A Review on the CO2 Emission Reduction Scheme and Countermeasures in China’s Energy and Power Industry under the Background of Carbon Peak

To reach the peak of carbon emission in China, the energy and power industry has the most arduous task and the heaviest responsibility. It should not only ensure efficient economic development, but also complete the arduous task of energy conservation and emission reduction. It is the main force in helping reach the peak of carbon emission. Taking the achievement of carbon peak in China’s power industry as the research object, this paper utilizes time series analyses to establish CO2 emission prediction models for China and its power industry under two scenarios: with and without a carbon peak target. The paper analyzes the current status of achieving carbon peak in China’s power industry and puts a forward CO2 emission reduction scheme for China and its power industry in the future. On this basis, countermeasures for China’s power industry to deal with carbon peak are explored.

Investigating the Possibility of Determining the Sequence Intersecting Lines between LaserJet Printing and Handwriting in Document Examination using Color Measurement Technique

In Questioned Documents Examination, the sequence of crossing lines in the intersection of handwriting and printed area can be important clues for detecting tampered documents. Recognition of such documents is a arduous task and requires people with experience and expertise. In the present work, we investigated the possibility of determining the sequence of intersecting lines between LaserJet printing and handwriting for a series of simulated laboratory specimens in the document examination using color measurement technique. The spectral reflectance curves and color coordinates of some points on and out of the cross lines were compared. Four different commercial ballpoint pens and a black toner LaserJet were used to prepare the specimens. The color change of the intersecting lines was subjectively considered through the captured images and a visual assessment process. It was also objectively determined by determining the color difference values from the colorimetric data in CIELAB and CIELCH color spaces in the visible range. The color change evaluation showed that the order in which printing or handwriting is applied alters colorimetric results. Moreover, the investigations showed small color difference values of less than 2 units between a point of printed area individually, and intersection could be applied as a tolerance limit for pass/fail judgments.

Proteomic Studies of Blood and Vascular Wall in Atherosclerosis. Review

The review is devoted to the analysis of literature data related to the role of proteomic studies in the study of atherosclerotic cardiovascular diseases. Diagnosis of patients with atherosclerotic plaques before clinical manifestations is an arduous task. The review presents the results of research on the new proteomic potential biomarkers of coronary heart disease, coronary atherosclerosis, acute coronary syndrome, myocardial infarction, carotid artery atherosclerosis. Also, the analysis of literature data on proteomic studies of the vascular wall was carried out. To assess the involvement of proteins in the pathological process of atherosclerosis, it is important to investigate the specific relationships between proteins in the arteries, expression and concentration of proteins. The development of proteomic technologies has made it possible to analyse the number of proteins associated with the development of the disease. Analysis of the proteomic profile of the vascular wall in atherosclerosis can help to detect possible diagnostically significant protein structures or potential biomarkers of the disease and develop novel approaches to the diagnosis of atherosclerosis and its complications.

Kamil Kilani’s Adaptation of Shakespeare in Arabic Children’s Literature: Acculturation Versus Enculturation

Children’s literature is a young literary genre which is guided by a complex set of motivational, cognitive and metacognitive considerations. In the Arab world, children’s literature emerged in tandem with the modern translation movement but has started to prosper as an independent literary form only recently. Translating for children is an arduous task with myriad challenges on the linguistic, sociocultural and educational levels. This paper aims to research Kamil Kilani’s Arabic adaptation of King Lear as a model to translate for children. Kilani’s translations are significant because they are adapted in a way which responds to the needs of children without simplifying the lexical and stylistic components of the source texts or compromising their cultural content. The paper adopts a descriptive methodology supporting the main argument with comparative examples from the source text and the target text. The analysis shows that Kilani’s adaptation revolutionized the source text’s form and structure, while preserving its conceptual content, language level and style exquisitely. The results suggest that translating for children does not have to embrace cultural adaptation strategies and can instead embrace a model of acculturation between the source text cultural content and the target text readers.

Characterizing Hippocampal Oscillatory Signatures Underlying Seizures in Temporal Lobe Epilepsy

Temporal Lobe Epilepsy (TLE) is a neurological condition characterized by focal brain hyperexcitability, resulting in abnormal neuronal discharge and uncontrollable seizures. The hippocampus, with its inherently highly synchronized firing patterns and relatively high excitability, is prone to epileptic seizures, and it is usually the focus of TLE. Researchers have identified hippocampal high-frequency oscillations (HFOs) as a salient feature in people with TLE and animal models of this disease, arising before or at the onset of the epileptic event. To a certain extent, these pathological HFOs have served as a marker and a potential target for seizure attenuation using electrical or optogenetic interventions. However, many questions remain about whether we can reliably distinguish pathological from non-pathological HFOs and whether they can tell us about the development of the disease. While this would be an arduous task to perform in humans, animal models of TLE provide an excellent opportunity to study the characteristics of HFOs in predicting how epilepsy evolves. This minireview will (1) summarize what we know about the oscillatory disruption in TLE, (2) summarize knowledge about oscillatory changes in the latent period and their role in predicting seizures, and (3) propose future studies essential to uncovering potential treatments based on early detection of pathological HFOs.

Fault Detection of Bearing: An Unsupervised Machine Learning Approach Exploiting Feature Extraction and Dimensionality Reduction

The monitoring of rotating machinery is an essential activity for asset management today. Due to the large amount of monitored equipment, analyzing all the collected signals/features becomes an arduous task, leading the specialist to rely often on general alarms, which in turn can compromise the accuracy of the diagnosis. In order to make monitoring more intelligent, several machine learning techniques have been proposed to reduce the dimension of the input data and also to analyze it. This paper, therefore, aims to compare the use of vibration features extracted based on machine learning models, expert domain, and other signal processing approaches for identifying bearing faults (anomalies) using machine learning (ML)—in addition to verifying the possibility of reducing the number of monitored features, and consequently the behavior of the model when working with reduced dimensionality of the input data. As vibration analysis is one of the predictive techniques that present better results in the monitoring of rotating machinery, vibration signals from an experimental bearing dataset were used. The proposed features were used as input to an unsupervised anomaly detection model (Isolation Forest) to identify bearing fault. Through the study, it is possible to verify how the ML model behaves in view of the different possibilities of input features used, and their influences on the final result in addition to the possibility of reducing the number of features that are usually monitored by reducing the dimension. In addition to increasing the accuracy of the model when extracting correct features for the application under study, the reduction in dimensionality allows the specialist to monitor in a compact way the various features collected on the equipment.

Problem-Based Learning in a Virtual Environment

The ongoing COVID-19 pandemic has disrupted teaching and learning in higher education institutions, presenting novel challenges for both staff and students alike. These challenges have had an immense impact in the way postgraduate research (PGR) teachers perform their dual responsibilities as both students and teachers. Achieving a seamless transition from in-person to virtual learning was an arduous task. To this end, pedagogies evolved to accommodate the use of remote conferencing, video capture and other real time communication tools that facilitate virtual collaboration between staff and students. In this paper, I highlight the challenges of integrating online learning with a problem-based learning (PBL), a signature pedagogy employed by law and business schools. I draw on my personal experiences as a student and PGR teacher during the pandemic, and suggest proactive mitigation responses.

Taxonomic Revision of Euphrasia (Orobanchaceae) in South America

Morphological boundaries between South American species of Euphrasia L. are controversial, rendering determination of specimens an arduous task. In this context, a comprehensive taxonomic revision of Euphrasia in South America is here provided for the first time. This study, based upon a classical morphological study of ca. 400 herbarium specimens, supports the recognition of eight species and one subspecies distributed in the Andean regions of Argentina, Bolivia, and Chile. From among native species, six belong to section Trifidae Benth. and one to the monotypic section Paradoxae Pugsley, endemic to Juan Fernández Islands; one adventive species, E. officinalis L., belongs to the section Euphrasia. The previously misunderstood presence of E. cockayniana Petrie is here untangled, and, consequently, the species is excluded from South America. A key to all Euphrasia taxa in South America, plus morphological descriptions, nomenclature items, geographical distribution and maps, habitat notes, illustrations, photographs, and discussion notes are included for the nine taxa. Eleven names are here synonymized, and lectotypes are designated for E. andicola Benth., E. debilis Wettst., E. flavicans Phil., E. intricata Phil., and E. philippii Wettst. Euphrasia andicola is reported for the first time for Argentina. This collaborative effort will represent a baseline for further investigations on Euphrasia in South America.

Optimization of Personnel Placement Scheme and Big Data Analysis Based on Multilayer Variable Neural Network Algorithm

People usually use the method of job analysis to understand the requirements of each job in terms of personnel characteristics, at the same time use the method of psychological measurement to understand the psychological characteristics of each person, and then put the personnel in the appropriate position by matching them with each other. With the development of the information age, massive and complex data are produced. How to accurately extract the effective data needed by the industry from the big data is a very arduous task. In reality, personnel data are influenced by many factors, and the time series formed by it is more accidental and random and often has multilevel and multiscale characteristics. How to use a certain algorithm or data processing technology to effectively dig out the rules contained in the personnel information data and explore the personnel placement scheme has become an important issue. In this paper, a multilayer variable neural network model for complex big data feature learning is established to optimize the staffing scheme. At the same time, the learning model is extended from vector space to tensor space. The parameters of neural network are inversed by high-order backpropagation algorithm facing tensor space. Compared with the traditional multilayer neural network calculation model based on tensor space, the multimodal neural network calculation model can learn the characteristics of complex data quickly and accurately and has obvious advantages.

Training Public Health Professionals to Tackle Women, Children, and Families Health Issues: A “Skill-Based Qualitative Learning Approach”

Purpose: There are several teaching and learning approaches but finding the one that is appropriate for a particular field or training program is an arduous task. The purpose of this paper is to introduce the “Skill Based Qualitative Learning Approach” (SBQLA) in training health professionals.Description: The SBQLA is a pedagogical approach via which learners are trained in developing qualitative questionnaires and interview skills to learn from experts in the Public Health (PH) field. This teaching approach arms students with interview skills that help them identify and address PH roadblocks and get them authentic information from experts. It also equips them with techniques on how to do formalized presentations and come up with projects and interventions that help mitigate and eliminate drivers of health problems among women, children and families.Assessment: Learners' field experiences are shared in a professional presentation style in a class to help trainees benefit from each other's information and to get formalized feedback on their presentation. Assessment in this learning approach is based on a synthesis and an analysis of data collected from professionals.Conclusion: Findings from this learning approach enables experts to shed light on true stories shared by real and authentic individuals whose faces can be associated with their shared experiences. This learning approach makes it possible for trainees to also initiate projects that help them tackle existing and emerging public health issues in their future work.