bluuebunny/crossref_metadata_2025 huggingface dataset:
Reemergence of arctic-like rabies virus in a rabies-free area in the Amur river basin
Authors: Botvinkin Aleksandr D. , Poleshchuk Elena M. , Chupin Sergey A. , Zarva Ivan D. , Meltsov Ivan V. , Yanovich Vasily A. , Zinyakov Nikolay G. , Sidorov Gennady N. , Andaev Evgeny , Metlin Artem E.
Published: October 2023

Background: A descriptive epidemiological study of the rabies outbreak in the Amur Region of Russia (ARR), which remained rabies-free from 1972 to 2017, was carried out. During 2018-2022, 49 animal rabies cases and one human rabies death were documented in the ARR. This study aimed to detect possible ways of incursion and features of the spatial spread of rabies virus (RABV) in ARR, which was considered rabies-free for 45 years.

Methods: After the beginning of the outbreak, 1,450 animal brain samples were tested using a direct fluorescent antibody test. Genetic analysis of RABV isolates was carried out (n = 9). GIS was used to analyze the spatial spread of rabies in ARR and adjacent territories.

Results: Rabies has been confirmed in red foxes (Vulpes vulpes ) - 38.8%, raccoon dogs (Nyctereutes procyonoides ) - 10.2%, wolves (Canis lupus ) - 14.3%, dogs - 18.4% and farm animals - 18.3% (n = 49). The first cases were detected south of the ARR, 30 km from the state border between Russia and China. In 2018-2022, the RABV spread within the forest-steppe landscapes of the Zeya-Bureya Plain northwesterly; the maximum distance from the first recorded event was 192.4 km (Me = 77.6). According to a phylogenetic analysis, the isolates from ARR belonged to the Acrtic-like-2 RABV lineage and showed the maximum similarity to the isolates recovered in the province of Heilongjiang (China, 2011, 2018) and the Jewish Autonomous Region (Russia, 1980).

Conclusion: Rabies was most likely introduced by wild carnivores from the adjacent territories of Russia or China, located downstream of the Amur River.
Influence of Porter’s diamond firm strategy, structure and rivalry construct on Pakistan automotive sector
Authors: Butt Muhammad Ahmed , Katuse Paul
Published: October 2023

The overriding purpose of the study was to ascertain through empirical research whether Porter’s diamond firm strategy, structure and rivalry construct influence the key automotive industry of Pakistan. The Researchers applied positivism research philosophy to help generalize the research study outcomes. Descriptive analytics was deployed to help determine the significance, direction and magnitude of the study. Primary data was obtained from senior managers working in the industry by applying a structured questionnaire for subsequent analysis. While soliciting data, an 85.6% response rate was documented. Statistical techniques were applied which included parametric and nonparametric to arrive at objectively based results to draw safe conclusions.  The research findings have created significant value for practitioners in the auto industry as well as the decision-makers at the helm of policy formulation. Pragmatic inferences to be drawn from the research findings include improving the performance of the automotive sector by internalizing Porter’s diamond construct firm strategy, structure, and rivalry. Results in other industries may be tested with future research to help establish generalizability in the area.
Using a hybrid approach for wind power forecasting in Northwestern Mexico
Authors:   Center for International Development and Environmental Research (ZEU), Senckenbergstrasse 3, D-35390 Giessen, Germany., Diaz-Esteban Yanet , López-Villalobos Carlos Alberto ,   Instituto de Energías Renovables, Universidad Nacional Autónoma de México, Privada Xochicalco s/n, Col. Azteca, 62588 Temixco, Morelos, México., Ochoa Moya Carlos Abraham ,   nstituto de Ciencias de la Atmósfera y Cambio Climático, Universidad Nacional Autónoma de México, Circuito de la Investigación Científica s/n, Ciudad Universitaria, 04510 Ciudad de México, México., Romero-Centeno Rosario ,   nstituto de Ciencias de la Atmósfera y Cambio Climático, Universidad Nacional Autónoma de México, Circuito de la Investigación Científica s/n, Ciudad Universitaria, 04510 Ciudad de México, México., Arturo Quintanar Ignacio ,   nstituto de Ciencias de la Atmósfera y Cambio Climático, Universidad Nacional Autónoma de México, Circuito de la Investigación Científica s/n, Ciudad Universitaria, 04510 Ciudad de México, México.
Published: October 2023

Wind energy is an important renewable source that has been considerably developed recently. In order to obtain successful 24-h lead-time wind power forecasts for operational and commercial uses, a combination of physical and statistical models is desirable. In this paper, a hybrid methodology that employs a numerical weather prediction model (Weather Research and Forecasting) and a neural network (NN) algorithm is proposed and assessed. The methodology is applied to a wind farm in northwestern Mexico, a region with high wind potential where complex geography adds large uncertainty to wind energy forecasts. The energy forecasts are then evaluated against actual on-site power generation over one year and compared with two reference models: decision trees (DT) and support vector regression (SVR). The proposed method exhibits a better performance with respect to the reference methods, showing an hourly normalized mean absolute percentage error of 6.97%, which represents 6 and 13 percentage points less error in wind power forecasts than with DT and SVR methods, respectively. Under strong synoptic forcing, the NN wind power forecast is not very accurate, and novel approaches such as hierarchical algorithms should be employed instead. Overall, the proposed model is capable of producing high-quality wind power forecasts for most weather conditions prevailing in this region and demonstrates a good performance with respect to similar models for medium-term wind power forecasts.
The facile synthesis and application of mesoporous silica nanoparticles with a vinyl functional group for plastic recycling
Authors: Lee Jong-tak , Kang Misun , Bae Jae Young
Published: October 2023

Due to growing concerns about environmental pollution from plastic waste, plastic recycling research is gaining momentum. Traditional methods, such as incorporating inorganic particles, increasing cross-linking density with peroxides, and blending with silicone monomers, often improve mechanical properties but reduce flexibility for specific performance requirements. This study focuses on synthesizing silica nanoparticles with vinyl functional groups and evaluating their mechanical performance when used in recycled plastics. Silica precursors, namely sodium silicate, and vinyltrimethoxysilane (VTMS), combined with a surfactant, were employed to create pores, increasing silica's surface area. Early-stage introduction of vinyl functional groups prevented the typical post-synthesis reduction in surface area. Porous silica was produced in varying quantities of VTMS, and the synthesized porous silica nanomaterials were incorporated into recycled Polyethylene to induce cross-linking. Despite a decrease in surface area with increasing VTMS content, a significant surface area of 883 m2/g was achieved. In conclusion, porous silica with the right amount of vinyl content exhibited improved mechanical performance, including increased tensile strength, compared to conventional porous silica. This study shows that synthesized porous silica with integrated vinyl functional groups effectively enhances the performance of recycled plastics.
Electrification of Last-Mile Delivery: A Fleet Management Approach with a Sustainability Perspective
Authors: Castillo Oscar , Álvarez Roberto
Published: October 2023

Carbon footprint mitigation actions have become a key issue for companies involved in urban freight transport. The electrification of the delivery fleets is one of the crucial sustainable actions carried out. To this end, it is necessary to explore several fleet renewal strategies over a finite planning horizon evaluating different types of electric powertrains for light commercial vehicles. The primary goal of this purpose-built analysis is to assist and boost the fleet managers' decisions when transitioning to alternative-fuelled vans, intending to maximize cost savings and reduce greenhouse gas emissions inventory. The model presented in this work is a Multi-Objective Linear Programming hybrid analysis taking into account the vehicle and energy supply infrastructure costs and the emissions reported in each inventory scope following the Greenhouse Gas Protocol sustainability guidelines. From this perspective, the conducted research offers a novel approximation to fleet management problems enabling a break-down cost analysis and an evaluation of the annual emissions company report. The comprehensive numerical simulations carried out over different study scenarios in Spain demonstrate that the optimization approach does not only show effective fleet renewal strategies but also identifies critical factors that impact the fleet's competitiveness, offering valuable insights for fleet managers and policymakers to meet carbon footprint reductions in a cost-effectively way.
Study on the Mechanism of Gas Intrusion and Its Transportation in Wellbore Under Shut-In Conditions
Authors: Zhu Haifeng , Xiang Ming , Yang Jicheng , Wang Xuerui , Liu Xueqi , Wang Zhiyuan
Published: October 2023

This paper presents a comprehensive study based on multiphase seepage and wellbore multiphase flow theories. It establishes a gas intrusion rate calculation model that considers various factors including formation pore permeability, bottomhole pressure difference, drilling fluid rheology, and surface tension. Experiments were conducted to investigate the gas intrusion mechanism under shut-in conditions, and the experimental results were employed to validate the reliability of the proposed gas intrusion rate calculation method. Furthermore, the research explores the transportation rates of single bubbles and bubble clusters in drilling fluid under shut-in conditions. Meanwhile, empirical expressions were derived for the drag coefficient for single bubbles and bubble clusters in the wellbore. These expressions can be used to calculate gas transportation rates for various equivalent radii of single bubbles and bubble clusters. Additionally, a method was developed for calculating the rising velocity of bubble clusters in water based on experimental results. The study reveals that the average bubble size in the bubble cluster is significantly smaller than the size of the single bubble generated from the orifice. When the viscosity of the drilling fluid is low, the bubble cluster transportation velocity exhibits a positive correlation with the average bubble diameter. When the average bubble diameter exceeds 1 mm, the bubble velocity no longer varies with the change in the bubble cluster diameter. The initial bubble size of intrusive gas, the transportation speed of intrusive gas in the wellbore, the gas intrusion rate, and variations of the wellbore pressure after gas intrusion were analyzed. The research results provide theoretical support for wellbore pressure prediction and pressure control under shutdown conditions.
“Life Starts at Less than Zero”: Trauma and Coping among Iraqi and Syrian Refugees
Authors: Ipekci Bediha
Published: October 2023

The ongoing conflict in Syria and Iraq has caused many residents of these countries to flee. This study explored how these refugees cope with traumatic experiences from pre-migration to post-migration phases and how they develop resilience experiences at the individual, familial, and community levels. Data were collected through individual interviews and analyzed using Interpretative Phenomenological Analysis. A discussion of the findings revealed (1) a cumulative effect of prolonged adversity from pre-migration to resettlement phases; (2) the impacts of post-migration context as it facilitates refugees’ adjustment or leads to re-traumatization through increased marginalization; and (3) finally, an overview of refugees’ ongoing survival. Clinical implications are presented along with policy implications and future directions for research.
Leveraging the Novel MSHA Model: A Focus on Adrenocortical Carcinoma
Authors: Mustapha Mubarak Taiwo , Uzun Berna , Ozsahin Dilber Uzun
Published: October 2023

This study aims to explore the utilization of deep learning models, specifically the innovative Multi-Modal Contextual Fusion Convolutional Neural Network (MSHA), for the detection and diagnosis of Adrenocortical Carcinoma (ACC) using computed tomography (CT) images. The objective is to develop an accurate and reliable model that can assist in the effective detection and classification of ACC. The study utilizes a dataset comprising contrast-enhanced CT images from 53 confirmed ACC patients. The MSHA model is employed, which incorporates a combination of mixed-scale dense convolution, self-attention mechanism, hierarchical feature fusion, and attention-based contextual information techniques. Evaluation metrics are used to assess the performance of the MSHA model, and a comparison is made with other established models, including ResNet50, VGG16, VGG19, and InceptionV3. The evaluation of the MSHA model demonstrates high performance, with an accuracy of 96.65% and precision, sensitivity, specificity, and F1 score of 96.0%. These results highlight the MSHA model's capability in accurately detecting and classifying ACC. Furthermore, compared to other models, the MSHA model outperforms ResNet50, VGG16, VGG19, and InceptionV3, indicating its superior performance in ACC detection and diagnosis. The findings of this study suggest that the MSHA model holds significant potential in assisting healthcare professionals with the detection and diagnosis of ACC. With its advanced features and contextual fusion techniques, the MSHA model achieves high accuracy and performance. The results highlight the clinical significance of this novel model and its potential to improve patient management and outcomes in the detection and diagnosis of ACC using CT images.
Examine Website Defacement Dataset by Exploiting Some Classifiers’ Capabilities
Authors: Zayid Elrasheed Ismail Mohommoud , Isah Ibrahim , Adam Yagoub Abbker , Farah Nadir Abdelrahman Ahmed , Alshehri Omar Abdullah
Published: October 2023

Website defacement is an illegal electronic act of changing a website. In this paper, robust machine learning classifiers’ capabilities were exploited to select the best input feature set for evaluating a website’s defacement risk. Zone-H, a private organization, offered us a defacement mining dataset. A sample of 93644 datapoints was concisely pre-processed and used for modelling purposes. Considering multidimensional features as input, reason and hackmode variables were chosen as outputs. Massive machine learning models were examined; however, decision tree (DT), k-nearest neighbour (k-NN), and random forest (RF) were the most powerful algorithms used to predict the target model. The input variables 'domain', 'system', 'web_server','redefacement', 'type', 'def_grade', and 'reason/hackmode' were tested and used to shape the final model. Using the cross-validation (CV) technique, the model’s key performance factors were calculated and reported. After calculating the average scores for the hyperbolic metrics (i.e., max-depth, min-sample-leaf, weight, max-features, and CV), both targets were evaluated, and the learning algorithms were ranked as RF, DT, and k-NN. The reason and hackmode variables were thoroughly analysed. The average score accuracies for the reason and hackmode targets were 0.85 and 0.585, respectively. The results showed a significant development in terms of modelling and optimizing website defacement risk. The study also successfully addresses the main cybersecurity concerns, in particular website defacement.
Organotypic Brain Slice Cultures to Investigate Early Age- and Sex-Dependent Astrocyte-Mediated Synapse Elimination in the Rat Prefrontal Cortex
Authors: Vivi Eugenia , Seeholzer Lea R , Nagumanova Anastasia , Di Benedetto Barbara
Published: October 2023

Clinical and pre-clinical studies of neuropsychiatric (NP) disorders show altered astrocyte prop-erties and synaptic networks. These are refined during early postnatal developmental (PND) stages. Therefore, investigating early brain maturational trajectories is essential to understand NP disorders. However, animal experiments are highly time-/resource-consuming, thereby calling for alternative methodological approaches.
The function of MEGF10 in astrocyte-mediated synapse elimination (pruning) is crucial for the refinement of neuronal networks during development and adulthood. To investigate the impact of MEGF10 during PND in the rat prefrontal cortex (PFC) and its putative role in brain disorders, we established and validated an Organotypic Brain Slice Culture (OBSC) system.
Using Western blot, we characterized the expression of MEGF10 and the synaptic markers syn-aptophysin and PSD95 in the cortex of developing pups. We then combined immunofluores-cent-immunohistochemistry with Imaris-supported 3D-analysis to compare age- and sex-dependent astrocyte-mediated pruning within the PFC in pups and OBSCs.
We thereby validated this system to investigate age-dependent astrocyte-mediated changes in pruning during PND. However, further optimizations are needed to use OBSCs to reveal sex-dependent differences. In conclusion, OBSCs can be a valid model to study physiological as-trocyte-mediated synaptic remodelling during PND and might be exploited to investigate pathomechanisms of brain disorders with aberrant synaptic development.
Development of a Model for Detecting the State of Instability of the Mechanical Part of a Synchronous Electric Drive System Operating with a Dynamic Load Using an Artificial Neural Network
Authors: Baghdasaryan Marinka K. Kajik , Hovhannisyan Vardan
Published: October 2023

Keywords: Аrtificial neural network, electric drive system, database, stability, training algorithm, technological mechanisms.
Investigating Transportation Needs in Clinical Nursing – Observational Task Assessment in Rural Clinics
Authors: Sommer Domenic , Kasbauer Jakob , Jakob Dietmar , Schmidt Sebastian , Wahl Florian
Published: October 2023

Transportation tasks in nursing are common and shorten the time for direct patient care in times of nursing shortage and an aging society. There is a lack of research on transportation needs, with most studies focusing on overall nursing task analysis without a deeper analysis of transportation tasks. In July and September 2023, we conducted weekly observations of nurses to assess clinical transportation needs. We aim to understand the economic impact and the methods nurses use for transportation tasks. We conducted a participant observation using a standardized app-based form over a seven-day observation period in two rural clinics. N=1,830 transports were made by nurses and examined by descriptive analysis. Non-medical supplies account for 27.05 % (n=495) of all transports, followed by medical supplies at 17.32 % (n=317), pharmacotherapy at 14.10 % (n=258) and other other categories like meals or drinks contributing 12.68 % (n=232). Most transports had a factual transport time of under a minute, with patient transport and lab samples displaying more variability. In total, 77.15 % of all transports were made by hand. Requirements to collect items or connect transports with patient care were included in 5 % of all transports. Our economic evaluation spotlighted meals as the most costly transport, with 9,596.16€ p.a. in the observed clinics. Budget-friendly robots would amortize over one year by transporting meals. We support understanding nurses’ transportation needs via further research on assistive robots to validate our findings and determine the feasibility of transport robots.
Reuse of Wood in the Light of Circular Building Construction in Slovenia, Serbia, North Macedonia, Bosnia and Herzegovina, and Montenegro
Authors: Kitek Kuzman Manja , Zbašnik-Senegačnik Martina , Kosanović Saja , Miloševska Janakieska Marija , Novaković Nevena , Rajković Irena , Grošelj Petra
Published: October 2023

This study explores the perspectives and practices of architects regarding the reuse of wood in construction across five South-Central European countries: Slovenia, Serbia, North Macedonia, Bosnia and Herzegovina (BiH), and Montenegro. Based on a survey of architectural professionals, the research explores their attitudes, challenges, and motivations for using reclaimed wood, with a focus on circular construction principles. Key findings reveal unanimous agreement among re-spondents that both their profession and government entities inadequately support or promote wood reuse within the context of circular construction. While architects value reclaimed wood, there are country-specific variations. Environmental benefits, unique aesthetics, historical value, and sustainability are cited as primary motivators for reclaimed wood use. Additionally, re-spondents emphasize the ecological aspect as the most important consideration in wood product reuse, followed by economic and technological factors. Common concerns revolve around the long-term quality and maintenance of wood products. This research provides insights into the challenges and opportunities surrounding reclaimed wood use in South-Central Europe, high-lighting the need for support mechanisms and increased awareness to advance circular construc-tion practices in the region. These findings can have implications for architects and investors by identifying market opportunities, promoting eco-friendly practices, and providing valuable in-sights for future building designs with a focus on reclaimed wood elements within circular con-struction.
A Finger Vein Liveness Detection Method Based on Multi-Scale Spatio-Temporal Image and Light-ViT Model
Authors: Chen Liukui , Guo Tengwen , Li Li , Jiang Haiyang , Luo Wenfu , Li Zuojin
Published: October 2023

Prosthetic attack is a problem that must be prevented in the current finger vein recognition application. To solve this problem, a finger vein living detection system was established in this article. The system first captures short-term static finger vein video by uniform near-infrared lighting, segments the veins by Gabor filters with current removing, calculates the multi-Scale spatial-Temporal maps(MSTmap) from the selected vein blocks, and trains the MSTmaps in the proposed Light-ViT network for the liveness detection. The MS maps are used to extract the coarse feature and Light-ViT is used to refine the liveness feature and predict the liveness result. Light-ViT, featuring an enhanced L-ViT backbone as its core, is constructed by interleaving multiple MN blocks and L-ViT blocks. This architecture effectively balances the learning of local image features, controls network parameter complexity, and substantially improves the accuracy of liveness detection. The accuracy of the Light-ViT network is verified to be 99.63% on the self-made living / prosthetic finger vein video dataset. This proposed system can also be directly applied to the finger vein recognition terminal after the model lightweighting.
Resonance Effects in Athletic Training: Unveiling the Impact of Exercise Similarity in Variable Practice for Handball, Basketball, and Volleyball Skills with CI and DL
Authors: Apidogo Julius Baba , Ammar Achraf , Salem Atef , Burdack Johannes , Schöllhorn And Wolfgang Immanuel
Published: October 2023

Effective sports training should be attuned to the athlete's specific conditionings and characteristics. In motor learning research, two often neglected factors that influence this resonance are the learner's athletic background and the structural diversity of exercises (e.g., relative similarity). This study examines the effects of three learning approaches (i.e., contextual interference (CI), differential learning (DL), and free-play control condition (CO)) on the parallel learning of Handball (HB), Volleyball (VB), and Basketball (BB) skills, considering participants' prior sport backgrounds. Forty-five males (15 HB, 15 VB, and 15 BB players) with a mean age of 22±1.4 years and at least 6 years of experience in the mastered discipline voluntarily participated in this study. A pre-post-retention test design including a 6-week-intervention program was employed. In terms of short-term (pre-post) gain, only the DL- group significantly improved their performance in both non-mastered disciplines (p=0.03 for the BB free-throw and p=0.05 for the HB shooting tests), with a trend (ES=0.53) towards an improvement in the performance of the mastered VB underarm-pass skill. In terms of relatively permanent gains, the CI group significantly improved their performances from pre- to retention-test only in the non-mastered BB free-throw skill (p=0.018). In contrast, the DL group significantly improved their performance at retention compared to the pre-test in both non-mastered BB (p=0.004) and HB (p=0.003) skills, with a trend (ES=0.4) towards improvement in the mastered VB test. In both short, and relatively permanent terms, higher composite score gains were observed in DL compared to CI (p=0.001 and 0.002) and CO (p=0.001 and <0.0001). In conclusion, the present findings provide additional support for the potential advantages of the DL model over those of CI. These findings can serve as the basis for tailored training and intervention strategies and provide a new perspective for addressing various issues related to individual and situational learning.
Improving Semantic Segmentation Performance in Underwater Images
Authors: Nunes Alexandra Pereira , Matos Aníbal
Published: October 2023

Nowadays, semantic segmentation is increasingly used in exploration by underwater robots, for example in autonomous navigation, so that the robot can recognise the nature and elements of its environment during the mission and act according to this classification to avoid collisions. Other applications can be found in the search for archaeological artefacts, in the inspection of underwater structures or in species monitoring. Therefore, it is necessary to try to improve the performance in these tasks as much as possible. To this end, we compare some methods for improving image quality and for data augmentation and test whether higher performance metrics can be achieved with both strategies. The experiments are performed with the SegNet implementations and the SUIM dataset with 8 common underwater classes to compare the obtained results with the already known ones. The results obtained with both strategies show that they are beneficial and lead to better performance results by achieving a mean IoU of 56% and an increased overall accuracy of 81.8%. The single result shows that there are 5 classes with an IoU value above 60% and only one class with an IoU value below 30%, which is a more reliable result and easier to use in real contexts.
Predictive Analysis of Electricity Consumption of a Mechanical Biological Waste Treatment Plant Using Machine Learning Techniques
Authors: Colantoni Andrea , Ortenzi Luciano , Lops Alessandro , Lombardi Pierpaolo
Published: October 2023

The aims of this paper can be traced back to goals number seven, twelve and thirteen introduced by Agenda 2030, namely "affordable and clean energy", “responsible consumption and production” and "climate action”, respectively.
This is due to the fact that the present work supports decarbonization processes acting in a direct way on the electricity consumption achieved by plants pertaining to the waste industry; in fact, this study aims at the realization of a machine learning model for predicting the energy consumption achieved by a mechanical biological treatment (MBT) waste plant located in central Italy, given the distribution of the entering waste. This model is implemented in MATLAB. The model can be used to tune the distribution of the entering waste in order to adapt the plant energy consumption to the capability of the energy sources and can serve as a play-ground model for other energy transformation plants.
The results of the study, which feed the literature on the application of artificial intelligence to real industrial plants, could be used to determine energy efficiency actions that could be incorporated into Property’s strategic planning.
Predicting Road Traffic Collisions Using a Two-Layer Ensemble Machine Learning Algorithm.
Authors: Oyoo James Oduor , Wekesa Jael Sanyanda , Ogada Kennedy Odhiambo
Published: October 2023

Road collisions are among the world’s critical issues causing many casualties, deaths, and economic losses. To deal with this scenario, precise analysis is required. In this paper, we use Machine Learning (ML) techniques to assess and predict road traffic collisions. An ensemble machine learning model with two layers was developed to analyze simulation data from the driving simulator. The first (base) layer integrates supervised learning techniques namely, k- Nearest Neighbors (k-NN) AdaBoost, Naive Bayes (NB), and Decision Trees (DT). The second layer predicts road collisions by combining the base layer outputs by employing the stacking ensemble method using logistic regression as a meta-classifier. The data for this study were imbalanced; therefore, we performed a SMOTE resampling strategy to handle the dataset imbalance. To simplify the model, the PSO algorithm was used to select the most important features in our dataset. The two-layer ensemble model proposed had the best outcomes with an accuracy of 88%, F1 score of 83%, and an AUC of 86% as compared with k-NN, DT, NB, and AdaBoost.
Double-Scale Expansions for a Logarithmic Type Solution to a Q-analog of a Singular Initial Value Problem
Authors: Malek Stephane
Published: October 2023

We examine a linear q-difference differential equation which is singular in complex time t at the origin. Its coefficients are polynomial in time and bounded holomorphic on horizontal strips in one complex space variable. The equation under study represents a q-analog of a singular partial differential equation, recently investigated by the author, which comprises Fuchsian operators and entails a forcing term that combines polynomial and logarithmic type functions in time. A sectorial holomorphic solution to the equation is constructed as a double complete Laplace transform in both time t and its complex logarithm log t and Fourier inverse integral in space. For a particular choice of the forcing term, this solution turns out to solve some specific nonlinear q-difference differential equation with polynomial coefficients in some positive rational power of t. Asymptotic expansions of the solution relatively to time t are investigated. A Gevrey type expansion is exhibited in a logarithmic scale. Furthermore, a formal asymptotic expansion in power scale is displayed, revealing a new fine structure involving remainders with both Gevrey and q-Gevrey type growth.
Ricci Vector Fields
Authors: Alohali Hanan , Deshmukh Sharief
Published: October 2023

We introduce a special vector field ω on a Riemannian manifold (Nm, g), such that the Lie derivative of the metric g with respect to ω is equal to ρRic, where Ric is the Ricci curvature of (Nm, g) and ρ is a smooth function on N^{m} and call this vector field a ρ-Ricci vector field. We use ρ-Ricci vector field on a Riemannian manifold (Nm, g) and find two characterizations of m-sphere Sm(α). In first result, we show that an m-dimensional compact and connected Riemannian manifold (Nm, g) with nonzero scalar curvature admits a ρ-Ricci vector field ω such that ρ is nonconstant function and the integral of Ric(ω,ω) has a suitable lower bound is necessary and sufficient for (Nm, g) to be isometric to m-sphere Sm(α). In second result, we show that an m-dimensional complete and simply connected Riemannian manifold (Nm, g) of positive scalar curvature admits a ρ-Ricci vector field ω such that ρ is a nontrivial solution of Fischer-Marsden equation and the squared length of the covariant derivative of ω has an appropriate upper bound, if and only if, (Nm, g) to be isometric to m-sphere Sm(α).
Design and Investigation of a Side-Chain Liquid Crystalline Polysiloxane with a Ntb Forming Side Chain
Authors: Jiang Wanhe , Mehl Georg H.
Published: October 2023

A new mesogenic non-symmetric dimeric monomer with a terminal olefin function, forming a twist bend nematic (Ntb) as well as a nematic (N) phase was synthesized, using an enhanced synthetic methodology, which avoids isomerization of the terminal double bond in the preparation of the dimer. This monomer was attached to a pentamethyldisiloxane group, resulting in SmA LC phase behaviour of the ensuing material. Linking the monomer to a siloxane main chain resulted in nematic phase behaviour. Detailed studies with the Ntb phase forming dimer DTC5C7 show full miscibility of the dimer and the new LC polymer in the LC state, suggesting that the side-chain LC polymer forms a Ntb phase as the low temperature nematic phase. Copolymerizing the monomer with a cyanobiphenyl based monomer allows to tune the glass transition and phase behavior further.
Observational Constraints on Dynamical Dark Energy Models
Authors: Avsajanishvili Olga , Chitov Gennady Y. , Kahniashvili Tina , Mandal Sayan , Samushia Lado
Published: October 2023

$\phi$CDM models provide an alternative to the standard $\Lambda$CDM paradigm, while being physically better motivated. These models lead to a time-dependent speed of sound for dark energy that is difficult to replicate by $w$CDM parametrizations.
We review the most up-to-date status of observational evidence for the $\phi$CDM models in this paper.
We start with an overview of the motivation behind these classes of models, the basic mathematical formalism, and the different classes of models.
We then present a compilation of recent results of applying different observational probes to constraining $\phi$CDM model parameters.
Over the last twenty years, the precision of observational data has increased immensely, leading to ever tighter constraints.
A combination of the recent measurements favors the spatially flat $\Lambda$CDM model, but a large class of $\phi$CDM models is still not ruled out.
Perkembangan Peserta Didik
Authors:   Universitas Muhammadiyah Sidoarjo, Liansari Vevy
Published: October 2023

Perkembangan dimaknai sebagai suatu proses perubahan dalam diri individu atau organism, secara fisik maupun psikis, menuju tingkat kedewasaan atau kematangan. Perkembangan itu berlangsung secara sistematis, progresif dan berkesinambangan. Secara psikis, perkembangan mengarah kepada pembentukan kepribadian yang sangat menentukan seseorang dalam bersosialisasi.
PLANT COMMUNITY CHANGE ACROSS THE PALEOCENE–EOCENE BOUNDARY IN THE GULF COASTAL PLAIN, CENTRAL TEXAS
Authors: WAGNER JENNIFER D. , PEPPE DANIEL J. , O’KEEFE JENNIFER M. K. , DENISON CHRISTOPHER N.
Published: October 2023

Long-term global warming during the early Paleogene was punctuated by several short-term ‘hyperthermal’ events, the most pronounced being the Paleocene–Eocene Thermal Maximum (PETM). During this long-term warming, tropical climates expanded into extra-tropical areas, creating a widespread band of thermophilic flora that reached into the paratropics, possibly as far north as mid-latitude North America in some regions. Relatively little is known about these paratropical floras, despite distribution across the North American Gulf Coastal Plain. We assess floras from the Gulf Coastal Plain in Central Texas before and after the Paleocene–Eocene boundary to define plant ecosystem changes associated with rapid global warming in this region. After the Paleocene–Eocene boundary, these floras suggest uniform plant communities across the Gulf Coastal Plain, but with high turnover rate and changes in community composition. Paleoecology and paleoclimate assessments from Central Texas Paleocene and Eocene floras suggest a warm and wet environment, indicative of tropical seasonal forest to tropical rainforest biomes. Fossil evidence from the Gulf Coastal Plain combined with the Bighorn Basin, Wyoming data suggest that early Paleogene warming helped create a paratropical belt that extended into mid-latitudes. Evaluating the response of fossil plant communities to rapid global warming has important implications for understanding and preparing for current global warming and climate change.
Consumption patterns in prehistoric Europe are consistent with modern behaviour
Authors: Ialongo Nicola , Lago Giancarlo
Published: October 2023

Have humans always sold and purchased things? This seemingly trivial question exposes one of the most conspicuous blind spots in our understanding of cultural evolution: the emergence of what we perceive today as basic economic behaviour. Here we test the hypothesis that consumption patterns in prehistoric Europe can be explained by standard economic theory, predicting that consumption is log-normally distributed and correlated to supply, demand and income. We identify metal fragments as money, address the statistical distribution of their mass values as a proxy of consumption, and observe that starting c. 1,500 BCE they become log-normally distributed. We simulate two alternative scenarios and find that 1) the null-hypothesis that random behaviour can produce log-normal distributions is inconsistent with the archaeological data, and 2) the highest consistency is obtained by modelling consumption as correlated to supply. We find no evidence that prehistoric economic behaviour was substantially different from modern behaviour.
ZnO/Chitosan Nanocomposite: Ecofriendly Biosynthesis, Characterization and Application for the Removal of Pyridoxine Hydrochloride (Vitamin B6) from Wastewater
Authors: Ali Samah , Dayo Marwa , Alahmadi Sana , Mohamed Amr
Published: October 2023

The bio-based ZnO/Chitosan nanocomposite was successfully biosynthesized by an easy, cost-effective, ecofriendly method using Chicory (Cichorium Intybus) plant extract. The synthesis was confirmed using uv-vis spectrometry which showed a SPR band at λmax of 380 nm, and the surface of the material was characterized by FT-IR spectroscopy which identified the material, and finally by scanning electron microscopy which confirmed the distribution of ZnO nanoparticles on the surface of Chitosan biopolymer (CS). The synthesized nanocomposite was applied in the adsorptive removal of Pyridoxine Hydrochloride (Vitamin B6) traces from wastewater by using batch technique. The removal capacity was studied under several adjustable conditions including pH, contact time, dose of adsorbent, and capacity of the drug. Langmuir and Freundlich isotherms were applied to describe of the adsorption process, and also by different kinetic models. Thermodynamic studies have been also carried out. The results were comparable to other adsorbents.
Antioxidant Capacity, in Silico Study and Histological Effects of Orange Peel on Obese Wistar Rats
Authors: Berroukeche Farid , Malti Nassima , Touil amina , Didi Amel , Mebarki Abdelouahab , Merzouk Hafida
Published: October 2023

Citrus peel is an industrial by-product, and it is far from being considered as waste. It is an inexhaustible source of bioactive substances, including phenolic acids, flavonoids, catechins, and tannins. For this purpose, we evaluated the biological activities of Citrus sinensis peel based on four parameters: HPLC phytochemical analysis, antioxidant properties (DPPH, FRAP), in vivo histology, and in silico modelling. In fact, in vivo study was designed as follows: adult male Albino Wistar rats were randomly divided into 5 groups. Group I: served as a control, group II: received 10% of orange peel, group III: received an obesogene diet (20% corn oil), group IV: received (20% corn oil, eight weeks + 10% of orange peel as corrective diet, four weeks), and group V: received (20% corn oil + 10% of orange peel protective diet). After 12 weeks, the liver, kidney, pancreas, and thyroid were removed for histology examination. Additionally, docking modelling was performed using Molecular Operating Environment software (MOE) to predict the interactions of thyroid hormone receptor with agonist ligands. Results revealed that methanolic and ethyl acetate extracts presented a high level of significant activity, respectively, in the DPPH and FRAP tests. Histology demonstrated that 10% of Citrus sinensis peel had a protective effect on the liver and possessed a hypothyroid effect on obese rats. In-silico data, illustrated that the complex of quercetin, (-)-epicatechin, and (+)-epicatechin with the target protein did not match with control at the binding site, suggesting that these docked ligands may play an antagonist action mode against thyroid hormone. To sum up, orange peels could be beneficial or harmful, depending on the drive and methods used.
VLDLR mediates alphavirus neuroinvasion through the blood-cerebrospinal fluid barrier
Authors: Martikainen Miika , Lugano Roberta , Pietilä Ilkka , Brosch Sofie , Cabrolier Camille , Sivaramakrishnam Aishwarya , Ramachandran Mohanraj , Yu Di , Dimberg Anna , Essand Magnus
Published: October 2023

Semliki Forest virus (SFV) is a neuropathogenic alphavirus which is of interest both as a model neurotropic alphavirus and as an oncolytic virus with proven potency in preclinical cancer models. The route of central nervous system (CNS) entrance of SFV is poorly understood but has been considered to occur through the blood-brain barrier. Here, we show that SFV primarily enters the CNS through the blood-cerebrospinal fluid barrier, and that VLDLR is crucial in enabling SFV infection of the choroid plexus epithelial cells.
SHAP Explanations for Multimodal Text-Tabular Models
Authors: Burton James , Moubayed Noura Al
Published: October 2023

Research on model transparency has remained relatively limited within the growing field of multimodal machine learning, particularly with text-tabular datasets. To address this research gap, we present a novel multimodal masking framework that extends SHapley Additive exPlanations (SHAP) to text-tabular datasets. This framework, which we make publicly available, enables the generation of SHAP explanations for any text-tabular dataset using any combination method. By masking features according to their modality, our framework ensures that features are treated consistently across unimodal and multimodal settings. Furthermore, by deferring the model input formation until after the masking call, we make the framework agnostic to how the input is formatted, avoiding the issues that arise when pre-forming the data into text and applying the existing text masker. In an extensive study, we examine the impact that combination strategies and language models have on SHAP explanations. Notably, the choice of combination method considerably influences the features identified as most important by the model. Furthermore, our findings reveal that methods converting all input to text tend to assign greater relative importance to text features over tabular features.
The effect of plant and animal-derived dietary oil supplementation on the reproduction performance of Dohne Merino ewes and the growth performance of their offspring
Authors: Sedupane Tebogo A , Roux Rina-Mari Le , Fourie Pieter J , Sedumedi Tshepiso A , Einkamerer Ockert B , O'Neill Hester A
Published: October 2023

The effect of different dietary oils on ewe reproductive performance and lamb growth was investigated. Fifty-second parity Dohne Merino ewes (body condition score 3.5±0.4) were randomly divided into five groups of ten each. Ewes received 30 ml of sunflower- or olive- or fish- or palm oil. The control group received no oil. Ewes received oils before and after laparoscopic artificial insemination (LAI). Two weeks after LAI, follow-up rams were introduced. Mean conception and lambing rates were 88%. Ewes that received olive oil had the highest conception rate and lambing rate of 100% but did not differ (P>0.05) from other groups (sunflower- 90%, fish- 90%, palm- 70% and control group 90%). The control group had the shortest gestation length (150 days). The gestation lengths of sunflower-, olive-, fish- and palm oil were 153, 156, 155, and 156 days. Birth weights of female lambs from control, sunflower-, olive-, fish- and palm oil were 5.41 ± 0.87, 5.70 ± 0.61, 5.8 ± 0.70, 5.8 ± 0.74 and 4.9 ± 0.50 kg (no difference between groups). The birth weights of male lambs from control, sunflower-, olive-, fish- and palm oil were 5.6 ± 1.19, 4.85 ± 0.70, 5.6 ± 0.84, 5.5 ± 0.49 and 5.4 ± 0.33 (no difference between groups). Weaning weights for male lambs were (28.8 ± 4.93, 25.5 ± 8.13, 29.0 ± 4.88, 30.2 ± 4.59, 28.8 ± 5.03) and female lambs were (28.5 ± 4.58, 28.3 ± 3.81, 25.8 ± 6.93, 28.9 ± 4.51, 27.6 ± 2.46), no difference between groups.
Global vulnerability and resilience of coastal ecosystems to tropical cyclones in a warming climate
Authors: Kropf Chahan , Vaterlaus Lisa , Bresch David , Pellissier Loïc
Published: October 2023

Coastal ecosystems are recognized to provide a large range of services including erosion prevention, clean water provision, or protection from tropical cyclones storms. However, with climate change, the increase in frequency and intensity of tropical cyclones may alter the composition of the ecosystems themselves potentially degrading the various services they provide. While in general ecosystems are adapted to external perturbations, extreme events can promote rapid and durable shifts. In this study, we quantify the vulnerability of all ecosystems described as ecoregions at a global scale by analyzing the wind fields of tropical cyclones generated from probabilistic historical tracks. We then investigate how tropical cyclone intensity and frequency shifting in a warming climate might lead to ecosystem modifications. We show that a combined 13% of the surface of all terrestrial ecosystems is susceptible to transformation due to cyclone pattern changes between 2020 and 2050 under current policies. Even for the most resilient ecosystems already experiencing winds above 60 m/s regularly, the average interval between two storms is projected to decrease from 19 to 12 years which is potentially close to their recovery time. Our global probabilistic approach being embedded in the established IPCC climate risk frameworks enables the study of a broad temporal range, and advocates for a shift in the consideration of the tropical cyclone impact from immediate damage to effects on long-term natural recovery cycles.
Sarcopenia worsening after transarterial radioembolization with Holmium-166 predicts progressive disease in patients with advanced hepatocellular carcinoma.
Authors: Trobiani Claudio , Ubaldi Nicolò , Teodoli Leonardo , Cappelli Federico , Ungania Sara , Vallati Giulio
Published: October 2023

Purpose:
To investigate the association between changes in sarcopenia before and after one-three months of Transarterial Radioembolization (TARE) treatment with Holmium-166 (166Ho) for hepatocellular carcinoma (HCC) and its impact on the rate of local response.
Methods:
A retrospective single center analysis was conducted on 20 HCC patients who underwent 166Ho-TARE. Patients were categorized into two groups based on the change in psoas muscle index (PMI) measured at the time of TARE and one-three months after: No-Sarcopenia group (deltaPMI stable or increased; n = 9) and Sarcopenia group (deltaPMI decreased; n = 7). DeltaPMI was associated to the local response rate, according to mRECIST criteria.
Results:
DeltaPMI was evaluated according to mRECIST criteria: non-responder group (standard and progression disease) and responder group (complete response or partial response).
Three months after TARE, a significant difference in sarcopenia status was observed (p = 0.041), with a median deltaPMI of -0.57 in the non-responder and 0.12 in the responder group. DeltaPMI measured three months after TARE can be considered as a predictive biomarker of the local response rate (p=0.028).
Conclusion:
Sarcopenia deterioration at three months from TARE with Holmium-166 is a reliable predictor of worse loco-regional response rate, evaluated radiologically, in HCC patients.