Concise Answer Research Articles

CPEQA: A Large Language Model Based Knowledge Base Retrieval System for Chinese Confidentiality Knowledge Question Answering

Large language models (LLMs) have exhibited remarkable performance on various natural language processing (NLP) tasks, particularly in the construction of the intelligent question-answering system. These systems, especially in specialized fields, usually rely on NLP through the retrieval of corpus and answering databases to efficiently provide accurate and concise answers. This paper focuses on the national confidentiality publicity and education field, aiming to address the dilemma of inaccurate knowledge retrieval in this field. Therefore, we design an intelligent confidentiality question-answering system CPEQA by comprehensively utilizing the LLMs platform and information retrieval technique. CPEQA is capable of providing professional answers to questions about Chinese confidentiality publicity and education raised by users. Additionally, we also integrate the conventional database retrieval technique and LLMs into the database query construction, enabling CPEQA to perform real-time queries and data analysis for both single-table and multi-table querying tasks. Through extensive experiments with generated query sentences, we show both methodological comparisons and empirical evaluations of CPEQA’s performance. Experimental results indicate that CPEQA has achieved competitive results on answering precision, recall rate and other metrics. Finally, we explore the challenges of the CPEQA system associated with these techniques and outline potential avenues for future research in this emerging field.

Comparative performance analysis of large language models: ChatGPT-3.5, ChatGPT-4 and Google Gemini in glucocorticoid-induced osteoporosis

BackgroundsThe use of large language models (LLMs) in medicine can help physicians improve the quality and effectiveness of health care by increasing the efficiency of medical information management, patient care, medical research, and clinical decision-making.MethodsWe collected 34 frequently asked questions about glucocorticoid-induced osteoporosis (GIOP), covering topics related to the disease’s clinical manifestations, pathogenesis, diagnosis, treatment, prevention, and risk factors. We also generated 25 questions based on the 2022 American College of Rheumatology Guideline for the Prevention and Treatment of Glucocorticoid-Induced Osteoporosis (2022 ACR-GIOP Guideline). Each question was posed to the LLM (ChatGPT-3.5, ChatGPT-4, and Google Gemini), and three senior orthopedic surgeons independently rated the responses generated by the LLMs. Three senior orthopedic surgeons independently rated the answers based on responses ranging between 1 and 4 points. A total score (TS) > 9 indicated ‘good’ responses, 6 ≤ TS ≤ 9 indicated ‘moderate’ responses, and TS < 6 indicated ‘poor’ responses.ResultsIn response to the general questions related to GIOP and the 2022 ACR-GIOP Guidelines, Google Gemini provided more concise answers than the other LLMs. In terms of pathogenesis, ChatGPT-4 had significantly higher total scores (TSs) than ChatGPT-3.5. The TSs for answering questions related to the 2022 ACR-GIOP Guideline by ChatGPT-4 were significantly higher than those for Google Gemini. ChatGPT-3.5 and ChatGPT-4 had significantly higher self-corrected TSs than pre-corrected TSs, while Google Gemini self-corrected for responses that were not significantly different than before.ConclusionsOur study showed that Google Gemini provides more concise and intuitive responses than ChatGPT-3.5 and ChatGPT-4. ChatGPT-4 performed significantly better than ChatGPT3.5 and Google Gemini in terms of answering general questions about GIOP and the 2022 ACR-GIOP Guidelines. ChatGPT3.5 and ChatGPT-4 self-corrected better than Google Gemini.

ChatGPT: A concise Google alternative for people seeking accurate and comprehensive carpal tunnel syndrome information

Popular artificial intelligence systems, like ChatGPT, may be used by anyone to generate humanlike answers to questions. This study assessed whether ChatGPT version 3.5 (ChatGPTv3.5) or the first five results from a Google search provide more accurate, complete, and concise answers to the most common questions patients have about carpal tunnel syndrome. Three orthopedic hand surgeons blindly graded the answers using Likert scales to assess accuracy, completeness, and conciseness. ChatGPTv3.5 and the first five Google results provide answers to carpal tunnel syndrome questions that are similar in accuracy and completeness, but ChatGPTv3.5 answers are more concise. ChatGPTv3.5, being freely accessible to the public, is therefore a good resource for patients seeking concise, Google-equivalent answers to specific medical questions regarding carpal tunnel syndrome. ChatGPTv3.5, given its lack of updated sourcing and risk of presenting false information, should not replace frequently updated academic websites as the primary online medical resource for patients.

Potential of Large Language Models (LLMs) as Supplementary Tools for Historical Learning: Users’ Interaction and Knowledge Acquisition

This study explores the strengths and limits of large language models (LLMs) in exploring the information on history, an area unexplored in the existing literature. ChatGPT and Gemini, as LLMs, have demonstrated superior performance in education, healthcare, and business. This study proposes utilizing the ChatGPT (ver. 3.5) and Gemini applications to acquire information on historical figures like Sher Shah Suri and Mughal Emperors and Sikhs in the subcontinent. To evaluate the proposed study, this study used two data sets: the first data set comprised a set of questions (n = 26) and the second data set contained questions (n = 35). The results indicate that ChatGPT provides concise answers to the questions of both datasets compared to the Gemini application. However, Gemini exhibited a higher accuracy (92.30%) than ChatGPT with accuracy (76.92%) for dataset 1. For the dataset 2, ChatGPT showed better accuracy (68.57%) than Gemini with accuracy (65.71%). Further research could expand on this study by employing additional artificial intelligence (AI) tools on large-scale datasets from diverse domains.

Impact of Migration on Global Politics and Security

Migration has presented a very important challenge at the global level in recent years, especially with the high economic disparity between countries and regions, wars and crises, and political instability. Even though migration policies and the legal system of asylum are at a solid level for the needs of society, the dilemma is still presented as to whether the flow of migrants affects and determines global politics and security or how this impact is at specific levels, especially at the level of income or economic capacity of regions or states. This is also the basic purpose of this study, where official data from the World Bank and United Nations give concise answers to the impact of the flow of migrants on political stability and global security. It turns out that at the global level, the flow of migrants does not represent a significant challenge in global and security policies, but at lower levels, according to countries, income represents a very significant determinant in political stability as well as Military Expenditure. Generally speaking, migration determines a country’s economic, social, and security image. After the increased migration that includes both refugees and asylum seekers in the last decade as a result of political instability, poverty, Arab Spring and Syrian war and wars in the recent past and the recent war in Ukraine and Israel, the dilemma arises whether migration as a phenomenon (in terms of percentage) has an impact on political stability and security at the world level. This is the basis of this study, which, through statistically valid data from the World Bank and the United Nations, analyzes and generalizes a conclusion about the impact of migration on political stability and security at the global level.

Improving the ability to discriminate medical multiple-choice questions through the analysis of the competitive examination to assign residency positions in Spain

IntroductionPsychometrics plays a vital role in evaluating educational research, including the analysis of multiple-choice exams. This study aims to improve the discriminatory ability of the “Médico Interno Residente” (MIR) medical exam in Spain, used to rank candidates for specialized healthcare training, through psychometric analysis.MethodsWe analyzed 2,890 MIR exam questions from 2009 to 2021 (totaling 147,214 exams), categorizing them based on methodology and response type. Evaluation employed classical test theory and item response theory (IRT). Classical test theory determined difficulty and discrimination indices, while IRT assessed the relationship between knowledge levels and question performance.ResultsQuestion distribution varied across categories and years. Frequently addressed knowledge areas included various medical specialties. Non-image-associated clinical cases were the easiest, while case-based clinical questions exhibited the highest discriminatory capacity, differing significantly from image-based case or negative questions. High-quality questions without images had longer stems but shorter answer choices. Adding images reduced discriminatory power and question difficulty, with image-based questions being easier. Clinical cases with images had shorter stems and longer answer choices.ConclusionsFor improved exam performance, we recommend using a clinical case format followed by direct short-answer questions. Questions should be of low difficulty, providing clear and specific answers based on scientific evidence and avoiding ambiguity. Typical clinical cases with key characteristic features should be presented, excluding uncertain boundaries of medical knowledge. Questions should have lengthy stems and concise answer choices, minimizing speculation. If images are used, they should be typical, clear, consistent with the exam, and presented within clinical cases using clinical semiotics and propaedeutics.

From Chatbots to Mind-bots: Navigating Assessments in the Advent of ChatGPT

Recently, the authors had a situation in a mobile chat group (WhatsApp) when a medical lecturer was asking what value a human papillomavirus (HPV) test adds to the pap smear for cervical cancer screening. The field experts in the group replied with detailed, erudite explanations. Soon after, another lecturer posted an answer from GPT-3 based chatbot. Despite needing more depth of an expert’s reply, the chatbot gave concise answers, reframing complex medical jargon in plain English without losing crucial medical information, and more. They were easier to understand. All these with the leisure of a human-like engagement. This narration is one of countless news related to ChatGPT, which have been making headlines, academic journals included, to illustrate how the large language model technology may have disrupted conventional educational practice. One discriminatory element distinguishes this technology from all its predecessors; it is not trying to mimic a human response but responding like a human. In this writing, we navigate discussion based on the most fundamental aspect of assessment, its purpose. We revisit the concept of fidelity from the field of simulation to explain how the technology may have rejuvenated the purpose of assessment for learning (formative assessment). Then, we articulate several associated challenges in the conduct of the high-stake assessment of learning (summative assessment). We conclude with an emphasis on the purposes as the guiding principles that remain the same despite the changes in the landscape on the conduct of the assessment.

The Emerging Role of AI in Patient Education: A Comparative Analysis of the Accuracy of Large Language Models for Pelvic Organ Prolapse

Introduction: This study aimed to evaluate the accuracy, completeness, precision, and readability of outputs generated by three large language models (LLMs); these are GPT by OpenAI, BARD by Google, and Bing by Microsoft, in comparison to patient education material on pelvic organ prolapse (POP) provided by the Royal College of Obstetricians and Gynecologists (RCOG). Methods: A total of 15 questions were retrieved from the RCOG website and input into the three LLMs. Two independent reviewers evaluated the outputs for accuracy, completeness, and precision. Readability was assessed using the Simplified Measure of Gobbledygook (SMOG) score and the Flesch-Kincaid Grade Level (FKGL) score. Results: Significant differences were observed in completeness and precision metrics. ChatGPT ranked highest in completeness (66.7%), while Bing led in precision (100%). No significant differences were observed in accuracy across all models. In terms of readability, ChatGPT exhibited higher difficulty than BARD, Bing, and the original RCOG answers. Conclusion: While all models displayed a variable degree of correctness, ChatGPT excelled in completeness, significantly surpassing BARD and Bing. However, Bing led in precision, providing the most relevant and concise answers. Regarding readability, ChatGPT exhibited higher difficulty. We observed that while all LLMs showed varying degrees of correctness in answering RCOG questions on patient information for POP, ChatGPT was the most comprehensive, but its answers were harder to read. Bing, on the other hand, was the most precise. The findings highlight the potential of LLMs in health information dissemination and the need for careful interpretation of their outputs.

QuerySum: A Multi-Document Query-Focused Summarization Dataset Augmented with Similar Query Clusters

Query-focused summarization (QFS) aims to summarize the source document(s) with regard to a specific aspect of information given in a query. It plays an important role in presenting users with a concise answer summary from a set of query-relevant documents retrieved by the information retrieval system. Nonetheless, the QFS research has long been hampered by the lack of adequate datasets in terms of both quality and quantity. In this paper, we introduce a large-scale multi-document query-focused summarization dataset, called QuerySum, which contains 27,041 data samples covering diverse topics and its quality is guaranteed through human verification. Unlike some previous QFS datasets constructed directly from the question answering datasets, 74% queries in our dataset are the challenging non-factoid What-, Why-, and How- questions. More importantly, we also provide a set of similar queries together with the corresponding summaries pairs for each query as the retrieved context, presenting a new feature of QuerySum. We aim to encourage research efforts in query intention understanding in the context of QFS. Leveraging QuerySum's depth, we propose a model for query-aware multi-document summarization and set a new QFS benchmark.

Implementasi Reward Asyik dalam Meningkatkan Kemandirian Anak Usia Dini

The purpose of this study is to evaluate how well ASYIK Rewards work to increase young children's learning independence. There is still a deficiency of independence in the educational setting of Ar-Raudhatul Ulum Kindergarten, particularly in autonomous study. Teachers must find workable solutions to get around this. At Ar-Raudhatul Kindergarten, one useful strategy might boost students' learning freedom. The second goal of In early childhood learning activities where there are problems with establishing self-confidence, the goal of this study is to increase independence. Those who had previously been bashful and hesitant to communicate with others or answer questions in front of friends and professors now feel comfortable and capable of providing solid, concise answers to any queries they pose online. Twenty students from group A of the Ar-Raudhatul Ulum Kindergarten served as the research subjects. With each signal, the Safe, Happy, Confident, Innovative, and Creative characteristics are the criteria that determine the evaluation score for early childhood learning independence with the ASYIK Reward. For the five observation scores, very good is represented by (√), good by (+), and minus by (-). There is an increase in scores following use, according to the findings of cycles I and II of the pre-cycle research. According to the research findings, the class completion scores from pre-cycle to cycles I and II seem to have increased. Prior to using the "Fun Reward" strategy, the kids' learning motivation score was 50%; in cycle I, it increased to 55%. Subsequently, after modifications were made in response to the outcomes of reflection on the execution of cycle I, improved outcomes were achieved. As a result, when a youngster gains enjoyable incentives to help them develop independence, and their actions typically lead to beneficial actions. The child's class completion percentage increased to 80% in Cycle II. Therefore, it may be claimed that the ASYIK Reward model encourages children's independence.

Novel Approaches for Improving Solubility of Pyridinium Anolytes in Non-Aqueous Redox Flow Batteries

Organic redox flow batteries (ORFBs) offer significant promise as a means of efficient grid-scale energy storage to compliment the intermittent power supply of renewable energy sources such as solar and wind power. ORFBs efficiently store energy in the form of redox-active organic molecules (ROMs) by passing them over two polarized electrodes to generate an oxidized (catholyte) and a reduced (anolyte) species, which are held in separate tanks until energy is needed. Structural diversity of anolytes and catholytes for ORFBs has expanded dramatically over the past decade as researchers have raced to discover or design ROMs suitable for use in grid-scale energy storage devices; however, a lack of correlations between ROM structure and a desired chemical or physical property has led to a “guess and check” approach that can overlook possibly transformative electrolyte structures. Recent work has demonstrated the ability to employ multivariate regression analysis to correlate computationally derived structural descriptors of ROMs to their corresponding stability. Based on this, we considered the possibility of employing a similar approach to identify quantitative structure-property relationships (QSPRs) for critical physical properties of ORFB electrolytes, such as solubility.Electrolyte solubility is an essential design characteristic in ORFBs as it directly impacts the corresponding battery’s energy storage capacity. Nevertheless, the fundamental question, “What makes an electrolyte soluble?” (especially in organic solvents) still lacks a concise answer despite decades of progress. As part of a collaboration with the Guarr group at the Michigan State University Bioeconomy Institute, our group has identified a novel class of pyridinium anolytes with exceptionally low redox potentials and highly persistent charged states. We will discuss our group’s recent progress to combine experimental and computational techniques to develop QSPR models to accurately predict solubility of pyridinium anolytes. The resulting models have provided new insights into the role that self-interaction between anolyte species in solution plays in dictating their corresponding solubility. Using these models, we have successfully expanded the solubility range for a class of pyridinium derivatives by >200% in acetonitrile.

Evaluating the Artificial Intelligence Performance Growth in Ophthalmic Knowledge.

We aim to compare the capabilities of Chat Generative Pre-Trained Transformer (ChatGPT)-3.5 and ChatGPT-4.0 (OpenAI, San Francisco, CA, USA) in addressing multiple-choice ophthalmic case challenges. Both models' accuracy was compared across different ophthalmology subspecialties using multiple-choice ophthalmic clinical cases provided by the American Academy of Ophthalmology (AAO) "Diagnosis This" questions. Additional analysis was based on image content, question difficulty, character length of models' responses, and model's alignment with responses from human respondents. χ2 test, Fisher's exact test, Student's t-test, and one-way analysis of variance (ANOVA) were conducted where appropriate, with p<0.05 considered significant. GPT-4.0 significantly outperformed GPT-3.5 (75% versus 46%, p<0.01), with the most noticeable improvement in neuro-ophthalmology (100% versus 38%, p=0.03). While both models struggled with uveitis and refractive questions, GPT-4.0 excelled in other areas, such as pediatric questions (82%). In image-related questions, GPT-4.0 also displayed superior accuracy that trended toward significance (73% versus 46%, p=0.07). GPT-4.0 performed better with easier questions (93.8% (least difficult) versus 76.2% (middle) versus 53.3% (most), p=0.03) and generated more concise answers than GPT-3.5 (651.7±342.9 versus 1,112.9±328.8 characters, p<0.01). Moreover, GPT-4.0's answers were more in line with those of AAO respondents (57.3% versus 41.4%, p<0.01), showing a strong correlation between its accuracy and the proportion of AAO respondents who selected GPT-4.0's answer (ρ=0.713, p<0.01). Our study demonstrated that GPT-4.0 significantly outperforms GPT-3.5 in addressing ophthalmic case challenges, especially in neuro-ophthalmology, with improved accuracy even in image-related questions. These findings underscore the potential of advancing artificial intelligence (AI) models in enhancing ophthalmic diagnostics and medical education.

PooRaa‐Agri KG: An agricultural knowledge graph‐based simplified multilingual query system

AbstractThe current work proposes PooRaa‐Agri KG, an agricultural knowledge graph‐based simplified multilingual query system that works in real time to provide concise answers for agriculture‐based queries. The proposed approach accommodates real‐time and low‐resource queries in English and Hindi with a novel multi‐stage solution consisting of data pre‐processing, sentence simplification, triplet extraction, knowledge graph generation, sentence reconstruction, query‐to‐reconstructed sentence matching, and machine translation as its sub‐modules. In this work, a novel combination of rule‐based sentence simplification and triplet extraction is carried out resulting in a triplet similarity score of 86.56% for the extracted triplets. This method is superior to the existing triplet extraction method whose triplet similarity score was found to be 60.65%. Further, the proposed work makes use of heuristic rules to reconstruct sentences which when evaluated by human evaluators for meaningfulness and grammar resulted in a score of 3.09/4 and 2.95/4 respectively. To complete end‐to‐end communication in the proposed system, a similarity‐based query answer system is proposed in this work.

Из опыта преподавания. XII. Зарождение кристалла и принцип равного ближайшего окружения

The article is devoted to the search for a concise answer to the question: how does the nucleus of a crystal arise? Although a simple and complete answer is impossible at the same time, an attempt has been made to clarify the problem in three aspects: according to which principle particles are combined into a nucleus, which nucleus have a chance to become crystal, and from which atomic configuration it can be recognized. To model the small polyhedral clusters, the principle of equal immediate environment is proposed. It is shown that such clusters with an odd number of atoms are impossible. Small polyhedral clusters should grow by attaching pairs of atoms. Previously, this was known for fullerenes because of their specific geometry. The topic is important because of the coverage of various quasi- and non-crystalline structures by crystallography and mineralogy.

Management of gastro-esophageal reflux disease: Practice-oriented answers to clinical questions

Gastro-esophageal reflux disease (GERD) is a condition which is frequently faced by primary care physicians and gastroenterologists. Improving management of GERD is crucial to maximise both patient care and resource utilization. In fact, the management of patients with GERD is complex and poses several questions to the clinician who faces them in clinical practice. For instance, many aspects should be considered, including the appropriateness of indication to endoscopy, the quality of the endoscopic examination, the use and interpretation of ambulatory reflux testing, and the choice and management of anti-reflux treatments, i.e., proton-pump inhibitors and surgery. Aim of the present review was to provide a comprehensive update on the clinical management of patients with GERD, through a literature review on the diagnosis and management of patients with GER symptoms. In details, we provide practice-oriented concise answers to clinical questions, with the aim of optimising patient management and healthcare resource use.

FAMILY NURSING CARE IN FAMILY WITH DIABETES MELLITUS WITH A FOCUS OF INAFFECTIVENESS MANAGEMENT OF TYPE 2 DIABETES MELLITUS DIET IN BANJARNEGARA DISTRICT

Diabetes is something that cannot be explained with clear and concise answers, but in general it can be described as a series of anatomical and chemical problems caused by various factors. Data from the World Health Organization (2015) shows that Indonesia has the seventh highest number of people with diabetes in the world, after China, India and the United States, with a population of 230 million in 2015 which reached 5.8% or around 8.5 million people suffer from diabetes. diabetes. The purpose of this study aims to obtain an overview in treating clients with diabetes mellitus with the problem of ineffectiveness of diet management of type 2 diabetes mellitus. The method of writing this case study uses descriptive analysis, is a nursing process method that focuses on one of the most important issues in each case, namely patient care. Diabetes, focuses on the ineffectiveness of the diet for type 2 diabetes. From the results and discussion that the client named Mr. S the reason the client was studied was to determine the client's dietary compliance. When the assessment is carried out, the client suffers from DM who has entered stage V with a developmental stage that has not been passed, namely stages VI to VIII. From the results of nursing care carried out, the problem of adherence to the Diabetes Mellitus diet was partially resolved. When the assessment is carried out, the client suffers from DM who has entered stage V with a developmental stage that has not been passed, namely stages VI to VIII. From the results of nursing care carried out, the problem of adherence to the Diabetes Mellitus diet was partially resolved. When the assessment is carried out, the client suffers from DM who has entered stage V with a developmental stage that has not been passed, namely stages VI to VIII. From the results of nursing care carried out, the problem of adherence to the Diabetes Mellitus diet was partially resolved.

Practical answers to frequently asked questions in minimally invasive lumbar spine surgery

BACKGROUND CONTEXTSurgical counseling enables shared decision-making (SDM) by improving patients’ understanding. PURPOSETo provide answers to frequently asked questions (FAQs) in minimally invasive lumbar spine surgery. STUDY DESIGNRetrospective review of prospectively collected data. PATIENT SAMPLEPatients who underwent primary tubular minimally invasive lumbar spine surgery in form of transforaminal lumbar interbody fusion (MI-TLIF), decompression alone, or microdiscectomy and had a minimum of 1-year follow-up. OUTCOME MEASURES(1) Surgical (radiation exposure and intraoperative complications) (2)Immediate postoperative (length of stay [LOS] and complications) (3) Clinical outcomes (Visual Analog Scale- back and leg, VAS; Oswestry Disability Index, ODI; 12-Item Short Form Survey Physical Component Score, SF-12 PCS; Patient-Reported Outcomes Measurement Information System Physical Function, PROMIS PF; Global Rating Change, GRC; return to activities; complications/reoperations) METHODSThe outcome measures were analyzed to provide answers to ten FAQs that were compiled based on the authors’ experience and a review of literature. Changes in VAS back, VAS leg, ODI, and SF-12 PCS from preoperative values to the early (<6 months) and late (>6 months) postoperative time points were analyzed with Wilcoxon Signed Rank Tests. % of patients achieving minimal clinically important difference (MCID) for these patient-reported outcome measures (PROMs) at the two time points was evaluated. Changes in PROs from preoperative values too early (<6 months) and late (≥6 months) postoperative time points were analyzed within each of the three groups. Percentage of patients achieving MCID was also evaluated. RESULTSThree hundred sixty-six patients (104 TLIF, 147 decompression, 115 microdiscectomy) were included. The following FAQs were answered: (1) Will my back pain improve? Most patients report improvement by >50%. About 60% of TLIF, decompression, and microdiscectomy patients achieved MCID at ≥6 months. (2) Will my leg pain improve? Most patients report improvement by >50%. 56% of TLIF, 67% of decompression, and 70% of microdiscectomy patients achieved MCID at ≥6 months. (3) Will my activity level improve? Most patients report significant improvement. Sixty-six percent of TLIF, 55% of decompression, and 75% of microdiscectomy patients achieved MCID for SF-12 PCS. (4) Is there a chance I will get worse? Six percent after TLIF, 14% after decompression, and 5% after microdiscectomy. (5) Will I receive a significant amount of radiation? The radiation exposure is likely to be acceptable and nearly insignificant in terms of radiation-related risks. (6) What is the likelihood that I will have a complication? 17.3% (15.4% minor, 1.9% major) for TLIF, 10% (9.3% minor and 0.7% major) for decompression, and 1.7% (all minor) for microdiscectomy (7) Will I need another surgery? Six percent after TLIF, 16.3% after decompression, 13% after microdiscectomy. (8) How long will I stay in the hospital? Most patients get discharged on postoperative day one after TLIF and on the same day after decompression and microdiscectomy. (9) When will I be able to return to work? >80% of patients return to work (average: 25 days after TLIF, 14 days after decompression, 11 days after microdiscectomy). (10) Will I be able to drive again? >90% of patients return to driving (average: 22 days after TLIF, 11 days after decompression, 14 days after microdiscectomy). CONCLUSIONSThese concise answers to the FAQs in minimally invasive lumbar spine surgery can be used by physicians as a reference to enable patient education.

آیات احکام میں تفسیر تیسیر القرآن کا منہج و اسلوب

Tafseer Taiseer-ul-Quran is comprised upon four volumes; current Tafseer of Moulana Abdul Rehman Killani is the first Tafseer of all Urdu Tafseer in which other verses of Quran and Sahih Hadith are most relied upon. Eighty percent of the Hadith are consulted from Bukhari and Muslim in it. The Hadith relating with the revelation of glory is also utilized to get the appropriate findings. An apologetic manner is avoided in this research work and it provides concise answers to irrational questions and objections. The main objective of this research is analyze the Tafseer of Moulana Abdul Rehman Killani in the focused context of bringing one of the most comprehensive Tafseerul Quran into the limelight. It contains Surahs, along with its virtues. The current study is qualitative in nature based upon descriptive and explorative research techniques. The methodology used in this research work revolves around primary and secondary sources both, Quran, Ahadith, Seerah and books. The glory of the Surahs is revealed and the timeline of revelation is also discussed. Number of verses in the name of Surahs, Rukuat,, words and number of letters are explained in this study. Maulana Abdul Rehman Killani has also paid much attention on the issue of Quranic sciences in his literary Tefseer. Tafeer has been compiled in every time and era, so Taisar-ul-Quran is also a unique addition to the literary world which will solve many contradictory questions of the scholars.

‘A poet is a profession like any other’

A journalist and Voprosy Literatury’s regular interviewer E. Konstantinova talks to the contemporary poet S. Stratanovsky, bringing up topics of his artistic philosophy and poetics, looking back at his publications in samizdat collections and association with the so-called ‘second culture’ of a censorship-free literary Leningrad, and following up with his contemporary publications in thick literary journals and opinions about poetry of the 2000s — 2010s. Konstantinova’s questions concern modern interpretation of classic quotations — by Pushkin or Tyutchev, the special ‘poet’s behaviour,’ the attitude towards Soviet poetry, etc. Stratanovsky’s concise and levelheaded answers not only provide the reader with a key to his own lyrical poetry but also present familiar works by 19th- and 20th-c. poets (e. g., E. Bagritsky, V. Khodasevich, and others) in a new light; in addition, they introduce poems by Stratanovsky’s fellow members of the so-called Leningrad’s ‘second culture.’

The sociology of white America: A teaching and learning guide

The sociology of white America: A teaching and learning guide