Alignment Issues Research Articles

The Relationship Between Core Muscle Endurance, Dynamic Balance, and Some Physiological Factors in Young Football Players with Genu Varum

Background: Genu varum, a common knee alignment issue, may negatively affect physical performance in athletes, particularly in sports requiring balance, agility, and strength, such as football. Objectives: This study aimed to examine the impact of genu varum on dynamic balance, core muscle endurance, and various athletic performance metrics in male football players. Methods: This descriptive and correlational study included 42 young football players, 21 with genu varum and 21 without. All participants trained at least three times per week and were screened for genu varum using a caliper. After applying inclusion and exclusion criteria, the participants completed tests to assess dynamic balance, core muscle endurance, lower body strength, agility, and speed. Key performance evaluations included the Sargent Jump test, a 9x4 meter agility test, and a 36-meter sprint. Independent t-tests were conducted to compare the two groups. Statistical analyses were performed using SPSS version 22, with a significance level set at 0.05. Results: Significant differences were found between the two groups. Players with genu varum showed reduced dynamic balance in all directions (anterior: P = 0.001, posterior-medial: P = 0.002, posterior-lateral: P = 0.004), lower body strength (P = 0.001), agility (P = 0.02), abdominal muscle endurance (P = 0.004), and sprint speed (P = 0.003) compared to players without genu varum. Conclusions: The findings indicate that genu varum adversely affects essential athletic performance components, including balance, strength, agility, and speed, in young football players. These deficits may increase the risk of injury and impede long-term athletic development.

A Fast 3D Range-Modulator Delivery Approach: Validation of the FLUKA Model on a Varian ProBeam System Including a Robustness Analysis

A 3D range-modulator (RM), optimized for a single energy and a specific target shape, is a promising and viable solution for the ultra-fast dose delivery in particle therapy. The aim of this work was to investigate the impact of potential beam and modulator misalignments on the dose distribution. Moreover, the FLUKA Monte Carlo model, capable of simulating 3D RMs, was adjusted and validated for the 250 MeV single-energy proton irradiation from a Varian ProBeam system. A 3D RM was designed for a cube target shape rotated 45° around two axes using a Varian-internal research version of the Eclipse treatment planning software, and the resulting dose distribution was simulated in a water phantom. Deviations from the ideal alignment were introduced, and the dose distributions from the modified simulations were compared to the original unmodified one. Finally, the FLUKA model and the workflow were validated with base-line data measurements and dose measurements of the manufactured modulator prototype at the HollandPTC facility in Delft. The adjusted FLUKA model, optimized particularly in the scope of a single-energy FLASH irradiation with a PMMA pre-absorber, demonstrated very good agreement with the measured dose distribution resulting from the 3D RM. Dose deviations resulting from modulator-beam axis misalignments depend on the specific 3D RM and its shape, pin aspect ratio, rotation angle, rotation point, etc. A minor modulator shift was found to be more relevant for the distal dose distribution than for the spread-out Bragg Peak (SOBP) homogeneity. On the other hand, a modulator tilt (rotation away from the beam axis) substantially affected not only the depth dose profile, transforming a flat SOBP into a broad, Gaussian-like distribution with increasing rotation angle, but also shifted the lateral dose distribution considerably. This work strives to increase awareness and highlight potential pitfalls as the 3D RM method progresses from a purely research concept to pre-clinical studies and human trials. Ensuring that gantry rotation and the combined weight of RM, PMMA, and aperture do not introduce alignment issues is critical. Given all the other range and positioning uncertainties, etc., not related to the modulator, the RM must be aligned with an accuracy below 1° in order to preserve a clinically acceptable total uncertainty budget. Careful consideration of critical parameters like the pin aspect ratio and possibly a novel robust modulator geometry optimization are potential additional strategies to mitigate the impact of positioning on the resulting dose. Finally, even the rotated cube 3D modulator with high aspect ratio pin structures (~80 mm height to 3 mm pin base width) was found to be relatively robust against a slight misalignment of 0.5° rotation or a 1.5 mm shift in one dimension perpendicular to the beam axis. Given a reliable positioning and QA concept, the additional uncertainties introduced by the 3D RM can be successfully managed adopting the concept into the clinical routine.

Drone-based mobile full-duplex optical communication.

Wireless optical communication plays a vital role in mobile communication networks, and both drones and vehicles are essential mobile communication nodes. Here, image identification technology, gimbal, and full-duplex optical communication systems are integrated to manage the challenging issues of light alignment and target tracking for mobile full-duplex optical communication under the transmission control protocol/internet protocol (TCP/IP) scheme. Both drones and vehicles are equipped with the proposed setup to realize full-duplex wireless optical data transmission at a communication rate of 2 Mbps in both air-to-ground and air-to-air scenarios. By further integrating wireless-fidelity (Wi-Fi) modules, drone-based mobile optical communication systems (MOCSs) can be interconnected with the network via the TCP/IP scheme, and real-time video transmission can be demonstrated. This work provides a feasible route toward a mobile optical communication network (MOCN) for diverse applications.

Where next for manufacturing productivity research? Propositions based on exploratory empirical investigation

Purpose The purpose of this research is to develop empirically grounded propositions for further research into UK manufacturing productivity.Design/methodology/approach Interviews were conducted with managers from strategic, tactical and operational levels from four manufacturing sectors to produce case studies. A modified strategic alignment theory framework was used to code, compare and contrast narratives on perceived productivity antecedents, definitions, compatibility with the definition from the UK Office for National Statistics, and vertical alignment issues within and across cases.Findings It was found that different key antecedents can facilitate and/or prevent strategic vertical alignment. Discussion reveals complex nuances in perceptions of manufacturing productivity and using the modified strategic alignment theory/productivity antecedent framework.Originality/value In revealing the alignment or otherwise of productivity definitions at different levels within the firm, the paper reveals nine propositions for future research including definitions, skills, metrics, performance measurement systems, people and system-centric perspectives, the value-added perspective of productivity and the role of innovation.

Development of a Predictive Model Based on the Alignment Tool in the Early Stages of Projects: The Case of Saudi Arabia Infrastructure Projects

The construction industry plays a substantial role in shaping the economies of many countries. Construction management faces various challenges that can lead to project failures, particularly in infrastructure projects struggling to meet cost and time requirements. Inadequate project planning and the intricate nature of construction projects can cause participants’ project goals to not align. It is crucial to address these challenges early in the planning stages to ensure project success. This research involved investigating previous studies to understand current practices for improving infrastructure project planning and selecting the best pre-project planning tool. Infrastructure projects in the Saudi construction industry are used as a case study. A questionnaire was prepared based on essential alignment issues affecting team alignment during pre-project planning. Participants rated the level of agreement with alignment issues and the overall success of a project they worked on. The study utilized descriptive and inferential analysis techniques to assess infrastructure project success rates and develop a predictive model driven by the alignment tool. Multiple linear regression techniques were used during the model’s development, and validation and reliability outputs were obtained. By evaluating all relevant stakeholders, the model generates a score to facilitate the pre-project planning process, increasing the likelihood of project success. The study found that the model’s predictive accuracy was 94%. This research is significant in creating a predictive model applicable to infrastructure projects, enhancing project management practices by enabling project teams to evaluate project progress, identify projects in need of corrective action, and ultimately improve project performance, leading to cost and time savings.

Leveraging Large Language Models with Chain-of-Thought and Prompt Engineering for Traffic Crash Severity Analysis and Inference

Harnessing the power of Large Language Models (LLMs), this study explores the use of three state-of-the-art LLMs, specifically GPT-3.5-turbo, LLaMA3-8B, and LLaMA3-70B, for crash severity analysis and inference, framing it as a classification task. We generate textual narratives from original traffic crash tabular data using a pre-built template infused with domain knowledge. Additionally, we incorporated Chain-of-Thought (CoT) reasoning to guide the LLMs in analyzing the crash causes and then inferring the severity. This study also examine the impact of prompt engineering specifically designed for crash severity inference. The LLMs were tasked with crash severity inference to: (1) evaluate the models’ capabilities in crash severity analysis, (2) assess the effectiveness of CoT and domain-informed prompt engineering, and (3) examine the reasoning abilities with the CoT framework. Our results showed that LLaMA3-70B consistently outperformed the other models, particularly in zero-shot settings. The CoT and Prompt Engineering techniques significantly enhanced performance, improving logical reasoning and addressing alignment issues. Notably, the CoT offers valuable insights into LLMs’ reasoning process, unleashing their capacity to consider diverse factors such as environmental conditions, driver behavior, and vehicle characteristics in severity analysis and inference.

Exploring experiences of TVET graduates’ work-integrated learning in a rural ecology: the case of TVET colleges in KwaZulu-Natal

This study investigates the experiences of Technical and Vocational Education and Training (TVET) graduates in rural ecology with work-based education (WBE), commonly known as work-integrated learning (WIL). The study was conducted at two campuses of Umfolozi TVET colleges in northern KwaZulu-Natal, focusing on post-graduation experiences. The National Certificate Vocational (NCV) qualification, introduced in January 2007, was the focal point, offered at Levels 2, 3, and 4 of the National Qualifications Framework, equivalent to Grades 10, 11, and 12. NCV graduates were selected as participants to explore their encounters with work-integrated learning during employment. Employing a qualitative case study design, the study utilized purposive sampling to identify six participants. Findings revealed systemic challenges faced by NCV graduates, particularly concerning the alignment of the curriculum with the needs of TVET students from rural areas. Additionally, it was noted that college management appeared unaware of the severity of the challenges encountered by students in securing in-service training opportunities. These findings underscore the need for educational institutions and policymakers to address curriculum alignment issues and enhance support mechanisms for TVET students, particularly those from rural backgrounds, to ensure the effectiveness of work-integrated learning initiatives.

Improved sub-genomic RNA prediction with the ARTIC protocol.

Viral subgenomic RNA (sgRNA) plays a major role in SARS-COV2's replication, pathogenicity, and evolution. Recent sequencing protocols, such as the ARTIC protocol, have been established. However, due to the viral-specific biological processes, analyzing sgRNA through viral-specific read sequencing data is a computational challenge. Current methods rely on computational tools designed for eukaryote genomes, resulting in a gap in the tools designed specifically for sgRNA detection. To address this, we make two contributions. Firstly, we present sgENERATE, an evaluation pipeline to study the accuracy and efficacy of sgRNA detection tools using the popular ARTIC sequencing protocol. Using sgENERATE, we evaluate periscope, a recently introduced tool that detects sgRNA from ARTIC sequencing data. We find that periscope has biased predictions and high computational costs. Secondly, using the information produced from sgENERATE, we redesign the algorithm in periscope to use multiple references from canonical sgRNAs to mitigate alignment issues and improve sgRNA and non-canonical sgRNA detection. We evaluate periscope and our algorithm, periscope_multi, on simulated and biological sequencing datasets and demonstrate periscope_multi's enhanced sgRNA detection accuracy. Our contribution advances tools for studying viral sgRNA, paving the way for more accurate and efficient analyses in the context of viral RNA discovery.

Controlled alignment imaging optical MIMO communication system based on light spot detection of arrayed light sources.

Visible light communication (VLC) technology has become one of the potential key technologies for 6 G due to its unique features, such as abundant license-free spectrum, high confidentiality, and low electromagnetic interference. In this paper, an optical multiple-input multiple-output (MIMO) communication system based on imaging reception is proposed, which can improve the data transmission rate and reduce interference between different sub-channels. To overcome the problem of optical communication link alignment, in this paper, we established an imaging optical MIMO communication system with a controlled light source array (CLSA) and proposed an optical alignment strategy to address the alignment issue. The scheme decouples the constraint that the light spot cannot be aligned with the detector in the communication system under the influence of optical parameters. Based on the light spot detection CLSA module, the communication system can ensure the stability of the link alignment when the communication distance changes. Experimental results show that when the communication distance is 20-80 m, the bit error rate (BER) of the communication system remains below 10-6. The single-channel communication rate of the system is 5 Mbps, and the total communication rate is 20 Mbps through the four-channel rate multiplexing. Moreover, if the communication distance increases to the range of 80-100 m, the BER increases, but it is always lower than the forward error correction (FEC) threshold.

Practical Performance Analysis of MDI-QKD with Orbital Angular Momentum on UAV Relay Platform.

The integration of terrestrial- and satellite-based quantum key distribution (QKD) experiments has markedly advanced global-scale quantum networks, showcasing the growing maturity of quantum technologies. Notably, the use of unmanned aerial vehicles (UAVs) as relay nodes has emerged as a promising method to overcome the inherent limitations of fiber-based and low-Earth orbit (LEO) satellite connections. This paper introduces a protocol for measurement-device-independent QKD (MDI-QKD) using photon orbital angular momentum (OAM) encoding, with UAVs as relay platforms. Leveraging UAV mobility, the protocol establishes a secure and efficient link, mitigating threats from untrusted UAVs. Photon OAM encoding addresses reference frame alignment issues exacerbated by UAV jitter. A comprehensive analysis of atmospheric turbulence, state-dependent diffraction (SDD), weather visibility, and pointing errors on free-space OAM-state transmission systems was conducted. This analysis elucidates the relationship between the key generation rate and propagation distance for the proposed protocol. Results indicate that considering SDD significantly decreases the key rate, halving previous data results. Furthermore, the study identifies a maximum channel loss capacity of 26 dB for the UAV relay platform. This result is pivotal in setting realistic parameters for the deployment of UAV-based quantum communications and lays the foundation for practical implementation strategies in the field.

Biomechanical innovations in correcting severe malocclusions

The correction of severe malocclusions presents significant challenges in orthodontics due to the complexity and precision required for effective treatment. Recent biomechanical innovations have transformed the landscape of orthodontic care, offering more precise, efficient, and patient-friendly solutions. Advancements in orthodontic bracket systems, including the development of self-ligating brackets and heat-activated arch wires, have enhanced the control and comfort of tooth movements, reduced treatment times and improving patient outcomes. Temporary anchorage devices (TADs) have provided stable anchorage points, enabling complex tooth movements that were previously difficult to achieve. TADs have proven particularly effective in managing open bites and class II malocclusions, offering non-surgical alternatives for severe cases. The integration of digital technology in orthodontics, such as 3D imaging and computer-aided design/manufacturing (CAD/CAM), has further revolutionized treatment planning and execution. These technologies facilitate accurate diagnostics and the creation of customized orthodontic appliances, ensuring precise and predictable treatment outcomes. Clear aligner therapy, initially limited to simple cases, has advanced through the use of improved thermoplastic materials and digital customization, allowing for the treatment of more complex malocclusions. Innovative force application techniques, including the use of attachments and elastics, have expanded the capabilities of aligners, making them a versatile and effective alternative to traditional braces. Collectively, these biomechanical innovations have significantly improved the management of severe malocclusions, enhancing treatment efficiency, precision, and patient comfort. The ongoing development of these technologies promises to further advance the field of orthodontics, providing more effective and aesthetically pleasing solutions for patients with complex dental alignment issues.

Board composition, ownership structure and dividend policy: Evidence from NSE listed companies in India

Purpose The purpose of the study is to analyse the influence of board composition and ownership structure on the dividend pay-out policy of selected listed Indian companies. Design/methodology/approach The top 30 companies listed on National Stock Exchange were examined for a period of three financial years from 2019 to 2021. Due to the cross-sectional and time-series character of the data, fixed effect panel model is used as the primary analytical approach. Findings The results revealed that there is a significant and positive association between board size and dividend policy. The results confirm that the firms with higher Indian ownership and non-institutional ownership distribute higher levels of dividend. No significant association was found between board independence and dividend decisions of ratios. Finally, it is observed that there is a positive impact of return on assets on dividend policy. Research limitations/implications Future studies can confirm the impact of ownership determinants and board characteristics on dividend distribution policy by taking stock dividends into account and enlarging the sample size of developing market businesses. They can also investigate ownership factors including management ownership and international ownership, in addition to other board attributes like qualification, tenure and age. This will offer a more thorough comprehension of how these variables relate to dividend policy. Practical implications Because they are better able to assess the financial position and make educated judgments, a diverse and independent board of directors may result in more strategic and cautious dividend policies. The dividend policy of a firm is also influenced by its ownership structure and strategic objectives; small or primary shareholders may exert pressure for larger payments. By focusing on long-term strategic goals rather than immediate requirements, managers may guarantee that interests are aligned. The findings of this study offer significant guidance to management and regulators about the dividend policy of publicly listed corporations. Social implications According to agency theory, the link between ownership structure, board composition and dividend policy may be explained by agency conflicts. Independent directors are necessary for the board to make decisions that effectively balance the interests of management and shareholders, particularly when it comes to paying dividends. According to studies, companies with non-executive directors have greater interest alignment and fewer agency issues. In addition, the ownership structure and makeup of the board are important factors in lowering agency conflicts and enhancing corporate governance procedures. Companies may improve their governance procedures and increase value for shareholders by resolving conflicts of interest. Originality/value The research paper contributes to the existing body of knowledge by analysing and uncovering significant and unknown relationship between board composition, ownership structure and dividend policy. The paper explores the relationship between specific ownership structures and dividend policy in context of a developing country where limited research exists and addresses a research question that has not been extensively studied before.

Issues in China's Alignment with DEPA Data Cross-Border Regulation

In the global digital governance game, the Digital Economy Partnership Agreement (DEPA) features innovative designs for digital trade, aligning with China's goals of internal data sharing openness and external breaking of "rule locks". However, China currently exhibits significant legislative differences in the "Data Issues" section of DEPA's fourth module, adopting stricter regulatory measures for cross-border data flow and data localization. Addressing DEPA's "principle & exception" regulatory standards, this paper argues that legitimate public policy objectives can be aligned with DEPA through proportionality principles, WTO general exception clauses, etc. By innovating customs supervision methods and following the path of the Hainan Free Trade Port trial, connecting international trade new rules, orderly implementing law enforcement systems and supporting mechanisms to ensure data security and orderly flow, China can gradually align with DEPA.

Simultaneous mapping of cathodoluminescence spectra and backscatter diffraction patterns in a scanning electron microscope

Electron backscatter diffraction and cathodoluminescence are complementary scanning electron microscopy modes widely used in the characterisation of semiconductor films, respectively revealing the strain state of a crystalline material and the effect of this strain on the light emission from the sample. Conflicting beam, sample and detector geometries have meant it is not generally possible to acquire the two signals together during the same scan. Here, we present a method of achieving this simultaneous acquisition, by collecting the light emission through a transparent sample substrate. We apply this combination of techniques to investigate the strain field and resultant emission wavelength variation in a deep-ultraviolet micro-LED. For such compatible samples, this approach has the benefits of avoiding image alignment issues and minimising beam damage effects.

Machine-learning-assisted orbital angular momentum recognition using nanostructures

The recognition of orbital angular momentum (OAM) in light beams holds significant importance in optical communication. The majority of current OAM recognition techniques are highly sensitive to stringent alignment issues. The speckle-based OAM recognition method reported in J. Opt. Soc. Am. A 39, 759 (2022)JOAOD61084-752910.1364/JOSAA.446352 is alignment-free in the transverse direction of light propagation and has been shown to operate successfully in the far-field region using macrostructures. This study introduces a proof-of-concept for speckle-learned OAM recognition with nanostructures, relaxing the strict alignment requirements in both the transverse and along the direction of light propagation. When the OAM beam interacts with random inhomogeneities at micron and/or nanoscale, it generates an OAM speckle field. Initially, a comprehensive examination of the dynamic evolution of OAM speckle fields, ranging from near field to far field, has been conducted using a ground glass diffuser, featuring random phase inhomogeneities at the micron scale. Subsequently, the investigation proceeds to randomly grown ZnO nanosheets on an aluminum substrate. To achieve rapid and precise OAM recognition, a tailored three-layer CNN is trained and tested on OAM speckle fields ranging from near field to far field to attain an accuracy surpassing 92%. This research expands the technique’s applicability, enabling recognition of OAM across near-field to far-field regimes, while leveraging micro- to nanostructures.

OFACD: An end-to-end change detection network for small UAVs remote sensing with viewpoint differences

Change detection using remote sensing images captured by small unmanned aerial vehicles (small UAVs) finds wide applications across various fields. However, there is a challenge when dealing with images captured at the same location by small UAVs at different times, leading to differences in viewpoint. These viewpoint differences present a significant challenge for most change detection methods. In this paper, we propose an end-to-end network, OFACD, designed to simultaneously address the issues of image alignment and change detection. Our network aligns feature maps using estimated optical flow and performs change detection concurrently. This approach enables the network to directly process images with viewpoint differences, effectively improving performance in scenarios with accumulated errors or large viewpoint variations, as well as enhancing throughput by eliminating repetitive feature extraction. Additionally, to fill the gap of the absence of change detection datasets with viewpoint differences and to evaluate our model, we created two change detection datasets with viewpoint differences. Extensive experimental results demonstrate that our method outperforms several state-of-the-art change detection methods in datasets involving viewpoint differences, exhibiting superior throughput and performance.

Optimization of Payroll System in the Education Sector of Timor-Leste: Ensuring Accuracy and Equity for Civil Servants and Teachers, A Case Study at the Ministry of Education in Dili

This study has the objective of enhancing the precision and equity of the payroll system within the education sector of Timor-Leste. By concentrating on crucial components such as compensation, incentives, auditing and control mechanisms, as well as level/scale adjustments, welfare, and professionalism, this investigation tackles significant concerns. Timor-Leste, having gained independence in 2002, encounters notable hurdles in administering its educational framework, particularly concerning civil servants and teachers. The Ministry of Education confronts challenges including wage inconsistencies, grade/category alignment issues, and teacher entitlements, all contributing to instructional difficulties and the potential for protests. The deficiencies of the current payroll system, encompassing salary disparities, outdated data, and workload discrepancies, amplify discontent, financial insecurity, and perplexity, thus emphasizing the pressing need for system enhancements. Employing a qualitative methodology involving interviews and document analysis, this study delves into the societal, cultural, and political contexts influencing the payroll system. The research scrutinizes the operational mechanisms of the system and the factors influencing its precision and equity, proposing specific measures such as streamlining processes, heightened utilization of information technology, human resources training, and enhanced communication to bolster effectiveness and fairness. These actions aspire to safeguard the well-being and contentment of educators and civil servants, thereby enhancing the calibre of education.

TPKE-QA: A gapless few-shot extractive question answering approach via task-aware post-training and knowledge enhancement

Few-shot extractive question answering (EQA) is a challenging task in natural language processing, whose current methods are mainly based on pretrained language models (PLMs). Data augmentation is often employed to improve the answer predictions of EQA models in few-shot settings. However, due to the differences between pretraining objectives and the EQA task, as well as embedding space alignment bottlenecks, the performance of few-shot EQA models must be improved. We propose TPKE-QA, a few-shot extractive Question Answering approach via Task-aware Post-training and Knowledge Enhancement, with entity-noun-oriented span selection in post-training, which can automatically generate EQA-style examples from a large-scale unlabeled corpus. By post-training based on generated examples, the gap between PLMs and the EQA task is effectively filled. To avoid embedding space alignment issues, a knowledge-enhanced sequence generation and knowledge injection approach for the EQA task enables gapless knowledge enhancement and fine-tuning on the post-trained model. In experiments, TPKE-QA achieved state-of-the-art results in most few-shot settings on the MRQA 2019 benchmark.

NAVIGATING ASSESSMENT ALIGNMENT IN BLENDED LEARNING: A COMPARATIVE ANALYSIS OF MAJOR AND NON-MAJOR SUBJECTS

A powerful pedagogical strategy has evolved in the age of developing educational technology: blended learning, which combines traditional instruction with online resources. In particular, it examines distinctions between major and non-major subjects in order to address the essential issue of assessment alignment in blend-ed learning. While investigations frequently evaluate the effectiveness of blended learning, few examine how examinations fit with its distinctive features. The assessment alignment in major and non-major subjects is compared in this study using the Correlation and Regression Tree (CART) model. Determining how assessments should align and how to make sure they measure topic mastery in this blended setting are among the key issues discussed. The study contributes to conversations about curriculum design and policy by assessing the literature and examining assessment procedures. The study offers knowledge that can be used to create evaluations for blended learning that are efficient while also promoting engagement among learners and academic success. Through this investigation, teachers receive tips on instructional design that will help them in the digital age to promote a unified learning experience across all topics.

Exploring Alternative Approaches to Language Modeling for Learning from Data and Knowledge

Despite their extensive application in language understanding tasks, large language models (LLMs) still encounter challenges including hallucinations - occasional fabrication of information - and alignment issues - lack of associations with human-curated world models (e.g., intuitive physics or common-sense knowledge). Moreover, the black-box nature of LLMs presents significant obstacles in training them effectively to achieve desired behaviors. In particular, modifying the concept embedding spaces of LLMs can be highly intractable. This process involves analyzing the implicit impact of such adjustments on the myriad parameters within LLMs and the resulting inductive biases. We propose a novel architecture that wraps powerful function approximation architectures within an outer, interpretable read-out layer. This read-out layer can be scrutinized to explicitly observe the effects of concept modeling during the training of the LLM. Our method stands in contrast with gradient-based implicit mechanisms, which depend solely on adjustments to the LLM parameters and thus evade scrutiny. By conducting extensive experiments across both generative and discriminative language modeling tasks, we evaluate the capabilities of our proposed architecture relative to state-of-the-art LLMs of similar sizes. Additionally, we offer a qualitative examination of the interpretable read-out layer and visualize the concepts it captures. The results demonstrate the potential of our approach for effectively controlling LLM hallucinations and enhancing the alignment with human expectations.