Set Of Values Research Articles

TWILIGHT OF METAPHYSICS: THE PROBLEM OF NOTHINGNESS AND NIHILISM IN THE MARTIN HEIDEGGER’S PHILOSOPHY

The article examines the problem of Nothingness and nihilism in Martin Heidegger’s views. In his early studies, Nothingness appears as a negation in the logical judgement. However, in further examinations, it relates to human existence. Nothingness is exposed in realization of the finitude of human life and death. Heidegger explains that human existence and Nothingness are connected through the experience of fear. To be fearful does not mean to be afraid of certain things, but to be able to see the perspective of non-existence. Nothingness that emerges in fear resembles transcendence, in other words, the ability to go beyond one’s own limits. Going outside the limitations of existence allows anyone to overcome the passiveness of life and start being concerned about one’s own existence. As a result, Heidegger’s philosophy of Nothingness was named nihilistic. However, Heidegger rethinks the problem of nihilism in the context of Nietzsche’s philosophy. He succeeds in showing that Nietzsche avoids the problem of Nothingness. In the Nietzschean vision, Nothingness is a characteristic of a world in which values are an appeal not to the earthly, but to the afterlife existence. That is why Nietzsche speaks of the need for a new set of values. Therefore, according to Heidegger, Nietzsche does not ask about the essence of Being, but simply assigns values to Being. Instead, in Heidegger’s philosophy, nihilism is a consequence of Western metaphysics ignoring the question of Nothingness. It is due to Nothingness that the question of Being becomes clear: without thinking of Nothingness, Being is not understood either. One can change the situation by asking again about Nothingness and Being. In general, the problem of nihilism in Heidegger’s philosophy changes significantly. First, he connects it with the historical movement of forgetting of Being, and later with a false interpretation of the essence of metaphysics. This thought is strengthened by the tendency of the dominance of technology, which dehumanizes the world. In conclusion, Heidegger’s philosophy is comparable to prophetism. Like Nietzsche, he seeks salvation from nihilism in art: poetry liberates from calculative rational thinking. Therefore, the question of the essence of thinking has not yet been raised in the West. The dominance of metaphysical thinking further distances a human from Being. At the same time, Heidegger insists on including the problem of Nothing in the question of Being, as ignoring it prevents the real overcoming of nihilism.

A model for determining the invariant component in the behavior of malware software based on the integration of fuzzy logic and genetic algorithms

The detection of polymorphic and metamorphic malware is a critical cybersecurity challenge due to its ability to evade detection by existing cyber defense systems by automatically modifying its own code and/or structure. In this context, an approach to the detection of polymorphic and metamorphic malware is proposed, which is based on the determination of an invariant component for each known type of malware during the analysis of its behavior. The essence of this approach is to define such an area of behavior that remains unchanged for a specific type of malicious software, regardless of the modifications made. To find the specified invariant component in the behavior of malware for each of its types, a set of values of the original feature space is described by fuzzy linguistic terms in order to obtain a set of fuzzy production rules for each type of malware. The next step is to determine the fuzzy invariant component for each known type of malicious software in the form of a fuzzy subset of features from the set of fuzzy production rules obtained in the previous step by means of genetic algorithms. The proposed model makes it possible to significantly increase the accuracy of detection of polymorphic and metamorphic software based on behavioral characteristics characteristic of already classified samples, which, in turn, contributes to increasing the overall effectiveness of the cyber security system.

DELTA-IRP AS A NEW HIGH-RESOLUTION MANOMETRY METRIC TO PREDICT THE POSITIVE OUTCOME OF LAPAROSCOPIC HELLER-DOR IN PATIENTS WITH ACHALASIA.

There is no consensus on the definition of failure after treatment in patients with achalasia. The Eckardt score (ES) is used to define clinical outcomes, but objective metrics are lacking. The aim of this study was to identify if any high-resolution manometry (HRM) parameters may be useful to predict a positive outcome after laparoscopic Heller-Dor (LHD). Patients undergoing LHD between 2012-2022 were enrolled. They were divided according to the outcome into success (SG) and failure group (FG). Alongside with the common HRM parameters, we also measured the difference between pre- and postoperative integrated relaxation pressure (∆-IRP). A ROC curve analysis was applied to assess the accuracy of each HRM parameter. Three-hundred-thirty-six patients (92.3%) were classified in the SG and 28 (7.7%) in the FG. No difference was found in terms of manometric types, symptom duration and history of previous treatments; preoperative lower esophageal sphincter (LES) pressure and IRP were higher in the SG than in the FG (41 vs 35mmHg, p=0.03 and 33 vs 26mmHg, p=0.002, respectively). The postoperative LES metrics were similar between the two groups, except for the ∆-IRP that was higher in the SG (23mmHg, IQR:15-31) than in the FG (14mmHg, IQR:9-17, p=0.0002). At univariable analysis, age, LES preoperative pressure, IRP and ∆-IRP were factors able to predict a positive clinical outcome. At multivariable analysis, the ∆-IRP was the only parameter independently related to clinical success (p<0.01, OR:0.94, CI 5%-95%:0.89-0.99). The ROC curve for the ∆-IRP showed an area under the curve of 0.71, with a threshold value set at 16.5mmHg (sensibility 71%, specificity 70%). Our data showed that the ∆-IRP with a threshold of 16.5mmHg could represent a new objective tool in order to predict the long-term positive outcome of LHD in patients with esophageal achalasia.

Inductor Current-Based Control Strategy for Efficient Power Tracking in Distributed PV Systems

This paper presents an inductor current-based maximum power point tracking (IC-MPPT) strategy and a single-inductor multi-input single-output (SI-MISO) structure with energy storage battery for distributed photovoltaic (PV) systems. In this study framework, the duty cycle of each PV channel can be controlled independently based on the presented IC-MPPT strategy, and the components/sensors costs are reduced through the presented SI-MISO PV system structure. In addition, a model predictive control (MPC) method is presented to regulate DC bus voltage, by controlling the bidirectional converter in the battery circuit. The presented control strategies have been rigorously derived and experimentally validated, and the experimental results demonstrate that each PV module can rapidly and efficiently track to the maximum power point in less than 0.016 s, while the bus voltage is stabilized near the set value, with an overshoot of less than 2.6%.

An Analytical Approach that Combines Knowledge from Germline and Somatic Mutations Enhances Tumor Genomic Reanalyses in Precision Oncology.

Background: Expanded analysis of tumor genomics data enables current and future patients to gain more benefits, such as improving diagnosis, prognosis, and therapeutics. Methods: Here, we report tumor genomic data from 1146 cases accompanied by simultaneous expert analysis from patients visiting our oncological clinic. We developed an analytical approach that leverages combined germline and cancer genetics knowledge to evaluate opportunities, challenges, and yield of potentially medically relevant data. Results: We identified 499 cases (44%) with variants of interest, defined as either potentially actionable or pathogenic in a germline setting, and that were reported in the original analysis as variants of uncertain significance (VUS). Of the 7405 total unique tumor variants reported, 462 (6.2%) were reported as VUS at the time of diagnosis, yet information from germline analyses identified them as (likely) pathogenic. Notably, we find that a sizable number of these variants (36%-79%) had been reported in heritable disorders and deposited in public databases before the year of tumor testing. Conclusions: This finding indicates the need to develop data systems to bridge current gaps in variant annotation and interpretation and to develop more complete digital representations of actionable pathways. We outline our process for achieving such methodologic integration. Sharing genomics data across medical specialties can enable more robust, equitable, and thorough use of patient's genomics data. This comprehensive analytical approach and the new knowledge derived from its results highlight its multi-specialty value in precision oncology settings.

Integrating traditional QSAR and read-across-based regression models for predicting potential anti-leishmanial azole compounds.

Leishmaniasis, a neglected tropical disease caused by various Leishmania species, poses a significant global health challenge, especially in resource-limited regions. Visceral Leishmaniasis (VL) stands out among its severe manifestations, and current drug therapies have limitations, necessitating the exploration of new, cost-effective treatments. This study utilized a comprehensive computational workflow, integrating traditional 2D-QSAR, q-RASAR, and molecular docking to identify novel anti-leishmanial compounds, with a focus on Glycyl-tRNA Synthetase (LdGlyRS) as a promising drug target. A feature selection process combining Genetic Function Approximation (GFA)-Lasso with Multiple Linear Regression (MLR) was used to characterize 99 azole compounds across ten structural classes. The baseline MLR model (MOD1), containing seven simple and interpretable 2D features, exhibited robust predictive capabilities, achieving an R2train value of 0.82 and an R2test value of 0.87. To further enhance prediction accuracy, three qualified single models (two MLR and one q-RASAR) were used to construct three consensus models (CMs), with CM2 (MAEtest = 0.127) demonstrating significantly higher prediction accuracy for test compounds than the MOD1. Subsequently, Support Vector Regression (SVR) and Boosting yielded 0.88 (R2train), 0.86 (R2test), 0.92 (R2train), and 0.82 (R2test), respectively. Molecular docking highlighted interactions of potent azoles within the QSAR dataset with critical residues in the LdGlyRS active site (Arg226 and Glu350), emphasizing their inhibitory potential. Furthermore, the pIC50 values of an accurate external set of 2000 azole compounds from the ZINC20 database were simultaneously predicted by CM2 + SVR + Boosting models and docked against the LdGlyRS, which identified Bazedoxifene, Talmetacin, Pyrvinium, Enzastaurin as leading FDA candidates, whereas three novel compounds with the database code ZINC000001153734, ZINC000011934652, and ZINC000009942262 displayed stable docked interactions and favourable ADMET assessments. Subsequently, Molecular Dynamics (MD) simulations for 100ns were conducted to validate the findings further, offering enhanced insights into the stability and dynamic behaviour of the ligand-protein complexes. The integrated approach of this study underscores the efficacy of 2D-QSAR modelling. It identifies LdGlyRS as a promising leishmaniasis target, offering a robust strategy for discovering and optimizing anti-leishmanial compounds to address the critical need for improved treatments.

Mentoring Across Generations: Design Considerations and Content

This paper addresses the use of mentor programs to explore the Post COVID-19 work environment. Changed working conditions such as remote and hybrid platforms, a rapid wave of leadership retirements, reconsideration of work priorities, and enhanced focus on diversity, equity, and inclusion have altered work approaches. Furthermore, younger generations of workers bring dramatically different approaches and value sets to the work world as they enter it. While all these changed conditions are susceptible to disagreements and conflicts, reflective conversation rooted in mentorship programs, can be a crucial tool for future planning. This paper explores the designs within mentor programs that serve across generations. Keywords: mentor programs, post Covid-19, work conditions, generations.

Value sets and the problem of redundancy in value set repositories.

Crafting high-quality value sets is time-consuming and requires a range of clinical, terminological, and informatics expertise. Despite widespread agreement on the importance of reusing value sets, value set repositories suffer from clutter and redundancy, greatly complicating efforts at reuse. When users encounter multiple value sets with the same name or ostensibly representing the same clinical condition, it can be difficult to choose amongst them or determine if any differences among them are due to error or intentional decision. This paper offers a view of value set development and reuse based on a field study of researchers and informaticists. The results emerge from an analysis of relevant literature, reflective practice, and the field research data. Qualitative analysis of our study data, the relevant literature, and our own professional experience led us to three dichotomous concepts that frame an understanding of diverse practices and perspectives surrounding value set development: Permissible values versus analytic value sets;Prescriptive versus descriptive approaches to controlled medical vocabulary use; andSemantic and empirical types of value set development and evaluation practices and the data they rely on.This three-fold framework opens up the redundancy problem, explaining why multiple value sets may or may not be needed and advancing academic understanding of value set development. In order for value set repositories to become more rather than less useful over time, software must channel user efforts into either improving existing value sets or making new ones only when absolutely necessary. This would require major, innovative changes to value set repository platforms. We believe the most direct path to giving value set developers the ability to leverage prior work is by encouraging them to compare existing value sets using advanced interfaces like VS-Hub, and by collecting and using metadata about code inclusion and exclusion decisions during the authoring process.

Molecular Modeling Studies of β-aminoacyl containing Homopiperazine derivatives as DPP4 Inhibitors

Dipeptidyl peptidase IV (DPP4) is a promising target for developing novel anti-diabetic and anti-obesity drugs. Pharmacophore based three dimensional quantitative structure activity relationship studies (3D-QSAR) and molecular docking were performed on a series of 56 β-aminoacyl-containing homopiperazine derivatives of DPP4 inhibitors to find out the structural relationship with the activity. The best predictive 3D-QSAR model with pharmacophore based alignment resulted in R2 (training set) value of 0.9407, Q2 (internal test set) value of 0.9053, Pearson-R value of 0.9517 and root mean square error (RMSE) 0.2838. The information rendered by pharmacophore based 3D-QSAR models and docking studies may afford valuable clues to optimize the lead and design of potent, selective inhibitors.

Susceptible-infected-recovered-susceptible processes competing on simplicial complexes

Contagions do not exist in isolation but in the presence of other propagating quantities, with which they may compete. Furthermore, higher-order interactions (interactions involving more than two nodes) are widely represented in real-world systems. We propose a stochastic model for susceptible-infected-recovered-susceptible (SIRS) processes competing in a simplicial complex that accommodates higher-order interactions. We also propose deterministic microscopic Markov chain (MMC) and mean-field (MF) forms of this model, which we analyze to reveal conditions for the persistence of each infection. We verify our analysis via numerical simulations, which also unveil critical mass effects, discontinuous phase transitions, and bistability. The stochastic model exhibits eight distinct classes of dependence of final state on initial conditions. All eight of these classes can be approximately reproduced with MMC, but only seven are seen under MF. The class observed in MMC and approximated in the stochastic model, but missing from MF, involves three distinct steady states for a single set of parameter values. However, in the absence of higher-order interactions MF exhibits an additional class, not observed in MMC or the stochastic model, corresponding to an infinite spectrum of steady states, in which the ratio of infection levels remains at its initial level. Both MMC and MF models can exhibit sustained oscillations in which one contagion disappears while the other tends to a limit cycle. Published by the American Physical Society 2024

Application of innovative solutions to improve the efficiency of the low-pressure cylinder flow part of a 1000 MW steam turbine for nuclear power plant

The outcomes of gas-dynamic computations for the low-pressure cylinder component of the K-1000-60/1500-2M type low-speed steam turbine intended for use in nuclear power plants are presented in the paper. Various strategies for enhancing the low-pressure cylinder, incorporating a novel approach, which was not previously employed in low-speed high-power steam turbines, have been identified. The flow part redesign has been carried out through the comprehensive methodology and software imple-mented in the IPMFlow package. This methodology encompasses gas-dynamic computations of varying complexities and ana-lytical profiling methods for spatial blade row shapes based on a limited set of parameterized values. Real thermodynamic prop-erties of water and steam were considered in 3D turbulent flow calculations. The final stage involved end-to-end 3D computations of the 7-stage low-pressure cylinder, employing parallel computing technology. The results indicate that the innovative solutions incorporated in the developed low-pressure cylinder led to a substantial increase in both efficiency and power.

What Can Popular Music and Library Music Learn from Each Other?

Because library music is made with the specific goal of future media use, its consideration might seem incongruous with broader histories of popular music or irrelevant to challenges facing the popular music industries. Yet both popular music and library music are defined against a shared set of cultural and commercial values, and both have adapted to shifts in media production, circulation, and consumption. In this article, I consider how popular music and library music have engaged with notions of art, responded to digitalisation, and revealed assumptions about the cultural and economic value of music. A side-by-side comparison reveals similarities and differences between popular music and library music that help us better understand changes affecting them both, as well as how we listen to music and how musicians make money from music.

Readiness of Agricultural Human Resources Based on Lokal Wisdom Lumbung Mataraman

The agricultural sector faces challenge of increasing its attractiveness to ensure the sustainability of agricultural human resources. The sustainability model of agricultural human resources emphasizes the resources owned by farmers to develop agricultural businesses into a more realistic approach. The contribution of this study is the development of the Lumbung Mataraman (LM) model as a local wisdom-based HR readiness model that anticipates limitations in the agricultural sector. LM is a local wisdom that has a set of Javanese cultural values to obtain optimal agricultural yields but also at the same time preserves nature. The intervention was carried out as a catalyst for the readiness of agricultural human resources to achieve food self-sufficiency. This research involved farmer groups in the Wonogiri and Sleman districts of Indonesia. The findings show that LM is local wisdom contributes to improving the quality of agricultural human resources in terms of skills, creativity, innovation, and local culture.

Exploring the use of the proverb “tsiwana i laiwa ndilani” by Vhavenda speaking people

The proverb, Tsiwana i laiwa ndilani”, was used in Vhavenda speaking communities to encourage children to be attentive and receptive to guidance and advice from anyone offering moral and ethical values. This paper aims is to explore how Vhaven?a people used the proverb Tsiwana i laiwa n?ilani. The reason of implementing it was to promote collective responsibilities of raising children with a set of values and manners desirable to society. A qualitative narrative inquiry was used, and data was collected through semi-structured interview. All Vhavenda speaking elders participated in the study. Purposive sampling was used to select six Vhavenda speaking elders who are knowledgeable with the use of the proverb. Data were merged into relevant themes using thematic analysis and coding also applied. This study was grounded by Symbolic Interactionism with Mead Theory as its theoretical framework. The proverb Tsiwana i laiwa n?ilani was the launching proverb for elders to express themselves about instilling norms and values in young children. Main findings of the study demonstrate that (i) a proverb Tsiwana i laiwa n?ilani is an idiom, (ii) it takes a village to raise an African child and finally a child is not an orphan per se. The study recommended that any adults who come across a misbehaving child, should feel free to instil guidance and advice as a way of promoting the cultural norms and values amongst the Vhavenda youth. It is also recommended that the programme to revive ndayo was suggested which encourages a collaborative effort between families, communities, schools, churches, and relevant government departments.

Using UMAP for Partially Synthetic Healthcare Tabular Data Generation and Validation.

In healthcare, vast amounts of data are increasingly collected through sensors for smart health applications and patient monitoring or diagnosis. However, such medical data often comprise sensitive patient information, posing challenges regarding data privacy, and are resource-intensive to acquire for significant research purposes. In addition, the common case of lack of information due to technical issues, transcript errors, or differences between descriptors considered in different health centers leads to the need for data imputation and partial data generation techniques. This study introduces a novel methodology for partially synthetic tabular data generation, designed to reduce the reliance on sensor measurements and ensure secure data exchange. Using the UMAP (Uniform Manifold Approximation and Projection) visualization algorithm to transform the original, high-dimensional reference data set into a reduced-dimensional space, we generate and validate synthetic values for incomplete data sets. This approach mitigates the need for extensive sensor readings while addressing data privacy concerns by generating realistic synthetic samples. The proposed method is validated on prostate and breast cancer data sets, showing its effectiveness in completing and augmenting incomplete data sets using fully available references. Furthermore, our results demonstrate superior performance in comparison to state-of-the-art imputation techniques. This work makes a dual contribution by not only proposing an innovative method for synthetic data generation, but also studying and establishing a formal framework to understand and solve synthetic data generation and imputation problems in sensor-driven environments.

Distance-based Similarity Measures of Hypersoft Sets under Uncertain Environment and Application in Customer Support Systems

Intuitionistic fuzzy hypersoft sets (IFHSS) represent a novel conceptual framework poised to overcome the limitations associated with intuitionistic fuzzy soft sets (IFSS) concerning the representation of multi-argument domains for parameter approximation. This model offers enhanced flexibility and reliability by facilitating the categorization of parameters into pertinent parametric valued sets. This study investigates the application of the IFHSS theory in enhancing similarity measurement within ChatBot systems. Through experimentation and analysis, the research demonstrates the efficacy of IFHSS-based approaches in handling uncertainties inherent in natural language interactions. We introduce distance measures (DM) along with their corresponding similarity measures (SM). These SMs tailored for IFHSS play a significant role in assessing similarity and facilitating the comparison of various factors. This article aims to develop six SMs based on their DMs and their axiomatic properties, theorems, and illustrative examples. Furthermore, we employ these measures to address real-world problems, particularly in the domain of computer sciences. By leveraging various technical factors, our analysis aids in pinpointing the best ChatBot for the satisfaction of customers. The methodologies proposed in this study hold promise for future case studies involving complex features and multiple decision-makers. Moreover, the suggested approach can be seamlessly integrated with existing structures.

Assessing the Direct Impact of Death on Discrete Choice Experiment Utilities.

The dead state can affect the value sets derived from discrete choice experiments (DCEs). Our aim was to empirically assess the direct impact of the immediate death state on health utilities using discrete choice experiment with time (DCETTO). A sample of the general population in Quebec, Canada, completed two approaches: DCETTO followed by a best-worst scaling with time (BWSTTO) (hereafter referred to as DCEBWS), versus DCETTO followed by the dominated option and the immediate death state (hereafter referred to as DCEDOD), both designed with the SF-6Dv2. In DCEBWS, all participants first completed 10 DCETTO choices (i.e., option A vs B), followed by 3 BWSTTO. In DCEDOD, the same participants first completed the same 10 DCETTO choices, followed by a repeated choice between the dominated option (i.e., A or B) and the immediate death state. A conditional logit model was used to estimate value sets. The performance of models was assessed using goodness of fit using Bayesian information criterion, parameters' logical consistency, and levels' significance. The direct impact of the death state on DCE latent utilities was evaluated by examining the magnitude of coefficients, assessing the agreement among the value sets estimated by DCETTO with DCEBWS and with DCEDOD using Bland-Altman plots, the proportion of worst-than-dead(WTD) health states, and analyzing the range of estimated values. From 398 participants, a total of 348 participants were included for final analysis. The number of parameters with illogical consistency and non-significant coefficients was lower in DCEBWS. The observed consistency in the relative importance of dimensions across all approaches suggests a stable and reliable ranking. The utility range for DCEDOD (-0.921 to 1) was narrower than for DCETTO (-1.578 to 1) and DCEBWS (-1.150 to 1). The DCEDOD estimated a lower percentage of WTD health states (20.01 %) compared to DCETTO (47.19 %) and DCEBWS (33.73 %). The agreement between DCETTO and DCEBWS was slightlystronger than between DCETTO and DCEDOD, and the mean utility values were higher in DCEDOD than in DCEBWS. The inclusion of the immediate death state directly within DCE increased utility values. This increase was higher when the immediate death was included in a sequence within a DCETTO (i.e., DCEDOD) than when it was included in a continuum of DCETTO (i.e., DCEBWS). The use of DCEDOD was potentially better suited to incorporate the dead state into a DCE.

A Temporal Convolutional Neural Network Fusion Attention Mechanism Runoff Prediction Model Based on Dynamic Decomposition Reconstruction Integration Processing

Accurate and reliable runoff forecasting is of great significance for hydropower station operation and watershed water resource allocation. However, various complex factors, such as climate conditions and human activities, constantly affect the formation of runoff. Runoff data under changing environments exhibit highly nonlinear, time-varying, and stochastic characteristics, which undoubtedly pose great challenges to runoff prediction. Under this background, this study ingeniously merges reconstruction integration technology and dynamic decomposition technology to propose a Temporal Convolutional Network Fusion Attention Mechanism Runoff Prediction method based on dynamic decomposition reconstruction integration processing. This method uses the Temporal Convolutional Network to extract the cross-temporal nonlinear characteristics of longer runoff data, and introduces attention mechanisms to capture the importance distribution and duration relationship of historical temporal features in runoff prediction. It integrates a decomposition reconstruction process based on dynamic classification and filtering, fully utilizing decomposition techniques, reconstruction techniques, complexity analysis, dynamic decomposition techniques, and neural networks optimized by automatic hyperparameter optimization algorithms, effectively improving the model’s interpretability and precision of prediction accuracy. This study used historical monthly runoff datasets from the Pingshan Hydrological Station and Yichang Hydrological Station for validation, and selected eight models including the LSTM model, CEEMDAN-TCN-Attention model, and CEEMDAN-VMD-LSTM-Attention (DDRI) for comparative prediction experiments. The MAE, RMSE, MAPE, and NSE indicators of the proposed model showed the best performances, with test set values of 1007.93, 985.87, 16.47, and 0.922 for the Pingshan Hydrological Station and 1086.81, 1211.18, 17.20, and 0.919 for the Yichang Hydrological Station, respectively. The experimental results indicate that the fusion model generated through training has strong learning ability for runoff temporal features and the proposed model has obvious advantages in overall predictive performance, stability, correlation, comprehensive accuracy, and statistical testing.

Tanggung Jawab Etis Penggunaan Artificial Intelligence Di Tanah Pendidikan: Formulasi Paradigma Baru Untuk Teknologi Otonom

This study examines the application of Immanuel Kant's ethical principles in the use of autonomous technology based on Artificial Intelligence (AI) in the academic domain. AI technology has significantly impacted academic efficiency and innovation but has also raised serious ethical challenges, such as algorithmic bias, dependency on technology, and threats to privacy and transparency. Using a Kantian ethical approach, this research identifies key issues and proposes an ethical framework that places justice, transparency, and respect for human dignity at the core of the design and implementation of autonomous technologies. The findings indicate that algorithmic bias can exacerbate academic inequities, while excessive reliance on AI risks diminishing the critical and creative capabilities of academics. Additionally, privacy and accountability in the management of academic data emerge as major concerns requiring ethically grounded policy interventions. This study also highlights the need for a new paradigm ensuring that autonomous technologies uphold humanistic values in academic settings, such as inclusivity, intellectual autonomy, and justice.

Performance enhancement of mid-infrared NH3 sensor using 9.06 μm QCL based on spectral optimization and NGO-LSTM model.

A detection sensor for mid-infrared ammonia (NH3) has been developed according to wavelength modulation spectroscopy-tunable diode laser absorption spectroscopy (WMS-TDLAS) technology, which can be applied in the chemical and aquaculture industries. A9.06 µm quantum cascade laser (QCL) and a41.5 m multipass gas cell (MPGC) were used to increase the detection limit of NH3. Spectral optimization and theNGO-LSTM (northern goshawk optimization-long short-term memory) model applied to gas detection are designed to improve the accuracy of sensor. Among them, the design of the temperature compensation and spectral drift correction reduces the effect of temperature and other environmental factors. Theoriginal second harmonic signal was denoised using the CEEMDAN-WPD (complete ensemble empirical mode decomposition with adaptive noise-wavelet packet decomposition) algorithm. And the NGO-LSTM algorithm was appliedto NH3 concentration inversion, adaptively optimizing theweight parameters. The experiment reflects that the measured value of the sensor has an excellent linear relationship with the set value (R2 0.9992). The long-term stability of the sensor was verifiedbased on 400 ppb NH3, with an RMSE (root mean square error) of 4.754 ppb. Allan-Werle bias analysis shows that the detection limit (LoD) is approximately 792 ppt at an integration time of 232 s. Subsequent response time and atmospheric environment simulation experiments have proven that this sensor provides an efficient approach for real-time monitoring of NH3.