bluuebunny/crossref_metadata_2025 huggingface dataset:
Çocuk Yoğun Bakımlarda Geliştirilmiş Sedo-Analjezi İzlem Protokollerinin Sedasyon Düzeyi İzlemine Etkisi: Bir Sistematik Derleme ve Meta-Analiz
Authors: Dernek Özlem Sultan , Bektaş Murat
Published: March 2024

Bu çalışma, çocuk yoğun bakımlarda geliştirilen sedo-analjezi izlem protokollerinin çocukların sedasyon düzeylerini izlemedeki etkinliğini değerlendirmek amacıyla sistematik derleme ve meta analiz tekniği kullanılarak gerçekleştirilmiştir. Çalışmaya 1 ay-18 yaş aralığındaki çocuklar alınmıştır. Hedge’s g etki büyüklüğü değeri etki büyüklüğü istatistiği olarak kullanılmıştır. Tarama sonucunda ulaşılan 11 çalışmadan dört tanesi çocukların sedasyon düzeylerinin izlenmesini önceki dönem ile sayısal olarak karşılaştırmaktadır. Bu dört çalışma meta–analize dahil edilmiştir. Analiz sonucunda Cochrane Q değeri anlamlı ve I2 değeri %88,37 bulunmuştur. Rastgele etki modelinde Hedges’s g=-0,038 ve p=0.839 olarak saptanmıştır. Bu çalışmada, çocuk yoğun bakımlarda geliştirilen sedo-analjezi izlem protokollerinin sedasyon izlemi üzerine etkisinin anlamlı olmayabileceği belirlenmiştir.
Usos y abusos de los nombres, Maya, Mayan, Yucatec Maya, Yukatek y Yucatec Mayan: una crítica al imperialismo científico de la terminología lingüística en el idioma inglés
Authors:   Indiana University, Castañeda Quetzil Eugenio
Published: March 2024

Este capítulo describe y analiza la desagradable confusión que provocan los nombres impuestos arbitrariamente por los lingüistas angloparlantes, con los cuales designan en inglés, tanto al pueblo maya como a su lengua. El uso de Yucatec Maya, Yucatec, Yucatecan, Mayan, Yucatec Mayan, Yucatecan Maya, Yucatecan Mayan como nombres étnicos es solo irrespetuoso, sino que además será siempre incorrecto. Este artículo explica cómo y por qué la lingüística anglo-americana ha denominado así a las hablantes mayas, a la lengua maya, y a las “cosas” culturales de los mayas. Además se abordan los efectos negativos y perjudiciales de este abuso desde la ética, la política, y la realidad empírica. Como conclusión, ofrecemos algunas pautas necesarias para corregir el atropello de imponer estos nombres sobrepuestos.
Sustentabilidad de Comunidades Mayas de la Zona Costera de Yucatán: Una Mirada Desde La Interculturalidad
Authors:   Universidad Autónoma Indígena de México, Ibarra Ceceña María Guadalupe
Published: March 2024

El estudio de sustentabilidad de algunas comunidades mayas de la zona costera de Yucatán y el diagnóstico de los recursos con que cuenta, se enfocará principalmente al ecosistema de manglar, ya que la península de Yucatán tiene el 55 % de la extensión de manglares en México. Sin embargo, es de las regiones más amenazadas, a pesar de que se ha incrementado el interés por la conservación y protección del ecosistema, con normas, leyes y creación de áreas naturales protegidas. El objetivo general es alcanzar el mejoramiento de la calidad de vida, promoviendo la conservación de los ecosistemas costeros para lograr un desarrollo sustentable, representando la disminución de problemas de contaminación y conflictos socioecológicos; en las comunidades de Ría Celestún, Sisal, Telchac, San Crisanto y Río Lagartos; además, conocer la percepción del ecosistema por sus habitantes, a través de la metodología participativa, obteniendo un Programa de Gestión Comunitaria en contextos interculturales.
Una aproximación a la salud mental entre los mayas: retos y reflexiones
Authors:   Universidad Intercultural Maya de Quintana Roo, Jiménez Balam Deira Patricia
Published: March 2024

La salud mental de las comunidades ha sido abordada desde una perspectiva descontextualizada. Existen nociones y significados culturales que conciben lo mental como parte de un todo más complejo y amplio que sobrepasa más allá la dicotomía convencional mente-cuerpo. El objetivo de este capítulo es reflexionar para aproximarnos a la construcción de la salud mental dentro de la comunidad maya, suscitando una indagación crítica sobre si el concepto occidental de salud mental es aplicable en este contexto. Para ello, se considerarán las experiencias del trabajo de campo en diversas comunidades del centro de Quintana Roo, México. A partir del análisis específico de un caso de chi’ichnak y del óol y su relación con las emociones y la salud, se proponen ideas para una atención psicológica culturalmente pertinente. También se plantean los retos que enfrentarían los profesionales de la salud mental que se aboquen al trabajo con comunidades mayas.
Ser y convertirse en Tihosuco, Quintana Roo, México: ‘La conjura de Xinum’ como cha’anil
Authors:   University of Idaho, Campbell Sarah Alice
Published: March 2024

En este capítulo, revisito algunas entrevistas que realicé durante la investigación de tesis doctoral en Tihosuco, Quintana Roo, México en 2017. Las entrevistas se realizaron con actores de la obra que se escenificada cada julio en Tihosuco, “La conjura de Xinum” para entender cómo los actores abordaron la creación de sus personajes. Lo hago a través de una lectura atenta de cómo los actores utilizan los recursos lingüísticos de la deixis en maya. Revisando estas entrevistas después de muchos años, y con consideración de conceptos desconocidos a ese tiempo para mí, intento situar una comprensión de la obra y su producción cada año como cha’anil. Este capítulo es parte de un libro en desarrollo que enfoca en un metamétodo para interpretar performances, teatro u otros eventos públicos en la península de Yucatán.
Del folclor al tsikbal: "El enano de Uxmal" o cómo derribar reyes y quizás trenes
Authors:   Appalachian State University, Worley Paul Marcus
Published: March 2024

La situación posee los rasgos de un romance arquetípico. Al encontrarse con las ruinas de una misteriosa ciudad antigua, un explorador blanco se dirige a su guía para obtener un poco de conocimiento local. El narrador nos informa de la situación: “Los indios consideran estas ruinas con una reverencia supersticiosa. No se acercan al lugar por la noche, y tienen la vieja historia de que entre ellas se esconde un inmenso tesoro. Cada uno de los edificios tiene su nombre dado por los indios. Esta se llama la Casa del Anano [sic], o Casa del Enano, y está consagrada por una leyenda salvaje, que, mientras estaba sentado en la puerta, recibí de labios de un indio, como sigue:…”1 Apelando a una autoridad basada en el relato directo del autor-narrador, estas palabras reflejan las actitudes y posturas en numerosas obras de etnografía, antropología, literatura de viajes y folclor.
IMPLEMENT DIRECT TORQUE AND FLUX CONTROL (DTC) INDUCTION MOTOR DRIVE MODEL: A REVIEW
Authors: Subbamma Dr. Manne Rama , Sindhuja Mrs. B. , Vijayalakshmi Rudru , Chandrikareddy Chintalappagari
Published: March 2024

Direct torque control ( DTC ) is considered one of the modern control methods, which wildly used in industrial variable speed drive systems to govern behaviors of induction motors. The main objective of this paper is to introduce a comprehensive study of a conventional direct torque of three phase induction motors. Moreover, the paper also aims to enhance and improve the transient response of the conventional DTC by proposing a novel design of its switching table, which considered as an essential component of DTC control circuit. At first, Space Vector theory was implemented to derive a mathematical model of a threephase induction motor. Second, the model was simulated by using Matlab Simulink software due to its simplicity. The conventional DTC and proposed DTC were applied on the computer model of the motor at no-load and when the motor shaft is loaded respectively. It was observed from the results that the transient response of the proposed DTC is better than the response of the conventional one. The paper concluded with number of important results, which obtained from the computer model of the two DTC control methods. Ripple
BİLGİ YÖNETİMİNİN BAŞARI KÜLTÜRÜNE ETKİSİ: SAĞLIK SEKTÖRÜ ÖRNEĞİ
Authors: Bakırcı Ali , Bozbayır Oğuzhan , Özata Musa
Published: March 2024

Günümüz işletmeleri hızlı gelişen bilim ve teknoloji ortamında başarılı olmak için birçok yönetimsel araç kullanmaktadır. Bu araçların etkinliğini ve verimliliğini arttırmak bilgi yönetiminin profesyonel şekilde icra edilmesinden geçmektedir. Bu bağlamda örgüt genelinde başarı kültürünün özümsenerek oturtulması için bilgi yönetim faktörlerinin tam olarak anlaşılması bir problem olarak karşımıza çıkmaktadır. Bu çalışmanın temel amacı sağlık çalışanlarının bilgi yönetim eğilimlerinin çalıştıkları kurumun başarı kültürüne etkilerini incelemektir. Ayrıca çalışma; sağlık kurumu çalışanlarının bilgi yönetimi eğilimleri, başarı kültürü algıları, bunların birbirleriyle olan ilişkisi ve birbirlerine olan etkilerini ortaya çıkarmayı amaçlamıştır. Tanımlayıcı nitelikte olan bu çalışmada genel tarama modeli ile ilişkisel araştırma modeli bir arada kullanılmıştır. Verilerin toplanmasında Bilgi Yönetim Ölçeği (BYÖ) ile Başarı Kültürü (BK) ölçeği kullanılmıştır. Yapılan güvenirlik analizlerinde ölçeklerin yüksek güvenirlikli oldukları görülmüştür. Çalışma 336 sağlık çalışanı ile yüz yüze anket tekniği kullanılmış olup verilerin toplanmasında tesadüfi örneklem yöntemi kullanılmıştır. Toplanan verilere bağımsız gruplar arası t testi, korelasyon ve yapısal eşitlik modeli ile çoklu regresyon analizleri yapılmıştır. Analizler sonucunda bilgi yönetimi ile başarı kültürü arasında orta şiddette pozitif yönlü ve istatistiksel açıdan anlamlı ilişkiler olduğu ayrıca bilgi yönetimi alt boyutlarının bir bütün olarak başarı kültüründeki değişimin %56’sını açıkladığı sonucuna ulaşılmıştır.
Proposing a framework for industry 4.0: An Asian perspective
Authors: Wu Deli & Afshan Ali
Published: March 2024

In this paper, a framework was presented for implementing Industry 4.0 in an Asian context. Industry 4.0, also known as the fourth industrial revolution, is a concept that described the inclusion of cutting-edge technologies such as automation and artificial intelligence into the manufacturing process. To successfully implement Industry 4.0 in the Asia region, the authors argued that it was necessary to take into account the region's specific cultural and socioeconomic backdrop. The authors argued that the successful implementation of Industry 4.0 in Asia was contingent on the effective management of six crucial elements, which were outlined in the suggested framework. Examples of these variables included the organizational structure, the matrix structure, the project teams, the flat hierarchy, the decentralization, the leadership style, and the human resource practices. The authors suggested that governments, industry leaders, and academics take into account the listed elements while establishing and implementing plans for Industry 4.0. The word "organizational structure" referred to an organization's overall architecture, whereas "matrix structure" referred to the use of cross-functional teams to address specific projects or initiatives. Project teams were temporary teams formed for a specific project or task, and a flat hierarchy was an administrative structure with fewer levels than standard hierarchies. Decentralization referred to the process of delegating decision-making authority to lower levels of an organization, whereas leadership style referred to the approach managers took when it came to inspiring and guiding employees. Lastly, when referring to human resource practices, the authors were talking about the rules and processes that aided in the recruitment, development, and retention of employees. The proposed framework provided a starting point for researchers to investigate how Industry 4.0 could be successfully adopted in Asian nations. If organizations considered the aforementioned characteristics, they would be able to build strategies that were more suited to the region's unique cultural and social milieu.
Data-driven Artificial Intelligence at the Crossroads: Investigating the Role of Affective Job Insecurity in the relationship between Artificial Intelligence Identity Threat and Employee Well-Being
Authors: Gull Aqeel , Dilawar Shumaila , Sher Falak
Published: March 2024

We developed a model based on Social Identity Theory, in which the identity threat posed by artificial intelligence (AI) has a negative impact on well-being of workers by increasing their feelings of job insecurity. This model is based on the observation that AI has the potential to displace jobs. The findings are supported by information obtained from a sample of 273 workers drawn from a variety of Pakistani businesses and industries and gathered over a period of time. We used a Confirmatory Factor Analysis to investigate the proposed model, and we used PROCESS MACRO to investigate direct and indirect effects of model's variables. It was ascertained that AI Identity Threat exerts an adverse impact on employees' well-being. Affective job insecurity was identified as a mediating factor in the relationship between AI Identity Threat and employee well-being. The empirical evidence derived from our study supports the assertion that an affective sense of job insecurity operates as a mediator, ameliorating the detrimental effects of AI identity threats on employee well-being. Our study's findings necessitate a discussion of both its conclusions and potential avenues for future research.
Strategic Impact of Industry 4.0 and Big Data on Supply Chain Efficacy in Pakistani Manufacturing
Authors: Arif Muhammad , Khan Sherbaz , Shah Adeel
Published: March 2024

The research aims to scrutinize the influence of Industry 4.0 indicators and Big Data Analytics in innovating and transforming supply chain processes in the manufacturing sector of Pakistan, highlighting how Industry 4.0 factors alter supply chain activities. This study was conducted within the manufacturing industry, specifically targeting employees from the automobile sector in Karachi, Pakistan. Data was collected through online Google Forms, utilizing a closed-ended questionnaire deployed on a Likert scale, to explore various independent variables and their impact on supply chain performance. Survey responses indicate that the variables—supply chain flexibility, supply chain agility, research and development, role of MIS, leadership style, and big data analytics—predominantly influence supply chain performance, albeit with one insignificant relationship noted between leadership style and supply chain performance. The research also affirms the significant mediating impact of supply chain performance indicators on organizational performance. The research may confront limitations related to its generalizability across varied industries and regions due to potential disparities in technological adoption and regulatory frameworks, and the exclusive focus on supply chain performance as the sole mediator might overlook alternative pathways through which the independent variables could influence organizational performance. The insights derived from this research could serve as a guide for managers and manufacturing organizations to comprehend the transformations invoked by Industry 4.0, offering a lens through which to perceive shifts in supply chain performance and activities. The study offers a pivotal examination of the evolution in supply chain practices induced by Industry 4.0 and Big Data Analytics within the context of Pakistan’s manufacturing arena, thereby providing a valuable resource for practitioners and academicians to understand and navigate the landscape of digitally transformed supply chains.
Designing Secure and Robust E-Commerce Plaform for Public Cloud
Authors: Ali Syed Afraz
Published: March 2024

The migration of e-commerce platforms to the public cloud has become a pivotal strategy for businesses seeking enhanced scalability, performance, and cost-efficiency. This paper explores the multifaceted design considerations critical to deploying robust e-commerce systems within the public cloud infrastructure. It delves into scalability, ensuring that platforms can handle varying loads with elasticity and grace. Security is examined as a paramount concern, addressing the need for stringent data protection, compliance with industry standards, and the implementation of best practices such as encryption and identity access management. Performance optimization is discussed, with a focus on leveraging content delivery networks and optimizing database operations to ensure swift customer experiences. The paper also covers reliability and availability, emphasizing the necessity of multi-regional deployment and sophisticated disaster recovery plans to guarantee uninterrupted service. Cost management is analyzed, highlighting the importance of understanding cloud pricing models and employing cost-effective resource utilization strategies. Data management is scrutinized, considering secure storage, privacy, and efficient data handling. User experience is identified as a critical component, with personalization and session management being key to customer satisfaction. The role of DevOps and automation in achieving efficient deployment cycles through continuous integration and delivery is outlined. The benefits of a microservices architecture are presented, along with the challenges of managing such distributed systems. Multi-tenancy and isolation are discussed in the context of security and resource optimization. Integration and APIs are explored for their role in facilitating extensibility and seamless third-party service incorporation. Compliance and legal considerations are addressed, underscoring the importance of data sovereignty and regular audits. Lastly, the paper touches on the incorporation of emerging technologies such as AI, ML, and IoT to stay at the forefront of innovation, and concludes with a discussion on environmental considerations for sustainable cloud practices. This comprehensive analysis provides a roadmap for businesses to navigate the complexities of cloud-based e-commerce, ensuring robust, secure, and efficient online retail operations.
Influential Factors of Impulsive Buying Behavior: Analyzing Retail Dynamics at Hyperstar Shopping Mall in Pakistan
Authors: Sangi Sartaj Ahmed , Pirzado Altaf Hussain , Abro Tanveer Ahmed , Ahmed Waseem , Shaikh Junaid Ahmed
Published: March 2024

In the perpetually shifting and dynamic realm of retail, the pivotal elements of differentiation and ensnaring customer trustworthiness stand as paramount imperatives.  The crucial strategy for retailers in steadfast customer retention and captivating is to understand the complex factors behind impulse buying behavior. Henceforth, this study explores the influential factors guiding impulse buying and their dominancy in securing customer loyalty. Our primary goal was to differentiate influential factors of impulse buying behavior and then discover their adeptness potential for customer acquisition. More importantly, our focus gravitated towards dissecting the shopping environment encasing Hyperstar, Karachi, to extract empirical data through a meticulously targeted survey mechanism. A quantitative approach was implemented, surveying 382 respondents within the boundaries of Hyperstar. Employing a 5-point Likert scale questionnaire, the scrutinization leveraged a conventional tool, ensuring an unwavering reliability quotient exceeding 0.7 thresholds for each differentiated factor. The survey questionnaire was borrowed from antecedent researchers to seamlessly align with the context while ensuring robustness.The research culminated in the discernment of distinct factors; sensation cues, promotional approaches, and window displays. Exhibiting a positive correlation with impulse buying bents for retail commodities. Most notably, sensation cues appeared as the foremost catalyst, exerting a strangely profound influence on impulse buying behavior.The research steadfastly emphasizes the pivotal role played by sensory stimuli in direction-finding impulse purchases. By purposefully managing sensation cues line-up promotional strategies and window displays, retailers can efficiently attribute impulse buying behaviors to adoptive customer engagement and customer loyalty, to fortify the market standing in the dynamic retail domain.
Digital Transformation and Its Impact on Business Performance in SMEs of Pakistan: An Empirical Study
Authors: Mushtaq Naila , Hussain Fawad , Dad Allah , Rehman Shafiq Ur , Waseem Muhammad
Published: March 2024

This study investigates the pivotal role of digital transformation in enhancing the business performance of Small and Medium Enterprises (SMEs) in Pakistan. Grounded in the Resource-Based View (RBV) theory, the research posits three hypotheses examining the relationships between technology adoption, digital literacy among management, and investment in digital infrastructure with business performance. A stratified random sampling approach was employed to collect data from managers and employees across various Pakistani SMEs. The findings reveal significant positive correlations between the elements of digital transformation and business performance. Specifically, the study indicates that SMEs embracing technological advancements, fostering digital literacy in management, and investing in digital infrastructure demonstrate improved operational efficiency and market adaptability. This research contributes to the understanding of digital transformation in the SME sector, offering valuable insights for practitioners and policymakers. It emphasizes the need for strategic digitalization approaches and highlights the benefits of supportive digital policies for SMEs. While the study offers robust evidence from the Pakistani context, it acknowledges limitations such as self-reporting biases and the need for further research in diverse geographical settings. Future studies are encouraged to explore the long-term impacts and sustainability of digital transformation strategies in SMEs.
Innovative Blind Stick for The Visually Impaired
Authors: Khan Moazzam Ali , Qureshi Myra Aslam , Zahid Hira , Agha Sidra , Mushtaq Taha , Nasim Shahzad
Published: March 2024

Visual impairment is an appellation that alludes to the state of being visionless. Individuals with optical blockage are likely to have limitations in their independence and movement, as well as a higher risk of falling, fractures, injuries, poor mental health, and social isolation. Thus, we propose the design of a blind stick to help blind people and assist them in familiarizing themselves with a clear path. In our proposed prototype, multiple sensors are attached to the individual's stick. The sensor detects an obstruction, the receiver's output activates, and the microcontroller detects this change since the receiver's outputs are inputs to the microcontroller. This stick detects the impediment in front and reacts by vibrating or mandating the user. Furthermore, a smoke sensor, fall detection sensor, heart rate, SPO2 level sensor, and Temperature sensor are utilized to detect multiple parameters and not only indicate the individual using the blind stick but to the caregiver as well through an application connected to the blind stick. Moreover, GPS tracking is also present which helps the individual with the stick and also informs the caregiver about the location of the blind person. The results of our proposed prototype demonstrate that this electronic device is a sufficient solution for overcoming the difficulties faced by visually impaired people and their caregivers and allowing them to live their lives independently. In conclusion, our innovative blind stick offers a transformative solution for visually impaired individuals, enhancing their mobility and independence. We invite readers to delve into the full paper to discover the full extent of its capabilities and its potential to make a meaningful difference in the lives of millions worldwide.
Song’s & Non-Song’s Pattern Classification Using Neural Network
Authors: Bhanbhro Areeza , Mukhtiar Abeer , Memon Muniba , Jalbani Akhtar Hussain , Bhangwar Sunbul , Khokhar Mohsin Ali
Published: March 2024

The problem of pattern classification between musical or non-musical song audio snippets and non-songs is the main subject of this paper. The machine learning approach accomplishes the goal of automatically interpreting recordings to see if they are songs or not. There are two types of songs: musical and non-musical. This is due to the possibility that it could be difficult to discern between non-musical songs and non-song patterns. Thus, it is necessary to have such systems that differentiate between non-song patterns and songs, whether or not they are melodic. The extraction and selection of characteristics have made advantage of the preprocessing phases. The features that were obtained were pitch, intensity, length (duration), tempo, and sample rate. The Back-propagation Multi-layer Perceptron Neural Network model is used for both dataset testing and model training. This technique will distinguish between a musical or non-musical song and a non-musical piece (such conversation and speech). Training data for the dataset included recordings of speeches and dialogues in Hindi and Urdu for non-song audio files, as well as audio of various male and female singers from Pakistan and India for song audio files in Hindi and Urdu. When the classifier was evaluated using a range of audio samples, 90% of its classifications for songs and non-songs were accurate.
RSS Feeds Filtering from multiple sources using automated techniques of Natural Language Processing
Authors: Jamali Mujeeb-ur-Rehman , Ali Najma Imtiaz , Maree Mujeeb-u-Rehman Maree , Soomro Akhtar Hussain , Soomro Safeeullah , Memon Abdul Ghafoor
Published: March 2024

The Internet's rapid growth has resulted in a surge of information, posing challenges for users trying to stay updated. The World Wide Web, with its diverse websites updated at varying intervals, necessitates efficient categorization of news articles. As the number of online news sources continues to rise, categorization becomes crucial for users to find information easily. Given the overwhelming volume of digital media information, categorizing news using algorithms and assigning multiple tags based on their category becomes essential. In the modern digital era, users face difficulties filtering and accessing relevant content tailored to their preferences due to the abundance of information from various sources. This study employs Natural Language Processing (NLP) methods for automated content filtering of RSS feeds. The developed system extracts feeds via RSS, generates headings and summaries, and employs NLP for categorization. This research has broad implications, offering opportunities for improved information management, personalized recommendations, and informed decision-making across diverse domains.
Alzheimer's MRI Recognition Based on Convolutional Neural Networks
Authors: Wang Mingjun
Published: March 2024

Alzheimer's disease (AD) is a progressively worsening neurodegenerative disorder, where early diagnosis is crucial for implementing effective disease management strategies. In multimodal diagnostic pathways, Magnetic Resonance Imaging (MRI) plays a pivotal role as a non-invasive imaging technique in disease identification and progression monitoring. Deep learning, particularly Convolutional Neural Networks (CNN), can extract key biomarkers from complex imaging data. By training CNN models to automatically interpret MRI scans, radiology experts can utilize these advanced analytical tools for more efficient and consistent pathological assessments, thereby enhancing clinical decision support systems. This study develops a deep learning-based method for the automatic classification of Alzheimer's brain MRI images, using convolutional neural networks to categorize MRI images into four stages: no dementia, very mild dementia, mild dementia, and moderate dementia. A CNN model is constructed, learning distinctive features of the images through multi-level feature extraction and performing feature map visualization. Early stopping is employed to prevent overfitting. The model is trained on a training set and evaluated on a test set, with performance metrics including confusion matrix, accuracy, precision, recall, F1 score, Kappa coefficient, Matthew’s coefficient, ROC curve with AUC value, and PQ curve with AP value. The results show that the proposed model effectively differentiates between various categories of MRI images, providing valuable tools for early diagnosis and condition monitoring.
Advances and Limitations on the Current Treatment for Schizophrenia and its Future Prospect
Authors: Li Jianning , Zhang Yunyang , Zhou Ruoyu
Published: March 2024

Schizophrenia is a challenging mental disorder marked by recurring psychosis. This article provides an in-depth overview of the current status of research and treatment techniques for schizophrenia. The introductory part gives a thorough explanation of the illness, including all of its many phases and symptoms. The review analyzes the genes involved in cell signaling and neurotransmission, including NRG1, DTNBP1, DISC1, and RGS4, which are important contributors to schizophrenia susceptibility. The article discusses the limitations and challenges associated with existing treatment modalities, including antipsychotic drugs (both typical and atypical), transcranial magnetic stimulation, deep brain stimulation, virtual reality, electroconvulsive therapy, and the ketogenic diet. These approaches have demonstrated efficacy in managing different aspects of schizophrenia symptoms, but they also come with limitations such as side effects, individual variability in response, and ethical concerns. To overcome these limitations, the study offers CRISPR gene editing as a viable future therapeutic option. To improve the efficacy and safety of schizophrenia therapies, the conclusion underlines the need for tailored treatment planning, continuous research, and creative approaches. The changing landscape of schizophrenia research and therapy gives promise for better results and quality of life for people living with the condition.
Risk Assessment of hematoma expansion in patients with hemorrhagic Stroke based on Stochastic Forest Prediction Model
Authors: Xiong Yuwei , Feng Xiaoyi , Gao Feiran
Published: March 2024

This paper discusses the prediction model of hematoma expansion probability in patients with hemorrhagic stroke. Hemorrhagic stroke is an acute and critical neurological disease. High mortality and disability make it a major challenge in the field of public health. Hematoma dilatation and peri-hematoma edema are important factors leading to secondary brain injury. therefore, accurate prediction of the risk of hematoma expansion in patients with hemorrhagic stroke has important guiding significance for clinical diagnosis and treatment. First of all, this study carries on the matching integration and abnormal processing of the patient data in order to improve the data quality. Then, a classification model based on support vector machine (SVM) and random forest is constructed to predict the probability of hematoma expansion. Through the comparative analysis of the classification accuracy, it is found that the prediction effect of the classification model based on random forest is better than that based on SVM. This result provides clinicians with a more accurate prediction tool, which is helpful to identify high-risk patients early, reduce the incidence of secondary brain injury, and improve the quality of life and prognosis of patients. This study provides a new idea for the clinical diagnosis, treatment and research direction of hemorrhagic stroke, and has important theoretical and practical value.
Manufacturing Process of CAR-T Therapy and Assessment of the Optimization Potential
Authors: Wang Jiayi , Wang Kaixin
Published: March 2024

One ground-breaking novel treatment that is being utilized extensively in the field of cancer treatment is chimeric antigen receptor (CAR)-T cell therapy. Three axes are used by CAR-T cells to promote tumor killing: Targeting the antigen-positive fraction is the Perforin and Granzyme axis; Targeting the antigen-negative fraction is the Fas and FasL axis; and Cytokine release is focused on the Stromal cell. As a result of this technology's widespread use, its benefits and applications have increased; yet, drawbacks and toxicities have also been discovered. The wide range of uses for this cutting-edge technology has resulted in an increase in its advantages and uses. Alongside these benefits, studies have also discovered negative effects and toxicities related to its usage. This work analyzes the complexities of the specialized immunotherapy mechanism, the difficulties associated with this technology, toxicity issues, and future directions in the treatment of CAR-T cells. Its potential to completely transform the way cancer is treated is becoming more and more evident as the medical community works to understand the intricacies of this innovative therapy.
Mitigating the Off-target Effects in CRISPR/Cas9-mediated Genetic Editing with Bioinformatic Technologies
Authors: Yuan Shixing
Published: March 2024

The biological and clinical fields have recognized the CRISPR/Cas9 (clustered regularly interspaced short palindromic repeat/CRISPR associated protein 9) system as a precise and efficient tool for editing the genome. Despite its merits, the system poses crucial challenges, notably the off-target effects which could lead to unintended mutations - a substantial impediment for clinical applications that may potentially compromise the validity of research and the safety of therapeutic applications. Bioinformatics plays a pivotal role in mitigating this risk. Utilizing more refined bioinformatic tools and algorithms, researchers can reduce off-target mutations remarkably. These instruments, powered by machine learning and computational modelling, are able to predict off-target effects and provide aid for the design of more efficient sgRNA. Despite these advancements, it remains crucial to continue to focus on the improvement and assessment of such bioinformatics strategies. This review aims to holistically explore the mechanism and applications of CRISPR/Cas9 genome editing, its off-target effects, and the consequent impacts, along with the potential of bioinformatics techniques to identify off-target risks and facilitate sgRNA design. This review will also incorporate a clinical trial on HIV-1 treatment as a case study to highlight the potential of bioinformatics in devising solutions to mitigate the potential off-target effects of CRISPR/Cas9-mediated genetic editing.
Immunotherapy in Non-Small Cell Lung Cancer (NSCLC): Chance and Challenge
Authors: Min Zhenning
Published: March 2024

Non-small cell lung cancer (NSCLC) is the major type of lung cancer, accounting for 85%. The common options for treatment include surgery, radiotherapy, chemotherapy, immunotherapy, and molecularly targeted therapy depending on the stage, histology, genetic changes, and health condition of patients. Sadly, just 15% of individuals with stage III NSCLC are still alive after five years, which indicates a bad outlook. Because of the use of PD-1 and CTLA-4 immune checkpoint inhibitors (ICIs), immunotherapy has unquestionably made the most progress in the field of NSCLC. This article offers a comprehensive exploration of NSCLC, encompassing its epidemiological characteristics, pathological processes of different classification, and the diverse treatment modalities available. It emphasizes the pivotal role of immunotherapy, particularly immune checkpoint inhibitors (ICIs) for treatment some classification of patients. The article discusses challenges and prospects, such as identifying patient populations that benefit most from immunotherapy and addressing issues like immune-related adverse events (irAEs) and immune resistance. Through this analysis, the article underscores the significant strides made in NSCLC treatment and the ongoing efforts to improve patient outcomes.
The Research of Risk Factor Prediction of Chronic Kidney Disease
Authors: Li Yuxi , Liu Yuzhi , Lu Guanyu
Published: March 2024

Kidney diseases, particularly chronic kidney disease (CKD), represent a significant health concern, particularly as one ages. This paper delves into an in-depth analysis of CKD, exploring its prevalence, risk factors, and the complex interplay of conventional and nontraditional determinants. Notably, socioeconomic, genetic, and lifestyle factors are pivotal in understanding CKD's multifaceted etiology. The research employs a diverse dataset from Bangladesh, applying statistical techniques including ANOVA, logistic regression, recursive feature elimination, and random forest to identify and evaluate crucial factors associated with CKD. The study aims to construct a robust predictive model for CKD risk assessment, integrating traditional and lesser-explored variables. This comprehensive approach holds promise in refining risk assessment models and guiding targeted intervention strategies for enhanced CKD prevention and management. The findings emphasize the necessity for a holistic understanding of CKD, emphasizing personalized approaches for effective disease management and prevention.
Nisin—A Promising Antimicrobial Compound from Lactic Acid Bacteria
Authors: Chen Guanchong
Published: March 2024

Nisin is a classical bacteriocin consisting of thirty-four amino acid residues and undergoing a high degree of post-translational modification to form a unique pentacyclic structure. Nisin is mainly synthesised by lactic acid bacteria. Under natural conditions, it exhibits strong antibacterial activity against Gram-positive organisms and, when combined with chelating agents, is effective against Gram-negative organisms. An excellent safety profile, low cytotoxicity and slow development of resistance are additional benefits. Currently, the application of nisin is focused on food preservation. This is due to the fact that nisin is naturally present in a wide range of historical fermented foods and its safety is fully guaranteed. With the continuous development of bacterial drug resistance and the consequent demand for new anti-infective drugs, its biomedical applications are being actively explored. However, due to the problems of drug stability, safety, and in vivo activity, there is still a certain distance from clinical application. In this paper, the author summarized the structure, biosynthetic mechanism, applications, and possible research directions of nisin.
AI and the Future of War: The Impact of Machine Learning in Security Practice
Authors: Li Lingjuan
Published: March 2024

This paper explores the relationship between artificial intelligence (AI) and future wars, and focuses on how machine learning strengthens safety practice. Based on Wu Qianze's paper in National Defense Science, Technology and Industry, Issue 5,2019, this paper expounds the wide application of AI in the military field and the great potential of machine learning in improving security protection. Through a review of existing studies, this paper presents a series of recommendations to enhance safety practices designed to provide guidance for future research and applications.
Intelligent Security Detection and Defense in Operating Systems Based on Deep Learning
Authors: Wang Hongbo , Wu Jian , Zhang Chenwei , Lu Wenran , Ni Chunhe
Published: March 2024

With the development of network technology, APT(advanced persistentthreat) attacks are increasing, and research on the security of enterprise assets requires effective detection of digital assets in the network space and effective management of assets through screening and combing, which is the key to real-time monitoring of the safe operation of the system. However, the accompanying malware also poses a threat to the user's property and privacy, so an effective method of detecting Android malware is necessary. In this research direction, although the feature processing capability of traditional machine learning has been improved, there are problems that feature extraction relies on expert experience and the accuracy is low. Therefore, this paper combines deep learning reinforcement technology with the operating system to detect and defend the system in advance, so as to achieve network security.
Sample Size Estimation and Statistical Analysis of Cholangiography Robots
Authors: Tang Rongli
Published: March 2024

In recent years, with the continuous innovation of medical devices encouraged by the government and the investment and attention of enterprises in research and development, coupled with the relevant requirements of the CDE (Center for Drug Evaluation) of the National Medical Products Administration, in order to improve the efficiency of trials and accelerate the launch of cholangiography robots, medical device research and development enterprises are increasingly focusing on the design of clinical trial plans, adopting more scientific, reliable, efficient and standardized statistical designs. Insufficient sample size may lead to issues with sample representativeness, while human centered trials are very expensive, and excessive sample size could increase the difficulty of clinical research. Therefore, this study used the "golden rule" of RCT (Randomized Controlled Trials), combined with the outcome indicators, test level, confidence, dropout rate, etc., to estimate the number of cases in the clinical efficacy comparison study of the cholangiography robot.
Analysis of different detection methods for pulmonary tuberculosis
Authors: Li Haowei
Published: March 2024

Tuberculosis is classified as a chronic respiratory disease with its pathogen being Mycobacterium tuberculosis (MTB). It poses a significant health threat due to its highly contagious nature and is difficulty in treatment. Various methods are employed to test for tuberculosis, including pathological, immunological and molecular biological tests. Each test has its unique principles and all are different in effects, and are widely used in clinical practice. In this research, principles, development and effects of each type of tests in testing pulmonary tuberculosis will be explored respectively. Additionally, the review will provide insights into the optimization of tuberculosis testing by comparing and contrasting different testing methods. Pathological tests represent the most traditional approach to testing for tuberculosis. Bacterial culture, a pathological test, is the gold standard of tuberculosis test. Nevertheless, it suffers from a long detection time. Meanwhile, immunological tests and molecular biological tests benefits from a fast detection, while the accuracy of the tests can be influenced by multiple factors. Thus, it will accelerate the detection speed while enhancing the precision to combine three different kinds of tests in clinical practice.
The Treatment of Bipolar Disorder
Authors: Li Xinyue
Published: March 2024

As a relatively large class of neurological diseases, bipolar has always been an important object of research and exploration by scientists. When bidirectional patients develop the disease, they are aggressive and a great burden for their families. Therefore, how to treat bipolar has been a hot topic. Current treatments for bipolar include medication, psychotherapy, and physiotherapy. Drug therapy includes mood stabilizers, antidepressants, anticonvulsants, antipsychotics and so on. Psychotherapy is aimed not only at the patient, but also with his or her spouse and relatives. Treatment modalities include psychoanalytic therapy, analytic psychotherapy, cognitive behavioral therapy, suggestive therapy, and supportive therapy. However, different treatment modalities have certain limitations. For example, drugs have certain side effects and may cause complications during treatment. Psychotherapy is more limited by the patient. Physical therapy may cause memory impairment, nausea, headaches, etc. This review is about current treatment modalities for bipolar and their limitations.
The Pet Attachment Affects The Person's Mental Condition
Authors: Feng Kewen
Published: March 2024

People are more willing to have pets in current society. The interaction with pets brings pet attachment to owners. Studies try to find the benefits of the pet; the pet attachment is the measurable variable to evaluate the benefits and function of the pet. The essay discusses the emotional support of pets by dividing it into positive and negative emotional conditions. Each condition introduces two studies to develop the benefits of pets. In the positive emotion condition, the studies show that different kinds of companion animals can increase the owners' well-being. The study also figures out that the interaction with and presence of pets contribute to the positive mood of the owners. In negative emotion, two studies studied two periods, which bring long-lasting stress and negatively impact people's mental health. The result shows that companion animals can display the function of buffering negative impacts and promoting positive moods. The studies mainly take surveys to measure the data from the participants. Studies conclude that pet attachment to companion animals has emotional support and positively impacts mental health in daily life.
Effect of reputation and social media on the financial performance of SMEs – a comparison between selected business sectors
Authors: Blajer-Golebiewska Anna , Vasa Laszlo
Published: March 2024

Research background: The growing popularity of social media increased its significance as a tool for enhancing corporate reputation. However, SMEs are less engaged in social media than large companies. For this reason, numerous SMEs miss the benefits they could derive from using the potential of social media and fail to optimize their reputation management strategies. Furthermore, substantial diversity among SMEs in their social media engagement practices is evident. Consequently, it is crucial to investigate this problem and identify SME owners' and managers’ perceptions of reputation and social media in specific business sectors. Purpose of the article: The aim of this study is to examine and quantify the effect of the importance of corporate reputations and social media on financial performance. Methods: The questionnaire was completed by 864 SMEs engaged in business activities within one of the following sectors: manufacturing, trade, services, and construction. Subjective perceptions of owners and managers of small and medium enterprises (SMEs) were analyzed. Data collection was conducted in the four Central European countries in the years 2022-2023. The statistical hypotheses were verified using correlation analysis and linear regression modelling. Analytical software IBM SPSS Statistics no. 28 was used in the statistical evaluation of the research data set. Findings & Value added: The corporate reputation of companies is a significant factor, which has a positive effect on the acceptable profit of a company in the business sector of manufacturing, trade, construction, and services. The strongest effect is present in the sector of construction. Social media is an important factor with a positive effect on the perception of a sufficient profit of a company by each business sector except construction. The strongest effect is present in the sector of manufacturing.  Corporate reputation was found to be insignificant for the perception of the ability to pay obligations (solvency) in the manufacturing sector. This effect is stronger in the construction sector in comparison to the trade and services sectors.
Factores psicosociales que inciden en el abandono temporal de estudios en universitarios de la UAB
Authors: Torrez Ramos Zulema
Published: March 2024

The objective of this research work is to identify the psychosocial factors that contribute to the temporary abandonment of studies in university students at the Adventist University of Bolivia. A qualitative approach was used, with a descriptive research with a non-experimental design. The information was collected using the in-depth interview technique and the specific instrument was a guide of questions to the which was used to provide a common thread to the dialogue. The main results highlight that students describe teachers as agents that demotivate them and is one of the causes that leads them to academic failure. Family and friends constitute a motivating agent for most students. The Adventist religion is described by some students as a demotivating factor. It is concluded that the majority of students consider teachers as demotivating agents, due to attitudes and behaviors, which could be contributing to university dropout.