Comprehensive Survey Research Articles

Quantity of delta-9-tetrahydrocannabinol consumption and cannabis use disorder among daily cannabis consumers.

Amid escalating cannabis legalization and daily consumption in the United States (US), determining the risk of cannabis use disorder (CUD) and relevant consequences among daily consumers has become a public health priority. Understanding these risks requires valid assessment of the daily quantity of delta-9-tetrahydrocannabinol (THC) consumed and its relation to consequences. This study characterized daily cannabis consumption using a new method for estimating milligrams of THC (mgTHC), and examined the relationship between daily mgTHC and CUD severity in a large national sample of daily consumers. US adult (aged 18+ years) daily cannabis consumers (n = 4134) completed a comprehensive online survey of cannabis consumption patterns (e.g. frequency, quantity, product types, potencies, administration methods) and Diagnostic and Statistical Manual of Mental Disorders, 5th edition (DSM-5) CUD criteria. The primary exposure was past-week daily mgTHC consumption, calculated from survey responses to queries about product type, amount and potency consumed and including adjustments for puff size and loss of THC from specific methods of administration. The primary outcomes were (1) number of CUD criteria (range = 0-11) and (2) CUD severity categories: none, mild, moderate, severe. Median daily consumption was ~130 mgTHC, with substantial variability (25% ≤ 50 mg and 25% ≥ 290 mg). On average, participants endorsed 2.5 CUD criteria, and 65% met criteria for CUD (39% mild, 18% moderate, 8% severe). Greater daily mgTHC predicted higher CUD criteria count [betalog(mgTHC) = 0.50, 95% confidence interval (CI) = 0.267-0.734] and higher odds of mild [log odds ratio (logOR) = 0.238, 95% CI = 0.184-0.292], moderate (logOR = 0.303, 95% CI = 0.232-0.374) or severe (logOR = 0.335, 95% CI = 0.236-0.435) CUD. Among daily consumers of cannabis, there appears to be a positive relationship between the daily quantity of cannabis consumed (measured in milligrams of delta-9-tetrahydrocannabinol) and both the risk and severity of cannabis use disorder.

Assessment of Facility Readiness for Pediatric Emergency and Critical Care Utilizing a 2-Phase Survey Conducted in Six Hospitals in Uganda and Cameroon: A Quality Improvement Study.

Each year, 5.3 million children under 5 years of age die in low-resource settings, often due to delayed recognition of disease severity, inadequate treatment, or a lack of supplies. We describe the use of a comprehensive digital facility-readiness survey tool, recently developed by the Pediatric Sepsis Data CoLaboratory, which aims to identify target areas for quality improvement related to pediatric emergency and critical care. Facility-readiness surveys were conducted at six sub-Saharan African hospitals providing pediatric emergency and critical care in Uganda (n = 4) and Cameroon (n = 2). The tool is a 2-phase survey to assess readiness to provide pediatric essential emergency and critical care: (1) an "environmental scan," focusing on infrastructure, availability, and functionality of resources, and (2) an "observational scan" assessing the quality and safety of care through direct observation of patients receiving treatment for common diseases. Data were captured in a mobile application and the findings analyzed descriptively. Varying levels of facility readiness to provide pediatric emergency care were observed. Only 1 of 6 facilities had a qualified staff member to assess children for danger signs upon arrival, and only 2 of 6 had staff with skills to manage emergency conditions. Only 21% of essential medicines required for pediatric emergency and critical care were available at all six facilities. Most facilities had clean running water and soap or disinfectants, but most also experienced interruptions to their electricity supply. Less than half of patients received an appropriate discharge note and fewer received counseling on postdischarge care; follow-up was arranged in less than a quarter of cases. These pilot findings indicate that facilities are partially equipped and ready to provide pediatric emergency and critical care. This facility-readiness tool can be utilized in low-resource settings to assist hospital administrators and policymakers to determine priority areas to improve quality of care for the critically ill child.

Sheaths are diverse and abundant cell surface layers in archaea.

Prokaryotic cells employ multiple protective layers crucial for defense, structural integrity, and cellular interactions in the environment. Archaea often feature an S-layer, with some species possessing additional and remarkably resistant sheaths. The archaeal sheath has been studied in Methanothrix and Methanospirillum, revealing a complex structure consisting of amyloid proteins organized into rings. Here, we conducted a comprehensive survey of sheath-forming proteins (SH proteins) across archaeal genomes. Structural modeling reveals a rich diversity of SH proteins, indicating the presence of a sheath in members of the TACK superphylum (Thermoprotei), as well as in the methanotrophic ANME-1. SH proteins are present in up to 40 copies per genome and display diverse domain arrangements suggesting multifunctional roles within the sheath, and potential involvement in cell-cell interaction with syntrophic partners. We uncover a complex evolutionary dynamic, indicating active exchange of SH proteins in archaeal communities. We find that viruses infecting sheathed archaea encode a diversity of SH-like proteins and we use them as markers to identify 580 vOTUs potentially associated with sheathed archaea. Structural modeling suggests that viral SH proteins can form complexes with the host SH proteins. We propose a previously unreported egress strategy where the expression of viral SH-like proteins may disrupt the integrity of the host sheath and facilitate viral exit during lysis. Together, our results significantly expand knowledge of the diversity and evolution of the archaeal sheath, which has been largely understudied but might have an important role in shaping microbial communities.

Comprehensive Survey of Image Steganography Systems based on FPGA Implementation

Steganography is the art of concealed communication. The basic idea of steganography is to hide secret data inside a cover media in an undetectable manner. Cover media and Secret data can be any popular digital media such as image, video, audio, or text. Various types of image steganography methods have been proposed according to the cover image domain, i.e. spatial domain and transform domain. The spatial domain methods are easy and simple while the transform domain methods are more complex. However, the transform domain methods are more robust against image processing operations. Besides, they are more secure and less detectable in an unsecured channel. The recent steganography methods are based on sophisticated algorithms that include several computational time-consuming tasks, and hence they are unable to embed and extract the hidden data in real-time. Therefore, the trend is to implement the steganography methods in the hardware to speed up their processing time and so improves the efficacy of steganography techniques. FPGA is used in various fields in the modern era due to lower development cost, flexibility, and reconfigurability. Most of the published steganography techniques carried out on FPGA are based on the spatial domain. In this paper, it has comprehensively studied and reviewed various existing implementations of image steganography on FPGAs. This includes studying their general operations, requirements, and performance evaluations. This review would assist researchers in finding the research gaps in FPGA implementation of steganography methods for real-time applications.

Teacher competency development model of higher vocational schools in Shandong province, China

Teacher competency is fundamental to successful vocational education reforms and quality enhancement. The rapid economic transformation and industrial upgrading in Shandong Province require highly skilled talents, prompting the implementation of modern vocational education (MVE) reform. However, the current competency of vocational teachers (VTs) often falls short of MVE standards, and there is an urgent need for a development model suitable for regional characteristics as a guide. In this study, we aim to assess the current state of VT competency development in Shandong Province and propose an integrated competency development model tailored to its specific needs. Our research employed a mixed-methods design, incorporating a cross-sectional survey of 280 VTs and 140 administrators and semi-structured interviews with management personnel. We conducted the exploratory factor analysis (EFA) on a comprehensive survey questionnaire and identified six key determinants of VT competency development. Based on these findings and expert interviews, we developed an integrated competency development model, comprising the teacher’s personal qualities and educational philosophy, educational technology competence, digital teaching competence, social policy, professional identity, and school culture and support. This model offers practical implications for designing effective teacher training programs and establishing supportive institutional environments, ultimately aiming to enhance the quality of vocational education through improved teacher competency.

Artificial Intelligences in Industrial Robots: A Framework Based on Gardner’s Multiple Intelligences

Industrial robots, both in manufacturing and non-manufacturing sectors, are evolving rapidly, driven by advancements in artificial intelligence (AI). This paper presents a comprehensive survey of industrial robots, framed through the lens of Howard Gardner’s theory of multiple intelligences. By categorising various AI capabilities in industrial robots—such as visual recognition, decision-making, and collaborative interaction—based on Gardner’s intelligence framework, we provide a novel taxonomy that bridges human cognitive abilities and artificial systems. The survey explores the historical development of industrial robots, the current state of AI implementation, and future trends in robotics. Additionally, we discuss the implications of these advancements for industries and their workforce, as well as the ethical considerations surrounding the growing autonomy of AI systems. This paper aims to serve as a reference point for researchers and professionals seeking to understand the intersection of cognitive science and industrial AI, highlighting the potential and challenges of integrating AI into robotic systems.

Evaluating Scheduling Efficiency for Complex Scientific Workflows in Cloud Infrastructures

Scientific workflow scheduling in Cloud computing is critical for efficiently managing data-exhaustive and compute-rigorous applications. With the rising demand of distributed computing, the interest in feasible assignment planning calculations has developed primarily. Revised text: This paper presents a comprehensive survey of advanced scheduling techniques focusing on minimizing energy consumption and improving resource utilization. Various scheduling algorithms, including heuristic-based, meta-heuristic-based, and reinforcement learning-based methods, are analyzed and compared. Additionally, the paper addresses the challenges of scheduling workflows with complex dependencies, offering a novel multi-objective workflow scheduling algorithm using reinforcement learning. The algorithm outperforms current methods in terms of makespan and energy consumption. Finally, the paper highlights open research issues and future directions in scientific workflow scheduling for distributed computing. Through our experiments, we have achieved significant improvements in scheduling efficiency, demonstrated by various performance metrics illustrated in our graphs.

MOF membranes for enhanced gas separation: materials, mechanisms, and application prospects—a comprehensive survey

MOF membranes for enhanced gas separation: materials, mechanisms, and application prospects—a comprehensive survey

Pharmacological activities and therapeutic potential of Hyperoside in the treatment of Alzheimer's and Parkinson's diseases: A systemic review.

Alzheimer's disease (AD) and Parkinson's disease (PD) are neurodegenerative disorders that significantly impact well-being. Hyperoside (HYP), a flavonoid found in various plant species, particularly within the genus Hypericin, exhibits diverse pharmacological properties. However, the precise mechanisms underlying the anti-AD and anti-PD effects of HYP remain unclear. This systematic review consolidated existing preclinical research on HYP by conducting a comprehensive literature survey and analysis. The objective was to corroborate the therapeutic efficacy of HYP in AD and PD models and to synthesize its potential therapeutic mechanisms. Searches were conducted in the PubMed, CNKI, and Web of Science databases. Reliability assessment of the 17 included studies confirmed the credibility of the mechanisms of action of HYP against AD and PD. We systematically assessed the neuroprotective potential of HYP in in vivo and in vitro models of AD and PD. Our findings indicated that HYP can mitigate, intervene in, and treat AD and PD animal models and associated cells through various mechanisms, including anti-oxidative, anti-inflammatory, anti-apoptotic, anti-Aβ aggregation, and cholinesterase inhibitory activities. Therefore, HYP potentially exerts anti-AD and anti-PD effects through diverse mechanisms, making it a promising candidate for therapeutic intervention in both AD and PD.

Exploring Feature Selection With Limited Labels: A Comprehensive Survey of Semi-Supervised and Unsupervised Approaches

Exploring Feature Selection With Limited Labels: A Comprehensive Survey of Semi-Supervised and Unsupervised Approaches

Advancements in Data Augmentation and Transfer Learning: A Comprehensive Survey to Address Data Scarcity Challenges

Abstract: Deep Learning (DL) models have demonstrated remarkable proficiency in image classification and recognition tasks, surpassing human capabilities. The observed enhancement in performance can be attributed to the utilization of extensive datasets. Nevertheless, DL models have huge data requirements. Widening the learning capability of such models from limited samples even today remains a challenge, given the intrinsic constraints of small datasets. The trifecta of challenges, encompassing limited labeled datasets, privacy, poor generalization performance, and the costliness of annotations, further compounds the difficulty in achieving robust model performance. Overcoming the challenge of expanding the learning capabilities of Deep Learning models with limited sample sizes remains a pressing concern even today. To address this critical issue, our study conducts a meticulous examination of established methodologies, such as Data Augmentation and Transfer Learning, which offer promising solutions to data scarcity dilemmas. Data Augmentation, a powerful technique, amplifies the size of small datasets through a diverse array of strategies. These encompass geometric transformations, kernel filter manipulations, neural style transfer amalgamation, random erasing, Generative Adversarial Networks, augmentations in feature space, and adversarial and meta- learning training paradigms. : Furthermore, Transfer Learning emerges as a crucial tool, leveraging pre-trained models to facilitate knowledge transfer between models or enabling the retraining of models on analogous datasets. Through our comprehensive investigation, we provide profound insights into how the synergistic application of these two techniques can significantly enhance the performance of classification tasks, effectively magnifying scarce datasets. This augmentation in data availability not only addresses the immediate challenges posed by limited datasets but also unlocks the full potential of working with Big Data in a new era of possibilities in DL applications.

Biofilm Production in Intensive Care Units: Challenges and Implications

The prevalence of infections amongst intensive care unit (ICU) patients is inevitably high, and the ICU is considered the epicenter for the spread of multidrug-resistant bacteria. Multiple studies have focused on the microbial diversity largely inhabiting ICUs that continues to flourish despite treatment with various antibiotics, investigating the factors that influence the spread of these pathogens, with the aim of implementing sufficient monitoring and infection control methods. Despite joint efforts from healthcare providers and policymakers, ICUs remain a hub for healthcare-associated infections. While persistence is a unique strategy used by these pathogens, multiple other factors can lead to persistent infections and antimicrobial tolerance in the ICU. Despite the recognition of the detrimental effects biofilm-producing pathogens have on ICU patients, overcoming biofilm formation in ICUs continues to be a challenge. This review focuses on various facets of ICUs that may contribute to and/or enhance biofilm production. A comprehensive survey of the literature reveals the apparent need for additional molecular studies to assist in understanding the relationship between biofilm regulation and the adaptive behavior of pathogens in the ICU environment. A better understanding of the interplay between biofilm production and antibiotic resistance within the environmental cues exhibited particularly by the ICU may also reveal ways to limit biofilm production and indivertibly control the spread of antibiotic-resistant pathogens in ICUs.

Psychosocial characteristics of suicide deaths by regional types in Korea: An analysis based on population inflow and outflow.

This study aims to investigate the psychosocial characteristics of suicide deaths in Korea according to population inflow and outflow, and to identify high-risk suicide groups by regional type in order to establish tailored suicide prevention plans for each region. The analysis included 4193 suicide deaths from four regional types in Korea. The data source was a comprehensive survey of suicide deaths through police investigation records from the Korean government. The analysis involved collecting demographic variables, suicide-related variables, and stress variables at the time of death for suicide deaths in each region and conducting cross-analysis and multinomial logistic regression analysis. Across all regions, factors such as male gender, unemployment, hanging in one's own home, and mental health problems were prevalent. However, in population-inflow urban areas, early-middle age and economic problems were higher, in population-outflow urban areas, late-middle age and economic problems were more prominent. Additionally, in both inflow and outflow rural areas, there was a relatively higher proportion of old age and physical health problems. This study is significant for confirming common and specific factors influencing suicide in each region, and for proposing considerations when formulating tailored suicide prevention plans for local communities.

A Comprehensive Survey on Brain Tumor Detection and Classification Techniques Using Machine Learning and Deep Learning Models

Abstract: Brain tumor detection and classification have become critical areas of research due to the complexities and challenges involved in accurate diagnosis. With the advancement of machine learning and deep learning techniques, numerous intelligent systems have been proposed to automate and enhance the process of detecting and classifying brain tumors. This survey paper reviews recent approaches and techniques used for brain tumor detection, focusing primarily on deep learning architectures like Convolutional Neural Networks (CNNs) and pre-trained models such as VGG, ResNet, and EfficientNet. In particular, methods using MRI images to classify tumors into categories such as glioma, meningioma, pituitary tumors, and healthy tissue are explored. A detailed analysis of various models, their architectures, training strategies, and evaluation metrics is provided. Moreover, the paper identifies the key challenges faced in the field, including imbalanced datasets, generalization issues, and computational efficiency. By comparing different approaches, we aim to highlight the strengths and limitations of each method and provide insights into how future research can address these challenges. The paper concludes with a discussion on promising future directions in the application of intelligent techniques for brain tumor detection, including the integration of multimodal data and the development of more interpretable models. This survey serves as a comprehensive resource for researchers interested in understanding the current landscape of brain tumor detection using advanced deep learning techniques.

Spatial Distribution and Disease Incidence of Guava Wilt Village Survey Across Prayagraj and Kaushambi, Uttar Pradesh, India

A comprehensive roving survey was conducted in the Prayagraj and Kaushambi districts of Uttar Pradesh, India, both prominent guava-producing regions. The survey, which included 30 villages, revealed widespread guava wilt with incidence rates ranging from 19.04% to 37.93%, averaging 30.19% across the region. Three forms of wilt these are quick, slow, and partial were identified. Kaushambi district had a higher incidence rate (31.56%) compared to Prayagraj district (28.82%). Indarapur village in Kaushambi recorded the highest wilt incidence (37.93%). The survey also found that 66.67% of farmers lacked formal training in guava cultivation, contributing to poor disease management. Guava wilt was most prevalent in older orchards (10+ years) and primarily affected the Allahabad Safeda variety, which had the highest wilt incidence (33.64%). In contrast, the Pant Prabhat variety exhibited the lowest wilt incidence (22.95%). The causal pathogen, Fusarium oxysporum f. sp. psidii, was isolated from diseased plants. Three fungal isolates displayed variations in colony morphology, growth, and sporulation. Microconidia were non-septate and kidney-shaped, while macroconidia were thin- walled and typically 2-3 septate. Two isolates also produced chlamydospores. This study highlights the need for enhanced farmer training and the potential for cultivating wilt-tolerant varieties to mitigate the spread of the disease in the region.

Unlocking Genome Editing: Advances and Obstacles in CRISPR/Cas Delivery Technologies

CRISPR/Cas9 (clustered regularly interspaced short palindromic repeats associated with protein 9) was first identified as a component of the bacterial adaptive immune system and subsequently engineered into a genome-editing tool. The key breakthrough in this field came with the realization that CRISPR/Cas9 could be used in mammalian cells to enable transformative genetic editing. This technology has since become a vital tool for various genetic manipulations, including gene knockouts, knock-in point mutations, and gene regulation at both transcriptional and post-transcriptional levels. CRISPR/Cas9 holds great potential in human medicine, particularly for curing genetic disorders. However, despite significant innovation and advancement in genome editing, the technology still possesses critical limitations, such as off-target effects, immunogenicity issues, ethical considerations, regulatory hurdles, and the need for efficient delivery methods. To overcome these obstacles, efforts have focused on creating more accurate and reliable Cas9 nucleases and exploring innovative delivery methods. Recently, functional biomaterials and synthetic carriers have shown great potential as effective delivery vehicles for CRISPR/Cas9 components. In this review, we attempt to provide a comprehensive survey of the existing CRISPR-Cas9 delivery strategies, including viral delivery, biomaterials-based delivery, synthetic carriers, and physical delivery techniques. We underscore the urgent need for effective delivery systems to fully unlock the power of CRISPR/Cas9 technology and realize a seamless transition from benchtop research to clinical applications.

Comprehensive survey on security challenges and solutions in cognitive radio networks

Comprehensive survey on security challenges and solutions in cognitive radio networks

Unveiling Regional Dynamics and Factors Influencing Fish Purchase and Consumption in Odisha, India

Despite India being a major global fish producer, per capita fish consumption remains notably low in several regions, raising concerns regarding the nutritional adequacy of diets. This study explores the factors influencing fish purchase and consumption patterns in the Cuttack and Khordha districts of Odisha, India. A comprehensive survey (n=400)was conducted, and a trans-regression analysis was applied to identify key factors. The results reveal that fish quality, with an emphasis on freshness, taste, and texture, is the most crucial factor influencing consumer preferences in both districts. Affordability, represented by fish prices, ranked second, while dressing facilities unexpectedly held significant importance, reflecting consumer preference for convenience in fish preparation. Sociocultural factors, including local traditions and dietary customs, also played a role but were less significant compared to other factors. The study highlights apromising trend of fish consumption exceeding recommended levels, contributing positively to nutritional goals. A comparative analysis betweenregions showed variability in meat consumption, with fish standing out as a primary dietary staple. These findings underscore the need for region-specific strategies to promote sustainable fish consumption, considering both economic and socio-cultural influences. The study contributes to the understanding of regional variations in fish consumption patterns and provides a foundation for targeted interventions to address nutritional challenges and promote sustainable dietary practices.

Recent advances in microbially derived chlorinated antiparasitic compounds.

Parasitic diseases remain a significant global health challenge, especially in developing countries, contributing to approximately one million deaths annually. Notably, among the 143 FDA-approved antiparasitic drugs, thirty-four possess chlorine in their chemical structure, highlighting the importance of chlorine substitution. This underscores the significance of chlorine atoms in elucidating structure-activity relationships crucial for drug discovery, aiming to develop safer, more selective, and environmentally friendly molecules with enhanced efficacy. Of particular interest some are naturally occurring chlorinated metabolites derived from PKS, NRPS, and PKS-NRPS biosynthetic pathways, which offer the potential for further manipulation. However, there is limited literature on antiparasitic chlorinated compounds from microbial sources. To address this, we conducted a comprehensive literature survey from 1963 to the present, identifying 28 chlorinated compounds with confirmed antiparasitic properties. This review underscores the potential of enzymatic machinery for selective chlorine substitution, offering insights for biochemists and synthetic chemists to develop versatile chlorinated compounds through synthetic biology, combinatorial chemistry, and organic synthesis.

A comprehensive survey of cybercrimes in India over the last decade

Since the 1990s, the integration of technology into daily life has led to the creation of an extensive network of interconnected devices, transforming how individuals and organizations operate. However, this digital transformation has also spurred the rise of cybercrime, criminal activities perpetrated through networks or computer systems. Cybercrime has become a global concern, presenting significant challenges to security systems. Although advancements in digital technology have enhanced efficiency, they have also opened new avenues for exploitation by cybercriminals, highlighting the urgent need for advanced cybersecurity measures. The escalating number of cyberattacks and associated risks in the past decade highlights the critical importance of protecting sensitive data and safeguarding information systems. Cybercrimes range from financial fraud and phishing scams to identity theft and online harassment, posing substantial risks to both individuals and organizations. In response, governments, law enforcement agencies, and cybersecurity units have intensified their efforts to address these threats. In recent years, India has experienced a significant surge in cybercrime incidents, with a notable increase in cases involving ransomware, data breaches, and social engineering attacks. The growing penetration of internet services, the expansion of e-commerce, and the rapid adoption of digital payment systems have made individuals and organizations more vulnerable to cyber threats. Key areas affected include banking, healthcare, and government sectors, which are frequently targeted due to the sensitive nature of the data they handle. To combat these risks, there is an increasing focus on public awareness, cybersecurity education, and robust regulatory frameworks. This paper provides an in-depth analysis of cybercrime, with a focus on developing innovative prevention strategies, strengthening internal security protocols, and classifying key cybercrime terminologies to better understand their implications for digital infrastructure.