Generic Functionality Research Articles

Benefits of Cognitive Behavior Therapy with Neuroscience-informed Psychoeducation in Young Adult Patients: A Preliminary Study

Background: Psychoeducation often is a recommended augmentation for cognitive behavior therapy (CBT) to enhance patients’ understanding of their clinical condition. But despite providing psychoeducation, there might be a lack of insightful engagement in the therapy process on the part of patients, affecting the therapy outcome. While there are neuroscience-informed psychoeducation models for addiction, there is rarely any literature on neuroscience-informed psychoeducation for CBT. It is postulated that a neuroscience-informed module of psychoeducation can bring better acceptance of CBT and improve its outcome. Methods: In this study, we used a pre- and poststudy design. Using purposive sampling, 30 consecutive young adult patients presented themselves with symptoms of depression and anxiety to receive standard CBT module. Fifteen of these received the CBT module with generic psychoeducation detailing the etiology, signs and symptoms, and treatment requirements for their condition. The other 15 received neuroscience-informed psychoeducation modules having specific inputs about the generic functioning of the human brain, the neuropsychological changes, and processes in the brain during and due to the psychotherapy (CBT) process. Acceptance and outcome of CBT were measured through changes in symptom rating as well as individual feedback, sentiment analysis of the feedback statements, and word cloud. Results: Participants of both the psychoeducation model groups – conventional psychoeducation (p < 0.001) and neuroscience-informed psychoeducation group (p < 0.001) – were found to have significant improvement in their clinical symptoms of the pre- and posttest scores. But the neuroscience-based psychoeducation group was better compliant to sessions and had better follow-up rates compared to conventional psychoeducation. Sentiment analysis of the feedback revealed a higher volume of sentiments in neuroscience-informed group. Conclusion: Neuroscience-based psychoeducation has the potential of changing perception and enhancing the credibility of CBT and can bring better acceptance and outcome for the CBT process.

Gut microbiome composition and functionality impact the responsiveness to a dairy-based product containing galacto-oligosaccharides for improving sleep quality in adults.

Sleep quality and duration can be impacted by diet, and has been linked to gut microbiota composition and function as the result of communication via the microbiota-gut-brain axis. As one strategy to improve sleep quality could be through the modulation of the gut microbiome, we assessed the effects of a dairy-based product containing whey protein, galacto-oligosaccharides, tryptophan, vitamins and minerals after a 3 weeks intervention on gut microbiota composition and (gut-brain related) functions on basis of 67 healthy subjects with moderate sleep disturbances. Associations of the gut microbiota with sleep quality and with response/non-response to the treatment were revealed by shotgun metagenomics sequencing of faecal DNA samples, and subsequent analyses of microbiota taxonomy and generic functionality. A database of manually curated Gut-Brain Modules (GBMs) was applied to analyse specific microbial functions/pathways that have the potential to interact with the brain. A moderate discriminating effect of the DP treatment on gut microbiota composition was revealed which could be mainly attributed to a decrease in Pseudomonas resinovorans, Flintibacter sp. KGM00164, Intestinimonas butyriciproducens, and Flavonifractor plautii. As interindividual variance in microbiota composition could have given rise to a heterogenous responsiveness of the subjects in the intervention group, we zoomed in on the differences between responders and non-responders. A significant difference in baseline microbiota composition between responders and non-responders was apparent, showing lower Bifidobacterium longum and Bifidobacterium adolescentis, and higher Faecalibacterium prausnitzii relative abundances in responders. The findings provide leads with respect to the effectiveness and potential underlying mechanisms of mode of action in sleep improvement that could support future nutritional interventions to aid sleep improvement.

Utilization of synthetic system intelligence as a new industrial asset

System knowledge and reasoning mechanisms are essential means for intellectualization of cyber-physical systems (CPSs). As enablers of system intelligence, they make such systems able to solve application problems and to maintain their efficient operation. Normally, system intelligence has a human-created initial part and a system-produced (extending) part, called synthetic system intelligence (SSI). This position paper claims that SSI can be converted to a new industrial asset and utilized as such. Unfortunately, no overall theory of SSI exists and its conceptual framework, management strategy, and computational methodologies are still in a premature stage. This is the main reason why no significant progress has been achieved in this field, contrary to the latent potentials. This paper intends to contribute to: (i) understanding the nature and fundamentals of SSI, (ii) systematizing the elicitation and transfer of SSI, (iii) exploration of analogical approaches to utilization of SSI, and (iv) road-mapping and scenario development for the exploitation of SSI as an industrial asset. First, the state of the art is surveyed and the major findings are presented. Then, four families of analogical approaches to SSI transfer are analyzed. These are: (i) knowledge transfer based on repositories, (ii) transfer among agents, (iii) transfer of learning resources, and (iv) transfer by emerging approaches. A procedural framework is proposed that identifies the generic functionalities needed for a quasi-autonomous handling of SSI as an industrial asset. The last section casts light on some important open issues and necessary follow-up research and development activities.

The (Self-Similar, Variational) Rolling Stones

Abstract The interplay between variational functionals and the Brunn–Minkowski Theory is a well-established phenomenon widely investigated in the last thirty years. In this work, we prove the existence of solutions to the even logarithmic Minkowski problems arising from variational functionals, such as the first eigenvalue of the Laplacian and the torsional rigidity. In particular, we lay down a blueprint showing that the same result holds for more generic functionals by adapting the volume case from Böröczky, Lutwak, Yang, and Zhang. We show how these results imply the existence of self-similar solutions to variational flow problems à la Firey’s worn stone problem.

Development of a Korean version of simple, intuitive descriptions for clinical use of the generic functioning domains in the ICD-11 Chapter V

Purpose To develop a Korean version of simple, intuitive descriptions (SIDs) for clinical use of the generic functioning domains in the International Classification of Disease 11th revision (ICD-11) Chapter V. Methods The initial Korean SID version proposal for the International Classification of Functioning, Disability, and Health (ICF) Rehabilitation set was translated following the Italian version. The remaining 17 codes were developed using original ICF descriptions; WHO Disability Assessment Schedule, Model Disability Survey, Korean Classification of Functioning, Disability, and Health; and previous studies. The final proposal for the Korean version of SIDs was selected through virtual conferences and three rounds of voting. Results This study developed SIDs for the 47 generic functioning domains in the Chapter V of ICD-11. However, the SID for 20 of the 47 codes was confirmed in vote A, for 23 codes in vote B and for the remaining 4 in the final vote. All experts agreed with the final SID proposal. Conclusions This is the first study in South Korea to attempt the development of SIDs for ICD-11 Chapter V. Therefore, the findings of this study could be used to evaluating of disability, functioning when ICD-11 is adopted for use in Korean clinical settings.

The Role of the OLS Program in the Development of echinopscis (an Extensible Notebook for Open Science on Specimens)

Starting in early 2022, biodiversity informatics researchers at Kew have been developing echinopscis: an "extensible notebook for open science on specimens". This aims to build on the early experiments that our community conducted with "e-taxonomy": the development of tools and techniques to enable taxonomic research to be conducted online. Early e-taxonomic tools (e.g., Scratchpads Smith et al. 2011) had to perform a wide range of functions, but in the past decade or so the move towards open science has built better support for generic functionality, such as reference management (Zotero) and document production (pandoc), skills development in automation and revision control to support reproducible science, as documented by the Turing Way (The Turing Way Community 2022), and an awareness of the importance of community building. We have developed echinopscis at Kew via a cross-departmental collaboration between researchers in biodiversity informatics and accelerated taxonomy. We have also benefitted from valuable input and advice from our many colleagues in associated projects and organisations around the world. OLS (originally Open Life Sciences) is a training and mentoring program for Open Science leaders with a focus on community building. The name was recently (2023) made more generic—"Open Seeds"—whilst retaining their well-known acronym "OLS"*1. OLS is a 16-week cohort-based mentoring program. Participants apply to join a cohort with a project that is developed through the 16 weeks. Each week of the syllabus alternates between time with a dedicated Open Science mentor and cohort calls, which are used to develop skills in project design, community building, open development & licencing, and inclusivity. Over 500 practitioners, experts and learners have participated across the seven completed cohorts of OLS' Open Seeds training and mentoring. Through this programme, over 300 researchers and open leaders from across six continents have designed, lauched and supported 200 projects from different disciplines worldwide. The next cohort will run between September 2023 and January 2024, and will be the eighth iteration of the program. This talk will briefly outline the work that we have done to setup and experiment with echinopscis, but will focus on the impact that the OLS program has had in its development. We will also include the use of techniques learned through OLS in other biodiversity informatics projects. OLS acknowledges that their program receives relatively few applications from project leads in biodiversity and we hope that this talk will be informative for Biodiversity Information Standards (TDWG) participants and can be used to build productive links between these communities.

Inferring ligand-receptor cellular networks from bulk and spatial transcriptomic datasets with BulkSignalR.

The study of cellular networks mediated by ligand-receptor interactions has attracted much attention recently owing to single-cell omics. However, rich collections of bulk data accompanied with clinical information exists and continue to be generated with no equivalent in single-cell so far. In parallel, spatial transcriptomic (ST) analyses represent a revolutionary tool in biology. A large number of ST projects rely on multicellular resolution, for instance the Visium™ platform, where several cells are analyzed at each location, thus producing localized bulk data. Here, we describe BulkSignalR, a R package to infer ligand-receptor networks from bulk data. BulkSignalR integrates ligand-receptor interactions with downstream pathways to estimate statistical significance. A range of visualization methods complement the statistics, including functions dedicated to spatial data. We demonstrate BulkSignalR relevance using different datasets, including new Visium liver metastasis ST data, with experimental validation of protein colocalization. A comparison with other ST packages shows the significantly higher quality of BulkSignalR inferences. BulkSignalR can be applied to any species thanks to its built-in generic ortholog mapping functionality.

Exposure to anticholinergic and sedative medication is associated with impaired functioning in older people with vertigo, dizziness and balance disorders-Results from the longitudinal multicenter study MobilE-TRA.

Introduction: Anticholinergic and sedative medication is prescribed for various conditions in older patients. While the general association between anticholinergic and sedative medication and impaired functioning is well established, its specific role in older individuals with vertigo, dizziness, and balance disorders (VDB) is still incompletely understood. The objective of this study was to investigate, whether an exposure to anticholinergic and sedative medication is associated with lower generic and lower vertigo-specific functioning in older patients with VDB. Methods: Data originates from the longitudinal multicenter study MobilE-TRA with two follow-ups, conducted from 2017 to 2019 in two German federal states. Exposure to anticholinergic and sedative medication was quantified using the drug burden index (DBI). Generic functioning was assessed by the Health Assessment Questionnaire Disability Index, appraising the amount of difficulties in performing activities of daily living (ADL). Vertigo-specific functioning was measured using the Vestibular Activities and Participation (VAP) questionnaire, assessing patient-reported functioning regarding activities of daily living that are difficult to perform because of their propensity to provoke VDB (Scale 1) as well as immediate consequences of VDB on activities and participation related to mobility (Scale 2). Longitudinal linear mixed models were applied to assess the association of exposure to anticholinergic and sedative medication at baseline and the level of generic and vertigo-specific functioning status over time. Results: An overall of 19 (7 from Bavaria) primary care physicians (mean age = 54years, 29% female) recruited 158 (59% from Bavaria) patients with VDB (median age = 78years, 70% female). Anticholinergic and sedative medication at baseline was present in 56 (35%) patients. An exposure to anticholinergic and sedative medication at baseline was significantly associated with lower generic functioning [Beta = 0.40, 95%-CI (0.18; 0.61)] and lower vertigo-specific functioning [VAP Scale 1: Beta = 2.47, 95%-CI (0.92; 4.02)], and VAP Scale 2: Beta = 3.74, 95%-CI [2.23; 5.24]). Conclusion: Our results highlight the importance of a close monitoring of anticholinergic and sedative medication use in older patients with VDB. When feasible, anticholinergic and sedative medication should be replaced by equivalent alternative therapies in order to potentially reduce the burden of VDB.

Semiparametric counterfactual density estimation

Abstract Causal effects are often characterized with averages, which can give an incomplete picture of the underlying counterfactual distributions. Here we consider estimating the entire counterfactual density and generic functionals thereof. We focus on two kinds of target parameters: density approximations and the distance between counterfactual densities. We study nonparametric efficiency bounds, giving results for smooth but otherwise generic models and distances. Importantly, we show how these bounds connect to means of particular nontrivial functions of counterfactuals, linking the problems of density and mean estimation. We propose doubly robust-style estimators, and study their rates of convergence, showing that they can be optimally efficient in large nonparametric models. We also give analogous methods for model selection and aggregation, when many models may be available and of interest. Our results all hold for generic models and distances, but we highlight results for L2 projections on linear models and Kullbach–Leibler projections on exponential families. Finally, we illustrate our method by estimating the density of the CD4 count among patients with HIV, had all been treated with combination therapy versus zidovudine alone, as well as a density effect. Our methods are implemented in the R package npcausal on GitHub.

A Survey of Software Metrics for Transitioning to an Updated Version of the Current Existing Framework or a New Framework

A software framework is an abstraction that allows to selectively updating software that provides generic functionality by adding additional code written by the user. Basically, a physical or conceptual structure is used to support or guide the construction of something that grows the structure into something productive. Many effective software teams start with frameworks as their foundation. Bundled code libraries, software modeling, APIs, and a myriad of other capabilities, which are included in these frameworks, make programming considerably faster and more effective. Therefore, software engineers often delve into various frameworks for development and other purposes. A survey has been conducted and opinions from teachers, students, and developers were solicited. This paper examines the results of the poll and tries to explain what software engineers, students, or seasoned developers experience when switching to a new framework in the middle of a project.

Framework for Dynamic Modelling of the Dam and Reservoir System Reduced Functionality in Adverse Operating Conditions

Dam and reservoir systems (DRSs) are crucial aspects of the infrastructure necessary for reliable water resource management. Nowadays, DRSs are being increasingly affected by numerous natural and anthropogenic impacts (aging and outdated infrastructure, climate change, natural hazards, global crises, etc.). Hence, additional pressure on DRS management is being applied as DRSs must be operated in adverse operating conditions, outside of their design envelopes. Since there is no practical way to redesign DRSs to meet all possible adverse conditions, efficient simulation tools are necessary for various “what-if” analyses. A system dynamics (SD) approach can be used, as it has shown the capacity to comprehend the intrinsic system complexity. In this paper, an 11-step framework for the dynamic modelling of reduced functionality in a DRS and the emulation of the system operation in adverse conditions is proposed. The framework covers the system model design, input scenario generation, system simulation, and performance evaluation steps. A focus is placed on the steps related to system decomposition, the identification of failure-indicative parameters, the definition and implementation of failure functions in the subsystem dynamic models, and dynamic failure modelling. Through these steps, a novel procedure is proposed for the dynamic modelling of the DRS subsystems’ failures (reduced functionality), common in the operation of DRSs under adverse conditions. For each subsystem prone to failure, failure-indicative parameters are identified. Failure functions employing generic functionality indicators, with values spanning from 0 to 1, are suggested to modify the values of the failure-indicative parameters in simulations and emulate the component failure impacts on DRS operation. Possibilities for modelling failure modes for different subsystems, varying in nature, duration, and magnitude are discussed. Potential physical damage to the system components, increases in measurement uncertainty, and the lack of the spare parts during periods of global crisis are applied as disturbances to the Pirot DRS case study to illustrate the possibilities of the suggested framework’s application for DRS failure modelling. It was concluded that the proposed framework allowed for the detection of severe impacts on system performance, emphasizing the need for DRS dynamic failure modelling in system analysis.

A model for self-organization of sensorimotor function: the spinal monosynaptic loop.

Recent spinal cord literature abounds with descriptions of genetic preprogramming and the molecular control of circuit formation. In this paper, we explore to what extent circuit formation based on learning rather than preprogramming could explain the selective formation of the monosynaptic projections between muscle spindle primary afferents and homonymous motoneurons. We adjusted the initially randomized gains in the neural network according to a Hebbian plasticity rule while exercising the model system with spontaneous muscle activity patterns similar to those observed during early fetal development. Normal connectivity patterns developed only when we modeled β motoneurons, which are known to innervate both intrafusal and extrafusal muscle fibers in vertebrate muscles but were not considered in previous literature regarding selective formation of these synapses in animals with paralyzed muscles. It was also helpful to correctly model the greatly reduced contractility of extrafusal muscle fibers during early development. Stronger and more coordinated muscle activity patterns such as observed later during neonatal locomotion impaired projection selectivity. These findings imply a generic functionality of a musculoskeletal system to imprint important aspects of its mechanical dynamics onto a neural network, without specific preprogramming other than setting a critical period for the formation and maturation of this general pattern of connectivity. Such functionality would facilitate the successful evolution of new species with altered musculoskeletal anatomy, and it may help to explain patterns of connectivity and associated reflexes that appear during abnormal development.NEW & NOTEWORTHY A novel model of self-organization of early spinal circuitry based on a biologically realistic plant, sensors, and neuronal plasticity in conjunction with empirical observations of fetal development. Without explicit need for guiding genetic rules, connection matrices emerge that support functional self-organization of the mature pattern of Ia to motoneuron connectivity in the spinal circuitry.

The electronic medical record in pediatrics: functionalities and best use practices

The implementation of electronic medical record tools in pediatric care implies the creation of use recommendations and a clear definition of functional features. The objective of this paper is to describe minimum specific functionalities for an electronic medical record in pediatrics, with the aim to contribute to a definition of design criteria and best practices for its use. In this report, we will go over the generic functionality of the electronic medical record; we will frame the legal context in Argentina; we will approach some implementation aspects; and finally, we will describe minimum pediatric specific functionalities. Despite the adoption barriers in the field of pediatrics when it comes to incorporating electronic medical records into their professional activity, there are multiple resources to enable this digital transformation process. It is also fundamental to understand that, like any other technology, there are responsibilities related to their appropriate use.

Assessment of Practice and Outcome of Exercise on Quality of Life of Women with Breast Cancer in Delta State.

Objectives:This study evaluated the effect of exercise on the quality of life of women with breast cancer. It determined the pre and post-intervention functional, symptoms, and global quality of life of women with breast cancer in the control and intervention groups. Methods:The quasi-experimental design study adopted a purposive sampling technique in selecting the women with breast cancer in the intervention (47) and control (47) groups. Data on QOL was measured using standardized instruments, namely the European Organization for Research and Treatment of Cancer Quality of Life Questionnaire-version 3 (EORTC QLQ-C30. Results:The results show the pre-intervention overall functional quality of life domain score was 59.2±21.98 for the intervention and 67.0± 20.13 for the control, an overall score for the symptom domain was 31.8±10.89 for the intervention and 29.8±10.24 for the control. The post-intervention overall generic functional domain score was 89.0±11.1 for the intervention and 51.2±17.8 for the control and for symptoms domain; their overall score was 16.4±10.2 for the intervention and 35.4±12.4 for the control. A significant difference existed in all post-intervention functional domain scores of the generic function and symptoms quality of life as the functional domains (p < 0.001) and global health status (p < 0.001). Conclusion:As the numbers of women who survive breast cancer continue to increase, there is a need for lifestyle modification like exercise to improve and maintain their overall QOL to live a fulfilled life devoid of post-treatment complications.

Robust and Reliable Process-Aware Information Systems

Over recent years, several sophisticated Process-Aware Information Systems (PAIS) have been proposed for managing business processes and automating large-scale scientific (e-Science) processes. Much of this success is due to their ability to provide generic functionality for modeling, execution and monitoring processes. These functionalities work well when process execution follows a well-behaved path towards achieving the models objectives. However, exceptions and anomalous situations that fall outside of the well-behaved execution path still pose a significant challenge to PAIS. The treatment for such exceptions usually involves interventions in systems by human operators, which result in significant additional cost for businesses. In this paper, we introduce a cost-aware recovery composition method that is able to find and follow recovery paths that reduce the cost of exception handling. From a practical point of view, our proposal reduces complexity and the need for manual interventions to handle exceptions. Finally, the feasibility of recovery mechanism is discussed from its implementation into WED-flow framework.

Deciphering the Molecular Role of Non-coding RNA in Malignant Cardiac Disease

Non-coding RNA (NcRNA) is a functional RNA which contributes to the gene regulation at both transcriptional and post-transcriptional level. NcRNA lack in the encoding of proteins, but this does not counteract the generic functioning of the cells. It is a group of RNA with versatile functioning. They are classified into small nuclear RNA, tRNA, rRNA, and microRNA. The cumulative functioning of ncRNA includes slicing as well as the management of chromosomes. The long ncRNA plays a vital role in malignant cardiovascular disease such as atherosclerosis, myocardial infarction, and injury. They act by different pathways and undergo different mechanisms that include binding to CBX, activating mesoderm posterior-1, modulating MKX25, and targeting bone morphogenetic proteins. They have an intricate role in cardiovascular disorders as some of the long ncRNAs contribute to the inhibition of plague formation in the heart, thus reducing the risk for atherosclerosis and thus retarding the development of heart failure. Whereby microRNA acts as a prognostic biomarker in the early detection of heart injury. MicroRNA has even been identified for the inhibition of angiogenesis, and Piwi-interacting RNA is important in genetic diversity. Thus, ncRNA is one of the factors in the leading cause of malignant cardiac disorder and is responsible for cardiac remodelling andheart failure.

A Model for Predictive Maintenance Based on Asset Administration Shell.

Maintenance is one of the most important aspects in industrial and production environments. Predictive maintenance is an approach that aims to schedule maintenance tasks based on historical data in order to avoid machine failures and reduce the costs due to unnecessary maintenance actions. Approaches for the implementation of a maintenance solution often differ depending on the kind of data to be analyzed and on the techniques and models adopted for the failure forecasts and for maintenance decision-making. Nowadays, Industry 4.0 introduces a flexible and adaptable manufacturing concept to satisfy a market requiring an increasing demand for customization. The adoption of vendor-specific solutions for predictive maintenance and the heterogeneity of technologies adopted in the brownfield for the condition monitoring of machinery reduce the flexibility and interoperability required by Industry 4.0. In this paper a novel approach for the definition of a generic and technology-independent model for predictive maintenance is presented. Such model leverages on the concept of the Reference Architecture Model for Industry (RAMI) 4.0 Asset Administration Shell, as a means to achieve interoperability between different devices and to implement generic functionalities for predictive maintenance.

A PSO-inspired architecture to hybridise multi-objective metaheuristics

Hybridisation is a technique that exploits and unites the best features of individual algorithms. The literature includes several hybridisation methodologies, among which there are general procedures, termed architectures, that provide generic functionalities and features for solving optimisation problems. Successful hybridisation methodologies have applied concepts of the multi-agent paradigm, such as cooperation and agent intelligence. However, there is still a lack concerning architectures for the hybridisation of multi-objective metaheuristics that fully explore these concepts. This study proposes a new architecture, named MO-MAHM, based on concepts from Particle Swarm Optimisation, to hybridise multi-objective metaheuristics. We apply the MO-MAHM to the Bi-objective Spanning Tree Problem. Four algorithms were hybridised within the MO-MAHM: three evolutionary algorithms and a local search method. We report the results of experiments with 180 instances, analyse the behaviour of the MO-MAHM, and compare to the results produced by algorithms proposed for the Bi-objective Spanning Tree Problem.

Geovisualization and Geographical Analysis for Fire Prevention

Swedish emergency services still have relatively limited resources and time for proactive fire prevention. As a result of this, there is an extensive need for strategic working methods and knowledge to take advantage of spatial analyses. In addition, decision-making based on visualizations and analyses of their own collected data has the potential to increase the validity of strategic decisions. The objective of this paper is to critically examine how some different geovisualization techniques—point data, kernel density and choropleth mapping—actively can complement each other and be applied in fire preventive work. The results show that each technique itself has limitations, but that, in combination, they increase the scope for interpretation and the possibilities of targeting different forms of preventive measures. The investigated geovisualization techniques facilitate various forms of fire prevention such as identifying which areas to prioritize for outreach, home visits, identification and targeting of different risk groups and customized information campaigns about certain types of fires in risk-prone areas. Furthermore, fairly simple mapping techniques can be utilized directly to evaluate incident reports and increase the quality of geocoded fire incidents. The study also shows how some of these techniques can be applied when analyzing residential fire incidents and their relation to underlying structural and socio-economic factors as well as spatio-temporal dimensions of fire incident data. The spatial analyses and supporting maps can help find and predict risk areas for residential fires or be used directly to formulate hypotheses on fire patterns. The generic functionality of the visualization methods makes them also useful for visual analysis of other types of incidents, such as reported crimes and accidents. Finally, the results are applicable to a work process adapted to the Swedish legislation on confidential data.

Goldstone equivalence and high energy electroweak physics

The transition between the broken and unbroken phases of massive gauge theories, namely the rearrangement of longitudinal and Goldstone degrees of freedom that occurs at high energy, is not manifestly smooth in the standard formalism. The lack of smoothness concretely shows up as an anomalous growth with energy of the longitudinal polarization vectors, as they emerge in Feynman rules both for real on-shell external particles and for virtual particles from the decomposition of the gauge field propagator. This makes the characterization of Feynman amplitudes in the high-energy limit quite cumbersome, which in turn poses peculiar challenges in the study of Electroweak processes at energies much above the Electroweak scale. We develop a Lorentz-covariant formalism where polarization vectors are well-behaved and, consequently, energy power-counting is manifest at the level of individual Feynman diagrams. This allows us to prove the validity of the Effective $W$ Approximation and, more generally, the factorization of collinear emissions and to compute the corresponding splitting functions at the tree-level order. Our formalism applies at all orders in perturbation theory, for arbitrary gauge groups and generic linear gauge-fixing functionals. It can be used to simplify Standard Model loop calculations by performing the high-energy expansion directly on the Feynman diagrams. This is illustrated by computing the radiative corrections to the decay of the top quark.