

The justification of an algorithm’s outcomes is important in many domains, and in particular in the law. However, previous research has shown that machine learning systems can make the right decisions for the wrong reasons: despite high accuracies, not all of the conditions that define the domain of the training data are learned. In this study, we investigate what the system does learn, using state-of-the-art explainable AI techniques. With the use of SHAP and LIME, we are able to show which features impact the decision making process and how the impact changes with different distributions of the training data. However, our results also show that even high accuracy and good relevant feature detection are no guarantee for a sound rationale. Hence these state-of-the-art explainable AI techniques cannot be used to fully expose unsound rationales, further advocating the need for a separate method for rationale evaluation.
A Survey on Methods and Metrics for the Assessment of Explainability Under the Proposed AI Act
Authors: Sovrano Francesco , Sapienza Salvatore , Palmirani Monica , Vitali Fabio
Published: December 2021

This study discusses the interplay between metrics used to measure the explainability of the AI systems and the proposed EU Artificial Intelligence Act. A standardisation process is ongoing: several entities (e.g. ISO) and scholars are discussing how to design systems that are compliant with the forthcoming Act and explainability metrics play a significant role. This study identifies the requirements that such a metric should possess to ease compliance with the AI Act. It does so according to an interdisciplinary approach, i.e. by departing from the philosophical concept of explainability and discussing some metrics proposed by scholars and standardisation entities through the lenses of the explainability obligations set by the proposed AI Act. Our analysis proposes that metrics to measure the kind of explainability endorsed by the proposed AI Act shall be risk-focused, model-agnostic, goal-aware, intelligible & accessible. This is why we discuss the extent to which these requirements are met by the metrics currently under discussion.
Reverse engineering of a mechanistic model of gene expression using metastability and temporal dynamics
Authors: Ventre Elias
Published: December 2021

Differentiation can be modeled at the single cell level as a stochastic process resulting from the dynamical functioning of an underlying Gene Regulatory Network (GRN), driving stem or progenitor cells to one or many differentiated cell types. Metastability seems inherent to differentiation process as a consequence of the limited number of cell types. Moreover, mRNA is known to be generally produced by bursts, which can give rise to highly variable non-Gaussian behavior, making the estimation of a GRN from transcriptional profiles challenging. In this article, we present CARDAMOM (Cell type Analysis from scRna-seq Data achieved from a Mixture MOdel), a new algorithm for inferring a GRN from timestamped scRNA-seq data, which crucially exploits these notions of metastability and transcriptional bursting. We show that such inference can be seen as the successive resolution of as many regression problem as timepoints, after a preliminary clustering of the whole set of cells with regards to their associated bursts frequency. We demonstrate the ability of CARDAMOM to infer a reliable GRN from in silico expression datasets, with good computational speed. To the best of our knowledge, this is the first description of a method which uses the concept of metastability for performing GRN inference.
A novel evacuation path planning method based on improved genetic algorithm
Authors: Zhai Longzhen , Feng Shaohong
Published: December 2021

In order to solve the problem of finding the best evacuation route quickly and effectively, in the event of an accident, a novel evacuation route planning method is proposed based on Genetic Algorithm and Simulated Annealing algorithm in this paper. On the one hand, the simulated annealing algorithm is introduced and a simulated annealing genetic algorithm is proposed, which can effectively avoid the problem of the search process falling into the local optimal solution. On the other hand, an adaptive genetic operator is designed to achieve the purpose of maintaining population diversity. The adaptive genetic operator includes an adaptive crossover probability operator and an adaptive mutation probability operator. Finally, the path planning simulation verification is carried out for the genetic algorithm and the improved genetic algorithm. The simulation results show that the improved method has greatly improved the path planning distance and time compared with the traditional genetic algorithm.
A novel multi-stage ensemble model for credit scoring based on synthetic sampling and feature transformation
Authors: He Fang , Zhang Wenyu , Yan Zhijia
Published: December 2021

Credit scoring has become increasingly important for financial institutions. With the advancement of artificial intelligence, machine learning methods, especially ensemble learning methods, have become increasingly popular for credit scoring. However, the problems of imbalanced data distribution and underutilized feature information have not been well addressed sufficiently. To make the credit scoring model more adaptable to imbalanced datasets, the original model-based synthetic sampling method is extended herein to balance the datasets by generating appropriate minority samples to alleviate class overlap. To enable the credit scoring model to extract inherent correlations from features, a new bagging-based feature transformation method is proposed, which transforms features using a tree-based algorithm and selects features using the chi-square statistic. Furthermore, a two-layer ensemble method that combines the advantages of dynamic ensemble selection and stacking is proposed to improve the classification performance of the proposed multi-stage ensemble model. Finally, four standardized datasets are used to evaluate the performance of the proposed ensemble model using six evaluation metrics. The experimental results confirm that the proposed ensemble model is effective in improving classification performance and is superior to other benchmark models.
An improved YOLO network for unopened cotton boll detection in the field
Authors: Zhang Yan , Yang Gongping , Liu Yikun , Wang Chong , Yin Yilong
Published: December 2021

Detection of cotton bolls in the field environments is one of crucial techniques for many precision agriculture applications, including yield estimation, disease and pest recognition and automatic harvesting. Because of the complex conditions, such as different growth periods and occlusion among leaves and bolls, detection in the field environments is a task with considerable challenges. Despite this, the development of deep learning technologies have shown great potential to effectively solve this task. In this work, we propose an Improved YOLOv5 network to detect unopened cotton bolls in the field accurately and with lower cost, which combines DenseNet, attention mechanism and Bi-FPN. Besides, we modify the architecture of the network to get larger feature maps from shallower network layers to enhance the ability of detecting bolls due to the size of cotton boll is generally small. We collect image data of cotton in Aodu Farm in Xinjiang Province, China and establish a dataset containing 616 high-resolution images. The experiment results show that the proposed method is superior to the original YOLOv5 model and other methods such as YOLOv3,SSD and FasterRCNN considering the detection accuracy, computational cost, model size and speed at the same time. The detection of cotton boll can be further applied for different purposes such as yield prediction and identification of diseases and pests in earlier stage which can effectively help farmers take effective approaches in time and reduce the crop losses and therefore increase production.
Optimization of APT attack detection based on a model combining ATTENTION and deep learning
Authors: Do Xuan Cho , Duong Duc
Published: December 2021

Nowadays, early detecting and warning Advanced Persistent Threat (APT) attacks is a major challenge for intrusion monitoring and prevention systems. Current studies and proposals for APT attack detection often focus on combining machine-learning techniques and APT malware behavior analysis techniques based on network traffic. To improve the efficiency of APT attack detection, this paper proposes a new approach based on a combination of deep learning networks and ATTENTION networks. The proposed process for APT attack detection in this study is as follows: Firstly, all data of network traffic is pre-processed, and analyzed by the CNN-LSTM deep learning network, which is a combination of Convolutional Neural Network (CNN) and Long Short Term Memory (LSTM). Then, instead of being used directly for classification, this data is analyzed and evaluated by the ATTENTION network. Finally, the output data of the ATTENTION network is classified to identify APT attacks. The optimization proposal for detecting APT attacks in this study is a novel proposal. It hasn’t been proposed and applied by any research. Some scenarios for comparing and evaluating the method proposed in this study with other approaches (implemented in section 4.4) show the superior effectiveness of our proposed approach. The results prove that the proposed method not only has scientific significance but also has practical significance because the model combining deep learning with ATTENTION network has helped improve the efficiency of analyzing and detecting APT malware based on network traffic.
Primary care providers need education and resources to provide optimal care for children and adults with spina bifida
Authors: Garver Amanda E. , Mohanty Salini , Dicianno Brad E. , Turchi Renee M.
Published: December 2021

PURPOSE: The objective of this study was to describe the knowledge, attitudes, and practices related to caring for youth with SB within a statewide pediatric primary care medical home network. METHODS: Health care professionals who were members of the Pennsylvania Medical Home Program (PAMHP) statewide network were surveyed on sociological demographics, confidence in and knowledge of topics that impact the spina bifida (SB) population, education on SB, designating responsibility of care, tasks completed in the office, and transition from pediatric to adult care from October 2016 through November 2016. RESULTS: Among 67 respondents, 78%believed counseling families/caregivers about the impact of SB on everyday life was a shared responsibility between primary care physicians (PCP) and SB clinics. Eighty five percent of participants indicated they had exposure to SB through clinical courses or didactics in training but only 54%of participants felt knowledgeable on the impact of SB on everyday life. CONCLUSION: Findings suggest a need for increased educational efforts, improved awareness of SB, and access to evidence-based guidelines for PCPs to promote accessible and coordinated care to individuals with SB.
Definición de sujeto en la formalización de los procesos organizacionales
Authors: Stassi Hector Martin
Published: December 2021

Este trabajo busca identificar las distintas definiciones de sujeto implícitas en los lenguajes administrativos utilizados en la formalización de los procesos organizacionales. Abordar este tema resulta de especial interés, ya que permite una aproximación a la aplicabilidad de las distintas teorías administrativas sobre el sujeto y entender de qué forma dicho sujeto se relaciona con los procesos administrativos. La metodología de investigación utilizada ha sido cualitativa-exploratoria. Se concluye que los sujetos administrativos implícitos en los lenguajes administrativos son de carácter contingente, es decir, que no hay una única definición de sujeto, sino que depende de la etapa del proceso administrativo en la que se encuentre, así como también de factores como la cultura organizacional, el sector y la estructura productiva de cada organización.