bluuebunny/crossref_metadata_2025 huggingface dataset:
A novel RGB image steganography algorithm using type-1 fuzzy logic
Authors: Dhaka Navita , Hooda Meenakshi , Yadav Vinita , Gill Sumeet
Published: October 2024

Steganography aims to conceal secret data within images without affecting image quality. Traditional methods often struggle with balancing simplicity, effectiveness and payload capacity while maintaining imperceptibility. Proposed algorithm: the paper proposed a novel steganographic mshEdgeRGB_T1 algorithm that combines Mamdani fuzzy type-1 logic with the least significant bit (LSB) method. The LSB method is chosen for its simplicity and effectiveness in hiding messages. The mshEdgeRGB_T1 algorithm focuses on embedding secret messages in edge pixels, detecting more edge pixels compared to other methods, thus increasing payload capacity. Findings: the algorithm’s performance is evaluated using metrics such as peak signal-to-noise ratio (PSNR), mean squared error (MSE) and histogram analysis to measure the similarity between the cover and Stego images, quantifying the level of imperceptibility. Experimental analysis demonstrates that the mshEdgeRGB_T1 algorithm offers improved payload capacity, enhanced security and reduced imperceptibility compared to many existing methods. Conclusion: the proposed mshEdgeRGB_T1 algorithm effectively balances simplicity, payload capacity and image quality, making it a better use for image steganography.
Attention based English to Indo-Aryan and Dravidian language translation using sparsely factored NMT
Authors: Dwivedi Ritesh Kumar , Nand Parma , Pal Om
Published: October 2024

Neural machine translation (NMT) is a sophisticated technique that employs a large, singular neural network to learn and execute automatic translation tasks. Unlike statistical machine translation systems, NMT handles the entire translation process in an end-to-end manner, removing the need for additional components. This approach has shown significant promise in translation quality and has become the prevalent method. In this study, we apply sparsely factored NMT to English and several Indo-Aryan (Hindi, Bengali) and Dravidian (Tamil, Malayalam) language pairs. Specifically, we develop the machine translation system using an attention-based mechanism. A significant problem with traditional transformers is the huge memory requirement. Therefore, a sparsely factored NMT (SFNMT) is used to reduce the memory requirement but also improves the training time, thereby, reducing the computing time. In this paper, take inspiration from Vaswani transformer and modify it to get the best results. The system’s performance was evaluated using the BLEU metric. The proposed model indtrl achieves a BLUE score of 32.13 (en→hi), 29.31 (en→be), 31.21 (en→ta), 21.12 (en→ml) and 32.67 (en→hi), 29.38 (en→be), 31.75 (en→ta), 21.17 (en→ml) without backtranslation and with backtranslation. To evaluate the performance of the system, we compared the results with those of existing systems. The developed system demonstrated a marginally higher BLEU score than both AnglaMT and Google translate.
A new intensity-modulated radiation therapy with deep learning heart rate prediction framework for smart health monitoring
Authors: Sivalingam Saravanan Madderi , Thisin Syed
Published: October 2024

This research paper monitors the patient’s health using sensor data, cloud, and big data Hadoop tools and used to predict heart attack and related results were discussed in detail. The integration of big data, and wearable sensors in pervasive computing has significantly enhanced healthcare services. This proposal focuses on developing an advanced healthcare monitoring system tailored for tracking the activities of elderly individuals. The wearable sensors are placed on humans at a right angle, left arm, right arm, and chest to collect the data. The large data are split into smaller segments using the map and reduce process of big data Hadoop tools. The intensity-modulated radiation therapy (IMRT) approach is used for the mapping phase and deep convolutional neural network (DCNN), deep belief network (DBN), and long short-term memory (LSTM) and proposed deep learning heart rate prediction (DLHRP) algorithms are used for the combiner/reduce phase. The reduction process combines similar segments of data to predict identical classes to predict the severity of human conditions. The proposed IMRT-DLHRP system has improved performance of 96.34% accuracy compared with 84.25%, 89.47%, and 91.58% compared to DCNN, DBN, and LSTM respectively, therefore proposed framework has significant improvement over existing approaches.
Enhanced SMS spam classification using machine learning with optimized hyperparameters
Authors: Hafidi Nasreddine , Khoudi Zakaria , Nachaoui Mourad , Lyaqini Soufiane
Published: October 2024

Short message service (SMS) text messages are indispensable, but they face a significant issue with spam. Therefore, there is a need for robust models capable of classifying SMS messages as spam or non-spam. Machine learning offers a promising approach for this classification, based on existing datasets. This study explores a comparison of several techniques, including logistic regression (LR), support vector machines (SVM), gradient boosting (GB), and neural networks (NN). Hyperparameters play a crucial role in the performance of these models, and their optimization is essential for achieving high accuracy. To this end, we employ an evolutionary programming approach for hyperparameter optimization. This approach evaluates the performance of these models before and after hyperparameter optimization, aiming to identify the most effective model for SMS spam classification.
Application of data mining for diagnosis of ENT diseases using the Naïve Bayes method with genetic algorithm feature selection
Authors: Wanti Linda Perdana , Adi Prasetya Nur Wachid , Awaludin Ihza , Aditya Saputra Muhammad Bintang , Furi Syamaidzar Nadifa , Dwi Kumara Dimas Maulana
Published: October 2024

Ear, nose, and throat (ENT) disease is a disorder that occurs in the eustachian tube in one of the organs, be it the ear, nose, or throat. Early signs of ENT disease include sore throat, painful swallowing, swollen and red tonsils, runny nose, nosebleeds, blocked nose, discharge from the ears, and others. To determine the diagnosis, it is necessary to carry out a physical examination of the ears, nose, and throat as recommended by an expert, namely an ENT doctor. The research carried out was implementing data mining for the diagnosis of ENT diseases using the Naïve Bayes (NB) method. This method was chosen because it can increase the accuracy, efficiency, and accessibility of health services and is also easy to understand and apply to classify ENT disease symptom data. The NB method was used to build an ENT diagnosis classification model and the model performance was evaluated using accuracy, precision, and recall metrics. To increase the accuracy of the NB algorithm predictions, feature selection using a genetic algorithm can be used. Genetic algorithms can help select the most relevant and significant features, improving the accuracy of NB models by eliminating irrelevant or noisy features. By applying this method, predictions for ENT diseases can be produced with an accuracy of 95.67%.
A hybrid firefly algorithm for the sales representative planning problem
Authors: Bouatouche Mourad , Belkadi Khaled
Published: October 2024

In the rapidly increasing pharmaceutical sector, sales representatives are employed by pharmaceutical manufacturers and distributors to inform and educate physicians. To convince providers to prescribe the medications to their patients, these representatives rely on their product expertise and people’s abilities to close deals. Instead of making direct sales, pharmaceutical sales representatives help medical professionals get the medications, treatments, and information they need to give their patients the best care possible. Furthermore, they inform the public about novel and occasionally life-saving treatments and share interesting medical developments. This study presents a hybrid methodology that integrates the benefits of local search and the firefly algorithm (FA) to determine the optimal planning for a sales representative. The objective is to maximize the rewards while adhering to certain constraints. The objective is to maximize the rewards while adhering to certain limits. Utilizing local search, the hybrid algorithm enhances firefly’s global search behaviour and produces the best possible sales presentation planning. The experimental findings demonstrate the superior performance of the suggested algorithm compared to the FA and other literature methods in the sense of enhancing the convergence rate and preventing local minima. Furthermore, it can enhance the best-known solution for most benchmark instances.
Lung cancer detection using hybrid integration of autoencoder feature extraction and ML techniques
Authors: Lakshmanarao Annemneedi , Gopal Nirmal , Vullam Nagagopiraju , Sridhar Mandapati , Kanth Modalavalasa Krishna , Rayudu Uma Maheswari
Published: October 2024

Lung cancer posed a significant global health challenge, necessitating innovative approaches for early detection and accurate diagnosis. In this paper, CT scan images for lung cancer with three classes namely benign, malignant, and normal are collected from Kaggle. We initially applied conventional machine learning (ML) algorithms including support vector machine (SVM), random forests (RF), decision trees (DT), logistic regression (LR), naive bayes (NB), and k-nearest neighbor for lung cancer detection. The results with these conventional algorithms are recorded. Later, we proposed a novel hybrid model that integrated diverse machine learning algorithms to further enhance accuracy. Our approach combined the power of autoencoders for feature extraction. Using Autoencoder technique, features from images are extracted and a new feature vector is created. Later, the same conventional ML classifiers applied and achieved enhanced performance. The hybrid model demonstrated remarkable performance in identifying lung cancer cases when compared to individual classifiers. Through extensive experimentation, we showcased the efficacy of our integrated framework, achieving high accuracy, precision, recall and F1-score metrics across multiple classifiers. This hybrid approach represented a significant advancement in lung cancer detection, offering a versatile and robust solution for early diagnosis and personalized treatment strategies in clinical settings.
A framework for dynamic monitoring of distributed systems featuring adaptive security
Authors: Periyasamy Sudhakar , Kaliyaperumal Prabu , Alagarsamy Abinaya , Elumalai Thenmozhi , Karuppiah Tamilarasi
Published: October 2024

Distributed systems play a crucial role in today’s information-based society, enabling seamless communication among governmental, industrial, social, and non-governmental institutions. As information becomes increasingly complex, the software industry is highly concerned about the heterogeneity and dynamicity of distributed systems. It is common for various types of information and services to be disseminated on different sites, especially in web 2.0. Since ‘information’ has become a prime tool for organizations to achieve their vision and mission, a high level of quality of service (QoS) is mandatory to disseminate and access information and services over remote sites, despite an unsecure communication system. These systems are expected to have security mechanisms in place, render services within an acceptable response time, dynamically adapt to environmental requirements, and secure key information. This research article proposes a framework for evaluating and determining a threshold up to which distributed systems can collect data to adapt to the environment. The study also proposes a dynamic security metric to determine the level of security disturbance caused by the monitoring system for adaptation and the measures to be implemented. Additionally, the paper details the role of the monitoring system in safeguarding the adaptive distributed system and proposes an adaptive monitoring system that can modify its functionality as per the environment.
Comparative analysis of different types of pulse width modulation techniques for multilevel inverters
Authors: R Palanisamy , Devi M. Nivethitha , T. R Manikandan , Devi K. Mekala , Mahajan Rashima , D Selvabharathi , K Selvakumar
Published: October 2024

Multilevel inverters have gained significant attention in recent years due to their ability to achieve higher voltage and lower harmonic distortion compared to conventional two-level inverters. Pulse width modulation (PWM) techniques play a crucial role in controlling multilevel inverters by generating the required switching signals for their power electronic devices. This paper presents a comprehensive comparative analysis of various PWM techniques employed in multilevel inverters, including sinusoidal pulse width modulation (SPWM), space vector pulse width modulation (SVPWM), carrier-based pulse width modulation (CBPWM), and selective harmonic elimination (SHEPWM). Each PWM technique's advantages, limitations, and suitability for different multilevel inverter topologies are discussed. Furthermore, recent advancements and hybrid PWM techniques are also examined to explore potential improvements in performance and efficiency. This paper aims to provide researchers, engineers, and practitioners with valuable insights into selecting the most appropriate PWM technique for their specific multilevel inverter applications, considering factors such as performance requirements, cost constraints, and ease of implementation.
Optimizing carrier transport properties in the intrinsic layer of a-Si single and double junction solar cells through numerical design
Authors: Prayogi Soni , Hamdani Dadan , Darminto Darminto
Published: October 2024

This research aims to improve the performance of a-Si: H solar cells, particularly in terms of carrier transport properties, through a numerical design approach utilizing AFORS-HET simulation software. By performing a series of rigorous computer simulations, we explore the potential regulation of the intrinsic layer thickness, carrier mobility, loading factor, and density of states (DoS) distribution in the solar cell's intrinsic layer. Recombination losses are reduced, and light absorption efficiency is significantly increased when the intrinsic layer thickness is adjusted, as shown by simulation findings. Moreover, reduction of transit times and enhancement of the total efficiency of the solar cells depend on increased carrier mobility. Parameters can be adjusted to attain optimal performance under various operating situations by adjusting the DoS and load factors. Furthermore, the simulations provide insightful information about the interactions between the junctions in solar cells with double junctions. Our results of this research provide an important contribution to efforts to develop more efficient and sustainable a-Si: H solar cells and emphasize the importance of numerical design approaches in photovoltaic technology.
Immune-Mediated Inflammatory Diseases, Dyslipidemia, and Cardiovascular Risk: A Complex Interplay
Authors: Wilkinson Michael J. , Shapiro Michael D.
Published: October 2024

Individuals with autoimmune inflammatory diseases, such as systemic lupus erythematosus, rheumatoid arthritis, and psoriasis, are at increased risk for cardiovascular disease. While these diseases share common features of systemic inflammation, the impact of individual autoimmune inflammatory conditions on circulating lipids and lipoproteins varies by specific disease, disease activity, and the immune-suppressing medications used to treat these conditions. A common feature observed in many autoimmune inflammatory diseases is the development of a proatherogenic dyslipidemic state, characterized by dysfunctional HDLs (high-density lipoproteins) and increased oxidation of LDLs (low-density lipoproteins). Various disease-modifying antirheumatic drugs also have complex and variable effects on lipids, and it is critical to take this into consideration when evaluating lipid-related risk in individuals with immune-mediated inflammatory conditions. This review aims to critically evaluate the current understanding of the relationship between immune-mediated inflammatory diseases and dyslipidemia, the underlying mechanisms contributing to atherogenesis, and the impact of various pharmacotherapies on lipid profiles and cardiovascular risk. We also discuss the role of lipid-lowering therapies, particularly statins, in managing residual risk in this high-risk population and explore the potential of emerging therapies with complementary anti-inflammatory and lipid-lowering effects.
PARP15
Is a Susceptibility Locus for Clarkson Disease (Monoclonal Gammopathy–Associated Systemic Capillary Leak Syndrome)
Authors: Chan Eunice C. , Ablooglu Ararat J. , Ghosh Chandra C. , Desai Abhishek , Schaible Niccole , Chen Xiuying , Zhao Ming , Olano M. Renee , Ganesan Sundar , Lack Justin B. , Krishnan Ramaswamy , Parikh Samir M. , Druey Kirk M.
Published: October 2024

BACKGROUND:

Vascular leakage is a deadly complication of severe infections, ranging from bacterial sepsis to malaria. Worldwide, septicemia is among the top 10 causes of lethality because of the shock and multiorgan dysfunction that arise from the host vascular response. In the monoclonal gammopathy–associated capillary leak syndrome (MG-CLS), even otherwise mundane infections induce recurrent septic-like episodes of profound microvascular hyperpermeability and shock. There are no defined genetic risk factors for MG-CLS or effective treatments for acute crises.

METHODS:

We characterized predicted loss-of-function mutations in PARP15 (poly[ADP-ribose] polymerase 15), a protein of unknown function that is absent in mice, in patients with MG-CLS. We analyzed barrier function in PARP15-deficient vascular endothelial cells and vascular leakage in mice engineered to express wild-type or loss-of-function variant human PARP15.

RESULTS:

We discovered several loss-of-function PARP15 variants associated with MG-CLS. These mutations severely reduced PARP15 enzymatic function. The presence of the most frequently detected variant (G628R) correlated with clinical markers of severe vascular leakage. In human microvascular endothelial cells, PARP15 suppressed cytokine-induced barrier disruption by ADP-ribosylating the scaffold protein JIP3 (c-Jun N-terminal kinase-interacting protein 3) and inhibiting p38 MAP (mitogen-activated protein) kinase activation. Mice expressing enzymatically inactive human PARP15(G628R) were significantly more prone to inflammation-associated vascular leakage than mice expressing wild-type PARP15 in a p38-dependent fashion.

CONCLUSIONS:

PARP15
represents a previously unrecognized genetic susceptibility factor for MG-CLS. PARP15-mediated ADP ribosylation is an essential and genetically determined mechanism of the human vascular response to inflammation.
Interleukin-6, C-Reactive Protein, and Recurrence After Stroke: A Time-Course Analysis of Individual-Participant Data
Authors: McCabe John J. , Walsh Cathal , Gorey Sarah , Arnold Markus , DeMarchis Gian Marco , Harris Katie , Hervella Pablo , Iglesias-Rey Ramon , Jern Christina , Katan Mira , Li Linxin , Miyamoto Nobukazu , Montaner Joan , Purroy Francisco , Rothwell Peter M. , Stanne Tara M. , Sudlow Catherine , Ueno Yuji , Vicente-Pascual Mikel , Whiteley William , Woodward Mark , Kelly Peter J.
Published: October 2024

BACKGROUND:

Inflammation promotes atherogenesis. Randomized controlled trials of anti-inflammatory therapies for prevention after stroke have not yet demonstrated clear benefit. IL-6 (interleukin-6) and hsCRP (high-sensitivity C-reactive protein) are independently associated with major adverse cardiovascular events poststroke and may guide patient selection in future randomized controlled trials. Optimal timing of hsCRP/IL-6 measurement poststroke is unknown, as early blood levels may be confounded by the inflammatory response to brain infarction.

METHODS:

Using individual-participant data from a systematic review, we performed a time-course analysis to investigate the association between hsCRP/IL-6 and recurrent events stratified by timing of sampling. The prespecified coprimary end points after sample measurement were: (1) recurrent major adverse cardiovascular events (first major coronary event, recurrent stroke, or vascular death) and (2) recurrent stroke (ischemic, hemorrhagic, or unspecified). The poststroke dynamics of IL-6/hsCRP were analyzed by plotting their median (interquartile interval) concentrations within each tenth of the sampling timeframe. Acute/postacute phases were defined for each biomarker according to the shape of this relationship.

RESULTS:

There were data for 9798 patients from 11 studies (19 891 person-years follow-up, 10 observational cohorts, and 1 randomized trial). Each marker was measured once. IL-6 was markedly elevated <24 hours poststroke compared with postacute levels (≥24 hours; 11.6 versus 3.02 pg/mL;
P
<0.001). HsCRP was elevated for 10 days. IL-6 was associated with recurrent major adverse cardiovascular events in the postacute phase (≥24 hours; risk ratio, 1.30 [CI, 1.19–1.41], per unit log
e
IL-6), but not in the acute phase (<24 hours; risk ratio, 1.10 [CI, 0.98–1.25];
P
interaction
=0.03). After adjustment for risk factors/medication, the association remained for postacute IL-6 when analyzed per log
e
unit (risk ratio, 1.16 [CI, 1.05–1.66]) and per quarter increase (risk ratio, 1.55 [CI, 1.19–2.02]; Q4 versus Q1), but not if measured acutely. Similar findings were observed for recurrent stroke. There was no evidence of time-dependent interaction with hsCRP.

CONCLUSIONS:

Timing of sample measurement after stroke modifies the association with recurrent major adverse cardiovascular events for IL-6 but not hsCRP. These data inform future randomized controlled trial designs incorporating biomarker-based selection of patients for anti-inflammatory therapies.
The Ālūsī Family between Baghdad and Istanbul: Imperial Connections and the Development of Salafi Commitments
Authors: Topal Omer Faruk
Published: October 2024

The Ālūsīs of Baghdad were one of the most influential families in the late Ottoman Empire, producing various prolific scholars. They were inspired by Ibn Taymiyya, supported the permissibility of ijtihād, and criticized excessive Sufi practices. Although accused of being Wahhabi owing to their Salafi ideas, they maintained strong ties with the Ottoman state, holding judicial and bureaucratic positions. This article situates the family within the Ottoman imperial framework and explains how they navigated as Salafi scholars in the predominantly Ḥanafī-Māturīdī structure of the Ottoman Empire.
Gender, Human Rights Networks, and the State of Emergency During the Arab Revolt (1936–1939)
Authors: Zichi Paola
Published: October 2024

Contrary to the current literature on the British state of emergency during the 1936–1939 Arab revolt, that sees women as victims of counterinsurgency policies or the differential objects within national-military cultures, this article argues that Palestinian women played an active role in fostering the rise of early human rights networks, and highlights how the politics of race and gender-based violence marked an embedded dimension of the imperial and colonial political authority and practice. By focusing on the emotional and divisive question of Palestine during the Arab Revolt, and using gender as a polyhedric tool of analysis, the article aims to show the centrality of gender and women’s activism in forging and grasping technologies of colonial emergency governance as tools of resistance to colonial violence. It enriches current histories of international law and adopts a critical feminist intersectional approach.
The makings of social meaning: Cassirer and Castoriadis
Authors: Rosengren Mats
Published: October 2024

Two of the most interesting attempts of the 20th century to conceptualise, assess and understand the production, functioning and institutionalisation of social imaginary meanings were made by, first, a major thinker in the post-Kantian philosophical anthropological tradition, the German philosopher Ernst Cassirer, and, years later, by French-Greek philosopher and psychoanalyst Cornelius Castoriadis. Both offer ways of thinking and understanding the current political contexts, and point to and offer concepts for articulating difficulties and obstacles facing us today. The affinities between Cassirer’s philosophy of symbolic forms and Castoriadis’s take on social imaginary significations are many and striking, despite the fact that Castoriadis never explicitly referred to his predecessor. This article pinpoints some of the affinities, and makes an argument for the political topicality of both thinkers still today.
Castoriadis reads Thucydides: The aspiration to autonomy and the acceptance of history
Authors: Árnason Jóhann P.
Published: October 2024

This essay discusses the third of Castoriadis’s seminars on Ancient Greece. Following the structure of the book, the first part deals with questions of autonomy and its limits. In that regard, Castoriadis’s analysis leads to more sceptical conclusions and modest expectations than in some earlier writings. This applies both to the social context and to the anthropological grounding of aspirations to autonomy. The second part focuses on some key points in Castoriadis’s reading of Thucydides. His interpretation of Athenian political culture in the fifth century bce, largely based on the funeral oration which Thucydides ascribes to Pericles, is shown to be problematic in some respects, especially with regard to power structures, but his key claims are nevertheless defended against the views of Nicole Loraux and Jonas Grethlein. The essay concludes with reflections on Thucydides’s account of interstate competition and geopolitical dynamics, and on its implications for our times.
Castoriadis in Search of Origins: Exploring Archaic Greece
Authors: Árnason Jóhann P.
Published: October 2024

The essay discusses the English translation of the first volume of Castoriadis´s seminars on Ancient Greece; its main focus is on the question of a primordial Greek grasp of the world, expressed in the metaphors of chaos, abyss and meaningless disorder, and on the religious imaginary that developed around this core. Arguments for and against a religious revolution in Homeric Greece are considered. A concluding section deals with the philosophical transformation of mythological themes.
On the Convergence Between Femme Theory and Popular Feminine Fiction
Authors: Medrano-González Claudia
Published: October 2024

Drawing on Femme Theory, this article explores the relationship between the devaluation of feminine knowledge and the academic abjection of feminine erotic romance. Here, I problematise femininity being inherently disempowering, arguing that it can be both a source of oppression (i.e., femmephobia) informed by intersectional axes of identity and a source of power that challenges gendered cultural frameworks. Applying a feminist, post-structuralist lens, I analyse how 100 girls (re)territorialise femininity through Spanish Young Adult erotic romance, contrasting their worldviews with the analysis of femmephobic regulatory discourse embedded in publicity. My findings suggest that publishers rework feminist discourse in an effort to appeal to girls’ presumed desire to assert (sexual) agency, simultaneously challenging gender binaries and revaluing femininity through the imposition of a culture of (sexual) self-confidence. Girls’ narratives support this finding, underscoring how Young Adult Erotic Romance is a gendered risk enforcing compulsory sexual agency, yet also a sex-positive space where they can co-construct fe(me)ninity outside the regulatory bounds.
Blumea axillaris (Inuleae, Asteraceae): the first record of an invasive exotic weed in the Americas and the first record of the genus in Brazil
Authors: TELES ARISTÔNIO MAGALHÃES , HEIDEN GUSTAVO
Published: October 2024

Blumea is one of the largest genus within the tribe Inuleae, Asteraceae, with approximately 100 species across tropical Asia, Africa, Oceania, and the Americas. It is characterized by disciform capitula, one large oxalate crystal in each cypsela epidermis cell, radial anther endothecium thickenings, and tailed anthers. There are no native species of Blumea in the Americas and Hawaii. However, two species have been documented, and both were introduced. Thus, this work documents the first records of Blumea axillaris in the Americas and the first report of this genus in Brazil. We provided a morphological description of the species, with ecological observations, a key for identifying the Blumea among the genera of the tribe Inuleae found in Brazil, and illustrations. This record serves as a warning since it is an invasive exotic species that, by all indications, has already firmly established itself as a weed and can represent significant challenges for agriculture and the environment.
Modelling the Relationship Between Merchandise Trade Flows and Some Macroeconomic Variables in Ghana
Authors: Ibrahim Azebre , Abonongo Anuwoje
Published: October 2024

Macroeconomic variables serve as economic indicators that offer valuable insights into the overall health and stability of an economy. Changes in these variables can have significant impacts on a country's trade balance and overall economic performance. This study employed multivariate time series analysis to study the relationship between Merchandise Trade Flows (MTF), Monetary Policy Rate (MPR), Commercial Lending Rate (CLR), Nominal Growth Rate (NGR) and Consumer Price Index (CPI) with Money Supply (MoS) as exogenous variable. The nature of trend in each series was investigated. The results revealed that quadratic trend model best models MTF, MPR, CLR and NGR whiles an exponential trend best models CPI. Johansen’s co-integration test with unrestricted trend performed revealed the existence of long-run equilibrium relationships between the variables and three (3) co-integrating equations described this long-run relationship. In terms of short-run relationships, the VEC (2) model revealed that, CLR, NGR, MoS have positive and significant impact on MTF. CLR, NGR and MoS have positive and significant impact on MPR, NGR have positive and significant impact on CLR, CPI and MoS have significant impact on NGR whiles NGR and MoS have significant impact on CPI. Model diagnostics performed on the VEC (2) model showed that, all the model parameters are structurally stable over time and the residuals of the individual models are free from serial correlation and conditional heteroscedasticity. Forecast error variance decomposition (FEVD) analysis showed that each variable primarily explained its own variance and the influence of other variables increase over time. Hence, adopting a broad perspective on macroeconomic variables can help policymakers anticipate and mitigate ripple effects across various economic sectors.
The Impact of Beneficial Microorganisms on Soil Vitality: A Review
Authors: Adal Yimam
Published: October 2024

The paper summarizes the literature on the critical impact of beneficial microorganisms on soil vitality. Common soil microorganisms, including bacteria, fungi, algae, protozoa, and viruses contribute significantly to enhancing soil fertility through processes such as nitrogen fixation, phosphorus solubilization and mobilization, sulfur cycle, composting, and heavy metal remediation. Their abundance and biomass vary significantly across taxa within the uppermost 15 cm of soil, with bacteria dominating numerically and fungi contributing substantially to biomass. These microorganisms mediate essential biogeochemical cycles in soil, including carbon, nitrogen, and phosphorus cycles, by facilitating the decomposition of organic matter and recycling soil nutrients. Nitrogen-fixing bacteria like Rhizobium are prevalent symbionts capable of biologically fixing nitrogen. Additionally, bacteria such as Micrococcus spp., Enterobacter aerogens, Pseudomonas capacia, fungi including Aspergillus niger, A. flavus, A. japonicas, Penicillum spp., and actinomycetes like Streptomyces play crucial roles in phosphorus solubilization, making phosphorus available for plant uptake. This synthesis underscores the critical role of beneficial microorganisms in maintaining soil vitality. These organisms interact with plants through beneficial relationships, influencing soil fertility dynamics by enhancing nutrient availability, promoting plant growth, and controlling pathogens. The use of biofertilizers has emerged as a sustainable strategy to improve crop yields and restore soil fertility, reducing environmental impacts linked to chemical fertilizers. Understanding the intricate dynamics of soil-beneficial microorganism and their interactions with Plants are pivotal for optimizing agricultural practices, ensuring long-term soil health, and enhancing productivity in sustainable farming systems.
Development Problems and Suggestions for the Giant Salamander Industry
Authors: Wang Wenbo , Liu Pin , Ning Yue , Feng Yalong , Dou Lingling , Wang Ping , Xi Ruimin , Yan Minfei
Published: October 2024

The Chinese giant salamander (Andrias davidianus), the American giant salamander (Cryptobranchus alleganiensis), and the Japanese giant salamander (A. japonicum) are precious aquatic protected animals that currently exist in the world. The latter two cannot be traded locally, whether wild or cultivated, and can only be protected, so they have not formed an industry. However, China allows the second generation of artificially bred offspring giant salamander to be traded and has already formed a factory scale breeding. The Chinese giant salamander has high economic value in food, health, medicine, beauty and other fields. In recent years, its artificial breeding industry has been widely developed in China and has a certain industrial scale. However, there are also many problems in the development of the giant salamander industry. In order to promote the high-quality development of the giant salamander industry and improve its quality, this article conducts research on the giant salamander industry, identifies existing problems, and provides suggestions. Firstly, this article analyzes the problems in the development process of giant salamanders from multiple perspectives, including management, service, market, research and development, and brand awareness. Secondly, corresponding industrial development strategies are proposed, including increasing regulatory efforts, enhancing service awareness, building market order, expanding sales channels, increasing research and development investment, reducing disease occurrence, extending the industrial chain, and strengthening brand awareness. Finally, the prospects for the development of the giant salamander industry are discussed.
Awareness of Officials and People About the Content of the People's Mobilization Work of the Government in Dong Nai Province
Authors: Loi Pham
Published: October 2024

This article aims to provide a comprehensive and systematic understanding of the awareness of both officials and the public regarding the government's people's mobilization work (PMW) in Dong Nai province today. People's mobilization efforts have garnered significant attention from multiple sectors, yet it is crucial to examine the perspectives of both government officials and ordinary citizens. The research focuses on identifying the levels of awareness, understanding, and engagement of these groups in relation to the government's PMW policies and strategies. By doing so, the study will delve into how effectively these efforts have been communicated, understood, and applied by both officials who implement policies and the public who are the target of these efforts. In addition, the article explores the challenges and successes of PMW in the province, aiming to pinpoint areas where improvements are needed to enhance collaboration between the government and the people. By offering a multidimensional perspective, the study contributes to the academic discourse on PMW while addressing practical issues surrounding the implementation of these strategies. The findings are expected to not only shed light on the current state of public awareness but also to provide policymakers with insights to refine their approach, thereby strengthening the relationship between government efforts and public participation in Dong Nai province.
Canning Quality Evaluation of Recently Released Ethiopian Chickpea Varieties
Authors: Seifu Muhaba , Yegrem Lamesgin , Kore Tamirat
Published: October 2024

Chickpea’s protein quality is better than other legumes. it has low levels of anti-nutritional factors and it is rich in some minerals and vitamins (thiamine and niacin). However, dried chickpea is needing prolonged cooking time. Owing to the need for more adequate alternatives to fulfill the demands of chickpea consumers in Ethiopia and other countries, adding value to the food via industrial processing and offering semi-prepared foods of high nutritional quality, sensory value and reduced cooking times is necessary. Hence, the aims of this study were to evaluate canning quality of Ethiopian chickpea varieties. Three kabuli Chickpea types and one desi type were used for this experiment. Three cooking temperatures (70, 80, and 90°C) were used. Cooking time, techno-functional, Canning quality of chickpeas, and Sensory evaluation of canned chickpeas were analyzed. The least significant difference (LSD) between the mean was reported at a significant level, p ≤ 0.05. The result indicates that Arerti variety cooked at 70 and 80°C had a maximum average of PWDWT, seed shape, splits, and degree of clumps. The maximum average of overall acceptability was observed from the Arerti variety followed by the koka variety. Finally, the canning quality evaluation results revealed that Arerti variety was suitable for canning, and has the potential to be used as a raw material for the canning industry.
Growth, Yield and Nutrient Performance of Salinity Tolerant Sunflower (cv. BARI Surjomukhi 2) Cultivar on Soils with different Salinity Levels
Authors: Halder Palash , Hossain Md. , Sarker Md. , Tania Anjuman , Sultana Papeya , Alam Md.
Published: October 2024

Salinity indeed is a great barrier for crop production. An experiment was carried growth, yield and nutrient performance of a salinity tolerant sunflower cultivar on soils with different salinity levels. Salinity's impact on seed germination and vigour exponent were studied. Pot experiment was done with sunflower on different salinity levels (0.55; 2.80; 3.55; 5.05; 6.95; 8.35; and 11.25 dSm-1) were created by using NaCl following CRD with 3 replications. Diverse agronomic parameters and chemical parameters were analyzed. The highest plant height (146.0±3.2 cm) was observed at 0.55 dSm-1 and the lowest (80.3±4.8 cm) was observed in 11.25 dSm-1. The greatest amount of seeds pot-1(300.0 ± 4.619) was observed in 3.55 dSm-1 and least number of seeds pot-1 (170.0 ± 2.887) was recorded in 11.25 dSm-1. The most seed yield (19.80 ± 0.1299 t ha-1) was recorded in 3.55 dSm-1 and the least (6.3 ± 0.1299 t ha-1) in 11.25 dSm-1 soil salinity level. The superior K content in shoot (2.540 ± 0.02309%) was observed in 0.55 dSm-1 and the inferior K contents (1.190± 0.005774%) was recorded in 11.25 dSm-1. The maximum Na contents (2.960 ± 0.03464 %) was observed in 11.25 dSm-1 and the minimum Na contents (0.5800 ± 0.01155%) was observed in 0.55 dSm-1 soil salinity level. The effects of salinity on all agronomic parameters were significant. All the chemical parameters show statistically significant on different salinity levels. The seed yield of sunflower growing on different salinity levels can be produced considerable seed yield up to 8.35 dSm-1.
Impact of Machine Learning Integration in Qur’anic Studies
Authors: Iqbal Arshad , Hassan Shabbir
Published: October 2024

The advancement in the field of computer science, especially in machine learning (ML), represents a flourishing innovation that carries great importance in the domain of education. The beneficial impact of ML can also be observed in the realm of Qur’anic studies, particularly in Arabic text recognition and recitation analysis. This paper presents a comprehensive analysis of 34+ published scholarly articles devoted to Qur’anic studies. This work explores the convergence of machine learning methodologies and Qur’anic studies, examining the innovative applications and methodologies for Arabic text and voice classification. The fusion of ML algorithms makes the work easy and accurate to analyze, interpret, and extract valuable insights from the sacred text. Subsequently, we delve deeper into the emergent field of ML algorithms like k-NN, ANN, BLSTM, MFCC, SVM, NB and DL approaches have been adapted for Qur’anic texts classification, recitation and recitation analysis on accuracy, speed, class recognition, response rate and biasness benchmark. This work covers a diverse range of applications, including automated Qur’anic exegesis and analysis of usage of Ahkam Al-Tajweed. The main contribution of the work is to provide insight into how ML facilitates in Arabic and Kufic textual analysis, linguistic subtleties, and thematic structures of the Qur’anic text. Using the deep learning approaches, the reciters, recitation style and of the Quranic text has also explained in the work.
Order and justice in ontological security studies
Authors: Steele Brent J
Published: October 2024

Order is a seemingly key concept in ontological security (OS). Yet work on “order” found in International Relations (IR) is often only treated tangentially (via anxiety, insecurity, and other more central referents) in ontological security studies (OSS). This article seeks to move the literature within OSS forward by taking on the challenge of acknowledging and speaking to its politics, and its ethics by centralizing the importance of order as a value. The article confronts criticisms against, and challenges of, OS by revisiting the pluralist versus solidarist debates in the “English School” of IR regarding order and justice. It then reads through recent work in OSS with these insights, examining the order versus justice tensions within several domains and topics in OSS. The article concludes that an ethics of OSS can confront and challenge inequalities and violence that follow from certain forms of order, and ordering, without concluding that one must resist all forms of order.
Physiological Predictors of Operator Performance: The Role of Mental Effort and Its Link to Task Performance
Authors: Pütz Sebastian , Mertens Alexander , Chuang Lewis L. , Nitsch Verena
Published: October 2024

Objective

The present study investigated how pupil size and heart rate variability (HRV) can contribute to the prediction of operator performance. We illustrate how focusing on mental effort as the conceptual link between physiological measures and task performance can align relevant empirical findings across research domains.

Background

Physiological measures are often treated as indicators of operators’ mental state. Thereby, they could enable a continuous and unobtrusive assessment of operators’ current ability to perform the task.

Method

Fifty participants performed a process monitoring task consisting of ten 9-minute task blocks. Blocks alternated between low and high task demands, and the last two blocks introduced a task reward manipulation. We measured response times as primary performance indicator, pupil size and HRV as physiological measures, and mental fatigue, task engagement, and perceived effort as subjective ratings.

Results

Both increased pupil size and increased HRV significantly predicted better task performance. However, the underlying associations between physiological measures and performance were influenced by task demands and time on task. Pupil size, but not HRV, results were consistent with subjective ratings.

Conclusion

The empirical findings suggest that, by capturing variance in operators’ mental effort, physiological measures, specifically pupil size, can contribute to the prediction of task performance. Their predictive value is limited by confounding effects that alter the amount of effort required to achieve a given level of performance.

Application

The outlined conceptual approach and empirical results can guide study designs and performance prediction models that examine physiological measures as the basis for dynamic operator assistance.
Direct democracy as a liberal political regime
Authors: Magni-Berton Raul
Published: October 2024

This article is a theoretical contribution to the understanding of direct democracy (DD) and its properties. It departs from common definitions of DD by distinguishing between a hard meaning, according to which direct democracies are characterized by the fact that legislative initiative and veto rights are extended to the entire citizenry, and a soft meaning, according to which they are not. Building on previous findings, it argues that hard DD profoundly alters the balance of power in representative systems and reinforces the liberal features of democratic systems, such as the independence of checks and balances and the empowerment of minorities. This analysis invites us to understand direct democracies as systems in which representatives of political groups and independent institutions negotiate to find a consensual way to implement legislation, and a direct vote of the people only takes place if agreement is not reached.
Amide proton transfer MRI may reflect effective reperfusion and predict functional outcomes in patients with ischemic stroke
Authors: Zhang Chi , Yong Xingwang , Cao Yuezhou , Hsu Yi-Cheng , Shi Haibin , Wu Feiyun , Zhang Yi , Lu Shanshan
Published: October 2024

Perfusion imaging is useful to assess tissue recovery in patients with acute ischemic stroke (AIS); however, it cannot reflect tissue metabolism. We postulated that amide proton transfer (APT) imaging can characterize the tissue status after reperfusion therapy, thus providing prognostic value for 90-day functional outcomes. We included 63 patients with AIS and large-vessel occlusion (LVO). The APT signals, including APT# and NOE# (nuclear Overhauser enhancement) were quantified. Ischemic lesions observed on APT# and diffusion-weighted imaging (DWI) were classified according to their mismatch patterns (APT# < DWI; APT# ≥ DWI). Predictors of 90-day good outcomes (modified Rankin scale score 0–2) were evaluated. Patients with successful reperfusion exhibited higher APT#, smaller percentage change of APT#, and a greater likelihood of presenting APT# < DWI compared to those with poor reperfusion (all P < 0.05). The APT# (odds ratio [OR] = 11.48, P = 0.046) and a mismatch pattern of APT# < DWI (OR = 7.41, P = 0.020) independently predicted good outcomes besides the clinical parameters. A mismatch pattern of APT# ≥ DWI was a significant marker of poor outcomes despite successful reperfusion ( P = 0.002). Our study provides preliminary evidence that APT may reveal tissue recovery after reperfusion and predict good outcomes at 90 days in patients with AIS and LVO.