Japanese Brown (JBR) cattle have moderately marbled beef compared to the highly marbled beef of Japanese Black (JBL) cattle; however, their skeletal muscle properties remain poorly characterized. To unveil interbreed metabolic differences over the previous results, we explored the metabolome network changes before and after postmortem 7-day aging in the trapezius muscle of the two cattle breeds by employing a deep and high-coverage metabolomics approach. Using both capillary electrophoresis (CE) and ultra-high-performance liquid chromatography (UHPLC)–Fourier transform mass spectrometry (FT/MS), we detected 522 and 384 annotated peaks, respectively, across all muscle samples. The CE-based results showed that the cattle were clearly separated by breed and postmortem age in multivariate analyses. The metabolism related to glutathione, glycolysis, vitamin K, taurine, and arachidonic acid was enriched with differentially abundant metabolites in aged muscles, in addition to amino acid (AA) metabolisms. The LC-based results showed that the levels of bile-acid-related metabolites, such as tauroursodeoxycholic acid (TUDCA), were high in fresh JBR muscle and that acylcarnitines were enriched in aged JBR muscle, compared to JBL muscle. Postmortem aging resulted in an increase in fatty acids and a decrease in acylcarnitine in the muscles of both cattle breeds. In addition, metabolite set enrichment analysis revealed that JBR muscle was distinctive in metabolisms related to pyruvate, glycerolipid, cardiolipin, and mitochondrial energy production, whereas the metabolisms related to phosphatidylethanolamine, nucleotide triphosphate, and AAs were characteristic of JBL. This suggests that the interbreed differences in postmortem trapezius muscle are associated with carnitine/acylcarnitine transport, β-oxidation, tricarboxylic acid cycle, and mitochondrial membrane stability, in addition to energy substrate and AA metabolisms. These interbreed differences may characterize beef quality traits such as the flavor intensity and oxidative stability. Full article

The aim of this study was to evaluate the findings of CT scans in patients with pathologically confirmed primary colorectal squamous-cell carcinoma (SCC). The clinical presentation and CT findings in eight patients with pathologically confirmed primary colorectal squamous-cell carcinoma were retrospectively reviewed by two gastrointestinal radiologists. Hematochezia was the most common symptom (n = 5). The tumors were located in the rectum (n = 7) and sigmoid colon (n = 1). The tumors showed circumferential wall thickening (n = 4), bulky mass (n = 3), or eccentric wall thickening (n = 1). The mean maximal wall thickness of the involved segment was 29.1 mm ± 13.4 mm. The degree of tumoral enhancement observed via CT was well enhanced (n = 4) or moderately enhanced (n = 4). Necrosis within the tumor was found in five patients. The mean total number of metastatic lymph nodes was 3.1 ± 3.3, and the mean short diameter of the largest metastatic lymph node was 16.6 ± 5.7 mm. Necrosis within the metastatic node was observed in six patients. Invasions to adjacent organs were identified in five patients (62.5%). Distant metastasis was detected in only one patient. In summary, primary SCCs that arise from the colorectum commonly present as marked invasive wall thickening or a bulky mass with heterogeneous well-defined enhancement, internal necrosis, and large metastatic lymphadenopathies. Full article

In this article, I consider how the epistemic problem of religious disagreement has been viewed within the Islamic tradition. Specifically, I consider two religious epistemological trends within the tradition: Islamic Rationalism and Islamic Traditionalism. In examining the approaches of both trends toward addressing the epistemic problem, I suggest that neither is wholly adequate. Nonetheless, I argue that both approaches offer insights that might be relevant to building a more adequate response. So, I attempt to combine insights from both by drawing a distinction between inferential and noninferential reflective responsibility. Given this distinction, I argue that it may be possible for a theist to remain steadfast in upholding their tradition-specific theistic belief, without having to hold that belief by way of inference; but nevertheless, having to be sufficiently reflectively responsible in forming their theistic belief noninferentially. Full article

The intelligent reflective surface (IRS) is a novel network node that consists of a large-scale passive reflective array to obtain a customized reflected wave direction by modulating the amplitude phase, which can be easily deployed to change the wireless signal propagation environment and enhance the communication performance under a non-line-of-sight (NLOS) environment, where location services cannot perform accurately. In this study, a low-rank matrix reconstruction-enabled fingerprint-based localization algorithm for IRS-assisted networks is proposed. Firstly, a 5G positioning system based on IRSs is constructed using multiple IRSs deployed to reflect signals. This enables the base station to overcome the influence of NLOS and receive the positioning signal of the point to be positioned. Then, the angular domain power expectation matrix of the received signal is extracted as a fingerprint to form a partial fingerprint database. Next, the complete fingerprint database is reconstructed using the low-rank matrix fitting algorithm, thereby considerably reducing the workload of building the fingerprint database. Finally, maximal ratio combining is used to increase the gap between the fingerprint data, and the Weighted K-Nearest Neighbor (WKNN) algorithm is used to match the fingerprint data and estimate the location of the points to be located. The simulation results demonstrate the feasibility of the proposed method to achieve sub-meter accuracy in an NLOS environment. Full article

The flatness of semiconductor substrates is an important parameter for evaluating the surface quality of semiconductor substrates. However, existing technology cannot simultaneously achieve high measurement efficiency, large-range thickness measurement, and nanometer-level measurement accuracy in the thickness measurement of semiconductor substrates. To solve the problems, we propose to apply the method that combines spectral-domain optical coherence tomography (SD-OCT) with the Hanning-windowed energy centrobaric method (HnWECM) to measure the thickness of semiconductor substrates. The method can be employed in the full-chip thickness measurement of a sapphire substrate, which has a millimeter measuring range, nanometer-level precision, and a sampling rate that can reach up to 80 kHz. In this contribution, we measured the full-chip thickness map of a sapphire substrate by using this method and analyzed the machining characteristics. The measurement results of a high-precision mechanical thickness gauge, which is widely used for thickness measurement in the wafer fabrication process, were compared with the proposed method. The difference between these two methods is 0.373%, which explains the accuracy of the applied method to some extent. The results of 10 sets of repeatability experiments on 250 measurement points show that the maximum relative standard deviation (RSD) at this point is 0.0061%, and the maximum fluctuation is 71.0 nm. The above experimental results prove that this method can achieve the high-precision thickness measurement of the sapphire substrate and is of great significance for improving the surface quality detection level of semiconductor substrates. Full article

Workforce indigenization in Gulf Corporation Council (GCC) countries is under-researched in international business literature, especially among expatriate academics from the Middle East and North Africa regions working in GCC countries. Therefore, drawing from the social exchange and conservation of resources theories, this study examines the moderating effect of perceived fairness on the relationship between positive affectivity (PA) and creativity in the context of enhanced indigenization of human resource (HR) policies in GCC countries. This study collects data from 228 mobile academics working in Saudi universities. Principal least squares structural equation modeling results show that PA positively impacts creativity. Further, perceived fairness is found to reinforce the connection between PA and creativity. This study’s results indicate that host universities must build appealing HR policies to cope with the diverse challenges related to the indigenization of HR policies. Furthermore, this study highlights the role of positive personality traits in enhancing creativity. Full article

In this paper, we consider the Cauchy problem for a three-component Novikov system on the line. We give a construction of the initial data $({\rho }_0,u_0,v_0)\in B_{p,\infty }^{\sigma -1}\left(\mathbb{R}\right)\times B_{p,\infty }^{\sigma }\left(\mathbb{R}\right)\times B_{p,\infty }^{\sigma }\left(\mathbb{R}\right)$ with $\sigma >max\left\{3+\frac{1}{p},\frac{7}{2}\right\},1\le p\le \infty$, such that the corresponding solution to the three-component Novikov system starting from $({\rho }_0,u_0,v_0)$ is discontinuous at $t=0$ in the metric of $B_{p,\infty }^{\sigma -1}\left(\mathbb{R}\right)\times B_{p,\infty }^{\sigma }\left(\mathbb{R}\right)\times B_{p,\infty }^{\sigma }\left(\mathbb{R}\right)$, which implies the ill-posedness for this system in $B_{p,\infty }^{\sigma -1}\left(\mathbb{R}\right)\times B_{p,\infty }^{\sigma }\left(\mathbb{R}\right)\times B_{p,\infty }^{\sigma }\left(\mathbb{R}\right)$. Full article

Nano-hydroxyapatite (n-HA) is the main inorganic component of natural bone, which has been widely used as a reinforcing filler for polymers in bone materials, and it can promote cell adhesion, proliferation, and differentiation. It can also produce interactions between cells and material surfaces through selective protein adsorption and has therefore always been a research hotspot in orthopedic materials. However, n-HA nano-particles are inherently easy to agglomerate and difficult to disperse evenly in the polymer. In addition, there are differences in trace elements between n-HA nano-particles and biological apatite, so the biological activity needs to be improved, and the slow degradation in vivo, which has seriously hindered the application of n-HA in bone fields, is unacceptable. Therefore, the modification of n-HA has been extensively reported in the literature. This article reviewed the physical modification and various chemical modification methods of n-HA in recent years, as well as their modification effects. In particular, various chemical modification methods and their modification effects were reviewed in detail. Finally, a summary and suggestions for the modification of n-HA were proposed, which would provide significant reference for achieving high-performance n-HA in biomedical applications. Full article

Ethical dilemmas affect several essential elements of humanitarian aid, such as the adequate selection of crises to which to provide aid and a selection of beneficiaries based on needs and not political or geostrategic criteria. Other challenges encompass maintaining neutrality against aggressors, deciding whether to collaborate with governments that violate human rights, and managing the allocation and prioritization of limited resources. Additionally, issues arise concerning the safety and protection of aid recipients, the need for cultural and political sensitivity, and recognition of the importance of local knowledge, skills, and capacity. The appropriateness, sustainability, and long-term impact of actions; security risks for aid personnel; and the need for transparency and accountability are also crucial. Furthermore, humanitarian workers face the duty to report and engage in civil activism in response to human rights violations and the erosion of respect for international humanitarian law. Lastly, the rights of affected groups and local communities in the decision-making and implementation of humanitarian aid are vital. The traditional foundations and approaches of humanitarian aid appear insufficient in today’s landscape of disasters and crises, which are increasingly complex and divergent, marked by a diminished capacity and shifting roles of various actors in alleviating suffering. This article reviews the historical evolution of the conceptualization of humanitarian aid and addresses some of its ethical challenges and dilemmas. Full article

This paper explores the application of Random Matrix Theory (RMT) as a methodological enhancement for portfolio selection within financial markets. Traditional approaches to portfolio optimization often rely on historical estimates of correlation matrices, which are particularly susceptible to instabilities. To address this challenge, we combine a data preprocessing technique based on the Hilbert transformation of returns with RMT to refine the accuracy and robustness of correlation matrix estimation. By comparing empirical correlations with those generated through RMT, we reveal non-random properties and uncover underlying relationships within financial data. We then utilize this methodology to construct the correlation network dependence structure used in portfolio optimization. The empirical analysis presented in this paper validates the effectiveness of RMT in enhancing portfolio diversification and risk management strategies. This research contributes by offering investors and portfolio managers with methodological insights to construct portfolios that are more stable, robust, and diversified. At the same time, it advances our comprehension of the intricate statistical principles underlying multivariate financial data. Full article

Background: Given the prevalence of ADHD and the gaps in ADHD care in Australia, this study investigates the critical barriers and driving forces for innovation. It does so by conducting a preparatory evaluation of an ADHD prototype digital service innovation designed to help streamline ADHD care and empower individual self-management. Methods: Semi-structured interviews with ADHD care consumers/participants and practitioners explored their experiences and provided feedback on a mobile self-monitoring app and related service innovations. Interview transcripts were double coded to explore thematic barriers and the enablers for better ADHD care. Results: Fifteen interviews (9 consumers, 6 practitioners) revealed barriers to better ADHD care for consumers (ignorance and prejudice, trust, impatience) and for practitioners (complexity, sustainability). Enablers for consumers included validation/empowerment, privacy, and security frameworks, tailoring, and access. Practitioners highlighted the value of transparency, privacy and security frameworks, streamlined content, connected care between services, and the tailoring of broader metrics. Conclusions: A consumer-centred approach to digital health service innovation, featuring streamlined, private, and secure solutions with enhanced mobile tools proves instrumental in bridging gaps in ADHD care in Australia. These innovations should help to address the gaps in ADHD care in Australia. These innovations should encompass integrated care, targeted treatment outcome data, and additional lifestyle support, whilst recognising the tensions between customised functionalities and streamlined displays. Full article

To ensure safe and stable operation, accurate fault localization within active distribution networks is required, and this has attracted much attention. Influenced by many factors such as the control strategy, control performance, initial state of the distributed generators, and distribution network topology, it is still difficult to reliably locate complex and variable single-phase short-circuit faults relying only on a single feature quantity, while localization methods incorporating intelligent algorithms are affected by the choice of a priori samples and the fact that the solution process is a black-box model. To address this challenge, in this work, an expression for the single-phase short-circuit current vector of a distribution network containing distributed generators is derived, and the differences in magnitude and phase angle of the short-circuit current vectors upstream and downstream of the fault point are analyzed. Based on measurement theory, a fault confidence distribution function that reacts to the relative size of the current magnitude difference and phase angle difference is established, and the fusion fault confidence of the short-circuit current vector is constructed with the help of evidence theory. Finally, a method of locating single-phase short-circuit faults in distribution networks that contain distributed generators is proposed. The simulation results show that the ratio of the fusion fault confidence of the short-circuit current vector between faulted and non-faulted sections under the influence of different distributed generator capacities, fault locations, and transition resistances differ significantly. The proposed single-phase short-circuit fault localization method is both adaptive and physically interpretable and has clear boundaries, sound sensitivity, and engineering practicability. Full article

Generative adversarial networks (GANs) and diffusion models (DMs) have revolutionized the creation of synthetically generated but realistic-looking images. Distinguishing such generated images from real camera captures is one of the key tasks in current multimedia forensics research. One particular challenge is the generalization to unseen generators or post-processing. This can be viewed as an issue of handling out-of-distribution inputs. Forensic detectors can be hardened by the extensive augmentation of the training data or specifically tailored networks. Nevertheless, such precautions only manage but do not remove the risk of prediction failures on inputs that look reasonable to an analyst but in fact are out of the training distribution of the network. With this work, we aim to close this gap with a Bayesian Neural Network (BNN) that provides an additional uncertainty measure to warn an analyst of difficult decisions. More specifically, the BNN learns the task at hand and also detects potential confusion between post-processing and image generator artifacts. Our experiments show that the BNN achieves on-par performance with the state-of-the-art detectors while producing more reliable predictions on out-of-distribution examples. Full article

With the rapidly growing interest in using structural timber, a need exists to inspect and assess these structures using non-destructive testing (NDT). This review article summarizes NDT methods for wood inspection. After an overview of the most important NDT methods currently used, a detailed review of Ground Penetrating Radar (GPR) and Ultrasonic Testing (UST) is presented. These two techniques can be applied in situ and produce useful visual representations for quantitative assessments and damage detection. With its commercial availability and portability, GPR can help rapidly identify critical features such as moisture, voids, and metal connectors in wood structures. UST, which effectively detects deep cracks, delaminations, and variations in ultrasonic wave velocity related to moisture content, complements GPR’s capabilities. The non-destructive nature of both techniques preserves the structural integrity of timber, enabling thorough assessments without compromising integrity and durability. Techniques such as the Synthetic Aperture Focusing Technique (SAFT) and Total Focusing Method (TFM) allow for reconstructing images that an inspector can readily interpret for quantitative assessment. The development of new sensors, instruments, and analysis techniques has continued to improve the application of GPR and UST on wood. However, due to the hon-homogeneous anisotropic properties of this complex material, challenges remain to quantify defects and characterize inclusions reliably and accurately. By integrating advanced imaging algorithms that consider the material’s complex properties, combining measurements with simulations, and employing machine learning techniques, the implementation and application of GPR and UST imaging and damage detection for wood structures can be further advanced. Full article

We evaluated subsequent virologic outcomes in individuals experiencing low-level virem ia (LLV) on dolutegravir (DTG)-based first-line antiretroviral therapy (ART) in Botswana. We used a national dataset from 50,742 adults who initiated on DTG-based first-line ART from June 2016–December 2022. Individuals with at least two viral load (VL) measurements post three months on DTG-based first-line ART were evaluated for first and subsequent episodes of LLV (VL:51–999 copies/mL). LLV was sub-categorized as low-LLV (51–200 copies/mL), medium-LLV (201–400 copies/mL) and high-LLV (401–999 copies/mL). The study outcome was virologic failure (VF) (VL ≥ 1000 copies/mL): virologic non-suppression defined as single-VF and confirmed-VF defined as two-consecutive VF measurements after an initial VL < 1000 copies/mL. Cox regression analysis identified predictive factors of subsequent VF. The prevalence of LLV was only statistically different at timepoints >6–12 (2.8%) and >12–24 (3.9%) (p-value < 0.01). LLV was strongly associated with both virologic non-suppression (adjusted hazards ratio [aHR] = 2.6; 95% CI: 2.2–3.3, p-value ≤ 0.001) and confirmed VF (aHR = 2.5; 95% CI: 2.4–2.7, p-value ≤ 0.001) compared to initially virally suppressed PLWH. High-LLV (HR = 3.3; 95% CI: 2.9–3.6) and persistent-LLV (HR = 6.6; 95% CI: 4.9–8.9) were associated with an increased hazard for virologic non-suppression than low-LLV and a single-LLV episode, respectively. In a national cohort of PLWH on DTG-based first-line ART, LLV > 400 copies/mL and persistent-LLV had a stronger association with VF. Frequent VL testing and adherence support are warranted for individuals with VL > 50 copies/mL. Full article

Maritime traffic is increasing more and more, creating more complex navigation environments for ships. Ship trajectory prediction based on historical AIS data is a vital method of reducing navigation risks and enhancing the efficiency of maritime traffic control. At present, employing machine learning or deep learning techniques to construct predictive models based on AIS data has become a focal point in ship trajectory prediction research. This paper systematically evaluates various trajectory prediction methods, spanning classical machine learning approaches and emerging deep learning techniques, to uncover their respective merits and drawbacks. In this work, a variety of studies were investigated that applied different algorithms in ship trajectory prediction, including regression models (RMs), artificial neural networks (ANNs), Kalman filtering (KF), and random forests (RFs) in machine learning, along with deep learning such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), long short-term memory (LSTM), gate recurrent unit (GRU) networks, and sequence-to-sequence (Seq2seq) networks. The performance of predictive models based on different algorithms in trajectory prediction tasks was graded and analyzed. Among the existing studies, deep learning methods exhibit significant performance and considerable potential application value for maritime traffic systems, which can be assessed by future work on ship trajectory prediction research. Full article

Since it is difficult to study the influence of different defect characteristics on the stress intensity factor of B-type sleeve fillet welds via experiments, this paper adopts ABAQUS finite element analysis software(Version 2019) to model the B-type sleeve fillet welds and studies the stress and stress intensity factor under different crack lengths, heights, and angles. The simulation results showed that with the increase in crack length and depth, the maximum stress intensity factor gradually increased, and with the increase in the crack inclination angle, the maximum stress intensity factor first increased and then decreased. Full article

Fuel level gauging in aircraft presents a significant flight mechanics challenge due to the influence of aircraft movements on measurements. Moreover, it constitutes a multidimensional problem where various sensors distributed within the tank must converge to yield a precise and single measurement, independent of the aircraft’s attitude. Furthermore, fuel distribution across multiple tanks of irregular geometries complicates the readings even further. These issues critically impact safety and economy, as gauging errors may compromise flight security and lead to carrying excess weight. In response to these challenges, this research introduces a multi-stage project in aircraft fuel gauging systems, as a continuum of studies, where this first article presents a computational tool designed to simulate aircraft fuel sensor data readings as a function of fuel level, fuel tank geometry, sensor location, and aircraft attitude. Developed in an open-source environment, the tool aims to support the statistical inference required for accurate modeling in which synthetic data generation becomes a crucial component. A discretization procedure accurately maps fuel tank geometries and their mass properties. The tool, then, intersects these geometries with fuel-level planes and calculates each new volume. It integrates descriptive geometry to intersect these fuel planes with representative capacitive level-sensing probes and computes the sensor readings for the simulated flight conditions. The method is validated against geometries with analytical solutions. This process yields detailed fuel measurement responses for each sensor inside the tank, and for different analyzed fuel levels, providing insights into the sensors’ signals’ non-linear behavior at each analyzed aircraft attitude. The non-linear behavior is also influenced by the sensor saturation readings at 0 when above the fuel level and at 1 when submerged. The synthetic fuel sensor readings lay the baseline for a better understanding on how to compute the true fuel level from multiple sensor readings, and ultimately optimizing the amount of used sensors and their placement. The tool’s design offers significant improvements in aircraft fuel gauging accuracy, directly impacting aerostructures and instrumentation, and it is a key aspect of flight safety, fuel management, and navigation in aerospace technology. Full article

The rising global prevalence of diabetes mellitus, a chronic metabolic disorder, poses significant challenges to healthcare systems worldwide. This study examined in-hospital mortality among patients diagnosed with non-insulin-dependent diabetes mellitus (NIDDM) of ICD-10, or Type 2 Diabetes Mellitus (T2DM), in Indonesia, utilizing hospital claims data spanning from 2017 to 2022 obtained from the Indonesia Health Social Security Agency or Badan Penyelenggara Jaminan Sosial (BPJS) Kesehatan. The analysis, which included 610,809 hospitalized T2DM patients, revealed an in-hospital mortality rate of 6.6%. Factors contributing to an elevated risk of mortality included advanced age, the presence of comorbidities, and severe complications. Additionally, patients receiving health subsidies and those treated in government hospitals were found to have higher mortality risks. Geographic disparities were observed, highlighting variations in healthcare outcomes across different regions. Notably, the complication of ketoacidosis emerged as the most significant risk factor for in-hospital mortality, with an odds ratio (OR) of 10.86, underscoring the critical need for prompt intervention and thorough management of complications to improve patient outcomes. Full article

Indoor wireless positioning has long been a dynamic field of research due to its broad application range. While many commercial products have been developed, they often are not open source or require substantial and costly infrastructure. Academically, research has extensively explored Bluetooth Low Energy (BLE) for positioning, yet there are a noticeable lack of studies that comprehensively compare traditional algorithms under these conditions. This research aims to fill this gap by evaluating classical positioning algorithms such as K-Nearest Neighbor (KNN), Weighted K-Nearest Neighbor (WKNN), Naïve Bayes (NB), and a Received Signal Strength-based Neural Network (RSS-NN) using BLE technology. We also introduce a novel method using Convolutional Neural Networks (CNN), specifically tailored to process RSS data structured in an image-like format. This approach helps overcome the limitations of traditional RSS fingerprinting by effectively managing the environmental dynamics within indoor settings. In our tests, all algorithms performed well, consistently achieving an average accuracy of less than two meters. Remarkably, the CNN method outperformed others, achieving an accuracy of 1.22 m. These results establish a solid basis for future research, particularly towards enhancing the precision of indoor positioning systems using deep learning for cost-effective, easy to set up applications. Full article

Background: Adopting advanced digital technologies as diagnostic support tools in healthcare is an unquestionable trend accelerated by the COVID-19 pandemic. However, their accuracy in suggesting diagnoses remains controversial and needs to be explored. We aimed to evaluate and compare the diagnostic accuracy of two free accessible internet search tools: Google and ChatGPT 3.5. Methods: To assess the effectiveness of both medical platforms, we conducted evaluations using a sample of 60 clinical cases related to urological pathologies. We organized the urological cases into two distinct categories for our analysis: (i) prevalent conditions, which were compiled using the most common symptoms, as outlined by EAU and UpToDate guidelines, and (ii) unusual disorders, identified through case reports published in the ‘Urology Case Reports’ journal from 2022 to 2023. The outcomes were meticulously classified into three categories to determine the accuracy of each platform: “correct diagnosis”, “likely differential diagnosis”, and “incorrect diagnosis”. A group of experts evaluated the responses blindly and randomly. Results: For commonly encountered urological conditions, Google’s accuracy was 53.3%, with an additional 23.3% of its results falling within a plausible range of differential diagnoses, and the remaining outcomes were incorrect. ChatGPT 3.5 outperformed Google with an accuracy of 86.6%, provided a likely differential diagnosis in 13.3% of cases, and made no unsuitable diagnosis. In evaluating unusual disorders, Google failed to deliver any correct diagnoses but proposed a likely differential diagnosis in 20% of cases. ChatGPT 3.5 identified the proper diagnosis in 16.6% of rare cases and offered a reasonable differential diagnosis in half of the cases. Conclusion: ChatGPT 3.5 demonstrated higher diagnostic accuracy than Google in both contexts. The platform showed satisfactory accuracy when diagnosing common cases, yet its performance in identifying rare conditions remains limited. Full article

The Least Absolute Shrinkage and Selection Operator (LASSO) regression technique has proven to be a valuable tool for fitting and reducing linear models. The trend of applying LASSO to compositional data is growing, thereby expanding its applicability to diverse scientific domains. This paper aims to contribute to this evolving landscape by undertaking a comprehensive exploration of the $L^1$-norm for the penalty term of a LASSO regression in a compositional context. This implies first introducing a rigorous definition of the compositional $L^p$-norm, as the particular geometric structure of the compositional sample space needs to be taken into account. The focus is subsequently extended to a meticulous data-driven analysis of the dimension reduction effects on linear models, providing valuable insights into the interplay between penalty term norms and model performance. An analysis of a microbial dataset illustrates the proposed approach. Full article