bluuebunny/crossref_metadata_2025 huggingface dataset:
Unveiling the composition of the single-charm molecular pentaquarks: insights from radiative decay and magnetic moment
Authors: Wang Fu-Lai , Luo Si-Qiang , Liu Xiang
Published: February 2025

In order to unravel the composition of the isoscalar DN, 

$$D^*N,$$

D
∗

N
,

$$D_1N,$$

D
1

N
,

and

$$D_2^*N$$

D
2
∗

N

molecular pentaquarks, we carry out a systematic investigation of their M1 and E1 radiative decays and magnetic moment properties. Using the constituent quark model and taking into account the S–D wave mixing effect and the coupled channel effect, our analysis yields numerical results indicating that the M1 and E1 radiative decays and the magnetic moment properties of these molecular pentaquarks provide some insights into their inner structures. These results can provide crucial clues for distinguishing their spin-parity quantum numbers and configurations in experimental studies. In addition, we highlight the importance of the electromagnetic properties as key observables for elucidating the inner structures of the observed

$$\Lambda _c^{+} (2940)$$

Λ
c
+

(
2940
)

and

$$\Lambda _c^{+} (2910).$$

Λ
c
+

(
2910
)

.

We hope that these findings can inspire our experimental colleagues to further explore the family of the single-charm molecular pentaquarks and to investigate the inner structures of the

$$\Lambda _c^{+} (2940)$$

Λ
c
+

(
2940
)

and

$$\Lambda _c^{+} (2910)$$

Λ
c
+

(
2910
)

in future studies.
Numerical study of buffer layers to obtain high performance solar cell
Authors: Sonawane Makrand E. , Kutwade Vishnu V. , Tonpe Dipak A. , Mazher Khan Faizan , Gajbar Prakash , Sumaiyya Shaikh , Sharma Ramphal
Published: February 2025

With the help of the SCAPS simulation program, this study investigates the possible buffer layer in copper zinc tin sulfide (CZTS)/zinc oxide (ZnO) thin-film solar cells. The tin sulfide (SnS[Formula: see text] and cadmium sulfide (CdS) are taken into consideration. Based on electronic band structure analysis, SnS2 exhibits a more favorable alignment of the conduction band than CZTS, which minimizes the barrier to energy transfer. SnS2 buffer layer showed enhanced carrier generation because of its higher absorption coefficient. Quantum efficiency plots highlighted SnS2’s superior performance in the 400–800[Formula: see text]nm range, where CZTS strongly absorbs. The [Formula: see text] buffer layer solar cell exhibited a larger J–V curve area, driven by a higher short-circuit current density ([Formula: see text].) of 24.27[Formula: see text]mA/cm2 compared to 23.69[Formula: see text]mA/cm2 for CdS, achieving an impressive 15.71% efficiency versus 15.35% for CdS. Because of improved band alignment, enhanced charge generation and higher quantum efficiency, [Formula: see text] is proving to be a promising buffer layer for high-efficiency CZTS solar cells.
An Empirical Study of Code Simplification Methods in Code Intelligence Tasks
Authors: Shen Zongwen , Li Yuning , Ge Jidong , Chen Xiang , Li Chuanyi , Huang Liguo , Luo Bin
Published: February 2025

In recent years, pre-trained language models have seen significant success in natural language processing and have been increasingly applied to code-related tasks. Code intelligence tasks have shown promising performance with the support of code pre-trained language models. Pre-processing code simplification methods have been introduced to prune code tokens from the model’s input while maintaining task effectiveness. These methods improve the efficiency of code intelligence tasks while reducing computational costs. Post-prediction code simplification methods provide explanations for code intelligence task outcomes, enhancing the reliability and interpretability of model predictions. However, comprehensive evaluations of these methods across diverse code pre-trained model architectures and code intelligence tasks are lacking. To assess the effectiveness of code simplification methods, we conduct an empirical study integrating these code simplification methods with various pre-trained code models across multiple code intelligence tasks.

Our empirical findings suggest that developing task-specific code simplification methods would be beneficial. Then, we recommend leveraging post-prediction methods to summarize prior knowledge, which can pre-process code simplification strategies. Moreover, establishing more evaluation mechanisms for code simplification is crucial. Finally, we propose incorporating code simplification methods into the pre-training phase of code pre-trained models to enhance their program comprehension and code representation capabilities.
Advances in Online Comprehensive Two-Dimensional Liquid Chromatography Method Development
Authors: Nell Eugene H.J. , Muller Magriet , de Villiers André
Published: February 2025

Comprehensive two-dimensional liquid chromatography (LC×LC) is increasingly being used to provide new information on the composition of complex samples. More widespread use of the technique is, however, hampered by the complexity of method development, which involves the selection and optimization of a very large number of experimental variables while considering their interdependence and relationship with conflicting analysis goals. This contribution summarizes the progress made in online LC×LC method development to date. Recent trends in advanced method optimization are highlighted to demonstrate how progress in the field enables the development of highly efficient LC×LC methods.
Comprehensive Analysis of Human Dendritic Spine Morphology and Density
Authors: Schünemann Kerstin D , Hattingh Roxanne M , Verhoog Matthijs B , Yang Danqing , Bak Aniella , Peter Sabrina , van Loo Karen M.J. , Wolking Stefan , Kronenberg-Versteeg Deborah , Weber Yvonne , Schwarz Niklas , Raimondo Joseph V , Melvill Roger , Tromp Sean Andrew , Butler James Thomas , Höllig Anke , Delev Daniel , Wuttke Thomas V , Kampa Björn M , Koch Henner
Published: February 2025

Dendritic spines, small protrusions on neuronal dendrites, play a crucial role in brain function by changing shape and size in response to neural activity. So far, in depth analysis of dendritic spines in human brain tissue is lacking. This study presents a comprehensive analysis of human dendritic spine morphology and density using a unique dataset from human brain tissue from 27 patients (8 females, 19 males, aged 18-71) undergoing tumor or epilepsy surgery at three neurosurgery sites. We used acute slices and organotypic brain slice cultures to examine dendritic spines, classifying them into the three main morphological subtypes: Mushroom, Thin, and Stubby, via 3D reconstruction using ZEISS arivis Pro software. A deep learning model, trained on 39 diverse datasets, automated spine segmentation and 3D reconstruction, achieving a 74% F1-score and reducing processing time by over 50%. We show significant differences in spine density by sex, dendrite type, and tissue condition. Females had higher spine densities than males, and apical dendrites were denser in spines than basal ones. Acute tissue showed higher spine densities compared to cultured human brain tissue. With time in culture, Mushroom spines decreased, while Stubby and Thin spine percentages increased, particularly from 7-9 to 14 days in vitro, reflecting potential synaptic plasticity changes. Our study underscores the importance of using human brain tissue to understand unique synaptic properties and shows that integrating deep learning with traditional methods enables efficient large-scale analysis, revealing key insights into sex- and tissue-specific dendritic spine dynamics relevant to neurological diseases.
ZIF‐8 Selective Dispersive Solid‐Phase Extraction‐LC‐MS/MS Method for the Determination of Aconitine alkaloids in Rat Plasma: Application in Pharmacokinetic Studies
Authors: Yang Yang , Renyan Liu , Lingyi Xin , Baodong Feng , Yu Zhang , Linqi Su , Tingwen Ming , Jingjian Liu , Qinhua Chen
Published: February 2025

Objective: Aconitine alkaloids, as the principal bioactive constituents of Fuzi, pose a significant challenge to its clinical application due to their toxicity. This study aimed to establish a rapid, efficient, and stable method for quantifying monoester‐type and diester‐type alkaloids in raw Fuzi using zeolitic imidazolate framework‐8 (ZIF‐8). The method was subsequently applied to pharmacokinetic studies in rats, offering valuable insights into the safe clinical use of Fuzi.

Methods: Synthetic ZIF‐8 was employed as the microextraction adsorbent, with optimization of extraction parameters such as ZIF‐8 content, shaker speed, extraction time, and sodium ion concentration to maximize enrichment efficiency. A dispersive solid‐phase extraction–liquid chromatography–tandem mass spectrometry (d‐SPE–LC–MS/MS) method, based on ZIF‐8, was developed and validated for method performance. The pharmacokinetics of five aconitine alkaloids in Fuzi were investigated, ensuring efficient extraction and analysis.

Results: Under the optimized conditions, the d‐SPE method demonstrated robust enrichment of aconitine alkaloids. A strong linear relationship was established for aconitine, hypaconitine, mesaconitine, lappaconitine, and benzoylaconitine within the concentration range of 0.3125–1000 ng/mL, with correlation coefficients exceeding 0.99. The LC–MS/MS assay achieved a detection limit as low as 0.104 ng/mL. Additionally, the pharmacokinetic analysis revealed rapid absorption of the five alkaloids, with benzoylaconitine exhibiting a Tmax of 0.25 h.

Conclusion: This study introduces a novel d‐SPE–LC–MS/MS method based on ZIF‐8 for the analysis of aconitine alkaloids in plasma, facilitating pharmacokinetic studies of Fuzi. These findings substantially contribute to a deeper understanding of the in vivo pharmacokinetics of aconitine alkaloids.
No increased risk of venous thromboembolism or infectious complications after JAK inhibitor exposure in patients with ulcerative colitis undergoing surgery
Authors: De Greef Ine , Bislenghi Gabriele , Terrasson Isabelle , Sabino João , Ferrante Marc , D'Hoore André , Verstockt Bram , Vermeire Séverine
Published: February 2025

Introduction
Total colectomy for ulcerative colitis (UC) is associated with postoperative morbidity, including venous thromboembolic events (VTE). In light of recent concerns on increased major adverse events associated with JAK inhibitor exposure, we aimed to evaluate the postoperative VTE risk as well as other complications in UC patients undergoing colectomy.
Methods
This single-center retrospective cohort study included all UC patients who underwent (procto)colectomy between 2013 and March 2022, and documented the 180-day postoperative non-infectious and infectious complications.
Results
One hundred seventy-five UC patients (43.4% women, median age 41.0 years) underwent colectomy. Forty-nine patients (28.0%) were operated in an urgent setting. In the twelve weeks prior to surgery, 53 (30.3%) patients had received anti-TNF agents, 40 (22.9%) anti-adhesion therapy, 16 (9.1%) anti-IL12/23 and 34 (19.4%) JAK inhibitors. Preoperatively, 26 patients (14.9%) received moderate to high doses of systemic corticosteroids. All except two patients received prophylactic LMWH postoperatively. During the 180-day postoperative period, 2 patients developed a thrombosis, all incidental findings on abdominal CT scan. No VTE was seen in the patients who underwent colectomy while on JAK inhibitor. Three out of 34 JAK-inhibitor treated patients (8.8%) developed a postoperative infectious complication, while the overall incidence of infectious complications was 17.1%.
Conclusion
Our findings suggest that the overall VTE risk in UC patients undergoing colectomy is low with adequate antithrombotic prophylaxis. JAK inhibitor use prior to surgery was not linked to increased short-term thromboembolic or infectious complications. However, the limited sample size warrants further study in larger cohorts.
Pathological resection depth in cold snare polypectomy and underwater endoscopic mucosal resection for superficial nonampullary duodenal epithelial tumors
Authors: Nukui Takayuki , Shimura Takaya , Inaguma Shingo , Sugimura Naomi , Fukusada Shigeki , Nishigaki Ruriko , Kojima Yuki , Kanno Takuya , Sasaki Makiko , Tanaka Mamoru , Ozeki Keiji , Kubota Eiji , Takahashi Satoru , Kataoka Hiromi
Published: February 2025

Introduction: Cold snare polypectomy (CSP) and underwater endoscopic mucosal resection (UEMR) are used for small superficial nonampullary duodenal epithelial tumors (SNADETs). This study aimed to compare the resection depth of SNADETs removed by CSP versus UEMR.
Methods: Data from SNADETs resected with CSP or UEMR between April 2018 and July 2023 at Nagoya City University Hospital were collected retrospectively. An independent pathologist, blinded to clinical information, evaluated the resected specimens and categorized resection depth into four groups: M (mucosa only), iMM (incomplete muscularis mucosae with defects), cMM (complete muscularis mucosae without submucosa), and SM (complete muscularis mucosae with submucosa).
Results: Among 109 SNADETs, 78 were resected with CSP and 31 with UEMR. The percentages of M, iMM, cMM, and SM for CSP were 23.1%, 50.0%, 5.1%, and 21.8%, respectively, while for UEMR they were 0%, 6.5%, 6.5%, and 87.1%. The UEMR group had a higher SM resection rate and significantly greater median submucosal depth than the CSP group (783 μm [range, 0–2220] vs. 0 μm [range, 0–1226]; P < 0.001). On the multivariate analysis, UEMR was an independent and significant factor for achieving SM resection.
Conclusion: UEMR achieved a significantly higher SM resection rate than CSP for all types of SNADETs. Clinically diagnosed duodenal high-grade dysplasia and adenocarcinoma should be treated with UEMR rather than CSP.
S-commerce: competition drives action through small medium enterprise top management
Authors: Sutomo Erwin , Abdul Rahman Nur Shamsiah , Romli Awanis
Published: February 2025

This study investigates the factors influencing the continued use of S-commerce in small and medium enterprises (SMEs), focusing on the roles of top management (TM) support, competitive pressure (CP), facilitating conditions, and service quality. Data were collected from 341 SME owners and analyzed using SEM. Data was analyzed with SmartPLS using a two-step approach. The findings indicate that TM support significantly impacts the continued use of S-commerce by influencing facilitating conditions and service quality while CP affects TM behavior and usage continuity. However, the findings reveal that operational factors, such as infrastructure and service quality, play a more critical role in sustaining S-commerce engagement than external pressures. Facilitating conditions, in particular, were found to have a strong influence on service quality and platform engagement, underscoring the importance of technical and organizational resources. The study extends prior research by highlighting the interplay between internal and external drivers in fostering the continuous use of S-commerce, offering practical insights for SMEs and future research directions.
Enhancing accessibility: deep learning-based image description for individuals with visual impairments
Authors: Shah Nidhi B. , Ganatra Amit P.
Published: February 2025

Technological developments in artificial intelligence, namely in the area of deep learning, have created new avenues for enhancing accessibility for those with visual impairments. In order to improve the capacity of people who are blind or visually impaired to understand and interact with visual material, this research investigates the creation and use of deep learning-based image description systems. We provide a comprehensive method that uses recurrent neural networks (RNNs) to generate natural language descriptions and convolutional neural networks (CNNs) and Autoencoders for extracting picture features. Our technology automatically creates comprehensive, context-aware descriptions of photographs by incorporating these models, giving users a better knowledge of their surroundings. We show the accuracy and reliability of the system on a wide range of photos through comprehensive testing. According to our research, deep learning-based picture description systems and converting the description in audio and making a promise to empower people who are visually impaired and foster diversity in the digital sphere.
Enhancing SDN security using ensemble-based machine learning approach for DDoS attack detection
Authors: Hirsi Abdinasir , Audah Lukman , Salh Adeb , Alhartomi Mohammed A. , Ahmed Salman
Published: February 2025

Software-defined networking (SDN) is a groundbreaking technology that transforms traditional network frameworks by separating the control plane from the data plane, thereby enabling flexible and efficient network management. Despite its advantages, SDN introduces vulnerabilities, particularly distributed denial of service (DDoS) attacks. Existing studies have used single, hybrid, and ensemble machine learning (ML) techniques to address attacks, often relying on generated datasets that cannot be tested because of accessibility issues. A major contribution of this study is the creation of a novel, publicly accessible dataset, and benchmarking the proposed approach against existing public datasets to demonstrate its effectiveness. This paper proposes a novel approach that combines ensemble learning models with principal component analysis (PCA) for feature selection. The integration of ensemble learning models enhances predictive performance by leveraging multiple algorithms to improve accuracy and robustness. The results showed that the ensemble of random forests (ENRF) model achieved the highest performance across all metrics with 100% accuracy, precision, recall, and F1-score. This study provides a comprehensive solution to the limitations of existing models by offering superior performance, as evidenced by the comparative analysis, establishing this approach as the best among the evaluated models.
Emerging approaches of artificial intelligence tools for distance learning: a review
Authors: Faouzi Ghita , Amrous Naila , El Faddouli Nour-Eddine , Khabouze Mostafa
Published: February 2025

Learning management system (LMS) is the best way to deliver educational content in the context of higher education, by settings students worldwide with high-quality educational material. This paper principally seeks to examine the use of e-learning platforms in the last years from 2019 to 2023, which has coincided with the pandemic period, by elucidating the benefits and limitations of e-learning platforms, analyzing the real-world artificial intelligence (AI) algorithms used and their operating context. A comprehensive literature search was conducted on different electronic databases to identify relevant studies related to e-learning and AI tools used during this period by applying inclusion, exclusion criteria and preferred reporting items for systematic reviews and meta-analysis (PRISMA) process. Based on this review the tools were necessary social media and free communication platforms that offer the flexibility and build autonomy to students. On the other hand, many challenges are arisen due to the lack of experience in the term of using those tools or due to technical problems, for this reason, the use of AI tools to enhance learning experience still one of the approved solutions.
An efficient load balance using virtual machine migration hybrid optimization technique in cloud computing
Authors: Sivalingam Saravanan Madderi , Prathapagiri Pavan Kumar
Published: February 2025

Cloud computing is becoming increasingly important to developers and companies because to the rapid development of information technology and the wide availability of internet applications. Every information technology industry has a significant role for cloud computing. Numerous multinational technology businesses, like Google, Microsoft, and Facebook, have established data centers across the world to offer processing and storage capabilities. Customers can submit their jobs to cloud centers directly. Reducing overall power usage is the primary goal, which was overlooked in the early stages of cloud development. Using gene expression programming (GEP), symbolic regression models of virtual machines (VMs) are developed using measured VM loads and the corresponding resource parameters. In order to minimize resource use, multidimensional resource load balancing of all the physical machines within the cloud computing platform is the aim of this analysis. The VMH loads estimated and the genetic algorithm that considers the current and the future loads of VMHs and decides an optimal VM-VMH for migrating VMs and performing load-balance. Hence, an efficient load balance using virtual machine migration hybrid optimization technique (HOT) in cloud computing shows better results in terms of accuracy, energy consumption, migration cost.
Plant leaf disease detection and classification using artificial intelligence techniques: a review
Authors: R Kusuma , Rajkumar R.
Published: February 2025

Agriculture is a cornerstone of human civilization, providing both food and economic stability. While not necessarily fatal, leaf diseases are a crucial threat to plant health. Accurate detection and classification of diseases in early stages are essential to minimize damage. Manual identification can be challenging, and delays in detection can lead to crop devastation. Fortunately, computer-aided image processing offers a solution. Researchers have explored several techniques for disease detection and classification by usage of affected leaf images, making significant progress over time. However, there's always room for improvement. Machine learning (ML), Deep learning (DL) techniques have shown hopeful results. ML, DL approaches act as black-box; eXplainable AI (XAI) provides clear explanations on decisions made by these black-boxes. This study aims to present a comprehensive review on plant leaf disease detection and classification by means of ML, DL and XAI methods with an overview of the outcomes of existing techniques, summarizes their performance, evaluation metrics, and analyses the challenges in existing systems, and offers the study's inferences.
Discrete wavelet transform and convolutional neural network based handwritten Sanskrit character recognition
Authors: Shelke Shraddha V. , Chandwadkar Dinesh M. , Ugale Sunita P. , Chothe Rupali V.
Published: February 2025

Sanskrit is one of the ancient languages from which the majority of present Indian languages are developed. Although the national mission for manuscripts (NMM) is digitizing handwritten Sanskrit manuscripts, there are still a lot of papers that need to be digitized. Recognition of handwritten script is a challenging task due to individual differences in writing styles and how those variations alter over time. The Sanskrit language is written in Devanagari script. A novel approach using discrete wavelet transform (DWT) and convolutional natural network (CNN) is proposed in this paper. Devanagari handwritten character dataset which includes 2000 handwritten images of 36 classes (2000*36=72000) is used in this research. Fine-tuned GoogLeNet model implemented here gave optimum values of epochs and learning rate of 15 and 0.01 respectively. Classification accuracy obtained by proposed DWT – CNN model is 98.97% with a loss of 0.098. Fine-tuned GoogLeNet model achieves 99.68% accuracy with a 0.0635 loss. Results obtained are also compared with existing approaches and found superior.
Internet of things meteorological station for climate monitoring and crop optimization in Carabayllo-Perú
Authors: Rumiche-Cardenas Jeremy Jared , Figueroa-Guevara Axel Walter , Gamarra-Pahuacho Deyvis Jhosmar , Quiroz-Grados Josue Daniel , Segovia-Ojeda Jamil , Cabana-Cáceres Maritza , Castro-Vargas Cristian
Published: February 2025

In the agricultural sector, monitoring environmental variables such as temperature, humidity, and atmospheric pressure is crucial for efficient and sustainable agriculture. However, conventional monitoring systems are expensive and need more autonomy, making their implementation difficult in small- and medium-scale agricultural operations. This study presents the design, implementation, and evaluation Internet of things (IoT)-based autonomous for watch remote critical climate variables in the Carabayllo region, Peru. The system uses a data acquisition, processing, and transmission architecture based on the ESP32 microcontroller, DHT22 sensors for measure climatic aspects, BMP180 for detection barometric, and the ThingSpeak cloud platform for data storage and visualization. Results show that the proposed system achieves accuracy comparable to commercial weather stations, making it accessible to small farmers. The implementation demonstrated the system’s ability to detect feasible local microclimates to monitor and predict weather patterns for proper crop growth. This approach enables farmers to monitor conditions in real time, receive early alerts on adverse weather events, and optimize agricultural practices such as irrigation and fertilization. The study concludes that the proposed IoT weather station represents a viable and cost-effective solution to improve agricultural decision-making in developing regions, potentially contributing to increasing crops.
Techniques of image segmentation: a review
Authors: Meinam Sharmila , Nongmeikapam Kishorjit , Singh N. Basanta
Published: February 2025

Image segmentation is a popular topic of research. Image segmentation divides an image into different parts that can be used for further analysis. By doing so, the image becomes simple and more meaningful information can be extracted. The segmentation techniques divide an image into multiple parts based on certain features of the image namely: color, texture, and intensity value of the pixel. Segmentation is considered as one of the toughest tasks for extracting features from an image, detection of objects and lastly classification of the image. The applications of image segmentation in every aspect of life such as satellite image analysis, object detection and recognition, in agricultural field, self-driving vehicles, and medical imaging. Has become indispensable. Till date, though researchers have developed many segmentation techniques, they are unable to design a generalized methodology for the image segmentation problems. A review of image segmentation techniques has been presented in this study. A summary of the advantages and disadvantages of these techniques has been presented. The focus of this manuscript is to provide a summary of the available research work on segmentation which will benefit the enthusiastic researchers in gaining better understanding about segmentation models in various application domains.
Assessment of cloud-free normalized difference vegetation index data for land monitoring in Indonesia
Authors: Hadiyanto Ahmad Luthfi , Sukristiyanti Sukristiyanti , Hidayat Arif , Pratiwi Indri
Published: February 2025

Continuous land monitoring in Indonesia using optical remote sensing satellites is difficult due to frequent clouds. Therefore, we studied the feasibility of monthly land monitoring during the second half of 2019, using moderate resolution imaging spectroradiometer (MODIS) normalized difference vegetation index (NDVI) data from Terra and Aqua satellites. We divide the Indonesian area into seven regions (Sumatra, Java, Kalimantan, Sulawesi, Nusa Tenggara, Maluku, and Papua) and examine NDVI data for each of the regions. We also calculated the cloud occurrence percentage every hour using Himawari-8 data to compare cloud conditions at different acquisition times. This research shows that Terra satellite provides more cloud-free pixels than Aqua while combining data from both significantly increase the cloud-free NDVI pixels. Monthly monitoring is feasible in most regions because the cloudy areas are less than 10%. However, in Sumatra, the cloudy area was more than 10% in October 2019. We need to include further data processing to improve the feasibility of continuous monitoring in Sumatra. This research concludes that monthly monitoring is still feasible in Indonesia, although some data require further processing. The use of additional data from other satellites in the monitoring can be an option for further research.
A recurrent network technique for energy optimization in 6G networks with dynamic device-to-device communication
Authors: Aneesh Sonia , Shaikh Alam N.
Published: February 2025

Energy efficiency has become a paramount concern in the design and deployment of 6G networks, driven by the exponential growth of connected devices and increasing traffic demands. For domain experts grappling with dynamic device-to-device (D2D) communication scenarios, optimizing energy consumption while maintaining reliable connectivity poses a significant challenge. To address this issue, we propose a novel recurrent network technique that dynamically configures D2D communication patterns, adaptively allocating temporary base stations among network nodes to enable efficient data transmission while minimizing energy expenditure. Our simulations demonstrate substantial energy savings, extended node lifetimes, and reliable performance, with a 37% reduction in overall network energy consumption and a 65% increase in average node lifetime compared to traditional cellular communication scenarios. In conclusion, this innovative approach paves the way for sustainable and energy efficient 6G communication systems, benefiting society by reducing operational costs, minimizing environmental impact, and prolonging the usability of mobile devices.
Detection of COVID-19 based on cough sound and accompanying symptom using LightGBM algorithm
Authors: Wiharto Wiharto , Abdurrahman Annas , Salamah Umi
Published: February 2025

Coronavirus disease 19 (COVID-19) is an infectious disease whose diagnosis is carried out using antigen-antibody tests and reverse transcription polymerase chain reaction (RT-PCR). Apart from these two methods, several alternative early detection methods using machine learning have been developed. However, it still has limitations in accessibility, is invasive, and its implementation involves many parties, which could potentially even increase the risk of spreading COVID-19. Therefore, this research aims to develop an alternative early detection method that is non-invasive by utilizing the LightGBM algorithm to detect COVID-19 based on the results of feature extraction from cough sounds and accompanying symptoms that can be identified independently. This research uses cough sound samples and symptom data from the Coswara dataset, and cough sound’s features were extracted using the log mel-spectrogram, mel frequency cepstrum coefficient (MFCC), chroma, zero crossing rate (ZCR), and root mean square (RMS) methods. Next, the cough sound features are combined with symptom data to train the LightGBM. The model trained using cough sound features and patient symptoms obtained the best performance with 95.61% accuracy, 93.33% area under curve (AUC), 88.74% sensitivity, 97.91% specificity, 93.17% positive prediction value (PPV), and 96.33% negative prediction value (NPV). It can be concluded that the trained model has excellent classification capabilities based on the AUC values obtained.
Novel intelligent trust computation for securing internet-of-things using probability based artificial intelligence
Authors: Fathima Nasreen , Sunitha Patel Mysore Shantharaj , Basavegowda Kiran
Published: February 2025

With rising demands of smart appliances with normal locations transforming themselves in smart cities, internet-of-things (IoT) encounters various evolving security challenges. The frequently adopted encryption-based approaches have its own limitation of identifying dynamic threats while artificial intelligence (AI) based methodologies are found to address this gap and yet they too have shortcomings. This manuscript presents an intelligent trust computational scheme by harnessing probability-based modelling and AI-scheme for monitoring the dynamic malicious behavior of an unknown adversaries. The study contributes towards a novel AI-model using reinforcement learning towards leveraging decision making for confirming the presence of unknown adversaries. The benchmarked study shows that proposed system offers significant improvement when compared to existing AI-models and other cryptographic schemes with respect to delay, throughput, detection accuracy, execution duration.
Roles of Sirtuins in Cardiovascular Diseases: Mechanisms and Therapeutics
Authors: Ding Yang-Nan , Wang Hui-Yu , Chen Xiao-Feng , Tang Xiaoqiang , Chen Hou-Zao
Published: February 2025

Cardiovascular diseases (CVDs) are experiencing a rapid surge and are widely recognized as the leading cause of mortality in the current aging society. Given the multifactorial etiology of CVDs, understanding the intricate molecular and cellular mechanisms is imperative. Over the past 2 decades, many scientists have focused on Sirtuins, a family of nicotinamide adenine dinucleotide–dependent deacylases. Sirtuins are highly conserved across species, from yeasts to primates, and play a crucial role in linking aging and diseases. Sirtuins participate in nearly all key physiological and pathological processes, ranging from embryogenic development to stress response and aging. Abnormal expression and activity of Sirtuins exist in many aging-related diseases, while their activation has shown efficacy in mitigating these diseases (eg, CVDs). In terms of research, this field has maintained fast, sustained growth in recent years, from fundamental studies to clinical trials. In this review, we present a comprehensive, up-to-date discussion on the biological functions of Sirtuins and their roles in regulating cardiovascular biology and CVDs. Furthermore, we highlight the latest advancements in utilizing Sirtuin-activating compounds and nicotinamide adenine dinucleotide boosters as potential pharmacological targets for preventing and treating CVDs. The key unresolved issues in the field—from the chemicobiological regulation of Sirtuins to Sirtuin-targeted CVD investigations—are also discussed. This timely review could be critical in understanding the updated knowledge of Sirtuin biology in CVDs and facilitating the clinical accessibility of Sirtuin-targeting interventions.
Infant Gut Microbiota and Childhood Blood Pressure: Prospective Associations and the Modifying Role of Breastfeeding
Authors: Liu Tiange , Stokholm Jakob , Zhang Mingyu , Vinding Rebecca , Sørensen Søren J. , Zhao Ni , Mueller Noel T.
Published: February 2025

Background

Germ‐free mice experiments indicate that human gut microbiota influence blood pressure (BP), but no studies have prospectively examined if infant gut microbiota affects their future childhood BP. We aim to investigate prospective associations of infant gut microbiota diversity and composition with childhood BP, examining effect measure modification by breastfeeding and mediation by a child's body mass index.

Methods and Results

In the Copenhagen Prospective Studies on Asthma in Childhood 2010 cohort, we measured infant gut microbiota (16S rRNA V4) at 1 week, 1 month, and 1 year and child BP at 3 and 6 years. We assessed α diversity‐BP, β diversity‐BP, and microbe abundances‐BP associations using linear regression, permutational multivariate analysis of variance, and beta‐binomial count regression, respectively. Data from 526 children showed that α diversity and several
Bifidobacterium
spp. had protective associations with BP but only in children breastfed for ≥6 months. For instance, a 1‐unit increment in 1 month Shannon index was associated with 1.86 mm Hg (95% CI, 0.66–3.05) lower 6‐year systolic BP in children breastfed ≥6 months but a 0.73 (95% CI, −1.00 to 2.45) higher 6‐year systolic BP in those breastfed <6 months (
P
‐interaction=0.02). Greater abundance of 2
Bifidobacterium
microbes at 1 week was negatively associated with 6‐year systolic BP when breastfeeding ≥6 months (
P
‐interaction<0.1). Further, abundance of 8 microbes at 1week or 1 month was linked to 3‐year or 6‐year BP (false discovery rate
P
<0.05), with 5 of them independent of a child's body mass index. Lastly, 1‐week unweighted UniFrac distance and 1‐year weighted UniFrac distance were associated with BP after adjustment (
P
<0.05).

Conclusions

Gut microbiota features at 1 week and 1 month of life were associated with BP at 6 years. Breastfeeding duration modified key associations including those for α diversity and
Bifidobacteria
.
Discovery of a new anaxyelid wasp (Hymenoptera: Anaxyelidae) in mid-Cretaceous Kachin Amber
Authors: OYAMA NOZOMU , JOUAULT CORENTIN , MITA TOSHIHARU
Published: February 2025

The family Anaxyelidae, known primarily from the fossil record, consists of two subfamilies—Syntexinae, and Anaxyelinae—spanning from the Middle Jurassic to the present. Among the latter, Syntexinae are mostly represented by fossil taxa from the Jurassic and Cretaceous periods, with Syntexis being the only extant genus, and no known Cenozoic occurrences. In this study, we describe a new genus and species of Syntexinae, Deresyntexis bethouxi gen. et sp. nov. from mid-Cretaceous Kachin amber. Although this discovery does not resolve existing diversity gaps, the well-preserved specimen provides valuable insights into the evolutionary traits and adaptations of Syntexinae during the Cretaceous. The unique morphology of the specimen, distinct from previously described genera, contributes to a deeper understanding of the evolutionary history and morphological diversity of the family.
An erotine net-winged beetle from Cenomanian Burmese amber confirms the Mesozoic origins of subfamilies (Coleoptera: Elateroidea: Lycidae: Erotinae)
Authors: MOTYKA MICHAL , KUSY DOMINIK , BOCAK LADISLAV
Published: February 2025

A new net-winged beetle, Dostaliella filiformis gen. et sp. nov., is described from mid-Cretaceous Burmese amber. Due to distinct morphology, the new tribe Dostaliellini trib. nov. is erected for this species in the subfamily Erotinae (Coleoptera, Elateroidea, Lycidae). Unlike previously described Cretaceous lycids, Dostaliella filiformis gen. et sp. nov. features a median pronotal areola. The presence of Erotinae in Cenomanian fauna is consistent with age estimates for Erotinae based on molecular phylogenies not calibrated with lycid data. The finding represents physical evidence for the timing of the radiation of the Erotinae and their low morphological disparity since the early phase of their evolution. Notably, all known lycid specimens preserved in Burmese amber are females with fully developed elytra, wings, and other appendages. Therefore, we still do not have any direct fossil evidence of neoteny in net-winged beetles and must reject the recent claim that the females of Cenomanian Lycidae, i.e., Burmolycini, were neotenic. None of the known Cretaceous lycid females resemble the fully larviform females seen in some modern groups of net-winged beetles.
A revision of Iguanura (Arecaceae, Arecoideae, Areceae)
Authors: HENDERSON ANDREW
Published: February 2025

A revision of Iguanura was carried out based on morphological data. Six hundred and seventy herbarium specimens were examined and scored for 15 quantitative and 25 qualitative variables. Application of the Phylogenetic Species Concept to 27 preliminary species resulted in recognition of 31 phylogenetic species. Of these, nine are recognized as new (I. beccarii, I. bruneiensis. I. exilis, I. grandis, I. lunata, I. mogeae, I. montana, I. sarawakensis, I. tomentosa). Nomenclature, descriptions, and distribution maps are provided for all species. Images of the type specimens of all new species are provided as well as images of most qualitative variables and of habits of selected species.
New records of Hydropsychinae (Insecta: Trichoptera: Hydropsychidae) from Assam, India
Authors: PANDHER MANPREET SINGH , KAUR SIMARJIT , PAREY SAJAD HUSSAIN
Published: February 2025

Three species of Hydropsychinae are recorded for the first time from the geographic limits of India. Hydropsyche dolosa Banks 1939, Potamyia flavata (Banks 1934), and Potamyia renatae Malicky 1997 (all from Manas National Park, Assam—a World Heritage site) are re-illustrated, redescribed, and recorded for the first time from India.
Near-Resonance between the Shelf Ocean and Semidiurnal Atmospheric Tidal Winds
Authors: Wei Hsu Fucent Hsuan , Schlichting Dylan , Shearman R. Kipp , Nash Jonathan D. , Hetland Robert D.
Published: February 2025

This manuscript illustrates the resonance between continental shelf oceans and the semidiurnal atmospheric tidal wind, explaining O(10−2) m semidiurnal sea surface height (SSH) variations in detided datasets. The resonance, similar to amplification of semidiurnal oceanic tides on the gentle and wide shelf, results in pronounced, offshore-attenuated standing waves on the shelf which is driven by the cross-shore pressure gradient force, Coriolis force, and the rotary wind stress. Observations and numerical results from the Texas-Louisiana shelf confirm this mechanism, where a significant presence of the semidiurnal tidal wind couples with O(10−2) m s−1 ocean currents, influencing SSH distribution and sustaining the wave structure. The consistency of the interaction and momentum budgets with the analytical solution suggests the robustness of the semidiurnal atmospheric tidal wind interacting with the shelf ocean. Notably, these findings suggest that similar resonances could occur on other gentle shelves known for enhancing semidiurnal oceanic tides and contribute 3 to 10% of the wind work.
Implementation of and Barriers to Collaborative Practices in the Public School Setting: A Survey of New York State Speech-Language Providers
Authors: Natali Cassandra , Duff Dawna
Published: February 2025

Collaboration between professionals allows for optimal student outcomes in the school setting. A shift in the educational system, including the reauthorization of the Individuals with Disabilities Education Act (IDEA) in 2004, the use of Multi-Tiered System of Supports (MTSS) and changes in curriculum standards have increased the need and expectation for school-based speech-language providers to engage in collaborative practices. The aim of this study was to identify practices used in speech-language providers in public schools to collaborate and barriers to collaboration. To address this question, speech-language providers working in public schools ( n = 151) were surveyed. Data was analyzed both qualitatively and quantitatively. Most collaborated regularly with special education teachers, occupational therapists (OTs), and school psychologists. Lack of time was reported to be a major barrier to more significant collaboration. Few providers reported having shared planning time with special education teachers. Clinical practices, implications, and real-world solutions are discussed.
Impacts of climate smart agriculture on livelihoods in sub-Saharan Africa: A meta-analysis
Authors: Simutowe Esau , Ngoma Hambulo , Thierfelder Christian
Published: February 2025

Sub-Saharan Africa is grappling with increasing food demand amid a growing population amidst a surging climate crisis. Climate Smart Agriculture (CSA) emerges as one option to address these challenges and enhance livelihoods. However, evidence of the impacts of CSA on livelihoods remains thin and mixed. We used a meta-analysis with 19 studies to assess the impacts of CSA practices on crop and household income, poverty, and food security in sub-Saharan Africa. Based on the literature, adoption of CSA is positively correlated with crop and household income, and food security. We do not find statistically significant effects on poverty and food security results. These results signify the need to integrate CSA with other complementary measures such as access to extension services, markets, climate-related risk insurance, and finance to improve food security and reduce poverty. We can conjecture that the positive effects on incomes are not sufficient to significantly drive up food security and poverty alleviation.
Preservation of Temporal Organisation of Tennis Service Following Ageing in Recreational Players
Authors: Shafizadeh Mohsen , Hizan Hazun , Davids Keith
Published: February 2025

Background: Ageing leads to functional changes in body systems. Purpose: This study sought to examine the age-based differences in temporal patterning, temporal variability and temporal regularity of tennis service in older adults. Research design: cross-sectional study. Study sample: Participants of this study were volunteers from young ( n = 10) and older ( n = 11) adults who were self-defined as non-competitive, participating in tennis at a recreational (sub-elite) level for 2–3 days per week regularly. Data collection: They were asked to perform 20 trials of the same serves (flat, slice or top-spin) on a regular size court. The timing (duration) of the serve action was recorded and analysed, using a high-speed digital camera. Participants performed the tennis serves during their routine training sessions on a tennis court. They undertook a structured general (10min) and a specific warm-up routine (10min) before performing services. There was a 30-second rest period between trials. Results: Findings showed that there were no statistically significant differences between the two groups in temporal patterning, whereas the younger group performed the serves with higher variability and regularity/consistency than the older adults in some phases of the action. Conclusion: Older adults who participate recreationally in sports are able to preserve some functional organisation of perceptual-motor capacities, such as timing, due to long-term practice effects. The findings illustrate how sport participation may help maintain functional movement organisation following ageing to mitigate age-related declines in perception and action in late adulthood.
Role of peak D-dimer in predicting mortality and venous thromboembolism in COVID-19 patients
Authors: Lee Rachel H , Wang Shan , Akerman Meredith , Joseph D’Andrea
Published: February 2025

Covid 19 patients often present with elevated D-dimer levels. The purpose of this study is to evaluate the role of D-Dimer levels in Covid 19 patients to predict mortality and venous thromboembolism (VTE) events. This is a retrospective chart review study from 1 April 2020 to 30 June 2020, during the peak Covid pandemic. A total of 350 patients were enrolled in this study; 69 (19.7%) patients died; 12 (3.4%) had a deep venous thrombosis; and 8 (2.3%) had a pulmonary embolism outcome. Peak D-dimer levels were collected with median levels of 765 ng/ml (266, 3135). Patients with VTE outcomes had significantly higher levels of peak D-dimers than patients in the non-VTE group (4876 vs 680, p < 0.0001). Patients who died had higher peak D-dimer levels than those who survived (4690 vs 501, p < 0.0001). The optimal cutoff point in peak D-dimer in predicting VTE events was 1437, yielding a sensitivity of 84.2% and a specificity of 65.0%. The optimal cutoff point in peak D-dimer in predicting mortality was 2004, yielding a sensitivity of 71.0% and a specificity of 77.9%. This study suggests that D-dimer levels can be elevated in Covid 19 hospitalized patients and can serve as indicators for mortality and VTE events.