bluuebunny/crossref_metadata_2025 huggingface dataset:
Efficient deep learning architecture for the classification of diseased plant leaves
Authors: Sadhasivam Muniyandi , Kalaiselvi Geetha Manoharan , Gladson Maria Britto James
Published: January 2024

The classification of plant leaf diseases via machine learning and deep learning algorithms has a great deal of potential for enhancing agricultural operations by allowing the early and accurate diagnosis of diseases. These systems can potentially develop into useful instruments for environmentally responsible farming and increased food safety as technological advancements continue. In this work, an efficient deep learning architecture has been developed to classify the diseased plant leaves. A ten-layer architecture is designed, which includes 5-convolutional layers using different numbers of filters (32, 64, 128, 256, and 512) and for dimension reduction, five max-pooling layers are used. The PlantVillage dataset which consists of more than 50,000 plant leaf samples is used to analyze the proposed architecture's performance. The performances are evaluated across different training and testing configurations and different dropout configurations. When compared to well-known transfer learning methods using visual geometric group (VGG16), AlexNet, and GoogleNet architectures, the proposed architecture obtains a higher level of performance with 98.18% classification accuracy.
A comparative study on time series data-based artificial intelligence approaches for classifying cattle feeding behavior
Authors: El Moutaouakil Khalid , Falih Noureddine
Published: January 2024

Cattle feeding behavior analysis is crucial for optimizing livestock management practices and ensuring animal well-being. This study presents a comparative analysis of three models: two machine learning algorithms including random forest and support vector machine (SVM), in addition to a deep learning convolutional neural networks (CNN) model, for classifying cattle feeding behaviors (eating, ruminating, and other) using time series data generated from a 3-axis accelerometer. The results of this study highlight the performance of these methods in accurately categorizing cattle feeding behaviors and demonstrate the importance of precise and efficient livestock monitoring and contributing to the improvement of animal well-being and enhancing the overall effectiveness of livestock operations.
Diagnosis and treatment of Guillain-Barre using the prolog expert system
Authors: Andrade-Arenas Laberiano , Molina-Velarde Pedro , Pucuhuayla-Revatta Félix , Yactayo-Arias Cesar
Published: January 2024

This research is mostly about Guillain-Barre syndrome (GBS), a complicated neurological condition with many subtypes that make diagnosis and treatment hard, even though medical care is always getting better. The main goal of this study is to build and test an expert system that can correctly diagnose these subtypes, with a focus on early detection and personalized treatments.
 The evaluation of the system was carried out using a dataset composed of 20 cases (12 positive and 8 negative). A confusion matrix was used to evaluate key metrics such as precision, sensitivity, and specificity. The findings demonstrate precision and sensitivity of 83% and specificity of 75%. These findings unambiguously demonstrate the efficacy of the system in correctly identifying positive Guillain-Barre cases while substantially reducing false negatives. In conclusion, this expert system offers a potentially useful tool to improve the accuracy of the diagnosis and treatment of Guillain-Barre patients. However, to take advantage of its full potential in clinical practice, it should be used as diagnostic support and not replace the medical staff, and it should be updated periodically to reflect new findings in medicine.
Encouraging hygiene permanence in tomato leaf and applying machine learning techniques
Authors: Madderi Sivalingam Saravanan , Devi Badabagni Lakshmi
Published: January 2024

Tomatoes are the major ingredient in food preparation, which leads to a huge food production rate. Most countries cultivate huge tomatoes at the same time that crop diseases affect the production rate due to many different types of diseases. The various types of diseases are bacterial spots, septoria leaf spot, left mold, late blight, early blight, arget and spot. Many research studies review these tomato leaf diseases with various statistics. The survey on disease will give a clear idea of reasons and prevention methods, also presenting how to reduce it in the early stages. In another study, tomato leaf images were taken to classify the diseased and non-diseased varieties. Few studies compare the standard model of disease prediction with the machine learning models. Therefore, this research study discusses tomato leaf disease detection and prevention methods used by various researchers in their studies and finally consolidate the observations. This study also deals with encouraging hygiene permanence in tomato leaf using machine learning algorithms. The convolutional neural network (CNN) was used to predict the early nature of the hygiene nature of leafy vegetable plants for the benefit of agriculture people and concluded with better future suggestions.
Deep neural network with fuzzy algorithm to improve power and traffic-aware reliable reactive routing
Authors: Murugesan Radhakrishnan , Kanapala Satish , Rajendran Subash , Banu Priya Prathaban , Ramadevi Rathinasabapathy , Duraichi Natarajan , Hema Rengaraj
Published: January 2024

In wireless networks, link breaks, and restricted resources create fundamental challenges for maintaining network applications. Several wireless network routing techniques concentrate on power efficiency to expand the network lifetime, but the traffic and reliability parameters are not the primary concern. Though, these techniques are not capable of dealing with the wireless network. Hence, this paper proposes deep neural network (DNN) with a fuzzy algorithm to improve power and traffic-aware reliable reactive routing (PTAR) in wireless networks. The wireless network is formed by clustering by the node power and selects the cluster head (CH) based on a fuzzy algorithm. The wireless node power level, node buffer space, and node reliability to consider the input parameters of the fuzzy system. Then thefuzzy algorithm gives the output for CH round length. This selected CH improves the node reliability, power efficiency with minimized network congestion. Then we use a DNN algorithm to choose an optimal relay by applying an adaptive load balance factor in the network. DNN is a machine learning algorithm, and it provides high accuracy. From the simulation results, the PTAR approach improves the network performance, such as packet received ratio, delay, residual energy, and routing overhead.
Hybrid model of convolutional neural network and long short term memory for heart disease prediction
Authors: Gupta Shubham , Sharma Pooja
Published: January 2024

Data mining is a process that assists in uncovering meaningful data from large, disorganized datasets. This research is being conducted to predict heart disorders by using available data to make predictions for the future. The approach is carried out in several stages, such as pre-processing the data, extracting relevant features, and classifying the data. all of these steps are essential for predicting heart disease. The deep learning models is already proposed by the researches for the heart disease prediction. This work introduces a hybrid deep learning model that combines convolutional neural network (CNN) and long short-term memory (LSTM) to predict heart disease. The proposed model has been implemented in python, and its accuracy, precision, and recall have been evaluated.
An improved secured cloud data using dynamic rivest-shamir-adleman key
Authors: Musa Ugbedeojo , O. Adebiyi Marion , Bukie Osang Francis , Aduragba Adebiyi Abayomi , Ariyo Adebiyi Ayodele
Published: January 2024

Encryption methods had been widely used for secure data transmission and communication in both public and private organizations against intruders. Rivest-shamir-adleman (RSA) encryption algorithm is one of the most popular and efficient encryption schemes that has been in used for decades. Due to technological advancement and innovation, there is a threat to this algorithm. It is believed that introduction of quantum computer will break RSA algorithm easily. In view of this, it is pertinent to research into how RSA algorithm could be strengthened against all adversaries. This research aim at protecting client/server communication and file sharing by generating dynamic public and private keys. The proposed method was implemented in visual basic.net 2008. The result shows that dynamic keys do not affect the performance of the system and it is capable of protecting communication and file sharing between client/server. As the key generated keeps changing at an interval, it will difficult for most advance computer to factor any of the keys before another key is generated. This is the basis of the security of the proposed system.
A hybrid model for data visualization using linear algebra methods and machine learning algorithm
Authors: Ali Mohsin , Choudhary Jitendra , Kasbe Tanmay
Published: January 2024

The t-distributed stochastic neighbor embedding (t-SNE) is a powerful technique for visualizing high-dimensional datasets. By reducing the dimensionality of the data, t-SNE transforms it into a format that can be more easily understood and analyzed. The existing approach is to visualize high-dimensional data but not deeply visualize. This paper proposes a model that enhances visualization and improves the accuracy. The proposed model combines the non-linear embedding technique t-SNE, the linear dimensionality reduction method principal component analysis (PCA), and the QR decomposition algorithm for discovering eigenvalues and eigenvectors. In Addition, we quantitatively compare the proposed model QRPCA-t-SNE with PCA-t-SNE using the following criteria: data visualization with different perplexity and different principal components, confusion matrix, model score, mean square error (MSE), training, testing accuracy, receiver operating characteristic curve (ROC) score, and AUC score.
A rest tremor detection system based on internet of thing technology
Authors: Faizah Safira , Nugraha Dian , N. Abdulrazaq Mohammed , Widi Dionova Brainvendra , Irsyad Abdullah Muhammad , Novianti Leni
Published: January 2024

This article outlines the creation of a health detection system designed to identify rest tremors in Parkinson's disease (PD). The system leverages internet of things (IoT) technology to measure frequencies derived from human activities, excluding other symptoms such as heartbeat and voice recording. The core components include the Arduino Nano microcontroller and the Node ESP MCU 8266 V3 for data processing. The system employs an accelerometer sensor positioned at the body's center axis to gauge the frequency of motor symptoms associated with resting tremors, particularly when the hands are at rest in the lap. The findings indicate that 9 samples displayed symptoms of rest tremor. The recorded p-value, standing at 0.884, signifies a robust correlation between the two variables at a significant threshold of 0.01 or 1%.
A novel artificial intelligent-based approach for real time prediction of telecom customer’s coming interaction
Authors: Hussien Reyad , Mahgoub Mohamed , Youssef Shahenda , Torky Ashraqat , K. Negied Nermin
Published: January 2024

Predicting customer’s behavior is one of the great challenges and obstacles for business nowadays. Companies take advantage of identifying these future behaviors to optimize business outcomes and create more powerful marketing strategies. This work presents a novel real-time framework that can predict the customer’s next interaction and the time of that interaction (when that interaction takes place). Furthermore, an extensive data exploratory analysis is performed to gain more insights from the data to identify the important features. Transactional data and static profile data are integrated to feed a deep learning model which is implemented using two methodologies: time-series approach and statistical approach. It is found that the time-series approach gives the best performance and fulfills all the requirements. The experiments show that the proposed framework introduces a good overall performance in comparison to existing approaches based on standard metrics like accuracy and mean absolute error (MAE) values. What makes the proposed work novel and special is that it is the first approach that addresses the telecom customer’s next future interaction not just churn prediction like the other approaches in literature.
Chili fruits maturity estimation using various convolutional neural network architecture
Authors: Mohd Hussin Najihah , Noorazlan Shah Zainudin Muhammad , Hidayat Mohd Saad Wira , Raihaan Kamarudin Muhammad , Muhammad Sufri , Shah Jehan Abd Razak Muhd
Published: January 2024

Agricultural robots recently become popular by helping the farmer to conduct their daily chores. A slow process of picking and grading will leads to an inaccurate result thus increasing the production cost. This study represents an innovative and economical alternative for farmers who require to undergone the process of estimating their maturity categories. A total of 1,200 chili images with 256×256 pixel are used, where 840 is used for training and the remaining 360 being served for testing. The maturity is determined by measuring the length of chili structure between the calyx and apex. Various convolutional neural network (CNN) architectures are applied to learn and recognize the chili fruits into three maturity categories; immature, moderately mature, and mature. ADAM and stochastic gradient descent with momentum (SGDM) optimizers with multiple CNN architectures is capable in recognising and classifying chilli fruits with an accuracy of above 85%.
Optimization of re-configurable multi-core processors and security based on field programmable gate arrays
Authors: Bachanna Prashant , Hari Sankar Palla , Kumar Tripath Mukesh , Shivendra Shivendra , Ravi Kumar Kadali , Bhosle Nilesh
Published: January 2024

n system-on-a-chip based complex processors has the problem of multithreading and miss-functionality due to their complexity and high-speed operations. In order to minimize these problems, the proposed design has machine learning based algorithms and cryptography systems for security has been incorporated. In the proposed work, the security level has been taken care of in three different stages such as data integrity, data authentication, and private and public keys encryption and decryption. In order to increase throughput with minimal latency, the proposed architecture with advanced high-performance protocol and advanced high-performance and advanced peripheral bus bridge is incorporated between the fabric dynamically re configurable multi-processor and peripherals along with security algorithms using secure hash algorithm (SHA-256) bits and advanced encryption standard (AES). In order to perform machine learning based applications, the proposed system is incorporated double-precision floating point arithmetic operations. The overall proposed architecture is developed in verilog hybrid deep learning (HDL) and quality checking using the LINT tool. The entire design is interfaced with the Zynq processor and software development kit (SDK) tool to verify data transfer between hardware and software. The obtained results are compared with existing state-of-art results and found that 18% improvement in throughput, a 21% improvement in power consumption savings, and a 34% reduction in latency.
Biomedical signal compression using deep learning based multi-task compressed sensing
Authors: Khadri Shruthi , K Bhoganna Naveen , Aravind Kumar Madam
Published: January 2024

Real-time transmission of biomedical signals is immensely challenging and requires cloud and internet of things (IoT) infrastructure. Security is also an important factor; however, to accomplish this, a reconstruction method is developed in which the entire signal is supplied as an input, the primary portion is considered here, and the signal is further encoded and transmitted to the destination. Electrocardiogram (ECG) compression for the lightweight wireless network is quite challenging for long-term healthcare monitoring. Compressed sensing (CS) involves efficient encoding mechanisms for error rate estimation for reconstruction and energy consumption for wireless transmission of data. We propose a multi-task compressed sensing (MT-CS) reconstruction mechanism in this study for ECG compression of data is most chosen for a wireless network system that has various sensors embedded in it. This model further extracts the essential adaptive features for correlation existing in the ECG signals. The performance of the proposed MT-CS reconstruction mechanism is evaluated on the multiparameter intelligent monitoring in intensive care (MIMIC-II) dataset, which ensures its robustness and generalization. The results obtained upon simulation ensure that the proposed MT-CS based reconstruction approach ensures excellent reconstruction signal with fewer measurements in comparison with the existing state-of-art CS techniques.
A new conjugate gradient for unconstrained optimization problems and its applications in neural networks
Authors: Luqman Ibrahim Alaa , Guhdar Mohammed Mohammed
Published: January 2024

We introduce a novel efficient and effective conjugate gradient approach for large-scale unconstrained optimization problems. The primary goal is to improve the conjugate gradient method's search direction in order to propose a new, more active method based on the modified vector , which is dependent on the step size of Barzilai and Borwein. The suggested algorithm features the following traits: (i) The ability to achieve global convergence; (ii) numerical results for large-scale functions show that the proposed algorithm is superior to other comparable optimization methods according to the number of iterations (NI) and the number of functions evaluated (NF); and (iii) training neural networks is done to improve their performance.
Reviewers For Vol 15 No. 1
Authors: Editor
Published: January 2024

Prof. Dr. Mirnawati Sudarmanto, Faculty of Veterinary Medicine, Bogor Agricultural University, Bogor (IPB), Indonesia Dr. Ratih Dewanti Hariyadi, Faculty of Agricultural Technology, Bogor Agricultural University (IPB), Bogor, Indonesia Prof. Dr. M.A. Chozin, Faculty of Agriculture, Bogor Agricultural University (IPB), Bogor, Indonesia Dr. Soekisman S. Tjitrosemito, Faculty of Mathematics and Natural Sciences, Bogor Agricultural University (IPB), Bogor, Indonesia Dr. Dahlia Sukma, Faculty of Mathematics and Natural Sciences, The State University of Jakarta (UNJ), Jakarta 13220, Indonesia Dr. Lisdar M. Sudirman,Â  Faculty of Mathematics and Natural Sciences, Bogor Agricultural University (IPB), Bogor, Indonesia Dr. Nurita L. Toruan-Mathius, Tissue Culture Laboratory, SMART Research Institute (SMARTRI), Bogor, Indonesia Ir. Soemaryono M.Sc., Biotechnology Research Unit For Estate Crops, Bogor, Indonesia Dr. Okky S. Dharmaputra, SEAMEO BIOTROP/Faculty of Mathematics and Natural Sciences, Bogor Agricultural University (IPB), Bogor, Indonesia Dr. Gayuh Rahayu, Faculty of Mathematics and Natural Sciences, Bogor Agricultural University (IPB), Bogor, Indonesia Dr. Yadi Haryadi, Faculty of Agricultural Technology, Bogor Agricultural University (IPB), Bogor, Indonesia Dr. Zainal Mahmud, Indonesian Agency for Agricultural Research and Development (Puslitbangbun), Bogor, IndonesiaÂ
Comparando os incomparáveis: resenha do livro Infini et disproportion chez Pascal, de João Cortese
Authors: Oliva Luís César Guimarães
Published: January 2024

Trata-da resenha do livro Infini et disproportion chez Pascal, de João Cortese.
Uma filosofia do encontro: resenha do livro O tempo e a ocasião, o encontro Espinosa Maquiavel, de Vittorio Morfino
Authors: Mendes Ricardo Polidoro
Published: January 2024

Trata-se da resenha do livro O tempo e a ocasião, o encontro Espinosa Maquiavel, de Vittorio Morfino
Endoplasmic Reticulum Protein 72 Regulates Integrin Mac-1 Activity to Influence Neutrophil Recruitment
Authors: Li Yaofeng , Xu Xulin , Wang Haoqing Jerry , Chen Yiyao Catherine , Chen Yaobing , Chiu Joyce , Li Li , Wang Lei , Wang Jinyu , Tang Zhaoming , Ren Lehao , Li Hongliang , Wang Xuanbin , Jin Si , Wu Yi , Huang Mingdong , Ju Lining Arnold , Fang Chao
Published: January 2024

BACKGROUND:

Integrins mediate the adhesion, crawling, and migration of neutrophils during vascular inflammation. Thiol exchange is important in the regulation of integrin functions. ERp72 (endoplasmic reticulum–resident protein 72) is a member of the thiol isomerase family responsible for the catalysis of disulfide rearrangement. However, the role of ERp72 in the regulation of Mac-1 (integrin αMβ2) on neutrophils remains elusive.

METHODS:

Intravital microscopy of the cremaster microcirculation was performed to determine in vivo neutrophil movement. Static adhesion, flow chamber, and flow cytometry were used to evaluate in vitro integrin functions. Confocal fluorescent microscopy and coimmunoprecipitation were utilized to characterize the interactions between ERp72 and Mac-1 on neutrophil surface. Cell-impermeable probes and mass spectrometry were used to label reactive thiols and identify target disulfide bonds during redox exchange. Biomembrane force probe was performed to quantitatively measure the binding affinity of Mac-1. A murine model of acute lung injury induced by lipopolysaccharide was utilized to evaluate neutrophil-associated vasculopathy.

RESULTS:

ERp72-deficient neutrophils exhibited increased rolling but decreased adhesion/crawling on inflamed venules in vivo and defective static adhesion in vitro. The defect was due to defective activation of integrin Mac-1 but not LFA-1 (lymphocyte function-associated antigen-1) using blocking or epitope-specific antibodies. ERp72 interacted with Mac-1 in lipid rafts on neutrophil surface leading to the reduction of the C654-C711 disulfide bond in the αM subunit that is critical for Mac-1 activation. Recombinant ERp72, via its catalytic motifs, increased the binding affinity of Mac-1 with ICAM-1 (intercellular adhesion molecule-1) and rescued the defective adhesion of ERp72-deficient neutrophils both in vitro and in vivo. Deletion of ERp72 in the bone marrow inhibited neutrophil infiltration, ameliorated tissue damage, and increased survival during murine acute lung injury.

CONCLUSIONS:

Extracellular ERp72 regulates integrin Mac-1 activity by catalyzing disulfide rearrangement on the αM subunit and may be a novel target for the treatment of neutrophil-associated vasculopathy.
The Contribution of Declines in Blood Lead Levels to Reductions in Blood Pressure Levels: Longitudinal Evidence in the Strong Heart Family Study
Authors: Lieberman‐Cribbin Wil , Li Zheng , Lewin Michael , Ruiz Patricia , Jarrett Jeffery M. , Cole Shelley A. , Kupsco Allison , O'Leary Marcia , Pichler Gernot , Shimbo Daichi , Devereux Richard B. , Umans Jason G. , Navas‐Acien Ana , Nigra Anne E.
Published: January 2024

Background

Chronic lead exposure is associated with both subclinical and clinical cardiovascular disease. We evaluated whether declines in blood lead were associated with changes in systolic and diastolic blood pressure in adult American Indian participants from the SHFS (Strong Heart Family Study).

Methods and Results

Lead in whole blood was measured in 285 SHFS participants in 1997 to 1999 and 2006 to 2009. Blood pressure and measures of cardiac geometry and function were obtained in 2001 to 2003 and 2006 to 2009. We used generalized estimating equations to evaluate the association of declines in blood lead with changes in blood pressure; cardiac function and geometry measures were considered secondary. Mean blood lead was 2.04 μg/dL at baseline. After ≈10 years, mean decline in blood lead was 0.67 μg/dL. In fully adjusted models, the mean difference in systolic blood pressure comparing the highest to lowest tertile of decline (>0.91 versus <0.27 μg/dL) in blood lead was −7.08 mm Hg (95% CI, −13.16 to −1.00). A significant nonlinear association between declines in blood lead and declines in systolic blood pressure was detected, with significant linear associations where blood lead decline was 0.1 μg/dL or higher. Declines in blood lead were nonsignificantly associated with declines in diastolic blood pressure and significantly associated with declines in interventricular septum thickness.

Conclusions

Declines in blood lead levels in American Indian adults, even when small (0.1–1.0 μg/dL), were associated with reductions in systolic blood pressure. These findings suggest the need to further study the cardiovascular impacts of reducing lead exposures and the importance of lead exposure prevention.
AmbiFC: Fact-Checking Ambiguous Claims with Evidence
Authors: Glockner Max , Staliūnaitė Ieva , Thorne James , Vallejo Gisela , Vlachos Andreas , Gurevych Iryna
Published: January 2024

Automated fact-checking systems verify claims against evidence to predict their veracity. In real-world scenarios, the retrieved evidence may not unambiguously support or refute the claim and yield conflicting but valid interpretations. Existing fact-checking datasets assume that the models developed with them predict a single veracity label for each claim, thus discouraging the handling of such ambiguity. To address this issue we present AmbiFC,1 a fact-checking dataset with 10k claims derived from real-world information needs. It contains fine-grained evidence annotations of 50k passages from 5k Wikipedia pages. We analyze the disagreements arising from ambiguity when comparing claims against evidence in AmbiFC, observing a strong correlation of annotator disagreement with linguistic phenomena such as underspecification and probabilistic reasoning. We develop models for predicting veracity handling this ambiguity via soft labels, and find that a pipeline that learns the label distribution for sentence-level evidence selection and veracity prediction yields the best performance. We compare models trained on different subsets of AmbiFC and show that models trained on the ambiguous instances perform better when faced with the identified linguistic phenomena.
Language Varieties of Italy: Technology Challenges and Opportunities
Authors: Ramponi Alan
Published: January 2024

Italy is characterized by a one-of-a-kind linguistic diversity landscape in Europe, which implicitly encodes local knowledge, cultural traditions, artistic expressions, and history of its speakers. However, most local languages and dialects in Italy are at risk of disappearing within a few generations. The NLP community has recently begun to engage with endangered languages, including those of Italy. Yet, most efforts assume that these varieties are under-resourced language monoliths with an established written form and homogeneous functions and needs, and thus highly interchangeable with each other and with high-resource, standardized languages. In this paper, we introduce the linguistic context of Italy and challenge the default machine-centric assumptions of NLP for Italy’s language varieties. We advocate for a shift in the paradigm from machine-centric to speaker-centric NLP, and provide recommendations and opportunities for work that prioritizes languages and their speakers over technological advances. To facilitate the process, we finally propose building a local community towards responsible, participatory efforts aimed at supporting vitality of languages and dialects of Italy.
Occurrence and potential health risk of aflatoxin M1 in raw, pasteurised, and UHT milk in Thailand
Authors: Poapolathep S. , Klangkaew N. , Phaochoosak N. , Jawjaroensri W. , Sroynum A. , Saipinta D. , Suriyasathaporn W. , Giorgi M. , Zhang Z. , Fink-Gremmels J. , Poapolathep A.
Published: January 2024

Aflatoxin M1 (AFM1) is a major metabolite of aflatoxin B1 occurring in many feed materials commonly used in the ration of dairy cows. The level of contamination of feed materials is currently increasing in many parts of the world due to climate change. These changes emphasise the necessity to monitor AFM1 levels in milk and dairy products as a precautionary measure to protect consumer health risk. In the current study, the AFM1 levels were measured in raw, pasteurised, and ultra-high temperature processed (UHT) milk commercially available in Thailand. In total, 900 milk samples were analysed, making this investigation one of Thailand’s first large-scale monitoring of milk contamination. Over a period of three consecutive years (2019 to 2021), each year 100 individual samples of either AFM1, pasteurised and UHT milk were collected, extracted using an immunoaffinity column for AFM1 and then quantified by a validated liquid chromatography analysis with fluorescence detection. The measured AFM1levels ranged between 65-1,810 ng/l, 12-87 ng/l, and 7-38 ng/l in the raw, pasteurised, and UHT milk, respectively, in 2019. In 2020, the corresponding AFM1 concentration range was 58-1,232 ng/l, 15-65 ng/l, and 7-29 ng/l and 52-1,432 ng/l, 20-59 ng/l, and 7-33 ng/l in 2021, respectively. According to international guidance documents, a direct comparison and formal risk analysis revealed that the measured AFM1 levels of the processed milk samples showed good compliance with the US regulatory limits and the Codex Alimentarius Commission recommendations. However, the latter applies particularly to milk and dairy products intended for the consumption of infants and young children, considering their relatively high consumption of milk and dairy products and the specific sensitivity of infants and toddlers under the age of 6 years to mutagenic and genotoxic contaminants in foods.
Time’s Arrow and Simultaneity
Authors: Barbour Julian
Published: January 2024

In his paper ‘Bridging the neuroscience and physics of time’ with Dean Buonomano (2021) in a forthcoming book Rovelli makes claims that I question because they rely uncritically on theoretical frameworks employed when the laws of thermodynamics and general relativity were discovered. Their reconsideration suggests growth of entropy is not the origin of time’s arrow and identifies a notion of universal simultaneity within general relativity.
Korean species of Hydrosmecta Thomson (Coleoptera: Staphylinidae: Aleocharinae) with descriptions of two new species
Authors: LEE SEUNG-GYU , AHN KEE-JEONG
Published: January 2024

A taxonomic study of the genus Hydrosmecta Thomson in Korea is presented. The genus is represented in Korea by three species including two new species, H. beolgokensis Lee & Ahn, sp. nov. and H. koreana Lee & Ahn, sp. nov. A key to Korean species, descriptions, and illustrations of habitus photographs and diagnostic characters are provided to facilitate identification.
A natural history note on the Pseudoneoponera ant-mimicking behaviour of Naddia sp. (Staphylinidae: Staphylininae: Staphylinini: Staphylinina), from Kalyani University campus, Kalyani, Nadia, West Bengal, India.
Authors: BHATTACHARJEE BANANI , CHAKROVORTY ARNOB , HÁVA JIŘÍ , SAMADDER ASMITA
Published: January 2024

A unique ant-mimicking behaviour has been observed in the rove beetle genus Naddia sp. (Coleoptera: Staphylinidae: Staphylininae: Staphylinini: Staphylinina) from the campus of the University of Kalyani, Kalyani, Nadia, West Bengal, India. The individual has been observed to undergo imperfect Batesian mimicry by mimicking ants of the genus Pseudoneoponera (Hymenoptera: Formicidae: Ponerinae: Ponerini).
The Physics behind Precipitation Onset Bias in CMIP6 Models: The Pseudo-Entrainment Diagnostic and Trade-Offs between Lapse Rate and Humidity
Authors: Emmenegger Todd , Ahmed Fiaz , Kuo Yi-Hung , Xie Shaocheng , Zhang Chengzhu , Tao Cheng , Neelin J. David
Published: January 2024

Conditional instability and the buoyancy of plumes drive moist convection but have a variety of representations in model convective schemes. Vertical thermodynamic structure information from Atmospheric Radiation Measurement (ARM) sites and reanalysis (ERA5), satellite-derived precipitation (TRMM3b42), and diagnostics relevant for plume buoyancy are used to assess climate models. Previous work has shown that CMIP6 models represent moist convective processes more accurately than their CMIP5 counterparts. However, certain biases in convective onset remain pervasive among generations of CMIP modeling efforts. We diagnose these biases in a cohort of nine CMIP6 models with subdaily output, assessing conditional instability in profiles of equivalent potential temperature, θe, and saturation equivalent potential temperature, θes, in comparison to a plume model with different mixing assumptions. Most models capture qualitative aspects of the θes vertical structure, including a substantial decrease with height in the lower free troposphere associated with the entrainment of subsaturated air. We define a “pseudo-entrainment” diagnostic that combines subsaturation and a θes measure of conditional instability similar to what entrainment would produce under the small-buoyancy approximation. This captures the trade-off between larger θes lapse rates (entrainment of dry air) and small subsaturation (permits positive buoyancy despite high entrainment). This pseudo-entrainment diagnostic is also a reasonable indicator of the critical value of integrated buoyancy for precipitation onset. Models with poor θe/θes structure (those using variants of the Tiedtke scheme) or low entrainment runs of CAM5, and models with low subsaturation, such as NASA-GISS, lie outside the observational range in this diagnostic.
A Radar-Based Hail Climatology of Australia
Authors: Brook Jordan P. , Soderholm Joshua S. , Protat Alain , McGowan Hamish , Warren Robert A.
Published: January 2024

In Australia, hailstorms present considerable public safety and economic risks, where they are considered the most damaging natural hazard in terms of annual insured losses. Despite these impacts, the current climatological distribution of hailfall across the continent is still comparatively poorly understood. This study aims to supplement previous national hail climatologies, such as those based on environmental proxies or satellite radiometer data, with more direct radar-based hail observations. The heterogeneous and incomplete nature of the Australian radar network complicates this task and prompts the introduction of some novel methodological elements. We introduce an empirical correction technique to account for hail reflectivity biases at C band, derived by comparing overlapping C- and S-band observations. Furthermore, we demonstrate how object-based hail swath analysis may be used to produce resolution-invariant hail frequencies, and describe an interpolation method used to create a spatially continuous hail climatology. The maximum estimated size of hail (MESH) parameter is then applied to a mixture of over 50 operational radars in the Australian radar archive, resulting in the first nationwide, radar-based hail climatology. The spatiotemporal distribution of hailstorms is examined, including their physical characteristics, seasonal and diurnal frequency, and regional variations of such properties across the continent.
Diagnosis of Pancreatic Cancer after Cholecystectomy in the Elderly
Authors: Fromer Marc W. , Xu Qian , Shindorf Mackenzie L. , Mouw Tyler J. , Kong Maiying , Myers John , Feygin Yana , Ghosh Indranil , Martin Robert C. G. , McMasters Kelly M. , Philips Prejesh , Scoggins Charles R. , Ellis C. T. , Egger Michael E.
Published: January 2024

Background

Previous studies evaluating whether recent cholecystectomy is associated with a pancreas cancer diagnosis are limited. We aimed to examine if cholecystectomy was performed more frequently in the year prior to cancer diagnosis than would be expected in a similar non-cancer population.

Methods

SEER-Medicare linked files were used to identify patients with pancreatic adenocarcinoma. Cancer diagnoses were considered to be “timely” if within 2 months of cholecystectomy or “delayed” if 2-12 months after cholecystectomy. Clinical factors and survival outcomes were compared using chi-square and Kaplan-Meier analyses.

Results

Rate of cholecystectomy in the year prior to diagnosis of cancer was 1.9% for the cancer group, compared to .4% in the non-cancer group (OR = 4.7, 95% CI 4.4-5.1). Differences in the cancer vs non-cancer cohorts at the time of cholecystectomy included a higher age (74 vs 70, P < .0001), more males (49.9% vs 41.7%, P < .0001), and more frequent open technique (21.0% vs 9.4%, P < .0001). Acute pancreatitis was nearly twice as common in the cancer cohort (19.1%) vs the non-cancer cohort (10.7%), P < .0001. There were no differences between patients who had a timely diagnosis after cholecystectomy compared to a delayed diagnosis with regard to age, gender, comorbidity index, race, or rural/urban designation. The rates of localized disease and subsequent resection were also similar between the delayed and timely groups. Overall unadjusted survival was no different between timely and delayed diagnoses, P = .96.

Discussion

Elderly patients diagnosed with pancreatic adenocarcinoma are more likely to have had a recent cholecystectomy compared to those without.
The Effectiveness of Cognitive-Focused Interventions for Adults With Diabetes: A Systematic Review
Authors: Kim Bohyun , Hu Jie
Published: January 2024

Background:

Adults with diabetes and impaired memory and executive functions are more likely to experience difficulties in diabetes self-management and achieving glycemic targets. The purpose of this systematic review was to evaluate the evidence of the effects of cognitive-focused interventions on cognitive ability, diabetes self-management, and management of glycemic levels for middle-aged adults and older adults with diabetes.

Methods:

A systematic review of randomized controlled/clinical trials published in English between 2012 and 2022 was conducted. A search was performed using 5 databases (PubMed, CINAHL, Embase, Web of Science, and Scopus) in addition to hand-searching. The search terms included diabetes, adults, cognitive-focused intervention, cognition, self-management, and hemoglobin A1C (HbA1C).

Results:

Eleven studies met the inclusion criteria. Cognitive ability and diabetes self-management were assessed using different measurements, and glycemic levels were measured with HbA1C. Nine studies applied cognitive training, one provided working memory training, and one used occupational therapy. Eight studies combined cognitive training with a co-intervention, including self-efficacy, lifestyle management, physical training, chronic disease self-management program, square-stepping exercise, psychoeducational intervention, and empowerment. Eight studies showed statistically significant improvements in at least one cognitive domain.

Conclusions:

Cognitive-focused interventions have a positive effect on improving memory and executive function. However, the evidence of cognitive-focused interventions on diabetes self-management and glycemic levels has not been established. Future studies to improve cognition using effective strategies to improve cognitive function enhancing diabetes self-management behaviors and managing glycemic levels are warranted.
Exploring the impact of coercion and voluntary entry on dropout rates in a therapeutic community for substance abuse rehabilitation: A survival analysis and logistic regression study
Authors: Haviv Noam , Kaplan Harel Gal , Sternfeld Michal , Wolff Moshe
Published: January 2024

High dropout rates challenge drug rehabilitation programs. Both therapists and academics aim to pinpoint predictors of retention, with individual motivation, both external and internal, being a key factor. This study compares the retention period for residents who joined the Hartuv residential therapeutic community as an alternative to imprisonment (i.e. due to external motivation) and residents who joined the community voluntarily. In this study, residents of the Hartuv residential therapeutic community between 2010 and 2015, were examined. The analysis of the data used both logistic regression and survival analysis. The logistic regression evaluated the predictors for completing or dropping out of the program. The survival analysis examined the retention time, measured over a 12-month follow-up period, which represents the completion of the program. Residents who entered the program as an alternative to imprisonment were significantly more likely to retain in treatment than those who arrived voluntarily. Retention in treatment is influenced by external motivation. Strengthening familial, social, and occupational relationships may increase external motivation among residents who enter the program voluntarily and therefore lack significant external motivation.
Can digitalization improve agriculture? Exploring the impact of ICT on grain food production in SAARC countries
Authors: Chandio Abbas Ali , Amin Asad , Khan Irfan , Rehman Abdul , Memon Aslam
Published: January 2024

In the last two decades, the use of information and communication technology (ICT) has greatly enhanced the productivity of the global agricultural sector. There has been a substantial increase in global sustainable food production and food consumption. This research investigates the enduring impacts of ICT on grain food production in SAARC countries. This study employs balanced panel data from 2002 to 2019. In order to accomplish the objective of the research, we use the Method of Moments Quantile Regression (MMQR). The results indicate that the use of Mobile phones and the Internet has a significant and favourable effect on grain food production across all quantiles. Furthermore, the results of the causality tests validate a significant bidirectional causal relationship between Internet usage, cropland, fertilizer usage, rural labour, and grain food production. On the contrary hand, there is an unidirectional connection where Mobile phones use influence grain production. The study's results suggest that authorities of SAARC countries should invest supplementary resources to improve ICT infrastructure in rural regions. Therefore, farmers may improve their agricultural productivity and increase their revenue by using digital technology.
Genetic variants in the mTOR pathway with renal cancer risk and subtypes in East Indian population
Authors: Malakar Subhajit , Chatterjee Srilagna , Das Madhusudhan , Pal Dilip Kumar
Published: January 2024

Introduction:

Renal Cell Carcinoma (RCC), which accounts for 2%–3% of all adult malignant neoplasms with a male-to-female predominance of 1.9 to 1 with typical presentation between 55 and 75 years. The phosphoinositide-3-kinase–protein kinase B/Akt (PI3KPKB/Akt) pathway is a main pathway in control of cell growth. mTOR pathway plays a key role in the pathogenesis of RCC.

Material and methods:

Its a prospective observational study. Tissue samples were collected and processed and DNA isolation and sequencing was done to see for any association and expression.

Results and analysis:

Polymorphism analysis of the sequence of three genes MTOR, AKT1, and PIK3CA done and found an intronic variant of the MTOR gene (rs3737611) and AKT1 gene (rs2498797) to be significantly associated with clear cell Renal Cell Carcinoma tumor samples.

Discussion:

This study will help to understand the pathogenesis better and the information can be used to develop new drugs and personalized treatment strategies that are tailored to an individual’s genetic makeup. The study identify individuals who are at heightened risk for developing renal cancer and could benefit from targeted screening or preventative measures. Some sample size and definite geographical sample pool remains the main limitation of the study which may not be externally validate the study results.
Financial Education Effects on Financial Behavior and Well-Being: The Mediating Roles of Improved Objective and Subjective Financial Knowledge and Parallels in Physical Health
Authors: Netemeyer Richard G. , Lynch John G. , Lichtenstein Donald R. , Dobolyi David
Published: January 2024

How does financial education lead to improved financial behavior and higher financial well-being? An influential Consumer Financial Protection Bureau model introduced in 2015 proposes that the goal of financial education is to improve financial well-being and that financial education does so by increasing financial knowledge, which improves financial behavior, which improves financial well-being. In this study, the authors test links in the Consumer Financial Protection Bureau model, examining the differential roles of objective and subjective knowledge. They also test whether an analogous model might capture effects of physical health education on physical health knowledge, behavior, and well-being. They report a quasi-experiment comparing changes in financial and physical health knowledge, behavior, and well-being at two time points in a semester for students enrolled in a personal finance class, a personal health class, or neither. This study reports the first causal estimates of flow from financial education to financial knowledge to financial behaviors to a validated measure of subjective financial well-being. Financial education caused large changes in both objective and subjective knowledge. Yet only subjective knowledge mediated the large effects of financial education on changes in downstream behaviors. The authors find weaker but similar results for physical health. The findings suggest that financial education efforts should be refocused to foster subjective knowledge and improved behavior.
How Does the “Us” versus “Them” Polarization Work? Capturing Political Antagonism with the Political Antagonism Scale
Authors: Hrbková Lenka , Macek Jakub , Macková Alena
Published: January 2024

This article introduces and validates a Political Antagonism Scale (PAS) aimed at capturing politically motivated antagonism among people in survey research. Recent trends indicate a rise in politically motivated polarization across various countries. This polarization often transcends mere ideological distance, fostering an identity-based “Us” versus “Them” political perspective. Consequently, this transforms the traditional agonistic political competition into a deeply adversarial antagonistic relationship, wherein political counterparts are viewed as enemies. Such conflict dynamics may have serious consequences for democratic politics and should be examined by scholars of political polarization across different political contexts and at different time points. However, a comprehensive survey tool addressing this form of polarization has been lacking. The PAS is designed to capture the complex nature of political antagonism and target individual antagonization towards their political outgroups on the level of social groups, political elites, the media, and everyday life. In addition, we present a regression model identifying potential sources of political antagonism. Our findings highlight the significant association between political antagonization and a prevailing sense of ontological insecurity.
Deciphering the pathways for deployment of different architecture of chemically treated and untreated fibers in polymeric materials for performance enhancement
Authors: Radhakrishnan Sidharth , Dwivedi Shashi Prakash , Sharma Bhasha , Gupta Sumit , Gupta Pallav , Chaudhary Vijay
Published: January 2024

Light weight polymer composites have been researched for a long time as potential replacements for metallic materials in every field of engineering. Material scientists are focused on using natural materials as constituents of polymer composite to make it bio-degradable, non-toxic, and safer for the environment. But an obstacle in place for this is the low mechanical properties of the bio-composites. Mechanical properties depend on reinforcement, which can be enhanced to a certain extent using different fiber architectures while introducing into the polymer matrix to develop the composite material. In this study, the various fiber reinforcement architectures are present and their influence on different properties has been discussed. In addition, the evolution in the fiber architecture used in polymer composite have also been discussed. The various difficulties faced have been discussed with possible methods to overcome them.