Essential Stage Research Articles

Heuristic-enabled active machine learning: A case study of predicting essential developmental stage and immune response genes in Drosophila melanogaster.

Computational prediction of absolute essential genes using machine learning has gained wide attention in recent years. However, essential genes are mostly conditional and not absolute. Experimental techniques provide a reliable approach of identifying conditionally essential genes; however, experimental methods are laborious, time and resource consuming, hence computational techniques have been used to complement the experimental methods. Computational techniques such as supervised machine learning, or flux balance analysis are grossly limited due to the unavailability of required data for training the model or simulating the conditions for gene essentiality. This study developed a heuristic-enabled active machine learning method based on a light gradient boosting model to predict essential immune response and embryonic developmental genes in Drosophila melanogaster. We proposed a new sampling selection technique and introduced a heuristic function which replaces the human component in traditional active learning models. The heuristic function dynamically selects the unlabelled samples to improve the performance of the classifier in the next iteration. Testing the proposed model with four benchmark datasets, the proposed model showed superior performance when compared to traditional active learning models (random sampling and uncertainty sampling). Applying the model to identify conditionally essential genes, four novel essential immune response genes and a list of 48 novel genes that are essential in embryonic developmental condition were identified. We performed functional enrichment analysis of the predicted genes to elucidate their biological processes and the result evidence our predictions. Immune response and embryonic development related processes were significantly enriched in the essential immune response and embryonic developmental genes, respectively. Finally, we propose the predicted essential genes for future experimental studies and use of the developed tool accessible at http://heal.covenantuniversity.edu.ng for conditional essentiality predictions.

Optimal Sand−Paving Parameters Determination of an Innovatively Developed Automatic Maize Seeding Machine

Maize is an important crop to ensure food safety. High-quality seeds can guarantee a good yield. The maize seed germination rate is the most important information for the maize industry, which can be obtained through the seed germination test. An essential stage in determining the germination rate is the planting of the seeds. The current seed planting process is fully manual, which is labor-intensive and costly, and it requires the development of an autonomous seeding machine. This research developed an automatic maize seeding machine, consisting of four operations: paving sand, seed layout, watering, and covering the seed. Among the four procedures, sand paving is a crucial step, the performance of which is affected by the gate opening size, conveyor speed, and sensor mounting location. Three performance evaluating factors are the weight of sand in the tray, the volume of sand left on the conveyor, and sand surface flatness. A full factorial experiment was designed with three variables and three levels to determine an appropriate factor combination. RGB-D information was used to calculate the volume of sand left on the conveyor and sand flatness. An analytic hierarchy process was employed to assign weights to the three evaluation indicators and score the various combinations of factors. The machine for paving sand achieved a satisfactory result with an opening size of 10.8 mm, a sensor distance of 9 cm, and a conveyor belt speed of 5.1 cm/s. With the most satisfactory factors determined, the machine shows superior performance to better meet practical applications.

Дифференциация плюсовых деревьев сосны обыкновенной по состоянию ксилемы

An essential stage in the establishment of seed orchards is the formation of an optimal composition, avoiding inbreeding depression in the produced seed material, which develops as a result of crossbreeding between closely related plus trees. The negative effect can be reduced by ensuring the genetic heterogeneity of the seeds. The purpose of this study is to give a relative evaluation of the plus trees of Scots pine (Pinus sylvestris L.) as one of the most economically significant tree species on the European continent according to their physiological conditions. The formation and lignification of the xylem of the plus trees were investigated in the Nizhny Novgorod region of Russia. A histochemical examination was performed using the reaction of phloroglucinol to lignin. Phenotypic differences in the physiological conditions of plus trees appeared on a uniform ecological background that indicates their genotypic determinism. This fact was confirmed by ANOVA. The share of the influence of differences between the proper plus trees was between 25.16±8.91 % and 53.98±5.48 %. According to the results of the factor and cluster analysis, the association of plus trees was generated by the similarity of the multidimensional estimates of the xylem conditions. The objects that were statistically most distant from the others were determined. The hereditary conditionality of the identified differences among plus trees allows us to consider their grouping into clusters as genotypically determined. This information provides a reasonable approach to the formation of an assortment of Scots pine clonal seed orchards. For citation: Besschetnova N.N., Besschetnov V.P., Babich N.A., Bryntsev V.A. Differentiation of Plus Trees of Scots Pine by Xylem Conditions. Lesnoy Zhurnal = Russian Forestry Journal, 2023, no. 4, pp. 9–25. (In Russ.). https://doi.org/10.37482/0536-1036-2023-4-9-25

Performance And Functional Testing With The Black Box Testing Method

Software testing is an essential stage in software development because it plays a role in maintaining the quality of the software produced. The objective of software testing is for verification and validation, namely ensuring that the software being developed works according to the expected functions when the software is designed. In addition to improving quality, the software being developed must also have minimal errors and test whether the software is feasible to use or not. This research employed the black box testing method. Black box technical testing was a test of the input or output functionality of the software. The research also described the testing model using automated testing, which was carried out by Apache JMeter tools on performance tests and Katalon Studio to functional tests on the Perbanas portal. The final results of this research depicted the test model from studying business processes, creating test scenarios for automated testing to analyzing the test results. The performance test results aimed to find out how much throughput (transactions per second) and Response Time are. Meanwhile, functional test results aimed to find out whether the expected functions and outputs were appropriate or not. The results of the performance test on the login feature that had been carried out, the best was taken, i.e., sample data that entered approximately 5000 samples for 250 users for a load test per 5 minutes; only 0.06% failed with an average of 15 transactions per second (TPS) by a maximum response time of 68 seconds and a minimum response time of 0.2 seconds. KEYWORDS: Apache JMeter, automation test, black box test, functional test, Katalon Studio, performance test

On a hybrid updating method for modeling vibroacoustic behaviors of composite panels

Composite panels, by virtue of their outstanding mechanical properties, have found various applications in different sectors like aerospace and transportation. Under certain assumptions like Kirchhoff–Love and Mead–Markus hypotheses, certain high-order differential equations can be used for vibroacoustic modeling of composite panels. However, for accurate modeling, updating parameters is an essential stage. Herein, we aim to theoretically and experimentally review this stage. For this purpose, a hybrid updating method is proposed, incorporating hierarchical functions, inhomogeneous wave correlation approach, and least squares optimizations. Then various laboratory measurements, including Laser Doppler Vibrometry measurements as well as sound pressure levels, are analyzed. The measurements were performed for a thick composite (sandwich) panel, and a thin composite (laminate) one, along with two isotropic (steel and aluminum) plates for additional validation. The experiments indicate the ability of the hybrid approach to adjust parameters and precisely model vibroacoustic behaviors of the panels. Furthermore, the proposed hybrid method can be used in studies whose goal is accurate vibroacoustic modeling for psychoacoustic issues and perceptual validations, which is also one of the future targets of this research.

Genome Instability of Hippocampal and Bone Marrow Cells in Male Mice after the Action of Immobilization and Pheromonal Stressor

Different stressors, affecting the cells of target organs, can lead to genomic instability and even disintegration, which can play a role in the formation of post-stress pathologies. We studied the effect of psycho-emotional stressors (immobilization and mouse stress pheromone – 2,5-dimethylpyrazine) on the DNA integrity of hippocampal and bone marrow cell in male mice of CD1, CBA and C3H strains. Cytogenetic and immunocytochemical methods (alkaline comet assay, ana-telophase analysis of mitotic disturbances and analysis of γH2AX foci) were used. It is shown that the classic mouse stressor (immobilization or restraint), similar as 2,5-dimethylpyrazine, damages the genome of the cells of both organs studied. The destabilization of the cell genome of various organs is considered as an essential stage in the development of a stress response, which is an attempt of the organism to adapt to extreme environmental influences.

The meaning and values of life during the transition stage of youth from studies to work

Currently, the transition period from studies to work presents an essential and complex stage in the personal and professional evolution of young people, often characterized by challenges and uncertainties that affect their adaptability and long-term success. This process signifies the transition from the academic to the professional environment, involving experimentation and re-evaluation of meaning and values in life. The theoretical bases of the formation of orientations in life related to the meaning of existence as an important condition of life activity, are grounded in the works of philosophers, such as G. Hegel, I. Kant, E. Fromm, psychologists К. A. Abulkhanova-Slavskaya, B.S. Bratusy, A.Н. Leontiev, D.A. Leontiev, V.V. Stolin, V.E. Chudnovsky, the author of logotherapy V. Frankl. These scientific sources provide valuable information about the time period in which individuals navigate the complex process of identity formation, search for meaning, and adaptation of values. The purpose of this article is to investigate, both theoretically and practically, the meaning and value of life in this period of transition to adulthood from a psychological perspective. Emphasizing identity development, the search for meaning, and the adaptation of values. The importance of this subject lies in identifying the factors that contribute to the development of social maturity, in order to be able to develop effective strategies to support young people in this stage of life. In this context, the specialized literature emphasizes the fact that the meaning of life and values have a significant impact on the behavior and attitudes of young people, as well as on their career expectations.

Oxylipin secretion by human CD3+ T lymphocytes in vitro is modified by the exogenous essential fatty acid ratio and life stage.

Immune function changes across the life stages; for example, senior adults exhibit a tendency towards a weaker cell-mediated immune response and a stronger inflammatory response than younger adults. This might be partly mediated by changes in oxylipin synthesis across the life course. Oxylipins are oxidation products of polyunsaturated fatty acids (PUFAs) that modulate immune function and inflammation. A number of PUFAs are precursors to oxylipins, including the essential fatty acids (EFAs) linoleic acid (LA) and α-linolenic acid (ALA). LA and ALA are also substrates for synthesis of longer chain PUFAs. Studies with stable isotopes have shown that the relative amounts of LA and ALA can influence their partitioning by T lymphocytes between conversion to longer chain PUFAs and to oxylipins. It is not known whether the relative availability of EFA substrates influences the overall pattern of oxylipin secretion by human T cells or if this changes across the life stages. To address this, the oxylipin profile was determined in supernatants from resting and mitogen activated human CD3+ T cell cultures incubated in medium containing an EFA ratio of either 5:1 or 8:1 (LA : ALA). Furthermore, oxylipin profiles in supernatants of T cells from three life stages, namely fetal (derived from umbilical cord blood), adults and seniors, treated with the 5:1 EFA ratio were determined. The extracellular oxylipin profiles were affected more by the EFA ratio than mitogen stimulation such that n-3 PUFA-derived oxylipin concentrations were higher with the 5:1 EFA ratio than the 8:1 ratio, possibly due to PUFA precursor competition for lipoxygenases. 47 oxylipin species were measured in all cell culture supernatants. Extracellular oxylipin concentrations were generally higher for fetal T cells than for T cells from adult and senior donors, although the composition of oxylipins was similar across the life stages. The contribution of oxylipins towards an immunological phenotype might be due to the capacity of T cells to synthesize oxylipins rather than the nature of the oxylipins produced.

Next-generation cell line selection methodology leveraging data lakes, natural language generation and advanced data analytics.

Cell line development is an essential stage in biopharmaceutical development that often lies on the critical path. Failure to fully characterise the lead clone during initial screening can lead to lengthy project delays during scale-up, which can potentially compromise commercial manufacturing success. In this study, we propose a novel cell line development methodology, referenced as CLD 4, which involves four steps enabling autonomous data-driven selection of the lead clone. The first step involves the digitalisation of the process and storage of all available information within a structured data lake. The second step calculates a new metric referenced as the cell line manufacturability index (MI CL) quantifying the performance of each clone by considering the selection criteria relevant to productivity, growth and product quality. The third step implements machine learning (ML) to identify any potential risks associated with process operation and relevant critical quality attributes (CQAs). The final step of CLD 4 takes into account the available metadata and summaries all relevant statistics generated in steps 1-3 in an automated report utilising a natural language generation (NLG) algorithm. The CLD 4 methodology was implemented to select the lead clone of a recombinant Chinese hamster ovary (CHO) cell line producing high levels of an antibody-peptide fusion with a known product quality issue related to end-point trisulfide bond (TSB) concentration. CLD 4 identified sub-optimal process conditions leading to increased levels of trisulfide bond that would not be identified through conventional cell line development methodologies. CLD 4 embodies the core principles of Industry 4.0 and demonstrates the benefits of increased digitalisation, data lake integration, predictive analytics and autonomous report generation to enable more informed decision making.

Assessing the performance of a waste management system towards a circular economy in the Global South: The case of Marrakech (Morocco)

Waste management is essential for the safety and wellbeing of any society; it also helps to tackle global problems, such as climate change and resource scarcity. To support the evolution of waste management systems (WMSs) towards a circular economy (CE), assessment methods are applied. This paper shows, using the case of Marrakech (Morocco), how a novel holistic approach called WMS development stage concept (WMS-DSC) is applied to assess WMSs and determine measures promoting a CE. The approach enables a simplified derivation of measures that are useful to municipal decision makers and consists of five stages; stage 1 is equal to a WMS lacking essential WM elements and stage 5 describes a functioning CE. The approach was specifically designed to be transferable to other Global South cities through the categorization into these universal stages.The results from the WMS-DSC show that components related to energy recovery and waste recycling are classified in stage 1. For the latter, a slight development towards stage 2 can be assumed due to the planned activities in Marrakech. The components “collection and transport” and “prevention and reuse” are mainly in stage 2. Nevertheless, the components “governance”, “sector and market” and “waste disposal” are assigned to stages 2 and 3 with a stronger trend towards stage 4. The results show that in Marrakech and Morocco, CE is still in its infancy, and with this current state, it is hard to achieve. However, 33 measures were determined to improve the WMS of Marrakech and promote a CE.

A single switch hybrid quadratic converter for grid‐integrated renewable energy system

SummaryTo interface the electricity grid with renewable energy system applications, the system voltage is to be stepped up to a higher level. Here, the interfacing converter along with the transformer unit becomes an essential stage. Due to this traditional approach and the presence of a transformer, the size and cost of the entire unit are higher and reduce the efficiency of the system drastically with large space requirements, especially under light load conditions. To overcome this limitation, various dc‐dc converters have been proposed by researchers; however, the components' utilization and operation range of duty cycles are higher to obtain a higher voltage level. This particular paper proposes a novel single‐switch hybrid quadratic converter (HQC) for a grid‐integrated renewable energy system. The main objective of this proposed approach is to minimize the components count utilized in the converter with lower device stress to achieve higher gain; hence, it improvises the efficiency. The operational waveforms and mathematical relations based on the corresponding equivalent circuit are discussed in detail. To understand and analyze the merits of various performance parameters, the detailed comparative performance of the proposed HQC with existing converters is summarized in this paper. Finally, to validate and verify the novelty, the 500‐W experimental prototype was built and experimental results were captured and presented in this paper. It is observed that the proposed HQC can yield an efficiency of more than 95% under a lower range of duty cycles.

The Reconstruction Logic and Institutional Realization of the Substantiation of Court Hearings in China's Commutation of Sentence and Parole Cases

The manuscript discusses the importance of sentence commutation and parole cases as an essential stage in the execution of criminal judgments, related to both judicial justice and trial authority. To clarify this issue, this article mainly adopts a literature research method to systematically review the research on China's establishment of parole and commutation since its inception, and uses case studies to provide examples to illustrate the existing situations.The author argues that substantive hearings for these cases can remove the issue of "paper-pushing" and strengthen procedural justice, transparency, and judicial justice. The manuscript suggests that the current "tripartite" linear model for parole hearings should be transitioned to a deliberative mode with statements from both prosecution and defense to achieve a substantive hearing. The author proposes that the "tripartite trial structure" reform should be the starting point for reconsidering the legal identity and rights of the participating subjects and their responsibilities.

A novel hybrid autoencoder and modified particle swarm optimization feature selection for intrusion detection in the internet of things network

The Internet of Things (IoT) represents a paradigm shift in which the Internet is connected to real objects in a range of areas, including home automation, industrial processes, human health, and environmental monitoring. The global market for IoT devices is booming, and it is estimated that there will be 50 billion connected devices by the end of 2025. This explosion of IoT devices, which can be expanded more easily than desktop PCs, has led to an increase in cyber-attacks involving IoT devices. To address this issue, it is necessary to create novel approaches for identifying attacks launched by hacked IoT devices. Due to the possibility that these attacks would succeed, Intrusion Detection Systems (IDS) are required. IDS' feature selection stage is widely regarded as the most essential stage. This stage is extremely time-consuming and labor-intensive. However, numerous machine learning (ML) algorithms have been proposed to enhance this stage to boost an IDS's performance. These approaches, however, did not produce desirable results in terms of accuracy and detection rate (DR). In this paper, we propose a novel hybrid Autoencoder and Modified Particle Swarm Optimization (HAEMPSO) for feature selection and deep neural network (DNN) for classification. The PSO with modification of inertia weight was utilized to optimize the parameters of DNN. The experimental analysis was performed on two realistic UNSW-NB15 and BoT-IoT datasets that are suitable for IoT environment. The findings obtained by analyzing the proposed HAEMPSO against the Generic attack in the UNSW-NB15 dataset gave an accuracy of 98.8%, and a DR of 99.9%. While the benign class revealed an accuracy of 99.9% and DR of 99.7%. In the BoT-IoT dataset, the DDoS HTTP attack revealed an accuracy of 99.22% and DR of 97.79%. While the benign class gave an accuracy of 97.54% and DR of 97.92%. In comparison with the state-of-the-art machine learning schemes, our proposed HAEMPSO-DNN achieved a competitive feat in terms of DR and accuracy.

“Chcę wrócić, jak emigrant, z podróży dalekiej, / Z papieru, z martwych liter, żywy. Do twych rąk . . . ” : życie i twórczość Kazimierza Wierzyńskiego (1894–1969)

“Chcę wrócić, jak emigrant, z podróży dalekiej, / Z papieru, z martwych liter, żywy. Do twych rąk . . . ” : życie i twórczość Kazimierza Wierzyńskiego (1894–1969)

An automatic alignment of the business process and business value models: a novel MDA method

With the massive development of end-users requirements, the model alignment has become an essential stage in software engineering, especially in the model driven architecture (MDA) approach, to absorb the end-user’s need. The purpose is to facilitate the alignment of new models from existing ones. Our contribution in this paper is to deal with the MDA higher abstraction lever by focusing on the automatic alignment of the business value with the business process models for the information system (IS). For our case, the data-flow diagram (DFD) illustrates the business process model, and the E<sup>3</sup>value model illustrates the business value model. However, the ATLAS-transformation language (ATL) ensures automatic alignment. The main goal is to facilitate and accelerate IS implementation while enhancing its quality.

Alignment of human aquaporin 4 and ß-amyloid proteins may indicate involvement of ß-amyloid in brain water homeostasis and prevention of brain edema.

Alignment of human aquaporin 4 and ß-amyloid proteins may indicate involvement of ß-amyloid in brain water homeostasis and prevention of brain edema.

Analysis of the Translation Problems of Words with Chinese Characteristics: Taking Chinese-English Translation as an Example

As a bridge and linkage of external communication, translation undertakes an important task for international communication. With the enhancement of China's soft power, the diffusion of culture has become increasingly important. That makes translation an essential stage. However, in the process of translation, many mistranslations cause the international community to misunderstand the original meaning and even misinterpret its culture. Starting from real situations and texts in Chinese to English translation, this study focuses on the problems of three phrases: “Taiwan question”, “The Communist Party of China”, and “People's democratic dictatorship” in Chinese to English translation by case analysis. As supplementary data, the researchers also conducted interviews with a Ph.D. in political science. It found that the problems may be created by the English meaning of the words, China's history and culture, and the translator's comprehension of the original language. Some suggestions for improving translation quality were also put forward from the perspectives of the country, the translation industry, and individuals.

Relationships between Pre-service Student Teacher’s Teaching Practice Anxiety, Self-determined Personal Characteristic and Academic Achievement

Teaching Practice (TP) is an essential and compulsory stage in teachers training, as it gives them a worthwhile opportunity to practice what they have learned during training. It involves real classroom teaching which leads to most students experience anxiety which interferes with their performance. This study was planned to examine the relationship between university student teachers’ anxiety levels towards TP with their self-determined learner’s personality and with academic performance in TP. The study involved two Kenyan universities; University of Kabianga and Laikipia University. Study population was 800 student teachers undertaking their TP exercise. A random sample of 105 students were involved. Research design was descriptive-correlational research design while research method was cross-sectional analytical survey. Three tools were used to correct data; The Student Teacher Anxiety Scale (STAS), Teaching Practice Assessment Score Sheet (TPASS) and Eysenck Personality Questionnaire (EPQ). Pearson correlation coefficient, r, was used to test the relationship between student teachers’ anxiety, personal characteristics, and academic achievement. Results indicate that; academic achievement in TP exercise and TP anxiety are high, there is a weak statistically significant negative relationship between TP anxiety and academic achievement and the negative relationship is statistically significant in evaluation and professional preparation anxieties only. Furthermore, there is a statistically insignificant relationship between learner personality and academic achievement in TP.

Feature selection method based on stochastic fractal search henry gas solubility optimization algorithm

In most data mining tasks, feature selection is an essential preprocessing stage. Henry’s Gas Solubility Optimization (HGSO) algorithm is a physical heuristic algorithm based on Henry’s law, which simulates the process of gas solubility in liquid with temperature. In this paper, an improved Henry’s Gas Solubility Optimization based on stochastic fractal search (SFS-HGSO) is proposed for feature selection and engineering optimization. Three stochastic fractal strategies based on Gaussian walk, Lévy flight and Brownian motion are adopted respectively, and the diffusion is based on the high-quality solutions obtained by the original algorithm. Individuals with different fitness are assigned different energies, and the number of diffusing individuals is determined according to individual energy. This strategy increases the diversity of search strategies and enhances the ability of local search. It greatly improves the shortcomings of the original HGSO position updating method is single and the convergence speed is slow. This algorithm is used to solve the problem of feature selection, and KNN classifier is used to evaluate the effectiveness of selected features. In order to verify the performance of the proposed feature selection method, 20 standard UCI benchmark datasets are used, and the performance is compared with other swarm intelligence optimization algorithms, such as WOA, HHO and HBA. The algorithm is also applied to the solution of benchmark function. Experimental results show that these three improved strategies can effectively improve the performance of HGSO algorithm, and achieve excellent results in feature selection and engineering optimization problems.

(Re)thinking transcription strategies: Current challenges and future research directions

Data transcription is often depicted as an essential and critical stage in qualitative research. As most researchers have experienced, it requires significant time and human resource investment. We focus on transcription strategies, a topic typically missing from the methodology discourse. We explore the biases and challenges of each of the transcription strategies. By analysing 434 academic refereed papers from top journals, we underline the lack of scrutiny over the transcription process, its impact, and strategies taken to conduct it. We also interviewed some of the authors to better understand the challenges associated with transcription. This paper aims at contributing to more reflexivity on the existing strategies regarding transcription and how to increase transparency in qualitative research.