bluuebunny/crossref_metadata_2025 huggingface dataset:
Adatok a Velencei-tó medrének pionír növényzetéhez
Authors: Riezing Norbert
Published: December 2023

Lake Velence is one of Hungary's largest saline lakes. Part of its natural dynamics is that certain parts of the lake bed regularly dry out, providing pioneer surfaces suitable for the emergence of plants of the mudflats and drying (saline) ponds. During the particularly dry year of 2022, the water level was per­manently and significantly low, allowing to study the vegetation of the dry lake bed. In the first half of the last century, the natural vegetation of the lakeshore (including salt steppes) was studied, but there are very few records of the pioneer species of the lake bed. The salt steppes around the lake have now largely declined, but due to low water levels, some of their characteristic species are still present in the lake bed (though, usually in low number of individuals). Examples include Crypsis aculeata, Cyperus pannonicus, Suaeda pannonica, the once abundant Suaeda prostrata, and the previously rare Crypsis schoenoides and Spergularia salina. Spergularia maritima was once widespread but currently not found in the lake bed. Although, Schoenoplectus litoralis is not a member of the ephemeral vegetation, but is a characteristic plant of the littoral zone of Lake Velence. This species probably appeared therein the middle of the last century and is currently spreading.
Az apró tyúktaréj (Gagea minima) előfordulása és élőhelyi viszonyai a Sorok-patak mentén (Vas megye)
Authors: Schmidt Dávid
Published: December 2023

Gagea minima (L.) Ker Gawl. is a Eurasian bulbous species. It occurs sporadically in Hun­gary, but so far only one current occurrence is known from the Western Transdanubia. In recent years, a large metapopulation has been found in a 15 km long section of the Sorok stream. Its habitat is the high sandy bank above the stream, where it lives mostly in nitrophilous vegetation under non-native tree species. Its long-term persistence in this secondary habitat may have been facilitated by its bulbs, which may have been regularly dispersed by periodic disturbance. Myrmecochory may also have played a role in the local establishment of subpopulations. A number of recently found populations indicate that data on the distribution of the species is still incomplete. The lack of data is probably due to its short flowering period and relative inconspicuousness. Gagea minima is considered a valuable relict species in the altered environment of the Sorok stream and its surroundings.
Astragalus vesicarius és más új elemek a Vértes flórájához
Authors: Bauer Norbert
Published: December 2023

A conservation survey on the condition of steppe and forest-steppe habitats in the Vértes Mts revealed several plant species from the flora of the area that have not been reported before. From a phytogeographical point of view, the most important is Astragalus vesicarius, whose small population in the Vértes fits very well into the native distribution pattern of the species in Hungary. Another important steppe plant is Stipa dasyphylla, which is found in two locations in the Vértes Mts. Besides other taxa of conservation interest (Orobanche teucrii, Phelipanche purpurea, Rosa hungarica), some under-recorded species (Cerastium tenoreanum, Stellaria pallida), and some spreading weeds (Elymus elongatus, Euphorbia lathyris) enrich our knowledge of the flora of the area.
Disabled People and the Intersectional Nature of Social Inclusion
Authors: Buettgen Alexis , Fontes Fernando , Eriksson Susan
Published: December 2023

This editorial introduces a thematic issue of Social Inclusion focusing on disabled people and the intersectional nature of social inclusion. This thematic issue includes transnational and transdisciplinary studies and expressions of lived experiences facing disabled people, their families, and allies across the globe from a social, human rights, and/or disability justice perspective. The articles comprising this issue include an explicit recognition and discussion of intertwined and socially constructed identities, labels, power, and privilege as explicated by pioneering Black feminists who introduced the concept of intersectionality. Taken together, the articles within this issue identify and articulate the powerful ideological forces and subsequent policies and practices working against transformational action. As such, we are not calling for the inclusion of disabled people into society as it is today—wrought with social, economic, and environmental crises. Rather, we seek a transformation of the status quo whereby disabled people are respected as an inherent part of human diversity with gifts and worthiness untangled from a capitalist and colonial system of exploitation, extraction, and oppression. This means that achieving social justice and inclusion requires radically reordering our economic and political systems. This thematic issue illuminates the impacts and root causes of exclusion to foment critical thinking about the possibilities for social inclusion from the perspective of those who are marginalized by the status quo.
A Security Model for the Classification of Suspicious Data Using Machine Learning Techniques
Authors: Boussi Grace Odette Et al.
Published: December 2023

Cybercrime first emerged in 1981 and gained significant attention in the 20th century. The proliferation of technology and our increasing reliance on the internet have been major factors contributing to the growth of cybercrime. Different countries face varying types and levels of cyber-attacks, with developing countries often dealing with different types of attacks compared to developed countries. The response to cybercrime is usually based on the resources and technological capabilities available in each country. For example, sophisticated attacks involving machine learning may not be common in countries with limited technological advancements. Despite the variations in technology and resources, cybercrime remains a costly issue worldwide, projected to reach around 8 trillion by 2023. Preventing and combating cybercrime has become crucial in our society. Machine learning techniques, such as convolutional neural networks (CNN), recurrent neural networks (RNN), and more, have gained popularity in the fight against cybercrime. Researchers and authors have made significant contributions in protecting and predicting cybercrime. Nowadays, many corporations implement cyber defense strategies based on machine learning to safeguard their data. In this study, we utilized five different machine learning algorithms, including CNN, LSTM, RNN, GRU, and MLP DNN, to address cybercrime. The models were trained and tested using the InSDN public dataset. Each model provided different levels of trained and test accuracy percentages.
Medical Estimating PF Machine Learning and IoT in Melancholy among Diabetic Patients
Authors: Chaitanya Kulkarni Et al.
Published: December 2023

To break down the frequency and related risk elements of sorrow in patients with type 2 diabetes mellitus in the nearby local area and give logical references to clinical anticipation and treatment of diabetes mellitus with wretchedness. Proposed strategies use AI more than 58 patients with type 2 diabetes mellitus were chosen by efficient inspecting, and pertinent surveys examined segment factors and related clinical variables and misery sub-scale (PHQ) was utilized to assess the level of sadness. Social help scale was utilized to survey the patients. SSRS are utilized to assess individual social help levels and lead measurable investigation. After assessment it's seen that among the 58 patients with type 2 diabetes, the rate of consolidated melancholy was 58%; the age, conjugal status, training level, occupation, family ancestry, term of diabetes, entanglements, work out. The distinction in friendly help was genuinely critical. The impacting elements of type 2 diabetes confounded with gloom incorporate age, conjugal status, training level, occupation, and family ancestry, span of diabetes, presence or nonappearance of confusions, exercise and social help. They have a high gamble of muddled sorrow and influence the improvement of diabetes.
Enhancing the Classification of Imbalanced Datasets through the Utilization of the Provisional Clarifier-Alternator (cPAen)
Authors: Sunil Kumar Et al.
Published: December 2023

Warmcomfort data is vital for enhancing heating and cooling system efficiency through ML (ML) models. Yet, these datasets often suffer from severe class imbalance due to subjective feedback. To tackle this issue, we introduce a data augmentation algorithm, Provisional Clarifier-Alternator (cPAen). In our research, we evaluated cPAen using real Warmcomfort data and found it outperformed SMOTE, ADASYN, and cWGAN-GP in terms of F1 scores. Notably, cPAen is over 5 times faster than cWGAN-GP while maintaining high test accuracy.
IoT-Based Environmental Control System for Fish Farms with Sensor Integration and Machine Learning Decision Support
Authors: Dhinakaran Et al.
Published: December 2023

In response to the burgeoning global demand for seafood and the challenges of managing fish farms, we introduce an innovative IoT-based environmental control system that integrates sensor technology and advanced machine learning decision support. Deploying a network of wireless sensors within the fish farm, we continuously collect real-time data on crucial environmental parameters, including water temperature, pH levels, humidity, and fish behavior. This data undergoes meticulous preprocessing to ensure its reliability, including imputation, outlier detection, feature engineering, and synchronization. At the heart of our system are four distinct machine learning algorithms: Random Forests predict and optimize water temperature and pH levels for the fish, fostering their health and growth; Support Vector Machines (SVMs) function as an early warning system, promptly detecting diseases and parasites in fish; Gradient Boosting Machines (GBMs) dynamically fine-tune the feeding schedule based on real-time environmental conditions, promoting resource efficiency and fish productivity; Neural Networks manage the operation of critical equipment like water pumps and heaters to maintain the desired environmental conditions within the farm. These machine learning algorithms collaboratively make real-time decisions to ensure that the fish farm's environmental conditions align with predefined specifications, leading to improved fish health and productivity while simultaneously reducing resource wastage, thereby contributing to increased profitability and sustainability. This research article showcases the power of data-driven decision support in fish farming, promising to meet the growing demand for seafood while emphasizing environmental responsibility and economic viability, thus revolutionizing the future of fish farming.
Learning to Detect Human Emotions in Digital World by Integrating Ensemble Voting Classifiers
Authors: Shyelendra Madansing Pardeshi Et al.
Published: December 2023

Due to the expansion of world of the internet and the quick acceptance of platforms for social media, information is now able to exchange in ways never previously imagined in history of mankind. A social networking site like Twitter offers a forum where people may interact, discuss, as well as respond to specific issues via short entries, like tweets of 140 characters and fewer. Users may engage by utilizing the comment, like and share tabs on texts, videos, images and other content. Although platforms for social media are now so extensively utilized, individuals are creating as well as sharing so much information than shared before, which can be incorrect or unconnected to reality. It is difficult to identify erroneous or inaccurate statements in textual content autonomously and find emotions of people. In this paper, we suggest an Ensemble method for sentiment and emotion analysis. Different textual features of actual and Emotion and sentiment have been utilized. We used a publicly accessible dataset of twitter sentiment analysis that included total 48,247 authenticated tweets out of 23,947 of which were authentic positive texts labeled as binary 0s  and 24,300 of which were  negative texts labeled as binary 1s. In order to assess our approach, we used well-known (ML) machine learning techniques, these are Logistic Regression (LR), AdaBoost, Decision Tree (DT), SGD, XG-Boost as well as Naive Bayes. In order to get more accurate findings, we created a multi-model sentiment and emotion analyzing system utilizing the ensemble approach and the classifiers stated above. Our recommended ensemble learner method outperforms individual learners, according to an experimental study.
Automated Pet Water Consumption Monitoring System
Authors: Sundar S Et al.
Published: December 2023

More than half of all people on the planet voluntarily keep pets as companions, but these animals are starting to strain their owners. While feeding their pets, owners experience a lot of stress. One of the pet feeders that may be managed online through a mobile application is the IoT pet feeder. This paper details an IoT based monitoring system which automates the process of detecting and replacing dirty water in a pet’s bowl in a cheap manner. It also describes methods of tracking the overall water consumption of the pet via the system and monitoring the pet’s health via detection of consumption anomalies.
A New Approach for Product Quality Check Based on Social Networks Opinions Analysis
Authors: Adil Enaanai Et al.
Published: December 2023

In this paper, we aim to enhance the relevance of e-commerce web sites by a prior quality checking of proposed products. This checking is done by analyzing social networks and YouTube videos comments.

To achieve this objective, we have broken down the work into a few steps. The first one consists in scraping text from social networks groups and storing it in a NoSQL graph database. Each scraped word is linked to one or many reactions that are coming from the social network. Therefore, we can utilize the database as a knowledge source that associates each set of terms with a specific type of reaction: positive, negative or neutral. Afterwards, we use a TF-IDF based filtering method to keep only relevant words and eliminate those which are a connected to all reactions. The advantage of this stage is the presence of a knowledge source that can be used for product quality checking.

In the e-commerce web site, data are coming from multiple e-commerce websites. The latter, offer products without quality checking. Concretely, we aim to allow users to check quality by a simple check button which call an implemented web service using human reactions and comments. After evaluating our approach, we have obtained an accuracy of 0,75. This result means that our method gives a three quarter of chance to have a good product.
A Tutorial on Cross-layer Optimization Wireless Network System Using TOPSIS Method
Authors: S. Nithya Et al.
Published: December 2023

Each other, leading to issues such as interference, limited bandwidth, and varying channel conditions. These challenges require specialized optimization techniques tailored to the wireless environment. In wireless communication networks is to maximize the overall system throughput while ensuring fairness among users and maintaining quality of service requirements. This objective can be achieved through resource allocation optimization, where the available network resources such as bandwidth, power, and time slots are allocated to users in an optimal manner. Optimization-based approaches in wireless resource allocation typically involve formulating the resource allocation problem as an optimization problem with certain constraints.. These techniques provide practical solutions with reduced computational complexity, although they may not guarantee optimality. In summary, optimization-based approaches have been instrumental in studying resource allocation problems in communication networks, including the wireless domain. While techniques from the Internet setting have influenced the understanding of congestion control and protocol design, specific challenges in wireless networks necessitate tailored optimization techniques that account for interference, limited bandwidth, and varying channel conditions. power allocation problem in wireless ad hoc networks Cross-layer optimization refers to the process of jointly optimizing the allocation of resources across different layers of wireless networks, the interactions between different layers become more complex due to the shared medium and time-varying channel conditions.  Nash equilibrium, where no user can unilaterally improve its own performance by changing its strategy. Game theory can capture the distributed nature of wireless networks and provide insights into the behavior of users in resource allocation scenarios Additionally, heuristics and approximation algorithms are often employed in wireless resource allocation due to the complexity of the optimization problems involved. In traditional cellular systems, each user is allocated a fixed time slot for transmission, regardless of their channel conditions. However, in opportunistic scheduling. Alternative parameters for “Data rate Ž kbps, Geographic coverage ,  Service requirements , cost ” Evaluation parameter for “Circuit-switched cell, CDPD, WLAN, Paging, Satellite.” “the first ranking training is obtained with the lowest quality of compensation.”
Design of Automated Website Phishing Detection using Sequential Mechanism of RCL Algorithm
Authors: C. Rajeswary Et al.
Published: December 2023

The phishing outbreaks in internet has become a major problem in web safety in recent years. The phishers will be stealing crucial economic data regarding the web user to perform economic break-in. In order to predict phishing websites, many blacklist-based phishing website recognition methods are used in this study. Traditional methods of detecting phishing websites rely on static features and rule-based schemes, which can be evaded by attackers. Recently, Deep Learning (DL) and Machine Learning (ML) models are employed for automated website phishing detection. With this motivation, this study develops an automated website phishing detection using the sequential mechanism of RCL algorithm. The proposed model employs Long-Short-Term Memory (LSTM), Convolutional Neural Network (CNN), and Random Forest (RF) models for the detection of attacks in the URLs and webpages by the similarity measurement of the decoy contents. The proposed model involves three major components namely, RF for URL phishing detection, CNN based phishing webpage detection, and LSTM based website classification (i.e., legitimate and phishing). The experimental result analysis of the RCL technique is tested on the benchmark dataset of Alexa and PhishTank. A comprehensive comparison study highlighted that the RCL algorithm accomplishes enhanced phishing detection performance over other existing techniques in terms of distinct evaluation metrics.
Classification of Cotton Leaf Diseases using Whales Optimization Algorithm based Deep Neural Network
Authors: T. Kalaiselvi Et al.
Published: December 2023

In the realm of agriculture, a current focal point of research revolves around the identification of plant diseases through leaf imagery. Employing image processing techniques for the recognition of agricultural plant diseases holds promise for reducing farmers' reliance on safeguarding their crops. This paper introduces a novel approach to classify cotton leaf diseases, utilizing a Deep Neural Network enhanced by the Whales Optimization Algorithm (WOA). The dataset comprises 10,000 cotton images sourced from Kaggle.com, directly captured from farm fields, covering healthy leaves, bacterial blight, Anthracnose, Cercospora leaf spot, and Alternaria diseases. Preprocessing involves the application of a median filter to eliminate image noise, and for segmenting diseased and normal regions, the Gustaffson-Kessel (G-K) fuzzy clustering method is employed. The WOA-augmented DNN demonstrates its effectiveness in classifying cotton images.
A Neural Radiance Field-Based Architecture for Intelligent Multilayered View Synthesis
Authors: Dhinakaran D. , Sankar S. M. Udhaya , Elumalai G. , Kumar N. Jagadish
Published: December 2023

A mobile ad hoc network is made up of a number of wireless portable nodes that spontaneously come together en route for establish a transitory network with no need for any central management. A mobile ad hoc network (MANET) is made up of a sizable and reasonably dense community of mobile nodes that travel across any terrain and rely solely on wireless interfaces for communication, not on any well before centralized management. Furthermore, routing be supposed to offer a method for instantly delivering data across a network between any two nodes. Finding the best packet routing from across infrastructure is the major issue, though. The proposed protocol's major goal is to identify the least-expensive nominal capacity acquisition that assures the transportation of realistic transport that ensures its durability in the event of any node failure. This study suggests the Optimized Route Selection via Red Imported Fire Ants (RIFA) Strategy as a way to improve on-demand source routing systems. Predicting Route Failure and energy Utilization is used to pick the path during the routing phase. Proposed work assess the results of the comparisons based on performance parameters like as energy usage, packet delivery rate (PDR), and end-to-end (E2E) delay. The outcome demonstrates that the proposed strategy is preferable and increases network lifetime while lowering node energy consumption and typical E2E delay under the majority of network performance measures and factors.
Improving Phishing Website Detection with Machine Learning: Revealing Hidden Patterns for Better Accuracy
Authors: Narayana Garlapati , Manchala Uma Devi , Naresh Usikela , Kiran Saggurthi , Kiran Medikonda Asha , Ch Ravi Kumar
Published: December 2023

Phishing attacks remain a significant threat to internet users globally, leading to substantial financial losses and compromising personal information. This research study investigates various machine learning models for detecting phishing websites, with a primary focus on achieving high accuracy. After an extensive analysis, the Random Forest Classifier emerged as the most suitable choice for this task. Our methodology leveraged machine learning techniques to uncover subtle patterns and relationships in the data, going beyond traditional URL and content-based restrictions. By incorporating diverse website features, including URL and derived attributes, Page source code-based features, HTML JavaScript-based features, and Domain-based features, we achieved impressive results. The proposed approach effectively classified the majority of websites, demonstrating the efficiency of machine learning in addressing the phishing website detection challenge with an accuracy of over 98%, recall exceeding 98%, and a false positive rate of less than 4%. This research offers valuable insights to the field of cyber security, providing internet users with improved protection against phishing attempts.
A Smart Waste Management System Framework Using IoT and LoRa for Green City Project
Authors: Laha Suprava Ranjan , Pattanayak Binod Kumar , Pattnaik Saumendra , Kumar Saurav
Published: December 2023

Waste management is a pressing concern for society, requiring substantial labor resources and impacting various social aspects. Green cities strive for achieving a net zero-carbon footprint, including efficient waste management. The waste management system deals with three problems that are interrelated: a) the timely checking of the status of bins to prevent overflow; b) checking the precise location of bins; and c) finding the optimal route to the filled bins. The existing systems fail to satisfy all three problem areas with a single solution. To track the overflow of the bin, the proposed model uses ultrasonic sensors, which are complemented with LoRa to transmit the exact location of the bins in a real-time environment. The existing models are not that efficient at calculating the exact bin-filled status along with the precise location of the bins. The Floyd-Warshall algorithm in the proposed model optimizes waste collection using the Floyd-Warshall algorithm to determine the shortest path. Leveraging low-cost IoT technologies, specifically LoRa modules for data transfer, our solution offers benefits such as simplicity, affordability, and ease of replacement. By employing the Floyd-Warshall algorithm with a time complexity of O (n^3), our method efficiently determines the most optimal waste pickup route, saving time and resources. This study presents a smart waste management solution utilising Arduino UNO microcontrollers, ultrasonic sensors, and LoRaWAN to measure waste levels accurately. The proposed strategy aims to create clean and pollution-free cities by addressing the problem of waste distribution caused by poor collection techniques.
Trusted Knowledge Infusion Model-based Recommender System for IoT based B2B applications
Authors: Partibha Ahlawat Et al.
Published: December 2023

The exponential growth and usage of Internet, social sites, and e-commerce results extensive amount of information everywhere. It becomes very difficult for the people to search something, so they go for the offered suggestions which are pertinent for them rather than searching from a potentially overwhelming number of options. Recommender Systems are the solutions for such difficulties. There are many powerful Recommender Systems available for e-commerce, websites, books, tourism, and documents, but recommendations for IoT-based applications need of new discoveries. Traditional recommendations methods are not sufficient for big data based scalable and heterogeneous IoT environment. In this paper, we propose a knowledge infusion model-based hybrid recommendation model for IoT-based B2B applications. The proposed model is analyzed with a real dataset, and the evaluation represents that model performs well in terms execution time, RMSE, precision and F1-score as compared with the existing models.
Implementation of Synthesize GAN Model to Detect Outlier in National Stock Exchange Time Series Multivariate Data
Authors: Swati Jain Et al.
Published: December 2023

This research work explores a novel approach for identifying outliers in stock related time series multivariate datasets, using Generative Adversarial Networks (GANs). The proposed framework harnesses the power of GANs to create synthetic data points that replicate the statistical characteristics of genuine stock related time series. The use of Generative Adversarial Networks to generate tabular data has become more important in a number of industries, including banking, healthcare, and data privacy. The process of synthesizing tabular data with GANs is also provided in this paper. It involves several critical steps, including data collection, preprocessing, and exploration, as well as the design and training using Generator and Discriminator networks. While the discriminator separates genuine samples from synthetic ones, the generator is in charge of producing synthetic data. Generating high quality tabular data with GANs is a complex task, but it has the potential to facilitate data generation in various domains while preserving data privacy and integrity. The results from the experiments confirm that the GAN framework is useful for detecting outliers.  The model demonstrates its proficiency in identifying outliers within stock-related time series data. In comparison, our proposed work also examines the statistics and machine learning models in related application fields.
Determinants of Family Empowerment in Utilizing Health Services in Kupang, Indonesia: A Cross-Sectional Study
Authors: Seran Agustina A. , Tat Florentine , Irfan Irfan , Krisyudhanti Emma , Rogaleli Yuanita Clara L. , Ludji In Suryani
Published: December 2023

Complications during pregnancy, childbirth, and postpartum are responsible for maternal deaths, however, childbirth in health facilities is far from the Strategic Plan target (88.9%). The majority of Timorese people still live with their parents, so care decisions depend on the parents/relatives. There is a tradition of "Se'i-tatobi), namely that mothers and babies will bathe in hot water and have a fire during the postpartum period. Apart from that, Timorese society is a patriarchal society where women do not have a significant position which can be seen from the weak position of women relative to men. Method. This research uses mixed method research, this research looks for the influence between variables, carried out with a cross-sectional research design. The research target population is families (mother-in-law, biological mother, or other relatives involved) who have pregnant women in the third trimester of pregnancy in Kupang Regency. The number of puskesmas in Kupang Regency is 26 puskesmas. The sample required is relatively large, the minimum required is up to 50 data samples for a predictor (independent) variable. It can select relationships because it uses a non-linear log transformation approach to predict odds so it uses a logistic regression analysis test. Result. Based on the data above, it shows that of the ten independent variables, there are 2 variables, namely parity and husband's job, which do not affect family empowerment, while the variables of mother's age, mother's education, husband's age, husband's education, place of birth of last child, helper, access, birth status are significant. influencing family empowerment. Conclusion Based on the data above, the mother's age, mother's education, husband's age, husband's education, place of birth of the last child, helper, and access also show family empowerment which significantly influences the family's ability to use it.
Effect of high light conditions on the response of Arabidopsis thaliana plants with suppressed mitochondrial alternative oxidase
Authors: Garmash Elena V. , Yadrikhinskiy Kirill V. , Shelyakin Mikhail A. , Belykh Elena S.
Published: December 2023

BACKGROUND: Plants as sessile organisms have developed biochemical pathways to protect themselves from the excess light energy. Mitochondrial alternative oxidase (AOX) participates in the oxidation of reductants exported from chloroplasts, thereby optimizing photosynthesis and protecting cells from photodamage.
AIM: The effect of high light on respiration and the relative transcripts content of a number of genes in Arabidopsis thaliana plants of the T-DNA insertional line for AOX1a (aox1a) was studied and compared with the response of the antisense silencing of AOX1a line (AS-12) and wild type line Col-0.
MATERIALS AND METHODS: Four-week-old A. thaliana plants of three lines grown at 90 µmol/m2 · s and then exposed to moderately high light conditions, 400 µmol/m2 · s, in a short-term experiment (8 h). Respiratory pathways activity, gene expression, and superoxide anion content were determined during experiment.
RESULTS: Plants of the aox1a line in response to high light were characterized by the absence of the total and alternative respiration reaction and the absence of the AOX1 protein in spite of the increased mRNA level of AOX1c, in contrast to the Col-0 and AS-12 lines. Also, an increased content of transcripts of only SAPX and CHS were found, while in the other lines a compensatory increase in the expression of many “defense” genes was revealed.
CONCLUSIONS: Thus, the aox1a line was characterized by a low compensatory effect at the level of defense systems activation. This is apparently caused by the absence of the AOX1 protein and, as a result, the weakening of the stress signal and stress response. The results obtained indicate the important role of AOX in the response of respiration to light stress; can be used to study the signaling pathways of regulation of AOX1a expression.
Senescence metabolomics of Nicotiana tabacum L. VBI-0 heterotrophic suspension cultures
Authors: Puzanskiy Roman K. , Kirpichnikova Anastasiia A. , Shavarda Alexey L. , Yemelyanov Vladislav V. , Shishova Maria F.
Published: December 2023

BACKGROUND: Heterotrophic cell cultures are widely used as a model in plant biology. During a culture cycle the composition of the medium changes: the sucrose and other substrates are depleted, metabolism products are accumulated and the density increases. Finally, arrest of a growth is followed by cell death in a short time. These processes are accompanied with physiological alterations, corresponding to senescence.
AIM: To resolve metabolic features of tobacco cells in growing and stationary senescent suspension cultures VBI-0.
MATERIALS AND METHODS: Nicotiana tabacum VBI-0 cells were cultured in suspension MS medium supplied with 3% sucrose. Cells were sampled at 7th day, during intensive growth, and at 28th day, when the culture was in the stationary phase. The GC-MS method was used to profile the metabolites.
RESULTS: Sucrose depletion in media caused starvation of heterotrophic tobacco cell culture and was associated with a decrease in the accumulation of free amino acids. At the same time, the level of pentoses and complex sugars, including sucrose, increased, while the levels of glucose and fructose were not changed significantly and levels of hexose phosphates decreased. During culture senescence cells showed higher levels of accumulation of malate, pyruvate and some other carboxylates.
CONCLUSIONS: The metabolomic data indicate that culture senescence was associated with a drop in amino acids metabolism, a decrease in the activity of the upper part of glycolysis, and the accumulation of complex sugars, pentoses and carboxylates.
Expresiones culturales tradicionales, Derecho de autor y cultura: un necesario diálogo
Authors: Vargas-Chaves Iván , Dermer-Wodnicky Miriam
Published: December 2023

El presente artículo aborda la problemática de la visión tradicional e instrumental que tiene el Derecho de autor desde la óptica de los derechos de explotación económica, la cual limita la protección del conjunto de conocimientos, prácticas, expresiones y elementos que son propios de la cultura de los pueblos indígenas. De este modo, se plantea un escenario el cual busca articular las tensiones y puntos en común entre el régimen del Derecho de autor, la cultura y las expresiones culturales tradicionales. Para lograr este objetivo, la metodología escogida por los autores contempló un enfoque de investigación jurídica aplicada sobre el fin de las normas, así como de investigación descriptiva a partir de técnicas de análisis sintético sobre teorías contemporáneas de la cultura. Como resultado se logró demostrar que existen limitaciones en el ordenamiento jurídico para normativizar la cultura, además de una falta de comprensión en la regulación de las dinámicas propias de la misma. En este sentido, se hace nece­sario redimensionar la relación entre c­ultura y derecho, contemplando primero sus tensiones y ambigüedades, con el fin de trazar una hoja de ruta que logre abarcar todas las expresiones culturales como parte de un único interés jurídico tutelado.
Diagnosis, Treatment, and Prevention of Iatrogenic Injury in the Lower Part of Common Bile Duct
Authors: Bai Ling , Jin Cheng , Wang Shuangquan
Published: December 2023

Iatrogenic bile duct injury is one of the important problems in abdominal surgery and its incidence has shown an upward trend in recent years. Iatrogenic bile duct injury threatens the quality of life and life safety of patients. At present, there is a lack of diagnostic and treatment norms that have a guiding significance based on large-scale evidence-based medicine research. The injury to the lower part of the common bile duct, especially the joint of the biliary and pancreatic intestines, generally is more serious. This article introduces the current status, classification, common causes, diagnosis, treatment, and prevention regarding the iatrogenic injury in the lower common bile duct. A deeper understanding of this injury may help minimize the incidence of injury, standardize diagnosis and treatment and improve the prognosis of patients.
The Prevalence of Hepatitis B and C among the Passively Screened Population and Blood Donors in Haryana, India: A Retrospective Analysis
Authors: Parveen Malhotra  , Vani Malhotra  , Usha Gupta  , Paramjeet Singh Gill  , Pushkar  , Yogesh Sanwariya
Published: December 2023

Background. Hepatitis B and C are significant problems in India, especially in certain hotspots such as Haryana state. Since the data derived from the large-scale studies are unavailable, reports from individual centers or blood bank data are largely relied on to get a glimpse of the prevalence rate in India. This retrospective study aims to estimate the prevalence of hepatitis B and C based on the data collected from blood banks and passive screening of patients at PGIMS, Rohtak. Methods. The first half of the dataset which contains routine hepatitis B and C test results was derived from blood banks. The other half of dataset was derived from the passive screening of patients from various outpatient departments and inpatient wards. The samples collected during the passive screening were tested for the positivity for HBsAg or anti-HCV antibody using ELISA tests. Results. Analysis of the blood bank data revealed that the prevalence rates of HBsAg and anti-HCV antibody positivity were 0.80% and 0.81%, respectively, whereas the rates of the same derived from passive screening data were 5.23% and 5.18%, respectively. Conclusion. The blood bank data did not depict the exact prevalence of hepatitis B and C. Concerns remain as they could possibly underestimate the rates. As opposed to that, the prevalence rates of passive screening data might possibly resemble to the genuine prevalence. Although it is sometimes inevitable to rely on blood bank data due to data paucity, cautiousness must be exercised when it comes to using this kind of data.
Conservative management in case of placenta accreta followed by hysterectomy
Authors: Nagraj Devina
Published: December 2023

With a rise in number of caesarean sections we are also seeing an increase in the adherent placenta that form a part of placenta accreta spectrum. Early identification and appropriate management and monitoring is very important in these patients as they can have hemorrhage that can cause significant maternal and fetal morbidity and mortality. This is a case of conservative management of a woman with placenta accreta spectrum, use of uterine artery balloon ligation to decrease the intra-operative blood loss, leaving placenta in situ, methotrexate therapy and follow up of the patient that ultimately led to hysterectomy. It highlights the importance of managing these women in adequately equipped center with the required expertise and intensive care support and multidisciplinary management to improve patient care leading to good maternal and fetal outcome.
Correlation of intraoperative squash smear and histopathology in central nervous system lesions: A 6-year retrospective study in a tertiary care hospital in Kerala
Authors: Chimmen Jeena V , Hasna KV Ayisha , Sebastian Angela Ann , Mathew Jassal , Nair Vismaya
Published: December 2023

Intraoperative squash smear cytology is considered to be of great value in intraoperative consultation of CNS pathology. It is a simple and reliable tool for rapid intraoperative diagnosis of CNS tumors.

The study was undertaken to assess the accuracy of intraoperative squash smear in the diagnosis of CNS lesions and to correlate the squash cytology diagnosis with histopathological diagnosis taking the latter as the gold standard.

A retrospective study of 38 cases of CNS lesions from which samples were collected by surgery or biopsy for intraoperative consultations and histopathological diagnosis. Radiologically confirmed cases were only included in the study. Smears were prepared from the biopsy sample obtained at the time of operation and were stained with hematoxylin and eosin. Remaining tissue submitted for histopathology was later correlated with intraoperative cytology diagnosis.

Out of 38 cases, histopathological diagnosis was compatible with cytological diagnosis in 36 cases, two cases showed discrepancies. The diagnostic accuracy of squash cytology was 94.73%. Even though two cases were diagnosed differently in cytology, it was proved to be a malignant tumor both in squash cytology and histopathological diagnosis. Hence sensitivity, specificity, positive predictive value and negative predictive value in detecting the neoplastic condition on cytology was 100% respectively.

Squash smear is a rapid, self-sufficient and cost effective method for the intraoperative diagnosis of CNS tumors. It can be considered as a mirror image to histopathological diagnosis and is of great value in intraoperative consultation of CNS pathology.
Incidental association: Acute myeloid leukemia and filariasis - A case report
Authors: Sampat Nakul Y , Valand Arvind Govind , Prajapati Rakshak Dinesh , Khan Pervaiz Ahmed , Wagh Pradeep
Published: December 2023

Acute myeloid leukemia (AML) is a hematological malignancy characterized by the abnormal proliferation of myeloid cells in the bone marrow. Symptoms include fatigue, frequent infections, easy bruising or bleeding, and shortness of breath. Filariasis is a parasitic infection caused by filarial worms (a nematode) transmitted through mosquito bites from infected individuals, leading to chronic lymphatic dysfunction, lymphedema, hydrocele, and elephantiasis. Here, we present the case of a 38-year-old male who was diagnosed with acute myeloid leukemia without maturation (M1) and an incidental finding of microfilariae in a peripheral blood smear. Interestingly, there was no accompanying eosinophilia in this case. This coexistence of filariasis and AML poses diagnostic challenges, as eosinophilia may not be a reliable indicator. Treatment options include Diethylcarbamazine, Ivermectin, Albendazole and Doxycycline for filarial and chemotherapy and stem cell transplantation for AML (M1). The prognosis for the patient is poor.
Analysis of Factors Affecting Public Interest in Investing in the Capital Market
Authors: Fajriyah Anis , Barokah Siti
Published: December 2023

This study aims to determine the factors that influence public interest in investing in the capital market in the people of Semarang Regency, Indonesia. This study uses a quantitative approach. The research sample is the community or residents of Semarang as many as 62 people. The instrument used is a questionnaire. Data processing using SPSS program. The method of analysis with multiple linear regression model. In this research, the data analysis technique and variable measurement used validity test, reliability test, and classical assumption test. The results of the research test are that the variables of knowledge, income, and motivation have a significant positive effect on people's interest in investing in the capital market. While the risk variable has no significant effect on public interest in investing in the capital market.Keywords: Intention, knowledge, income, motivation, risk, investment
Identification of AHNAK expression associated with the pathogenesis of chronic obstructive pulmonary disease by bioinformatic analysis
Authors: Zhang Chunhui , Liu Yu
Published: December 2023

Background: Chronic obstructive pulmonary disease (COPD) was a risk factor for lung cancer tumorigenesis. This study aimed to discover novel diagnostic biomarkers for COPD patients and determine their underlying pathogenetic mechanisms. Materials and methods: Differentially expressed genes (DEGs) in COPD samples and normal controls were analyzed and utilized to construct a network associated with a high risk for COPD occurrence. Enrichment analysis was applied on the strength of Gene Ontology (GO) annotations and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis. The RT-qPCR analysis was performed to determine 10 hub genes in COPD. ELISA assay was utilized to measure IL-1β, IL-6, and IL-10 levels. Spearman’s correlation analysis was conducted to detect the correlation between inflammatory cytokines and AHNAK expression. Cell proliferation and apoptosis were evaluated by CCK-8 and flow cytometry assays. Results: AHNAK was significantly increased in COPD serum samples compared with non-COPD smokers and strongly correlated with inflammation. AHNAK level could also discriminate COPD from non-COPD with high accuracy. Conclusion: AHNAK may be a feasible biomarker playing crucial functions in the diagnosis and progression of COPD.
The cadherin protein CDH19 mediates cervical carcinoma progression by regulating AKT/NF-κB signaling
Authors: Yu Jia , Sun Xin , Yu Yani , Cui Xiaorong
Published: December 2023

The cell adhesion protein cadherin 19 (CDH19) has been reported to be involved in various types of cancer, but its role in cervical carcinoma remains unknown. We collected and analyzed the patients’ data using the GEPIA Kaplan-Meier plotter databases. CDH19 was overexpressed in cervical carcinoma cells to assess its effect on cell proliferation and activation of AKT and NF-κB signaling pathways. A xenograft mouse model was established to study the function of CDH19 in vivo. We found that CDH19 expression was significantly downregulated in cervical carcinoma tissues compared to adjacent normal tissues. Patients with high expression of CDH19 had a significantly better overall survival rate than those with low CDH19 expression. CDH19 expression was negatively correlated with the expression of the proliferation marker Ki-67, and overexpression of CDH19 significantly inhibited cervical carcinoma cell proliferation. Furthermore, overexpression of CDH19 suppressed the activation of the AKT and NF-κB signaling pathways, and CDH19-overexpressing cervical carcinoma tumors exhibited significantly slower growth in vivo. CDH19 plays an important role in cervical carcinoma by suppressing both cell proliferation and the activation of AKT and NF-κB signaling pathways. Therefore, CDH19 may be a potential therapeutic target for cervical carcinoma.
Indoor Environmental Quality, Climate Change, and Life Quality
Authors: Madvari Rohollah Fallah
Published: December 2023

The Article Abstract is not available.
Domino Effects in Process Industries
Authors: Laal Fereydoon
Published: December 2023

The Article Abstract is not available.
Hand dermatitis, Prevalence and Risk Factors among Healthcare Workers during the COVID-19
Authors: Motamedrezaei Omolbanin , Lotfi Hamed , Jahani Farnaz , Sharifzadeh GholamReza , Rajabipour Hadis , Laal Fereydoon
Published: December 2023

Background: Healthcare workers (HCWs) need to perform new preventive measures to protect themselves and patients against ongoing COVID-19 transmission, which can increase hand dermatitis (HD) among them. This study aims to investigate the prevalence of HD among HCWs and its possible risk factors in IRAN.
Methods: A survey of 159 HCWS working in university hospitals was performed from August to September 2020 in a cross-sectional study. Research data were collected via standardized self-administered questionnaire. The history of HD was determined via standardized Nordic Occupational Skin Questionnaire (NOSQ-2002).
Results: The prevalence of HD in the study population was 51.6%. A significant association was found between the prevalence of HD among HCWs and sex, age, working hours per week, and wearing gloves (P<0.05).  Females had a 3.84 fold higher risk of HD than males (confidence interval (CI): 1.85-8). HCWs older than 40 and those who aged 30-39 had a 9.6 and 1.72 fold higher risk of HD than those aged 20-29 (CI: 2.6-35.7; CI: 0.87-3.4, respectively).
Conclusion: Possible risk factors for developing HD among HCWs are female gender, age, wearing gloves, and fewer working hours per week. Preventive measures for HD are needed for HCWs, especially during the COVID-19 pandemic.