bluuebunny/crossref_metadata_2025 huggingface dataset:
Investigating the Short Food Supply Chain for Fresh Products in Jordan: Insights from Farmers, Intermediaries, and Retailer
Authors: Kareem Muna Abdul , Shbikat Nizar , Alsoussi Abdelrahim
Published: January 2025

This study investigates short food supply chains (SFSCs) for fresh products in Jordan from the perspectives of key stakeholders, including farmers, intermediaries, and retailers. A qualitative approach is employed, utilizing 30 semi-structured interviews. Thematic analysis was used for data analysis. Through thematic analysis, this research identifies critical themes such as market dynamics, supply chain relationships and communication, operations and efficiency, sustainability practices, and challenges and opportunities. The results reveal that trust and communication are critical for maintaining SFSCs in Jordan, though these interactions are largely informal and reliant on intermediaries. Market dynamics demonstrate a growing preference for locally sourced produce driven by affordability, quality, and availability. Sustainability practices across economic, social, and environmental dimensions are evident, but challenges persist including limited infrastructure, financial challenges, and climate-related vulnerabilities. This research addresses the limited literature on SFSCs in developing countries. It provides comprehensive insights into their benefits and challenges. The findings confirm the role of SFSCs in enhancing Jordan’s food security, reducing dependence on imports, and supporting local communities. Practical recommendations are offered to stakeholders and policymakers to improve SFSC's resilience and sustainability.
Exploring the Role of Traditional Ecological Knowledge in Restoring and Managing Miombo Woodlands: A Case Study from the Lubumbashi Charcoal Production Basin, Haut-Katanga, DR Congo
Authors: N'tambwe Nghonda Dieu-donné , Khoji Muteya Héritier , Mpanda Mukenza Médard , Cabala Kaleba Sylvestre , Malaisse François , Koy Justin Kyale , Masengo Kalenga Wilfried , Bogaert Jan , Useni Sikuzani Yannick
Published: January 2025

The over-exploitation of forest resources in the Lubumbashi Charcoal Production Basin in south-eastern DRC is accelerating deforestation and miombo woodlands degradation, jeopardizing the livelihoods of local communities. In this situation, current forestry policies remain ineffective, not least due to the failure to integrate traditional ecological knowledge (TEK). This study explores this knowledge through focus groups and individual interviews in four villages (Maksem, Mwawa, Nsela and Texas), selected according to the availability of forest resources and the number of inhabitants. Interviews gathered data on sacred sites and trees, conservation practices, the transmission of traditional knowledge, the role of ceremonies and the socio-demographic determinants of their application. These data were analyzed using descriptive statistics (citation frequency), Fisher's exact test for associations between variables, and Jaccard's similarity index to compare villages. The results show that 75% of people surveyed in both types of villages were aware of sacred sites in their village, as opposed to cemeteries, where logging is forbidden. Thirty sacred wood species were identified, with stricter observance of related prohibitions in villages with a high availability of forest resources. Knowledge is transmitted orally, via family councils and traditional ceremonies. Conservation practices include small-scale farming, intercropping, the ban on cutting trees on sacred sites and the use of dead wood. However, only the first two are still widely practiced, especially in villages with limited resources (64%). These practices are mainly applied by women and the elderly, and those involved in charcoal production and the collection of non-timber forest products. Nevertheless, the application of these TEK is strongly affected by population growth and the consequent low availability of forest resources, amplifying the pressure on miombo woodlands. These results underline that the integration of this traditional ecological knowledge into DRC forestry policies would significantly strengthen forest restoration initiatives, through a fine-tuned understanding of local ecosystems, sustainable practices and key woody species, while fostering community involvement. Decision-makers must integrate these TEK into DRC forest policy to strengthen biodiversity conservation and miombo woodlands restoration efforts.
Data-Driven Decision-Making in Marketing
Authors: Rosário Albérico Travassos , Cruz Rui Nunes
Published: January 2025

Data-driven decision-making (DDDM) has emerged as a transformative force in marketing, enabling organizations to leverage data analytics, artificial intelligence, and big data to enhance customer engagement and gain a competitive edge. This study investigates the potential of DDDM to optimize personalization, predict consumer behaviour, and align marketing strategies with sustainability and innovation goals. Researchers conducted a systematic literature review and analyzed case studies to explore how industries integrate DDDM practices. The research examined practical applications, identified key challenges, and assessed emerging marketing analytics and decision-making trends. The findings demonstrate that DDDM enhances marketing precision and operational efficiency, significantly improving customer segmentation, campaign effectiveness, and lifetime value management. Additionally, the study identifies challenges such as data privacy concerns, ethical considerations, and the complexity of integrating diverse data sources across omnichannel platforms. The study proposes strategies for balancing automation with human creativity and ensuring ethical data use. DDDM holds transformative potential for the marketing domain, fostering innovation and long-term value creation. Aligning DDDM practices with organizational goals and ethical standards is essential for sustainable success. This study offers a comprehensive framework and actionable insights for academics and practitioners to harness the full potential of data-driven approaches.
Climate Change in Southwestern Anatolia (Turkey) and Its Possible Impacts on Plant Biodiversity
Authors: Sütgibi Semra
Published: January 2025

Biodiversity is of great importance for ecosystems such as protecting soils against erosion, increasing soil fertility, providing clean water to streams/rivers, contributing to the healthy continuation of nutrient cycling, pollinating plants, acting as a buffer against diseases. These characteristics of biodiversity are called “ecosystem functions” or “ecosystem services”. However, many factors threaten biodiversity, such as changes in land cover and land use, pollution, invasion of exotic species and climate change. Among these, it would not be wrong to say that the anthropogenic climate change experienced today is one of the most important threats to biodiversity. As is known, the Mediterranean basin is one of the most sensitive areas in terms of global climate change. This study aimed to reveal the possible effects of climate change on plant biodiversity in south-western Anatolia. For this purpose, daily, maximum and minimum temperature data and daily total precipitation data of 10 meteorological stations located in the research area were evaluated. The non-parametric Mann-Kendall test was used to test the changing trends in temperature and precipitation. Then, the possible effects of changes and trends in temperature and precipitation on plant biodiversity in the study area are explained.
Clinical, Electrocardiographic and Echocardiographic Predictors of Atrial Fibrillation Recurrence after Pulmonary Vein Isolation
Authors: Karanikola Aikaterini-Eleftheria , Tzortzi Melpomeni , Kordalis Athanasios , Doundoulakis Ioannis , Antoniou Christos Konstantinos , Laina Ageliki , Tsioufis Panagiotis , Argyriou Nikos , Sakalidis Athanasios , Pamporis Konstantinos , Tsioufis Konstantinos , Tsiachris Dimitrios
Published: January 2025

Atrial fibrillation (AF) is a supraventricular arrhythmia and the most common heart rhythm disorder in the adult population worldwide with an estimated prevalence of 2% to 4% of the population. Cases of AF have shown an increasing trend in recent decades, while its frequency is expected to rise even more. Given the significant impact on patients’ quality of life, as well as its major complications -including thromboembolic events- effective rhythm control strategies other than antiarrhythmic medication have emerged, with catheter ablation (CA) being the cornerstone of these. During recent years, CA has been upgraded into first-line treatment for selected patients. However, complications do exist and arrhythmia-free survival is not always warranted. The need to better identify patients more suitable for this specific therapeutic measure is crucial in improving outcomes and preventing arrhythmia recurrences. This review aims to present currently identified predictors of AF recurrence after catheter ablation based on clinical characteristics, electrocardiographic and echocardiographic parameters, in an era of increasing interventional rhythm control approaches for the management of AF.
Toward Anode-Free Lithium-Ion Battery Using Amorphous Titanium Oxide Thin Films by Atmospheric-Pressure Mist Chemical Vapor Deposition
Authors: Fukushima Hibiki , Shirai Hajime , Sone Hirotaka , Kurihara Hideki , Ohno Toshinori , Watanabe Takuya
Published: January 2025

We demonstrate the potential of Lithium titanate (LiTiO) and amorphous titanium oxide (a-TiOx) thin films synthesized from titanium diisopropoxide bis acetylacetonate C16H28O6Ti [Ti(acac)2(OiPr)2] using atmospheric-pressure mist chemical vapor deposition method as negative electrode and solid electrolyte for anode-free lithium-ion battery (LIB). LTO thin films synthesized from Ti(acac)2(OiPr)2 and LiNO3 at 500 ℃ act as a negative electrode in LIB. In a-TiOx synthesized at 200-300 ℃, Li-ion permeability improved with charge/discharge cycles and acts as a solid electrolyte. The high diffusivity of Li ions demonstrated its superior behavior as a solid electrolyte. The a-TiOx solid electrolyte battery achieved an charge/discharge efficiency of 94%. These results imply that a-TiOx holds promise for realizing anode-free lithium metal batteries.
Advancing Pressure-Based Flow Rate Soft Sensors: Signal Filtering Effects and Non-Laminar Flow Rate Determination
Authors: Brumand-Poor Faras , Kotte Tim , Hanifa Abdulaziz , Reese Christian , Schmitz Katharina
Published: January 2025

Precise flow measurement is pivotal in hydraulic systems, serving as an essential parameter for fluid power control. When combined with pressure data, it also facilitates calculating hydraulic power, proving invaluable for applications like predictive maintenance. Conventional flow sensors in fluid power systems are typically invasive, often disrupting the flow and delivering unreliable measurements, particularly during transient conditions. One common approach relies on determining the flow rate by measuring the pressure difference along a pipe and applying the Hagen-Poiseuille law, constrained to steady, laminar, and incompressible flows. This study presents the capability of a previously introduced analytical soft sensor to determine transient pipe flow for non-laminar flow rates. This method does not require a flow rate sensor and utilizes two pressure transducers to determine the pipe flow. Furthermore, the relevance of filtering is investigated, and the robustness of the soft sensor is demonstrated by the outcome that even basic filtering results in a good match between the soft sensor and test rig. The findings underscore the soft sensor’s capability to deliver accurate and computationally efficient measurements of transient volumetric flow based solely on pressure data for laminar and non-laminar flow with simple filtering of real-world signals.
Evaluating Advanced Cybersecurity Technologies for Cloud Environments
Authors: Athukorale Nirmal Kavindu , Yi Chua Jing , Xin Loh Zi , Yasmine Alysha , Qi Choo Jia , Yu Dang Zi , Jia Wei Jason Soo , Hady Filbert , Zhe Lim Shi , Eu Chua Chong , Sindiramutty Siva Raja
Published: January 2025

This paper addresses the rise of cyberattacks that pose significant threats to computer and network systems, including data breaches, malware, and unauthorized access, which are becoming increasingly prevalent (Aslan et al., 2023). It explores how security measures in cloud computing protect sensitive data and ensure operational integrity as more organizations adopt cloud environments. The paper examines advanced cloud security technologies concerning their mechanisms, components, and role in addressing modern cybersecurity challenges. Key elements such as data encryption, network security, and auditing are discussed regarding their role in safeguarding sensitive information within cloud environments. Essential processes such as identifying, protecting, detecting, responding, and recovering are considered in developing a comprehensive cybersecurity framework for cloud systems. Besides that, the paper also evaluates examples of advanced cloud security measures, such as encryption technologies, and discusses their benefits, including multi-layered defence, scalability, and cost efficiency. The presented limitations, which involve compliance challenges, multi-tenancy risks, and reduced direct control are critically assessed. Furthermore, the paper discusses future directions in cloud security, including edge-cloud computing and hybrid cloud solutions. Finally, it proposes advanced countermeasures, such as intelligent deception technologies using honeytokens and honeynets to dynamically mitigate evolving threats. This study emphasizes the importance of adaptive and proactive strategies for cloud environment security as well as provides valuable insights relevant to both academic research and practical implementation.
Development of Transient Hydrodynamic and Hydrodispersive Models in Semi-Arid Environments
Authors: Hakimi Samir , Hessane Mohamed Abdelbasset , Bahir Mohammed , Faraj Turki Kh.
Published: January 2025

The article presents a hydrogeological study through numerical modeling of the coastal basin of Rharb, located in the northwest of Morocco, a region characterized by a semi-arid environment. This basin consists of two aquifers of Plioquaternary and Upper Quaternary age, separated by a lower Quaternary aquitard. The Plioquaternary aquifer hosts abundant and good-quality groundwater, while the Upper Quaternary aquifer hosts a mediocre quality groundwater with negligible hydrogeological interest. In this semi-arid context, for health reasons, a significant portion of the population exploits the second, deeper aquifer. Unfortunately, this is not the case for the majority of the population, who cannot access the second aquifer due to financial constraints (exploitation costs). Overexploitation of the Plioquaternary aquifer by agricultural pumping causes water stress, leading to a decline in piezometric levels and seawater intrusion, causing a degradation of the local hydrosystem. The main objective of the study is to develop a decision support tool, in the form of a Multicodes numerical environment, to understand the circulation and storage mode of groundwater, aimed at maintaining this resource and preserving the environment, considering both quantitative and qualitative constraints. The creation of this digital environment detailing the intrinsic characteristics of the basin leads to monitoring the quantity and quality of both aquifers in response to external influences such as recharge, exploitation and pollution. The development of a transient hydrodynamic model over a 15 years period confirms that overexploitation of the deep aquifer is the main cause of the decline in groundwater level. Subsequently, the development of a hydrodispersive model indicates higher salt concentrations in the shallow aquifer than in the deeper aquifer, and locates the seawater. Despite the qualitative constraints, the quality of the deep aquifer is relatively preserved thanks to the clayey aquitard acting as a barrier against the infiltration of agricultural pollutants in the central basin, and the renewal of these waters in the recharge zones at the peripheries. However, deficit hydraulic balances signal the beginning of a relatively critical period.
Records of Ground Deformation in Northern Kefalonia by Cosmogenic 36Cl Geochronology
Authors: Athanassas Constantin D. , Braucher Regis , Vakalas Ioannis , Apostolopoulos George
Published: January 2025

The cosmogenic 36Cl exposure ages in this study provide the first direct chronology of landscape evolution and ground deformation in the Ionian Islands, with a focus on the Thinia Valley and the anticline near Zola in northern Kefalonia, western Greece. At the primary Zola site, cosmogenic 36Cl exposure ages indicate persistent deformation on the eastern limb of the anticline from the Middle Quaternary to historical times. This broad range, spanning both ancient and recent ages, suggests that the anticline has experienced multiple deformation events rather than a single episode. Older ages correspond to a Middle Pleistocene phase of exhumation, while younger ages suggest additional mass-wasting events influenced by climatic and tectonic factors. At the secondary site, southeast of the previous site, ages indicate a steady-state surface where erosion and decay balance cosmogenic 36Cl production, implying a true age beyond the dating range and leaving the slope’s origin indeterminate. Nonetheless, recent tectonic activity on a fault cutting through this surface suggests an actively evolving tectonic landscape continuing into the Holocene.
Leveraging Reinforcement Learning for an Efficient Automation of Windows Registry Analysis During Cyber Incident Response
Authors: Ghanem Mohamed Chahine , Benkhelifa Elhadj , Wojtczak Dominik , Ferrag Mohamed Amine , Tihanyi Norbert , Nepomuceno Erivelton Geraldo
Published: January 2025

Windows represents the most common platform found in seized computers due to its widespread presence. This disparity has become worse due to the introduction of Microsoft’s Windows. Post Cyber Incident analysis of Microsoft Windows machines has become increasingly challenging due to the ever-evolving nature of digital threats. Traditional digital forensics methods often struggle to keep pace with modern cybercrime activities’ volume, sophistication, and complexity, which either target or originate from Windows machines. In response to these challenges, this research introduces WinRegRL, a framework that combines Reinforcement Learning (RL) and Rule-Based Artificial Intelligence (RB-AI) to enhance the efficiency, effectiveness and accuracy of digital investigations in the context of Windows Operating Systems. WinRegRL fully captures key information, elaborates the MDP environment, solves the RL problem and extracts expertise for later use. Implementation and testing of WinRegRL validated the research hypothesis by enabling optimised analysis and correlation of Registry forensics. Results prove that the proposed RL model outperforms all previous approaches including bling automation and human expert performance in terms of time, the number of artefacts explored, and the accuracy of results. Another advantage of the proposed framework is the ease of repetition, especially in this context, where more than one machine of the same configuration is under investigation, a context often faced in real DFIR practice.
Neural Networks for Statistical Modeling: Achieving Highly Accurate Chemical Oxygen Demand Prediction in Herbicide-Contaminated Wastewater
Authors: EL HAMZAOUI YOUNESS , Campos Beatriz Fernanda Tejero , Arellano Juan A Álvarez , Arcos Leonardo Palemón , Cruz Homero Toral
Published: January 2025

Herbicide-contaminated wastewater poses a critical environmental challenge, largely due to the difficulty of accurately predicting Chemical Oxygen Demand (COD) removal efficiency. This study introduces a systematic Artificial Neural Network (ANN) approach to model COD dynamics during the advanced oxidation (sonophotocatalytic) degradation of two commercial herbicides (Alazine and Gesaprim). Multiple backpropagation algorithms—including Gradient Descent, Conjugate Gradient, Quasi-Newton, and Levenberg-Marquardt—were evaluated to identify the most effective training strategy for COD prediction.
The optimal ANN architecture (6-10-1) consisted of six input neurons, ten hidden neurons, and one output neuron, using a hyperbolic tangent sigmoid transfer function in the hidden layer and a linear transfer function in the output layer. Trained on a dataset of 500 samples (70% for training; 30% for validation), this model achieved near-perfect correlation between measured and predicted COD values (R² = 0.9999, RMSE = 0.0140).
Key findings include:
• Parameter Characterization: Six operational variables (reaction time, pH, TiO₂ concentration, UV intensity, ultrasound frequency, and herbicide concentration) were assessed for their impact on COD removal.
• Dominant Variables: Reaction time emerged as the most influential parameter (59.91%), followed by herbicide concentration (8.3%) and TiO₂ concentration (7.5%).
• Algorithm Performance: Levenberg-Marquardt provided superior predictive performance (p < 0.01) among the tested backpropagation algorithms.
• Robust Validation: Friedman tests confirmed the reliability of the ANN models for COD prediction.

Uniqueness of the Present Work
While prior studies have underscored the growing importance of ANNs in wastewater treatment and herbicide degradation, none directly match the objectives and depth of this research. Beyond comparing multiple backpropagation algorithms and integrating sensitivity analysis, a novel “Neural Networks for Statistical Modeling” methodology was developed, complete with a comprehensive Graphical User Interface (GUI) and advanced statistical tests. This unique combination of algorithmic evaluation, in-depth statistical analysis, and user-friendly implementation offers new perspectives on optimizing herbicide degradation processes, with potential to reduce operational costs and environmental risks in agricultural and industrial applications.
Future studies will focus on practical implementation strategies and scaling up to full-scale operations, further solidifying the model’s efficacy for real-time monitoring and process optimization in advanced oxidation processes.
Environmental and Infrastructure Impacts in Brazilian Agrarian Reform Settlements: Contrast between Agropastoral and Agroextrative Projects in Brazil
Authors: Callejas Ivan Julio Apolonio , Durante Luciane Cleonice , Nora Giseli Gomes Dalla , Rossetto Onelia Carmem , Venere Paulo Cesar , Teixeira Raoni Florentino da Silva , Lima Diogo Marcelo Delben Ferreira de , Rosseti and Karyna de Andrade Carvalho
Published: January 2025

The Moju I and II Settlement Project (SP) and the Juruti Velho Agro Extractivist Project (ESP) are configured as different types of rural settlement implemented in the Lower Amazonas Mesoregion of the State of Pará / Brazil. This research surveyed the environmental impacts resulting from the degradation of the landscape, infrastructure conditions, and habitability conditions due to the implementation of conventional settlement projects (Moju I and II) and agro extractivist (Juruti Velho), concurrently carrying out an analysis of the contrast between them. To quantify impacts, the Leopold Matrix tool was applied to the database developed in the RADIS/UFMT Project. In turn, the environmental and infrastructure indicators refer to access to water, basic sanitation, telecommunications, electricity, and housing conditions. Asymmetries were evident between the two types of settlements. The lowest environmental impacts are concentrated in ESP due to exploration, which impacts the environment less in terms of deforestation. ESP compared to SP also have better access to water supply and telephone infrastructure, but worse access to internet and electrification. Other impacts are similar in both types of settlements, with little difference. The Agroextractive Settlement Projects (ESP) are a settlement model that promotes sustainable development in the region.
Postmortem Study and Attributable Mortality Due to Candidiasis in Non-Neutropenic Critical Patients
Authors: Ibañez Nolla Jordi , Garcia Felip , Carrasco Miquel A. , Nolla-Salas Miquel
Published: January 2025

A prospective study was made in non-neutropenic ICU patients in whom Candida spp. has been detected, defining Invasive Candidiasis (IC) based on Dissemination and Multifocality, therapeutic protocol and the methodology of the postmortem study. Attributable mortality is distinguished according to statistical, histopathological and clinical criteria. The presence of Candida spp. was observed in 145 cases of 3389 ICU discharges (4.3%). There are 120/145 cases (83%) classified as IC and 25/145 colonization (17%). ICU mortality is 35% (51/145) and hospital mortality were 46% (67/145). The postmortem study has been carried out in 71% of those who died in the ICU (36/51). Candida albicans is the most frequently isolated species (87%), followed by Candida glabrata (18%). Isolation in bronchial secretions is the most frequent focus (86%), followed by digestive foci (pharynx 83% and gastric 78%). The attributable mortality has been 25% by statistical methods, 28% the postmortem study and 23% clinical data. Abdominal surgery and non-Candida albicans are risk factors for attributable mortality, and correct antifungal treatment is a protective factor. In conclusion, histopathological attributable mortality is like that defined by both statistics and clinical data, and the simplification of the definition of IC allows for faster and more targeted therapeutic action.
Gaussian Process Regression with Soft Equality Constraints
Authors: Kochan Didem , Yang Xiu
Published: January 2025

This study introduces a novel Gaussian process (GP) regression framework that probabilistically enforces physical constraints, with a particular focus on equality conditions. The GP model is trained using the quantum-inspired Hamiltonian Monte Carlo (QHMC) algorithm, which efficiently samples from a wide range of distributions by allowing a particle's mass matrix to vary according to a probability distribution. By integrating QHMC into the GP regression with probabilistic handling of the constraints, the approach balances the computational cost and accuracy in the resulting GP model as the probabilistic nature of the method contributes to shorter execution times compared with existing GP-based approaches. Additionally, an adaptive learning algorithm is introduced to optimize the selection of constraint locations, further enhancing the method's flexibility. The effectiveness and efficiency of this approach are demonstrated through several applications: estimating hyperparameters for high-dimensional GP models under noisy conditions and reconstructing a sparsely observed steady-state heat transport problem. The numerical results indicate that the proposed approach accelerates the process while maintaining the accuracy.
Towards Green Transportation: Predictive Modeling of Intersection Congestion Using Machine Learning for Sustainable Urban Traffic Management
Authors: Muktar Bappa , Fono Vincent , Nouboukpo Adama
Published: January 2025

One of the main consequences of urban pollution is intersection congestion, which occurs due to frequent vehicle stops. These interruptions lead to increased fuel consumption and greenhouse gas emissions (CO2), along with other pollutants such as nitrogen oxides (NOX) and fine particulates. These pollutants can adversely affect the respiratory, cardiac, and neurological health of city residents. To address the growing demand for smart and sustainable transportation systems in large cities, predicting intersection congestion using artificial intelligence offers a promising solution. In this study, we present a predictive modeling approach to classify congestion levels at intersections controlled by traffic lights. Using the CN+ dataset collected in Bremen, Germany, our methodology incorporates vehicle and environmental features to predict congestion levels, optimize traffic flow, and reduce pollutant emissions. We employ data preprocessing, feature engineering, and machine learning techniques, including an innovative feature selection method called Dual Importance Intersection Feature Selection (DIFS), which combines Random Forest (RF) and Chi-square analysis. We tested various classifiers, including RF, XGBoost, LightGBM, CatBoost, and Artificial Neural Network (ANN), utilizing SMOTE balancing to address the class imbalance. Performance metrics such as precision, recall, F1 score, overall accuracy, and Quadratic Weighted Kappa (QWK) demonstrate promising results, with F1 and QWK scores reaching 100%. This makes our approach a robust tool for managing traffic sustainably and efficiently.
Clinical Implications and Surgical Approach of the Fetal-Type Posterior Communicating Artery Aneurysms
Authors: Toader Corneliu , Radoi Mugurel Petrinel , Aljboor Ghaith Saleh R. , Covache-Busuioc Razvan-Adrian , Tulemat Aoun , Al-Saedi Ali Ridha , Papacocea Toma Marius
Published: January 2025

The fetal-type posterior communicating artery (PCoA) variant, present in approximately 17.5% of adults, has significant clinical and surgical implications, particularly in the management of cerebrovascular disorders such as aneurysms. This variant alters cerebral hemodynamics, increasing the risk of ischemic events and complicating neurosurgical interventions. Surgical management of aneurysms associated with fetal-type PCoA requires precise anatomical knowledge and advanced imaging to tailor the approach. Techniques such as pterional craniotomy and temporary clipping of the internal carotid artery facilitate safe dissection and aneurysm obliteration while preserving vascular integrity. This paper discusses the challenges posed by fetal-type PCoA anatomy, highlights tailored surgical techniques, and explores the role of endovascular alternatives. A case presentation demonstrates the successful management of a ruptured PCoA aneurysm, emphasizing the importance of individualized surgical strategies. Long-term follow-up is crucial to prevent complications such as recurrence or ischemic events, underscoring the need for a multidisciplinary approach to optimize patient outcomes.
A Cost-Effective and Easy to Assemble 3D Human Microchannel-Blood-Brain Barrier Model and Its Application in Tumor Metastasis
Authors: Fu Bingmei M , Li Yunfei
Published: January 2025

By utilizing PDMS, collagen hydrogel and a cell line for human cerebral microvascular endothelial cells, we generated a 3D microchannel-BBB model under physiological flows. This 3D BBB has a circular shaped cross-section and a diameter of ~100m, which can properly mimic the cerebral microvessel responsible for material exchange between the circulating blood and brain tissue. The permeability of the 3D microchannel-BBB to a small molecule (sodium fluorescein with molecular weight 376) and that to a large molecule (Dex-70k) are the same as those of rat cerebral microvessels. This 3D BBB model can replicate the response to a plasma protein, orosomucoid, a cytokine, VEGF, and an enzyme, heparinase III, in either rat cerebral or mesenteric microvessesels in terms of permeability and glycocalyx (heparan sulfate). It can also replicate the adhesion of a breast cancer cell, MDA-MB-231, in rat mesenteric microvessels with no treatment and with treatments with VEGF, orosomucoid and heparinase III. Because of difficulties in accessing human cerebral microvessels, this inexpensive and easy to assemble 3D human BBB model can be applied to investigate the BBB modulating mechanisms in health and in disease and to develop therapeutic interventions targeting tumor metastasis to the brain.
Correlative Analysis Among experimental and Theoretical Structural, Thermochemical, and Molecular Spectroscopic Parameters of Crystals of Mandelic Acid
Authors: Ivanova Bojidarka
Published: January 2025

Crystals of mandelic acid are of significant importance. They are commercial pharmaceutics formulations modulating active ingredient solubility and its pharmacological effect. Commercial pharmaceuticals are at about 50 % crystals. Salt formulation is among the most used strategy for improving properties of medications. Salt crystallization screening is routinely implemented into pharmaceutical industry. Via disproportionation there is produced free therapeutics forms. The process is thermodynamically and kinetically driven. It is tackled by crystallographic and quantum chemical methods for salt screening as integral parts of development workflow in pharmaceutical industry. Correlations among crystallographic, Fourier-transform infrared, and electronic spectroscopic data on salts, and theoretical thermochemical approaches are of primary importance for determining relations among molecular structure « crystal structure « properties of crystals. This paper presents novel structural and molecular spectroscopic data on crystals of mandelic acid such as DL-mandelic acid (1), 4-phenyl-pyridinium mandelate mandelic acid (2) ¾ first, reported, herein, ¾ and catena-((μ3-DL-mandelato)-silver(I)) (3). It also utilizes chemometrics. The major conclusion follows from relation between crystallographic potential energy data on bond critical point using Abramov’s formula and theoretical bond dissociation energy showing |r|=0.9999. The approach seems best characterizes experimental crystallographic energetics of chemical bonds of molecules fitted off theoretical data.
Mutational Scanning and Binding Free Energy Computations of the SARS-CoV-2 Spike Complexes with Distinct Groups of Neutralizing Antibodies: Energetic Drivers of Convergent Evolution of Binding Affinity and Immune Escape Hotspots
Authors: Alshahrani Mohammed , Parikh Vedant , Foley Brandon , Raisinghani Nishank , Verkhivker Gennady
Published: January 2025

The rapid evolution of SARS-CoV-2 has led to the emergence of variants with increased immune evasion capabilities, posing significant challenges to antibody-based therapeutics and vaccines. In this study, we conducted a comprehensive structural and energetic analysis of SARS-CoV-2 spike receptor-binding domain (RBD) complexes with neutralizing antibodies from four distinct groups (A-D), including group A LY-CoV016, group B AZD8895 and REGN10933, group C LY-CoV555, and group D antibodies AZD1061, REGN10987, and LY-CoV1404. Using coarse-grained simplified simulation models, rapid energy-based mutational scanning, and rigorous MM-GBSA binding free energy calculations, we elucidated the molecular mechanisms of antibody binding and group-specific escape mechanisms, identified key binding hotspots, and explored the evolutionary strategies employed by the virus to evade neutralization The structural analysis and mutational profiling revealed distinct binding mechanisms and epitope specificities for each antibody group, which directly influence their neutralization potency and susceptibility to escape mutations. The MM-GBSA binding free energy analysis identified critical binding hotspots and quantified the contributions of van der Waals and electrostatic interactions to antibody binding. The results showed that key binding hotspots, such as F456, F486, and V445, are dominated by van der Waals interactions, while residues like K417, E484, and K444 contribute significantly through electrostatic interactions. Mutations at these sites, including K417N, E484K, and K444Q, are frequently observed in emerging variants and enable immune evasion by disrupting antibody binding. We identified synergistic effects of van der Waals and electrostatic interactions at key residues K417, F486, and K444 underscore their dual role in binding and immune evasion. Mutations at these sites disrupt both types of interactions, leading to significant reductions in binding affinity and susceptibility to escape mutations. These findings align with experimental data, which identify these residues as dominant escape hotspots. The residue-based decomposition analysis revealed energetic mechanisms and thermodynamic factors underlying the effect of mutations on antibody binding. The results of this energetic analysis demonstrate excellent qualitative agreement between the predicted binding hotspots and critical mutations with respect to the latest experiments on average antibody escape scores. These findings provide valuable insights into the molecular determinants of antibody binding and viral escape, highlighting the importance of targeting conserved epitopes and leveraging combination therapies to mitigate the risk of immune evasion.
All About Lekking: A Comprehensive Examination of Lekking and the Lek Mating System in the Black Soldier Fly, Hermetia illucens (Diptera: Stratiomyidae)
Authors: Lemke Noah B.
Published: January 2025

The black soldier fly, Hermetia illucens (Diptera: Stratiomyidae), is an economically important fly that is now mass-reared globally to produce livestock protein and frass that can serve as sustainable alternatives to conventional fishmeal and synthetic fertilizer. The species’ mating system has repeatedly been described as lekking, though there have been doubts raised as to the appropriateness of this terminology, especially for captive populations. Hence, the purpose of this review is to formally evaluate the mating system of the black soldier fly using Bradbury’s expanded criteria for lekking species to better understand what indeed the natural mating system and behavior of the black soldier fly is. From an applied perspective, accurately describing the mating system is important for understanding whether the industrial breeding system is inducing (unintentional) phenotypic changes. However, it is also important to point out that because the black soldier fly are not being mass-reared for release into the wild, as would be the case for those reared for Sterile Insect Technique (SIT), unless a particular mating system can be linked to an increase in fertile egg production, then there is a very limited reason why that mating system should be preserved in favor of the more productive one, other than perhaps the discovery of correlated genetic erosion or through the desire to promote insect-welfare, which places an emphasis on emulating a natural environment, maintaining natural behaviors, and reducing stress of confined animals.
Comprehensive Profiling of Immune Cell Populations and Cytokine Signatures in Colorectal Cancer Patients
Authors: Huynh Chien Dinh , Nguyen Phuong Minh , Ngo Trung Dinh , Nguyen Hung Xuan , Nguyen Tu Dac , Mai Hien Thi , Le Huyen Thi , Hoang Duy Mai , Le Linh Khac , Nguyen Quan Khoi , Nguyen Hoang Viet , Kelley Keith Wayne
Published: January 2025

Colorectal cancer (CRC) ranks as the fifth most prevalent malignancy in Vietnam. Cytological and molecular assays currently serve as crucial tools for assessing the immune status of patients. Here we document extensive alterations in both the cellular immune system in CRC patients using state of the art multi-color flow cytometry and its products (cytokines). We report a significant reduction in B and T lymphocyte populations in CRC patients, coupled with relatively stable NK cell and monocyte levels. These changes may be considered broadly characteristic of the disease. More specifically, several B cell subpopulations were increased in CRC patients, including B_CD21low, B_transitional and B_class-switched cells. Similarly, CRC patients displayed greater proportions T cell activation markers, including T_CD69+, T_CD8+CD28- and T_CD8 effector and T_CD8 effector memory cells. Leukocytes from CRC patients also exhibited an upregulation of NK cell receptors (CD25, CD57, CD69, KLRG1, p44) and enhanced cytotoxic capability of NK cells. Five out of 15 cytokines were significantly elevated in CRC patients. This study provides a comprehensive profile of cellular immune markers and cytokines that may serve as immune biomarkers and treatment response monitoring in CRC patients. These findings contribute to our understanding of the new and coordinated immunological landscape in CRC and will inform future diagnostic and therapeutic strategies.
Decoding Mini-Puberty and Its Clinical Significance: A Narrative Review
Authors: Serbis Anastasios , Kosmeri Chrysoula , Atzemoglou Natalia , Lampropoulou Aikaterini , Giaprou Lida-Eleni , Giapros Vasileios
Published: January 2025

Mini-puberty refers to the transient activation of the hypothalamic-pituitary-gonadal (HPG) axis during early infancy, lasting up to six months in boys and 12-24 months in girls. This phase represents the second activation of the HPG axis, following its initial activation during the second half of fetal life. At birth, the removal of the suppressive effect of placental estrogens on the HPG axis prompts a rise in both gonadotropins and sex steroid hormones, resulting in distinct clinical and laboratory markers of mini-puberty. While the clinical significance of mini-puberty remains partially understood, emerging evidence underscores its essential role in several aspects of human growth and development. In boys, testosterone influences penile growth, increases Sertoli cell numbers in the testes, and lays the foundation for future spermatogenesis. In girls, the increase in estradiol levels promote follicular maturation and stimulate breast and uterine growth. Beyond the gonadal effects, mini-puberty appears to impact body composition, affecting body weight and promoting accelerated growth. Additionally, it may affect early psychosomatic and neural maturation, playing a role in several key aspects of the infantile brain. This narrative review examines recent findings on the physiology of the activation of HPG axis before and after birth along with its significance in various aspects of human growth and development. In addition, mini-puberty unique features in specific groups, such as preterm and small-for-gestational-age infants are presented.
What Have Urban Digital Twins Contributed to Urban Planning and Decision Making? A Systematic Literature Review and a Socio-technical Research and Development Agenda
Authors: Azadi Shervin , Kasraian Dena , Nourian Pirouz , Wesemael Pieter van
Published: January 2025

Urban Digital Twins (UDTs) were first discussed in 2018. Seven years later we ask: What has been their contribution to urban planning and decision-making so far? Here, we systematically review 84 peer-reviewed articles to map and compare UDTs’ ambitions with their realized contributions. Our results indicate that despite the vast technical developments, socio-technical challenges have remained largely unaddressed which caused much of the UDTs’ ambitions to remain unrealized. We identify three categories in these socio-technical challenges: interdisciplinary integration (II), consensual contextualization (CC), and procedural operationalization (PO). Accordingly, we consolidate a socio-technical research and development agenda to realize the ambitions of UDTs for urban planning and decision-making: Augmented Urban Planning (AUP).
Exploring AI-Driven Machine Learning Approaches for Optimal Classification of Peri-Implantitis Based on Oral Microbiome Data: A Feasibility Study
Authors: Pais Ricardo Jorge , Botelho João , Machado Vanessa , Alcoforado Gil , Mendes José João , Alves Ricardo , Bessa Lucinda J.
Published: January 2025

Background: Machine-learning (ML) techniques have been recently proposed as a solution for aiding in the prevention and diagnosis of microbiome-related diseases. Here, we applied auto-ML approaches on real-case metagenomic datasets from saliva and subgingival peri-implant biofilm microbiomes to explore a wide range of ML algorithms to benchmark best-performing algorithms for predicting peri-implantitis (PI). Methods: A total of 100 metagenomes from the NCBI SRA database (PRJNA1163384) were used in this study to construct biofilm and saliva metagenomes datasets. Two AI-driven auto-ML approaches were used on constructed datasets to generate 100 ML-based models for the prediction of PI. These were compared with statistically significant single microorganism-based models. Results: Several ML algorithms were pinpointed as suitable bespoke predictive approaches to apply to metagenomic data, outperforming the single microorganism-based classification. Auto-ML approaches rendered high-performing models with AUC values, sensitivities and specificities between 80% and 100%. Among these, classifiers based on ML-driven scoring of combinations of 2-4 microorganisms presented top-ranked performances and can be suitable for clinical application. Moreover, models generated based on the saliva microbiome showed higher predictive performance than those from the biofilm microbiome. Conclusions: This feasibility study bridges complex AI research with practical dental applications by benchmarking ML algorithms and exploring oral microbiomes as foundations for developing intuitive, cost-effective, and clinically relevant diagnostic platforms.
Nutritional Stress Leads to Persistence and Persister-Like Growth in Staphylococcus aureus
Authors: Risoen Katie , Shaw Claire , Weimer Bart C
Published: January 2025

Staphylococcus aureus is a versatile zoonotic pathogen capable of causing a wide range of infections. Due to the organism’s ability to persist, recalcitrant and recurring infections are a major concern for public and animal health. This study investigated the establishment of persistence using two S. aureus strains—ATCC 29740, a bovine mastitis isolate, and USA300, a human clinical isolate—under substrate depletion. This stress established a persistence phenotype where the strains remained persistent for >120 days at notable concentrations and developed persister-like growth, including small colony variant formations. With qRT-PCR we found the cell density was higher than represented by the plate count while the intracellular ATP remained constant during the persistence phase. These findings indicate that S. aureus has complex survival strategies to support its persistent state, providing a host-specific perspective when addressing recurrent infections in human and animal infectious disease.
The Relationship between Adenovirus Infection and Intussusception via Pathological Evidence Confirms
Authors: Lin Lung- Huang , Huang Chi-Jung , Lo Cheng-Yu , Lee Yu-Hsien , Chen Yung-Chuan
Published: January 2025

Although some cases of intussusception in older children are associated with pathological changes such as lymphoma or polyps, the cause of most cases in infants is unknown. Several reports have identified an association between adenovirus infection and intussusception in children. However, much of the evidence is indirect, such as stool samples or throat swab data. Our study analyzed intestinal tissue, which may be more direct evidence of the relationship between adenovirus infection and intussusception.
We retrospectively reviewed children 0.05, with no statistical difference, while the P value of PCR was