bluuebunny/crossref_metadata_2025 huggingface dataset:
Quantum Approximate Optimization Algorithms for Maximum Cut on Low-Girth Graphs
Authors: Li Tongyang , Su Yuexin , Yang Ziyi , Zhang Shengyu
Published: February 2025

Maximum cut (MaxCut) on graphs is a classic NP-hard problem. In quantum computing, Farhi, Gutmann, and Goldstone proposed the Quantum Approximate Optimization Algorithm (QAOA) for solving the MaxCut problem. Its guarantee on cut fraction (the fraction of edges in the output cut over all edges) was mainly studied for high-girth graphs, i.e., graphs with only long cycles. On the other hand, low-girth graphs are ubiquitous in theoretical computer science, including expander graphs being outstanding examples with wide applications in theory and beyond. In this paper, we apply QAOA to MaxCut on a set of expander graphs proposed by Mohanty and O'Donnell known as additive product graphs. Additionally, we apply multi-angle QAOA (ma-QAOA) to better utilize the graph structure of additive product graphs in ansatz design. In theory, we derive an iterative formula to calculate the expected cut fraction of such graphs. This formula also extends to the quantum MaxCut problem. On the other hand, we conduct numerical experiments to compare between best-known classical local algorithms and QAOA with constant depth. Our results demonstrate that QAOA outperforms the best-known classical algorithms by 0.3% to 5.2% on several additive product graphs, while ma-QAOA further enhances this advantage by an additional 0.6% to 2.5%. In particular, we observe cases that ma-QAOA exhibits superiority over best-known classical algorithms but QAOA does not. Furthermore, we extend our experiments to planar graphs such as tiling grid graphs, where QAOA also demonstrates an advantage.
A content analysis of how biology textbook tasks address system thinking skills in the context of the carbon cycle
Authors: Düsing Katharina
Published: February 2025

Understanding the global cycling of carbon is critical to participate in the current public discussion on climate change in an informed manner. Fostering system thinking is key to reach this goal. Of the various influences on students' development of system thinking in relation to the carbon cycle, the present study focuses on the influence of textbooks – both their explanatory text and the activities they provide for students. Research-based information pertaining to ways students work on acquiring system thinking skills when solving textbook tasks on the carbon cycle is scarce. Therefore, this study investigates two research questions: (1) Which system thinking skills are addressed in German biology textbook tasks on the carbon cycle? and (2) Which entities and activities from different levels of biological organization do students deal with when working on the tasks? The second research question relates to the argument that the multi-level character of the carbon cycle causes particular learning difficulties for students. Following a qualitative content analysis, two coding schemes are developed and applied. The main results: the system thinking skills “identifying system organization” and “analyzing system behavior” are more frequently addressed than “system modeling”. The selection of entities and activities of the carbon cycle in the tasks depicts a variety of individual aspects but often does not allow a continuous tracing of dynamics in the carbon cycle along carbon flows. We suggest a greater emphasis on systems modeling in carbon cycle tasks and that the selection of entities and activities should support knowledge integration.
Enhancing Boro Rice Productivity and Profitability in Flash Flood-Prone Haor Areas of Bangladesh: The Influence of Planting Times, Varieties, and Management Practices
Authors: Akter Nurunnaher , Islam Md. Rafiqul , Karim Md. Abdul , Miah Md. Giashuddin , Rahman Md. Mizanur , Ahmed Sharif
Published: February 2025

Flash floods in the Haor regions of Bangladesh frequently destroy Boro rice crops at maturity or near maturity, leading to farmers' reluctance to invest adequately in cultivation, resulting in poor crop management and reduced productivity. However, adopting improved agricultural practices, such as selecting suitable rice varieties and optimizing planting schedules, can significantly enhance Boro rice yields and mitigate the impact of flash floods. An on-farm experiment was conducted from December 2021 to May 2022 in the flood-prone region of Sunamganj, Bangladesh, using a three-factor split-split plot design with three replications. The study evaluated the effects of sowing dates (15 December and 30 December), rice varieties (BRRI dhan28, a short-duration variety; and BRRI dhan92, a long-duration variety), and management practices (poor, moderate, and good) on Boro rice production. Results indicated that good management practices led to significant yield increases of 22.2% and 55%, along with gross margin improvements of 23.8% and 64.5%, compared to moderate and poor management. The long-duration variety (BRRI dhan92) performed best with early planting, while the short-duration variety (BRRI dhan28) excelled with later planting. Despite the high yield potential of BRRI dhan92, early planting posed a higher risk of flash flood damage at maturity. These findings highlight the importance of strategic cultivar selection, optimized planting times, and improved management practices to maximize Boro rice productivity in flood-prone regions and reduce the risk of crop loss due to flash floods.
Clinical Presentation and Outcome of Hodgkin's Lymphoma in Children: A Retrospective Study
Authors: Dongre Amol , Sood Karan , Dongare Trupti , Arora Brijesh
Published: February 2025

Background
Hodgkin’s Lymphoma (HL) is one of the most curable malignancies of childhood, with chemotherapy alone or in combination with radiotherapy (RT) being the preferred treatment modality, albeit associated with significant acute and late toxicities. Therefore, this retrospective study aims to comprehend clinical-biological differences from the west, analyse local prognostic factors, and evaluate outcomes and toxicity with current therapy.
Methods
This retrospective study included 355 treatment-naive patients with classical HL. All patients received 4–8 cycles of upfront ABVD-based chemotherapy followed by involved field radiotherapy (IFRT) to the bulky/residual sites. Relapsed patients received one of the commonly used salvage regimens (GDP, ICE, or MINE). Analyses were conducted for interim response assessment and end-of-treatment response.
Results
Out of the 355 patients evaluated, 167 presented with advanced-HL (Stage IIBX, III & IV), and 15 of these 167 were ineligible for the final analysis due to various reasons. The median age was 9.7 years, and the male-to-female ratio was 5.9:1. The primary histologic subtype was mixed cellularity. The majority (73%) had stage III disease, with 80 out of 152 (53%) presenting 'B' symptoms and 69 out of 152 (45%) having bulky disease. Extranodal involvement was observed in 26% of patients. The first-line treatment regimen was ABVD for all patients, with IFRT in 68 (44.7%) cases. At the end of therapy, 83% of patients achieved complete remission (CR), while 32 (21%) progressed or relapsed. The median follow-up was 37 months, and the overall survival (OS) and event-free survival (EFS) rates were 83.6% and 78% at three years, respectively. These survival rates were lower than those reported in studies from the west. The CHIPS score was a prognostic factor that evaluated four of the mentioned factors (Stage IV, bulky mediastinal disease, serum albumin, and fever). The event-free survival rate was 83% for patients with a CHIPS score of 0 or 1, 70% for those with a CHIPS score of 2, and 61% for those with a CHIPS score of 3 or 4.
Conclusion
The CHIPS score can predict the outcome of children with advanced HL treated with ABVD-based therapy but requires prospective validation.
Predicting current and future distributions of wild medicinal shrubs Ephedra alata Decne (Ephedraceae) under climate change in Tunisia
Authors: Wibowo Andrio
Published: February 2025

Ephedra alata is a common wild medicinal plant species in Tunisia. The ongoing utilization of E. alata for medicinal applications and uses is threatened by the changing climate, and this can lead to the potential decline of E. alata's geographic distribution range in Tunisia. In this study, we use species distribution modeling to estimate both the current and future potential distribution of E. alata, providing a basis for E. alata suitable habitats across Tunisia. Herein, the future climate model was based on two future representative concentration pathways representing optimistic scenarios (RCP 2.6) and pessimistic scenarios (RCP 8.5) in the years 2070. The mean temperature of the coldest quarter, the temperature seasonality, and the precipitation of the driest quarter, known as bioclimatic variables, contribute significantly to the E. alata distributions. Under an optimistic scenario, the distribution ranges of E. alata remained stable. On the contrary, under a pessimistic scenario in 2070, the distribution ranges of E. alata across governorates in Tunisia will decline, with ranges of 44.17–95.35%, or climate change will cause the disapearance of E. alata. In the future, E. alata will only be available in the western region, where it was previously common in the central and eastern regions.
WITHDRAWN: Knowledge and Practices on Frozen Lamb Handling among the Catering Industry in UAE: A Cross-sectional Study
Authors: saad Fadi , Rozalli Norazatul Hanim Mohd
Published: February 2025

This paper evaluates the food safety knowledge of 628 catering staffs involved in handling frozen lamb across critical stages, including loading, receiving, transportation, storage, thawing, and food safety responsibilities and awareness. A validated and structured questionnaire was used to assess adherence to food safety protocols and common practices in frozen lamb handling. Findings revealed significant associations between job roles and practices in temperature monitoring (p<0.001) and safe thawing methods (p<0.001). While 94.7% of respondents reported receiving food safety training, the results indicated a need for improvement in regular microbial testing during storage. This study emphasizes the importance of continuous training and strict adherence to food safety protocols to maintain product integrity and enhance food safety within the UAE catering industry.
Enhancing Hydroponic Organic Tomato Resilience through Grafting and Bioprotection Strategies
Authors: Dash Prosanta K. , Guo Bing , Leskovar Daniel I.
Published: February 2025

The infestation of major greenhouse pests such as whiteflies, leafminers, and thrips and hot and humid conditions pose significant challenges in controlled environment horticulture, particularly for organic tomato production. Low tomato productivity is attributed to pests and inadequate stress tolerance of existing cultivars, which hinder the ability to optimize fruit set and yield. Although the individual effects of bioprotection and grafting strategies of conventional production systems have been extensively studied, their combined effects in controlled environments have been less explored. This study aimed to assess the efficacy of grafting (‘Maxifort’ × ‘Valdeon RZ’) and bioprotection strategies (yellow sticky traps, Spinosad, and Bacillus thuringiensis) on the mitigation of greenhouse pest infestation and enhancement of the yield of organically grown hydroponic tomatoes in adverse environmental conditions in Qatar. The experimental design used a strip plot with grafted ‘Valdeon RZ’ and nongrafted ‘Valdeon RZ’ as the main plot treatments; Yellow sticky traps, Spinosad, and Bacillus thuringiensis were randomly assigned to the subplots. Tomato cultivar Valdeon RZ grafted on Maxifort exhibited superior seedling quality, as evidenced by increased stem diameters and improved root attributes. Grafted ‘Valdeon RZ’ plants treated with Spinosad demonstrated an enhanced net assimilation rate (27%) and stomatal conductance (17%), as well as reduced transpiration loss (22%) and electrolyte leakage (18%); however, the intercellular CO2 concentration was maintained. Flowering of grafted ‘Valdeon RZ’ plants treated with Spinosad occurred 4 days earlier than that of untreated and nongrafted counterparts. Compared with the control plants, grafted plants treated with Spinosad exhibited superior fruit sets (22%) and pollen viability (18%), as well as fewer incidences of flower drops (28%). Grafted ‘Valdeon RZ’ plants treated with ‘Spinosad’ outperformed the control in terms of marketable fruit yields, with a significantly higher yield (26%). Additionally, fruits collected from grafted plants demonstrated superior postharvest quality, including firmness, soluble solids content, acidity, and color dynamics. Among the bioprotection strategies, Spinosad exhibited superior pest control efficiency, followed by Bacillus thuringiensis and yellow sticky traps. Spinosad-treated plants showed a 40% reduction in leafminer, 28% reduction in whitefly, and 22% reduction in thrips compared with untreated control plants. Our findings can lead to practical strategies that minimize greenhouse pest infestations while improving tomato yield in an organic hydroponic system within a protected environment.
Nitrogen Fertilizer Effects on Hemp Biomass Production Detected by Drone-based Spectral Imaging
Authors: Serrano Tamara , Brym Zachary T. , Monserrate Luis A. , Her Young Gu , Stanford Jill , Bhadha Jehangir H. , Singh Hardeep , Sharma Lakesh K. , Upadhyaya Yogendra R. , Griffin Winniefred D. , Shellenbarger Hayden T.
Published: February 2025

The recent introduction of hemp (Cannabis sativa L., <0.3% Δ9-tetrahydrocannabinol) to the United States has been met with a limited understanding of cultivation practices and environmental monitoring techniques. Aerial imaging can contribute to site-specific management and improve hemp production by monitoring crop growth and health in response to agricultural inputs. Drone-based spectral imaging was deployed with the objective of identifying a spectral signature for floral hemp variety ‘Wife’; detecting differences in plant size, yield, and spectral reflectance in response to nitrogen (N) fertilizer application rates; and assessing field-level variation in crop health and yield observable by aerial imaging to advance hemp management decisions. We conducted an in-field N experiment to test the effects of six N application rates (0, 56, 112, 168, 224, and 280 kg·ha−1) on the high-cannabinoid flower variety ‘Wife’ from 2020 to 2022. A quadcopter with a multispectral sensor collected images from an altitude of 61 m between final fertilization and harvest. Each plant canopy was defined in the image and evaluated for size and reflectance as Normalized Difference Vegetation Index (NDVI) and Green Normalized Difference Vegetation Index (GNDVI). The N application rate of 224 kg·ha−1 N resulted in significant increase in hemp biomass from 112 and 56 kg·ha−1, whereas significant increases in floral yield and canopy area were observed just at 112 and 56 kg·ha−1 N. The N application rate of 224 kg·ha−1 N produced significantly greater NDVI values compared with those of the application rate of 112 kg·ha−1 N, whereas GNDVI was significantly greater at 224 kg·ha−1 N when compared with N rates less than 168 kg·ha−1 N. Canopy area and plant indices consistently showed strong positive linear correlation with aboveground biomass. This study revealed that drone-based aerial imaging can be an effective tool for monitoring crop growth and health while informing N management decisions in hemp cropping systems.
Methyl Isothiocyanate Concentration, Distribution, and Persistence in Sandy Soil as Influenced by Dazomet Post-application Practices
Authors: Polli Estefania G. , Gannon Travis W. , Rogers Ronald R. , LeCompte Mathieu C. , Ahmed Khalied , Silcox Charles
Published: February 2025

Dazomet is a fumigant commonly used to control soil seedbanks and plant tissues of weed species in highly infested turfgrass areas. This fumigant reacts with water in the soil when in the presence of oxygen and releases methyl isothiocyanate (MITC) gas that kills seeds and plant tissues within the soil. Previous studies have reported varying levels of weed control by dazomet. As MITC is highly water soluble, mobile in soil, and volatile, inconsistencies in dazomet efficacy may be related to post-application practices of tilling, rolling, irrigation, and tarping. Therefore, the objective of this study was to analyze the effect of two practices commonly performed following dazomet application: tarp treatment (tilling, rolling, irrigation, and tarping), and water-seal treatment (post-irrigation at 0, 1, 2, and 3 days after application) on MITC concentration, distribution, and persistence in sandy soil. Field studies were conducted at Sandhills Research Station in Jackson Springs, NC, USA, in 2022 and 2023. MITC concentration and persistence varied between treatments and years. In 2022, MITC concentrations were notably higher in the tarp treatment compared with the water-seal treatment, whereas in 2023, the difference between treatments was less pronounced and more soil depth– and sample timing–dependent. Both treatments presented longer persistence, up to 168 hours after application (HAA), in 2023 compared with 120 HAA in 2022. In addition, MITC was highly concentrated in the top 15 cm of the soil and was detected as deep as 31 cm down from the soil surface in both treatments across both years.
Pymablock: An algorithm and a package for quasi-degenerate perturbation theory
Authors: Araya Day Isidora , Miles Sebastian , Kerstens Hugo , Varjas Daniel , Akhmerov Anton R.
Published: February 2025

A common technique in the study of complex quantum-mechanical systems is to reduce the number of degrees of freedom in the Hamiltonian by using quasi-degenerate perturbation theory. While the Schrieffer-Wolff transformation achieves this and constructs an effective Hamiltonian, its scaling is suboptimal, it is limited to two subspaces, and implementing it efficiently is both challenging and error-prone. We introduce an algorithm for constructing an equivalent effective Hamiltonian as well as a Python package, Pymablock, that implements it. Our algorithm combines an optimal asymptotic scaling and the ability to handle any number of subspaces with a range of other improvements. The package supports numerical and analytical calculations of any order and it is designed to be interoperable with any other packages for specifying the Hamiltonian. We demonstrate how the package handles constructing a k.p model, analyses a superconducting qubit, and computes the low-energy spectrum of a large tight-binding model. We also compare its performance with reference calculations and demonstrate its efficiency.
Consumer willingness to adopt digital coupons in post-demonetization and COVID-19 in India
Authors: Anirvinna C. , Jha Deepak , David Goodwin Rapaka , Pokhriyal Deepak
Published: February 2025

This study aims to examine the factors influencing consumers’ willingness to adopt digital coupons in India. It focuses on the impact of two major events: demonetization in 2016 and the COVID-19 pandemic. Together, these events have caused a shift toward digital payments and digital coupons, changing consumer behavior in favor of digital solutions. This study specifically focuses on consumers in Jaipur, both urban and rural, to capture the unique dynamics of this geographical region. In this study, 110 respondents from different demographic groups were given a structured questionnaire. 12 respondents were selected for in-depth qualitative interviews to learn more about the factors that promote and hinder the use of digital coupons. Quantitative data analysis is conducted using SmartPLS 4 software, and the qualitative interview data are analyzed thematically. The regression analysis reveals that convenience and perceived value drives the use of digital coupons, with 75% of respondents reporting their adoption. The findings bring into perspective how the digital consumer landscape of India is evolving and what role incentives play in digital marketing in driving consumer preference and shaping the long-term feasibility of the strategy. The conclusion reinforces that the use of digital incentives for consumers will be influential in choices and underlines the feasibility of digital approaches in the new consumer environment in India.
AcknowledgmentThe authors would like to thank the Deanship of Manipal University Jaipur for supporting this work.
The impact of individual and organizational factors on employee innovative work behavior: Empirical evidence from private companies in Vietnam
Authors: Hai Yen Tran , Tien Minh Chu , Ngoc Diep Nguyen , Dai Long Khuc
Published: February 2025

Employee innovative work behavior plays a vital role in innovation management in private companies, especially in an emerging market like Vietnam. This study investigates the influence of individual factors (including employee creativity and innovative self-efficacy) and organizational factors (constituting innovation climate and organizational support) on innovative work behavior and the impact of innovative work behavior on employee job performance. To test the hypotheses quantitatively, the study uses a two-stage second-order partial least squares structural equation modeling (PLS-SEM) method and a questionnaire-based study with 706 employees from private businesses in Vietnam. The findings indicate that individual factors substantially impact workers’ innovative work behavior (scoring 0.491) compared to organizational factors (scoring 0.395). In addition, all factors, including employee creativity, innovative self-efficacy, innovation climate, and organizational support, positively impact workers’ innovative work behavior. Specifically, innovative self-efficacy exerts the most significant influence on innovative work behavior (with a score of 0.360), followed by organizational support (scoring 0.272) and employee creativity (scoring 0.157). Simultaneously, the innovation climate exerts a minor influence on innovative work behavior, with a score of 0.142. Finally, innovative work behavior directly and positively affects employee job performance, scoring 0.641.
Experimentação e materiais alternativos: um olhar para a formação inicial dos professores de química
Authors:   , Silva Maria Verônica da
Published: February 2025

Na atualidade é possível perceber a necessidade de renovação nas formas de ensinar e aprender o que pode contribuir para a melhoria do ensino. Se tratando do ensino de Química as atividades experimentais apresentam-se como uma metodologia capaz de promover a relação entre teoria e prática, em que os estudantes conseguem perceber vários conceitos difíceis de serem visualizados em aulas estritamente teóricas. Dentro deste contexto os materiais de baixo custo que são empregados em atividades cotidianas podem ser incluídos nos roteiros das aulas, servindo para substituir equipamentos e reagentes bem como contribuindo para que os estudantes visualizem a aplicação dos conteúdos estudados. Apesar das várias discussões existentes sobre a importância de tais recursos, na formação inicial observa-se poucas abordagens quanto a este assunto. Pensando desta forma, este trabalho de dissertação teve como objetivo investigar o que é previsto para os cursos de formação inicial dos professores de Química, no que se refere a experimentação e materiais alternativos, bem como as abordagens e trabalhos feitos sobre esta temática por docentes e discentes. Para isto realizou-se esta pesquisa no Instituto Federal de Educação, Ciência e Tecnologia do Rio Grande do Norte (IFRN) – Campus Apodi, através de quatro etapas: i) realização de um estado do conhecimento referente ao tema de experimentação e materiais alternativos, ii) análise do PPC da Licenciatura em Química com o objetivo de verificar a existência de referências no documento ao tema em estudo, iii) entrevistas individuais com docentes que atuam no curso e iv) entrevistas com grupo focal a uma turma do 8° período do referido curso. Quanto a análise dos dados do estado do conhecimento, PPC do curso e entrevistas foi empregado o método de análise de conteúdo de Bardin (1977). Após finalizada a pesquisa, foi possível perceber que, no que diz respeito ao estado do conhecimento, existem diversas publicações relacionadas ao tema, mas que se limitam a propostas de atividades experimentais, contudo, existem poucas publicações que se referem a atividades que tenha sido aplicada realmente nas escolas. Já no PPC foram encontradas poucas referências ao tema. As entrevistas com os docentes demonstraram que eles reconhecem a importância dos materiais alternativos para a melhoria do ensino de Química, no entanto, nota-se que existem poucos trabalhos que são desenvolvidos no curso relacionados ao tema. Por fim, os discentes demostram também conhecer a temática e já ter desenvolvido atividades a ela relacionadas
Gene Therapy: Transforming the Battle Against Pancreatic Cancer
Authors: Sharma Rohit , Kumar Sourabh , Ghosh Rashmi , Komal Kumari , Kumar Manish
Published: February 2025

Pancreatic cancer remains one of the most aggressive and lethal malignancies, with a
dismal prognosis despite advancements in conventional treatment modalities. Gene therapy has
emerged as a promising approach to combat pancreatic cancer by targeting the underlying genetic
alterations and harnessing the power of the immune system. This review explores the current landscape
of gene therapy strategies for pancreatic cancer, including gene replacement therapy, gene silencing,
immunotherapy enhancement, and oncolytic virotherapy. Gene replacement therapy aims
to restore the function of tumor suppressor genes, such as TP53, while gene silencing targets oncogenes
like KRAS (Kirsten rat sarcoma viral oncogene homolog) to inhibit tumor growth. Immunotherapy
enhancement, particularly through chimeric antigen receptor (CAR) T-cell therapy,
has shown potential in overcoming the immunosuppressive tumor microenvironment. Oncolytic
viruses, engineered to replicate in and destroy cancer cells selectively, have demonstrated efficacy
in preclinical models and are being evaluated in clinical trials. Recent advances, including the successful
treatment of a patient with advanced pancreatic cancer using neoantigen T-cell receptor
gene therapy, highlight the potential of personalized gene therapy approaches. However, challenges
such as precise gene delivery, tumor heterogeneity, and ethical considerations must be addressed
to realize the potential of gene therapy for pancreatic cancer fully. Ongoing research and
clinical trials are expected to facilitate the way for the development of safe and effective gene therapies,
offering hope for improved outcomes in pancreatic cancer.
Formulation, Development and Evaluation of Effervescent Tablet of Green
Tea (Camellia sinensis)
Authors: Kaur Prabhjot , Deswal Geeta , Chopra Bhawna , Kriplani Priyanka , Grewal Ajmer Singh , Dhingra Ashwani K.
Published: February 2025

Background:

Camellia sinensis has an extensive variety of pharmacological properties,
including neuroprotection, photo-aging resistance, stress resistance, antioxidant, anti-tumour, hypoglycemic, antibacterial, and antiviral effects tracing a good potential in addressing illness and
preventing disease. Challenges with conventional dosage forms include patient incompatibility,
improper dosing, the inclusion of microplastics, etc.

Objective:

The present work aims to deliver a novel formulation devoid of microplastics and with
improved consumer compliance

Methods:

Wet granulation was used to formulate 500 mg Camellia sinensis effervescent tablets,
with improved effervescent time and rapid release. Citric acid and sodium bicarbonate's impacts on
disintegration time and pH were examined using a factorial design. Pre-compression variables were
assessed for the granule mixture. Post-compression criteria were employed to assess effervescent
tablets. The optimum formulation was selected using a central composite response surface and assessment criteria

Results:

The physicochemical characteristics of the developed formulations were significantly influenced by the independent factors. Low concentrations of sodium bicarbonate have positive impact on pH whereas high concentrations of sodium bicarbonate as well as citric acid enhance disintegration time. The design outcomes showed that the optimized effervescent tablets (F10) prepared
with 697.5 mg and 448.38 mg of citric acid and sodium bicarbonate respectively had good physicochemical properties.

Conclusion:

In compliance with present quality standards, factorial design was efficiently utilized
for the development of Camellia sinensis effervescent tablets. It was concluded that green tea effervescent tablet (F10) would function as a substitute for conventional green tea powder along with
green tea bags as a means of administration.
The Psychosocial Account of the Social Added Value of Nonprofit and Voluntary Organizations: A Participatory Study
Authors: Tommasi Francesco , Meneghini Anna Maria , Pistoni Carlo , de Cordova Federica
Published: February 2025

Background

The psychosocial perspective of the Social Added Value (SAV) is a valuable account of the social impact of Nonprofit and Voluntary Organizations (NPVOs). This is because it recognizes the economic impacts of NPVOs. However, the existing top-down operationalization of SAV still needs the integration of a bottom-up perspective (i.e., NPVOs’ members' perspective).

Objectives

Following a participatory research approach, the study seeks to complement the existing psychosocial perspective with bottom-up perspectives on SAV.

Methods

The study uses concept mapping, a participatory stakeholder-driven process, to generate a framework of SAV on a sample of NPVO managers (N = 105).

Results

The results of the participatory study reveal that the elements characterizing NPVOs’ SAV act in a circuit of benefits comprising the contextualization of the individual in the community and contributing to the lives of people in and around the community. This bottom-up operationalization of NPVOs’ SAV comprises six main benefits, namely (a) state and territory, (b) care and awareness, (c) create and valorize relationships, (d) community and social promotion, (e) well-being and (f) self-determination, and local activation.

Conclusion

The findings of the study provide indications to complement the psychosocial perspective of SAV and its implications for theory and practice. Notably, these findings appear to reflect the ecological perspective of community psychology. As such, they offer a starting point for tailoring interventions to support and promote communities and NPVOs.
A Method Based on Machine Learning to Classify Text for the Field of Cybersecurity
Authors: Sriram Siddharth
Published: February 2025

Rapid advancements in networks and computer systems have opened a new
door for immoral acts like cybercrime, which threaten public safety, and security, as
well as the global economy. The purpose of this proposal is to analyse IP fraud and
cyberbullying as two distinct types of cybercrime. The primary goals of this study are
to use instances of cybercrime to provide a short examination of cybercrime activities,
and the family member principles, and propose a pairing schema. Using the Naive
Bayes (NB) & Support Vector Machine (SVM) artificial intelligence techniques,
cybercrime instances are categorised according to their ideal qualities. The Twitter data
in the Kaggle database has been clustered using K-means. User ID, sign-up date,
referral, browser, gender, and age as well as IP address are just a few of the most useful
information used to educate the computer. Total 151,113 datasets were used for
experimental analysis of the suggested algorithm's performance. The accuracy of the
suggested approach, 97%, is higher than that of the current method (NB). The
challenge of regression may be easily surmounted with the use of the random forest
method for the categorization of the resultant cybercrimes. The planned study uses age
categories as the foundation for identifying the different offenses.
AI Model for Text Classification Using FastText
Authors: Sharma Sorabh
Published: February 2025

The purpose of text categorization, a machine learning technique, is to
automatically assign tags or categories to texts. Natural language processing (NLP)-
based text classifiers can quickly analyse vast volumes of text and classify it based on
emotions, themes, and human intent. FastText was created by Facebook's AI Research
team and is available to the public as a free library. Its primary goal is the efficient and
accurate processing of big datasets in order to provide scalable remedies for the
problems of text categorization and representation. Traditional machine learning
techniques used in most text categorization models suffer from issues including the
curse of dimensionality and subpar performance. This research offers a fastText-based
AI text classification model to address the aforementioned issues. The fastText
approach allows our model to create a low-dimensional, continuous, and high-quality
representation of text by mining the text for relevant information through feature
engineering. The experiment uses Python to define the text dataset, and the results
demonstrate that our model outperforms the baseline model trained using classic ML
methods in terms of accuracy, recall, and F values.
Classification of Medical Text using ML and DL Techniques
Authors: Mahajan Sulabh
Published: February 2025

The use of sarcasm in everyday conversation has recently risen to
prominence. All the kids of our age utilise sarcasm to convey a negative message in a
more nuanced manner. With the advancement of AI and machine learning techniques
in the area of natural language processing (NLP), it has become more difficult to
reliably and effectively identify sarcasm. This research provides a new method for
sarcasm detection using machine studying and deep learning, hoping to make a
meaningful contribution to this expanding area of study. In order to prepare the phrase
for a hybrid deep learning model for training and classification, this method employs
bidirectional encoder representations from transformers (BERT). The combination of
CNN and LSTM creates the hybrid model used here. The suggested model has been
tested on two datasets, with the goal of identifying sarcasm from nonsarcastic words.
The trained model obtained a 99.63% accuracy rate, a 99.33% precision rate, a 99.83%
recall rate, and an F1-score of 99.56%. These findings are based on 10 rounds of crossvalidation performed on the model that was suggested using the medical datasets.
Evaluation of ML and Advanced Deep Learning Text Classification Systems
Authors: Kapoor Tarun
Published: February 2025

Classifying texts into groups determined by their content is called text
classification. In this process, automatic labelling of documents written in natural
languages is carried out according to predetermined labels. Both text comprehension
systems, which perform transformations on texts such as creating summaries,
answering queries, and extracting data, and text-retrieving systems, which obtain texts
in fulfillment of a user query, rely heavily on text categorization. In order to learn
effectively, current algorithms for supervised learning for text classification need a
large enough training set. This research introduces a novel text categorization
algorithm that uses artificial intelligence techniques (machine studying and deep
learning techniques) and needs fewer documents for training than previous methods.
To generate a feature set from already-classified text documents, we resort to “word
relation,” or association rules based on these words. To classify the data, we use the
idea of a Convolutional Neural Network with Deep Convolution to the extracted
features and then employ a single genetic algorithm approach. The suggested method
has been built and thoroughly tested in a working system. The results of the
experiments show that the suggested system is an effective text classifier.
Textual Classification Utilizing the Integration of Semantics and Statistical Methodology
Authors: Gandhi Ayush
Published: February 2025

Effectively classifying texts is possible using several classification
techniques. Machine learning constructs a classifier by studying and memorising the
characteristics of several classes. For text categorization, deep learning provides similar
advantages since it can function with great precision using simpler architecture and
processing. In order to categorise textual information, this research makes use of
machine learning and deep learning methods. There is a great deal of extraneous details
in textual data that must be removed during pre-processing. To prepare it for analysis,
we remove duplicate columns and impute missing data. In the next step, we use deep
learning techniques for classification, including long short-term memory (LSTM),
artificial neural network (ANN), as well as gated recurrent unit (GRU). According to
the findings, GRU obtains 92% accuracy, which is higher than that of any other model
or baseline investigation.
The Use of Machine Learning Techniques to Classify Content on the Web
Authors: Sharma Dikshit
Published: February 2025

In text categorization, texts are sorted into groups according to their content.
It is the process of automatically classifying texts written in natural languages
according to a set of guidelines. Both text comprehension systems, which perform
transformations on text such as creating summaries, answering queries, and extracting
data, and retrieval of text systems, which collect texts in reaction to a user query on the
internet content, rely heavily on text categorization. In order to learn effectively,
current algorithms for supervised learning for text classification need a large enough
training set. This research introduces a novel text categorization system that makes use
of an AI approach and needs fewer articles for training over information found on the
web. To generate a feature set from already categorised texts, we resort to “word
relation,” or association rules based on these terms. The obtained characteristics are
then processed by a Support Vector Machine, and ultimately, a single genetic algorithm
idea is introduced for classification. The suggested approach has been developed and
validated in a working system. The experimental results verify the effectiveness of the
proposed system as a text classifier.
Method for Adaptive Combination of Multiple Features for Text Classification in Agriculture
Authors: Singh Jaskirat
Published: February 2025

When applying conventional text classification techniques, the values in
agricultural text are converted into characters, which destroys the original semantic
representation of numerical aspects. A unique text classification approach is suggested,
based on the dynamic fusion of several characteristics, to completely mine the deep
latent semantic characteristics in agricultural literature. The global key semantic
characteristics of the text were extracted using the Bi-directional Gated Recurrent
Neural Networks (GRU) model with attention mechanism, while the local semantic
data about the text at various levels was extracted using the multiple windows
Convolution Neural Network. Finally, the number that features essential semantic
expressions was obtained using a machine learning approach for creating the
quantitative value feature vector. To further enhance the deep semantic expression
found in agricultural text and successfully improve the impact of farm text
categorization with phenotypic numerical type, we use a focus technique to
dynamically fuse the derived numerous semantic characteristics.
Domain Knowledge-based BERT Model with Deep Learning for Text Classification
Authors: Kalia Akhilesh
Published: February 2025

Lexical model BERT already trained on BookCorpus and Wikipedia works
well on two NLP tasks after downstream fine-tuning. The requirements of BERT
model include strategy analyses and task-specific and domain-related data. The
problems of task awareness as well as instruction data in BERT-DL, a BERT-based
text-classification model, are addressed through auxiliary sentences. The pre-training,
training, and post-training steps for BERT4TC's domain challenges are all provided.
Learning speed, sequencing duration, and secret state vectors that select fine-tuning are
all investigated in extended trials over 7 public datasets. The BERT4TC model is then
contrasted using a variety of auxiliary terms and post-training goals. On multiple-class
datasets, BERT4TC with the ideal auxiliary phrase outperforms previous state-of-theart feature-based algorithms and fine-tuning approaches. Our domain-related corpustrained BERT4TC beats BERT on binary sentiment categorization datasets.
An Algorithm for Categorizing Opinions in Text from Various Social Media Platforms
Authors: Saini Pavas
Published: February 2025

More individuals are sharing their thoughts and feelings through internet
videos as social media platforms proliferate. While successful emotional fusion in
multimodal data is a key component of multimodal sentiment analysis, most existing
research falls short in this area. Predicting users' emotional inclinations through their
expressions of language is made easier by multi-modal sentiment detection. As a result,
the field of multi-modal sentiment detection has grown rapidly in recent years. As a
result, multimodal sentiment analysis is quickly rising to the forefront of academic
interest. However, in actual social media, visuals and sentences do not always work
together to represent emotional polarity. Additionally, there are several information
modalities, each contributing in its unique way to the overall emotional polarity. A
multimodal approach to sentiment analysis that takes into account contextual
knowledge is presented as a solution to these issues. The approach begins by mining
social media texts for subject information that comprehensively describes the comment
material. Additionally, we use cutting-edge pre-training models to identify emotional
qualities that span several domains. Then, we provide methods for merging features at
different levels, such as cross-modal global fusion as well as cross-modal high-level
semantics fusing. At long last, we run our tests on a multimodal dataset that really
exists in the real world. Results show that the proposed approach can correctly classify
the tone of heterogeneous online reviews, and it also outperforms the standard
approach in many other ways as well.
Classification Algorithms for Evaluating Customer Opinions using AI
Authors: Khurana Saniya
Published: February 2025

There are now a great deal of consumer reviews of items that are written
entirely in text. The reviews express their opinions. Opinion mining is another name for
sentiment analysis. A common way for businesses to keep tabs on how customers feel
about their brands and goods is through the use of sentiment analysis on textual data.
Naive Bayes, Random Forest, Decision Tree, and Support Vector Machine classifiers
are all used and compared in this study. In this study, we evaluate the efficacy of
several classifiers by measuring their ability to correctly categorize mobile product data
sets of varying sizes. Data were collected from popular online retailers like Amazon,
Flipkart, and Snapdeal and analyzed to determine categorization accuracy. Naive
Bayes, Random Forest, Decision Tree, and Support Vector Machines are some of the
categorization algorithms compared here.
A Contrast Between Bert and Word2vec's Approaches to Text Sentiment Analysis
Authors: Nagpal Manish
Published: February 2025

A novel approach to creating a dataset to train a neural network that can
analyze the tone of social media postings is proposed in this study. The paper goes on
to detail how the word2vec and BERT algorithms may be used in a neural network to
analyze social media messages and evaluate their emotional tone. A hybrid of cosine
similarity as well as ontological mappings based on a tweaked version of the Term
Frequency-Inverse Document Frequency (TFIDF) characteristics is used by the
algorithm for semantic searching. The execution includes sentiment analysis, phrase
extraction process, textual belief indicator, keyword-based search, as well as text
summary, among other things. Additionally, trials were carried out proving the efficacy
of the methods presented. The efficiency of stemming and lemmatization of the text in
creating a training set for sentiment analysis was also tested experimentally.
Multimodal Sentiment Analysis in Text, Images, and GIFs Using Deep Learning
Authors: Minhas Deepak
Published: February 2025

More and more people are inclined to use GIFs, videos, and photographs on
social media as a way to convey their feelings and thoughts. We developed a Pythonbased multimodal sentiment analysis tool for various Twitter formats, taking into
account not just the text of a tweet but also its accompanying GIFs and pictures, for
more precise sentiment scoring. We employ fine-tuned CNN for image sentiment
analysis, VADER for text analysis, and image sentiment and facial expression analysis
for GIFs, with each frame individually analyzed. Our research shows that combining
textual and picture data yields superior outcomes compared to models that depend only
on either images or text. The output scores from our text, picture, and GIF modules will
be aggregated to get the final sentiment score for the incoming tweets.
CNN-based Deep Learning Techniques for Movie Review Analysis of Sentiments
Authors: Garg Prateek
Published: February 2025

Twitter, Facebook, Instagram, etc. are just a few of the many online
discussion platforms that have sprung up as a result of the explosion in internet use and
popularity, giving individuals a place to air their views on current events. Films get
both acclamation and criticism from the general public. As a major form of
entertainment, they inspire user evaluations of film and television on websites like
IMDB and Amazon. Scientists and researchers give careful thought to these critiques
and comments in order to extract useful information from the data. This data lacks
organisation but is of critical importance nevertheless. Opinion mining, also known as
sentiment classification, is a growing field that uses machine learning and deep
learning to analyse the polarity of the feelings expressed in a review. Since text
typically carries rich semantics useful for analysis, sentiment analysis has grown into
the most active investigation in NLP (natural language processing). The continuous
progress of deep learning has substantially increased the capacity to analyse this
content. Convolutional Neural Networks (CNN) are commonly utilised for natural
language processing since they are one of the most successful deep learning
methodologies. This paper elaborates on the methods, datasets, outcomes, and limits of
CNN-based sentiment analysis of film critics' reviews.
Sentiment Analysis of Hotel Reviews Based on Deep Learning
Authors: Sohal Jagmeet
Published: February 2025

Many hotel evaluations have been written and shared online these days.
Machine learning sentiment classification requires complicated artificial design
features and feature extraction technique, whereas emotion dictionary-based sentiment
classification requires a large amount of emotional database resources. In this study, we
present the idea of long short-term memory. The text categorization method is used to
determine the general tone. First, the brief comment text is processed into the LSTM
network using word2vec and word segmentation technology; next, a dropout technique
is implemented to avoid overfitting in order to get the final rating model. By using the
LSTM network's superior short-term memory, a positive impact has been realized on
sentiment categorization of reviews of hotels, with a precision of more than 95%.