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The aim of this study is to verify and assess the quality of lessons in traditional and electronic textbook on general standards of textbooks quality. The method of theoretical analysis and content analysis was used. For the purpose of analyzing the contents of the sample we took teaching unit Measures and measurement from two textbooks: the traditional and the electronic. The electronic textbook, which has been the subject of research, is of quality and meets the standards of textbook quality.

Group action analysis developed and applied mainly by Louis Michel to the study of N-dimensional periodic lattices is the central subject of the book. Di¬fferent basic mathematical tools currently used for the description of lattice geometry are introduced and illustrated through applications to crystal structures in two- and three-dimensional space, to abstract multi-dimensional lattices and to lattices associated with integrable dynamical systems. Starting from general Delone sets the authors turn to di¬fferent symmetry and topological classifications including explicit construction of orbifolds for two- and three-dimensional point and space groups.

This book investigates trends in skills demand and supply over the past two decades for insights into ways to build (and use) the critical skills needed to sustain competitiveness of the Philippine economy. Part one of the book investigates trends in demand for skills in the country overall and by sectors, explores its possible determinants, and attempts to identify emerging skills gaps. Part two turns to the analysis of the supply of skills in the country with a focus on the ability of education and training to provide highly skilled labor, keeping workers' skills updated, and providing skills development opportunities for the unskilled. It explores employers' perceptions on the quality of institutions and provides detailed analysis of the main characteristics, outcomes, and challenges in four key (or growing) subsectors of the provision of skills in the country: higher education, postsecondary technical-vocational education, non-formal secondary education, and postemployment training. It concludes with a summary of policy recommendations.

The article argues for raising class consciousness among community college students and describes how the author employs the writings of Charles Bukowski to reach an ethnically diverse, but predominantly working-class student population.

To form a strong grounding in human-related sciences it is essential for students to grasp the fundamental concepts of statistical analysis, rather than simply learning to use statistical software. Although the software is useful, it does not arm a student with the skills necessary to formulate the experimental design and analysis of a research project in later years of study or indeed, if working in research. This textbook deftly covers a topic that many students find difficult. With an engaging and accessible style it provides the necessary background and tools for students to use statistics confidently and creatively in their studies and future career. Key features: Up-to-date methodology, techniques and current examples relevant to the analysis of large data sets, putting statistics in context Strong emphasis on experimental design Clear illustrations throughout that support and clarify the text A companion website with explanations on how to apply learning to related software packages This is an introductory book written for undergraduate biomedical and social science students with a focus on human health, interactions, and disease. It is also useful for graduate students in these areas, and for practitioners requiring a modern refresher.

This short, practical book introduces students to the important components of the information-seeking process. Unlike guides that describe the research process but do not explain its logic, this book focuses entirely on basic concepts, strategies, tools, and tactics for research--in both electronic and print formats. --from publisher description.

There are more than 33,000 species of living fishes, accounting for more than half of the extant vertebrate diversity on Earth. This unique and comprehensive reference showcases the basic anatomy and diversity of all 82 orders of fishes and more than 150 of the most commonly encountered families, focusing on their distinctive features. Accurate identification of each group, including its distinguishing characteristics, is supported with clear photographs of preserved specimens, primarily from the archives of the Marine Vertebrate Collection at Scripps Institution of Oceanography. This diagnostic information is supplemented by radiographs, additional illustrations of particularly diverse lineages, and key references and ecological information for each group. An ideal companion to primary ichthyology texts, Fishes: A Guide to Their Diversity gives a broad overview of fish morphology arranged in a modern classification system for students, fisheries scientists, marine biologists, vertebrate zoologists, and everyday naturalists. This survey of the most speciose group of vertebrates on Earth will expand the appreciation of and interest in the amazing diversity of fishes.

This paper focuses on developing nominal synsets for the Saraiki language (SL), a lesser-studied language spoken in Pakistan. Nominal synsets are groups of nouns that share semantic characteristics and are crucial for natural language processing tasks such as information retrieval, machine translation, and text classification. The research aims to create Saraiki Nominal Synsets (SNS) using the Gurumukhi Punjabi WordNet. The study employs a hybrid approach, combining merge and expansion techniques for analysis and gathers data from PDF textbooks, online sources, and the Saraiki Wikimedia incubator. The collected data is limited to texts published between 2000 and 2019, and manually tagged using Antconc 3.4.4.0 wordlist due to the unavailability of a tagger for the Saraiki Language. The study builds a 2.2 million Saraiki word corpus and a list of 750 nouns, then categorizes and semantically organizes the Saraiki Nominal Synsets based on the list of Saraiki nouns. To identify and classify nouns in SL based on their semantic properties, a corpus-based approach is utilized, and nominal synsets are constructed using a combination of manual and automatic methods. Evaluating the quality of the synsets involves comparing them to existing lexical resources and conducting a semantic similarity analysis. The results demonstrate the effectiveness of the approach in capturing semantic relations among nouns in SL and producing synsets useful for various NLP applications. Overall, this study contributes to the development of linguistic resources for lesser-studied languages and provides valuable support for researchers and developers working on natural language processing tasks involving SL.

Legal professionals in Malawi rely on a limited number of textbooks, outdated law reports and inadequate library services. Most documents available are in image form, are un-structured, i.e. contain no useful legal meta-data, summaries, keynotes, and do not support a system of citation that is essential to legal research. While advances in document processing and machine learning have benefited many fields, legal research is still only marginally affected. In this interdisciplinary research, the authors build semi-automatic tools for creating a corpus of Malawi criminal law decisions annotated with legal meta-data, case and law citations. We used this corpus to extract legal meta-data, including law and case citations as used in Malawi by employing machine learning tools, spaCy and Gensim LDA. We set the foundation for a new methodology for classifying Malawi criminal case law according to the recently introduced International Classification of Crime for Statistical Purposes (ICCS).

In order to meet the differentiated needs of students and improve the satisfaction of college mental health textbook recommendation, a college mental health textbook recommendation scheme based on improved deep learning algorithm is proposed. Based on the analysis of the principle of deep learning data recommendation system, the data interest is calculated according to the browsing records of students on college mental health textbooks. Combine the deep learning algorithm and collaborative filtering algorithm to collect the demand data of college mental health textbooks, then use the Naive Bayesian classification method to divide the college mental health textbooks into interested and uninterested parts, and recommend the interested college mental health textbooks to the students in need. Experiments show that the longest recommendation time of the college mental health textbook recommendation scheme based on the improved deep learning algorithm proposed in this paper is 4.5 min, the highest recommended recall rate is 95.18%, the average accuracy is 97.2%, the highest content richness is 0.8, the system stability coefficient is 1.06, and the overall average praise rate is 97.79%. It has a good recommendation effect.