A Systematic Review of Cyber Threat Intelligence: The Effectiveness of Technologies, Strategies, and Collaborations in Combating Modern Threats.

Cyber threat intelligence (CTI) has become critical in enhancing cybersecurity measures across various sectors. This systematic review aims to synthesize the current literature on the effectiveness of CTI strategies in mitigating cyber attacks, identify the most effective tools and methodologies for threat detection and prevention, and highlight the limitations of current approaches. An extensive search of academic databases was conducted following the PRISMA guidelines, including 43 relevant studies.

Post-stroke upper limb rehabilitation: clinical practices, compensatory movements, assessment, and trends.

Stroke, a vascular disorder affecting the nervous system, is the third-leading cause of death and disability combined worldwide. One in every four people aged 25 and older will face the consequences of this condition, which typically causes loss of limb function, among other disabilities. The proposed review analyzes the mechanisms of stroke and their influence on the disease outcome, highlighting the critical role of rehabilitation in promoting recovery of the upper limb (UL) and enhancing the quality of life of stroke survivors.

Impact of promoting physical exercise on migraine - A video-based randomized clinical trial.

Introduction: Migraine significantly impacts quality of life, and a substantial proportion of subjects experience refractory migraine. Non-pharmacological strategies, such as physical exercise, have shown potential benefits in headache control. This study aims to evaluate the effectiveness of a video-based educational intervention in promoting physical activity and improving migraine-related outcomes.

Benchmarking Controllers for Low-Cost Agricultural SCARA Manipulators.

Agriculture needs to produce more with fewer resources to satisfy the world's demands. Labor shortages, especially during harvest seasons, emphasize the need for agricultural automation. However, the high cost of commercially available robotic manipulators, ranging from EUR 3000 to EUR 500,000, is a significant barrier.

Life-Cycle Assessment in Agri-Food Systems and the Wine Industry-A Circular Economy Perspective.

Rapid population growth, climate change, and resource depletion pose significant challenges to global food production, demanding sustainable solutions. A Life-Cycle Assessment (LCA) provides a structured framework for evaluating the environmental impact of food systems throughout their entire life cycle. This review examines how an LCA can be integrated with circular economy principles to address sustainability challenges, optimize resource use, and minimize waste in food and alcoholic beverage production.

Implementation of an Internet of Things Architecture to Monitor Indoor Air Quality: A Case Study During Sleep Periods.

Most human time is spent indoors, and due to the pandemic, monitoring indoor air quality (IAQ) has become more crucial. In this study, an IoT (Internet of Things) architecture is implemented to monitor IAQ parameters, including CO and particulate matter (PM). An ESP32-C6-based device is developed to measure sensor data and send them, using the MQTT protocol, to a remote InfluxDBv2 database instance, where the data are stored and visualized.

Optimization of Magnetoplasmonic Behavior in Ag/Fe Bilayer Nanostructures Towards Refractometric Sensing.

Magneto-optic surface plasmon resonances (MOSPRs) rely on the interaction of magnetic fields with surface plasmon polaritons (SPP) to modulate plasmonic bands with magnetic fields and enhance magneto-optical activity. In the present work, a study on the magnetoplasmonic behavior of Ag/Fe bilayers is carried out by VIS-NIR spectroscopy and backed with SQUID measurements, determining the thickness-dependent magnetization of thin-film samples. The MOSPR sensing properties of Ag/Fe planar bilayers are simulated using Berreman's matrix formalism, from which an optimized structure composed of 15 nm of Ag and 12.

AI-based models to predict decompensation on traumatic brain injury patients.

Traumatic Brain Injury (TBI) is a form of brain injury caused by external forces, resulting in temporary or permanent impairment of brain function. Despite advancements in healthcare, TBI mortality rates can reach 30%-40% in severe cases. This study aims to assist clinical decision-making and enhance patient care for TBI-related complications by employing Artificial Intelligence (AI) methods and data-driven approaches to predict decompensation.

Technological Resources for Physical Rehabilitation in Cancer Patients Undergoing Chemotherapy: A Scoping Review.

: Cancer patients undergoing chemotherapy often face challenges that reduce their physical function and quality of life. Technological resources offer innovative solutions for physical rehabilitation, but the extent of their application in this context remains unclear. This scoping review aims to explore and map the various technological tools used to support physical rehabilitation in cancer patients during chemotherapy, focusing on their potential to improve outcomes and enhance patient care.

Prospective Validation and Usability Evaluation of a Mobile Diagnostic App for Obstructive Sleep Apnea.

: Obstructive sleep apnea (OSA) classification relies on polysomnography (PSG) results. Current guidelines recommend the development of clinical prediction algorithms in screening prior to PSG. A recent intuitive and user-friendly tool (OSABayes), based on a Bayesian network model using six clinical variables, has been proposed to quantify the probability of OSA.

Phase-Shifted Fiber Bragg Grating by Selective Pitch Slicing.

This paper presents a new type of phase-shifted Fiber Bragg Grating (FBG): the sliced-FBG (SFBG). The fabrication process involves cutting a standard FBG inside its grating region. As a result, the last grating pitch is shorter than the others.

A survey on cell nuclei instance segmentation and classification: Leveraging context and attention.

Nuclear-derived morphological features and biomarkers provide relevant insights regarding the tumour microenvironment, while also allowing diagnosis and prognosis in specific cancer types. However, manually annotating nuclei from the gigapixel Haematoxylin and Eosin (H&E)-stained Whole Slide Images (WSIs) is a laborious and costly task, meaning automated algorithms for cell nuclei instance segmentation and classification could alleviate the workload of pathologists and clinical researchers and at the same time facilitate the automatic extraction of clinically interpretable features for artificial intelligence (AI) tools. But due to high intra- and inter-class variability of nuclei morphological and chromatic features, as well as H&E-stains susceptibility to artefacts, state-of-the-art algorithms cannot correctly detect and classify instances with the necessary performance.

Are Aptamer-Based Biosensors the Future of the Detection of the Human Gut Microbiome?-A Systematic Review and Meta-Analysis.

The gut microbiome is shaped early in life by dietary and lifestyle factors. Specific compounds in the gut affect the growth of different bacterial species and the production of beneficial or harmful byproducts. Dysbiosis of the gut microbiome has been linked to various diseases resulting from the presence of harmful bacteria and their byproducts.

Calibration and Modeling of the Semmes-Weinstein Monofilament for Diabetic Foot Management.

Diabetic foot is a serious complication that poses significant risks for diabetic patients. The resulting reduction in protective sensitivity in the plantar region requires early detection to prevent ulceration and ultimately amputation. The primary method employed for evaluating this sensitivity loss is the 10 gf Semmes-Weinstein monofilament test, commonly used as a first-line procedure.

Plasmonic nanoparticle sensors: current progress, challenges, and future prospects.

Plasmonic nanoparticles (NPs) have played a significant role in the evolution of modern nanoscience and nanotechnology in terms of colloidal synthesis, general understanding of nanocrystal growth mechanisms, and their impact in a wide range of applications. They exhibit strong visible colors due to localized surface plasmon resonance (LSPR) that depends on their size, shape, composition, and the surrounding dielectric environment. Under resonant excitation, the LSPR of plasmonic NPs leads to a strong field enhancement near their surfaces and thus enhances various light-matter interactions.

Research priorities in veterinary palliative care.

Veterinary palliative care consists of end-of-life care of companion animals suffering from terminal or life-limiting diseases. Despite the growing need for veterinary palliative care, little research has been conducted on this topic. This perspective intends to provide an overview of the existing concerns and identify knowledge gaps to motivate further research.

Human versus Artificial Intelligence: Validation of a Deep Learning Model for Retinal Layer and Fluid Segmentation in Optical Coherence Tomography Images from Patients with Age-Related Macular Degeneration.

Artificial intelligence (AI) models have received considerable attention in recent years for their ability to identify optical coherence tomography (OCT) biomarkers with clinical diagnostic potential and predict disease progression. This study aims to externally validate a deep learning (DL) algorithm by comparing its segmentation of retinal layers and fluid with a gold-standard method for manually adjusting the automatic segmentation of the Heidelberg Spectralis HRA + OCT software Version 6.16.

Neurophotonics: a comprehensive review, current challenges and future trends.

The human brain, with its vast network of billions of neurons and trillions of synapses (connections) between diverse cell types, remains one of the greatest mysteries in science and medicine. Despite extensive research, an understanding of the underlying mechanisms that drive normal behaviors and response to disease states is still limited. Advancement in the Neuroscience field and development of therapeutics for related pathologies requires innovative technologies that can provide a dynamic and systematic understanding of the interactions between neurons and neural circuits.

Guidelines for reproducible analysis of adaptive immune receptor repertoire sequencing data.

Enhancing the reproducibility and comprehension of adaptive immune receptor repertoire sequencing (AIRR-seq) data analysis is critical for scientific progress. This study presents guidelines for reproducible AIRR-seq data analysis, and a collection of ready-to-use pipelines with comprehensive documentation. To this end, ten common pipelines were implemented using ViaFoundry, a user-friendly interface for pipeline management and automation.

Optical pH Sensor Based on a Long-Period Fiber Grating Coated with a Polymeric Layer-by-Layer Electrostatic Self-Assembled Nanofilm.

An optical fiber pH sensor based on a long-period fiber grating (LPFG) is reported. Two oppositely charged polymers, polyethylenimine (PEI) and polyacrylic acid (PAA), were alternately deposited on the sensing structure through a layer-by-layer (LbL) electrostatic self-assembly technique. Since the polymers are pH sensitive, their refractive index (RI) varies when the pH of the solution changes due to swelling/deswelling phenomena.

Bi-directional hyperspectral reconstruction of cherry tomato: diagnosis of internal tissues maturation stage and composition.

Introduction: Precision monitoring maturity in climacteric fruits like tomato is crucial for minimising losses within the food supply chain and enhancing pre- and post-harvest production and utilisation.

Objectives: This paper introduces an approach to analyse the precision maturation of tomato using hyperspectral tomography-like.

Methods: A novel bi-directional spectral reconstruction method is presented, leveraging visible to near-infrared (Vis-NIR) information gathered from tomato spectra and their internal tissues (skin, pulp, and seeds).

Enhanced Sensitivity in Optical Sensors through Self-Image Theory and Graphene Oxide Coating.

This paper presents an approach to enhancing sensitivity in optical sensors by integrating self-image theory and graphene oxide coating. The sensor is specifically engineered to quantitatively assess glucose concentrations in aqueous solutions that simulate the spectrum of glucose levels typically encountered in human saliva. Prior to sensor fabrication, the theoretical self-image points were rigorously validated using Multiphysics COMSOL 6.