Specific Model Research Articles

MizBERT: A Mizo BERT Model

This research investigates the utilization of pre-trained BERT transformers within the context of the Mizo language. BERT, an abbreviation for Bidirectional Encoder Representations from Transformers, symbolizes Google’s forefront neural network approach to Natural Language Processing (NLP), renowned for its remarkable performance across various NLP tasks. However, its efficacy in handling low-resource languages such as Mizo remains largely unexplored. In this study, we introduce MizBERT , a specialized Mizo language model. Through extensive pre-training on a corpus collected from diverse online platforms, MizBERT has been tailored to accommodate the nuances of the Mizo language. Evaluation of MizBERT’s capabilities is conducted using two primary metrics: Masked Language Modeling (MLM) and Perplexity, yielding scores of 76.12% and 3.2565, respectively. Additionally, its performance in a text classification task is examined. Results indicate that MizBERT outperforms both the multilingual BERT (mBERT) model and the Support Vector Machine (SVM) algorithm, achieving an accuracy of 98.92%. This underscores MizBERT’s proficiency in understanding and processing the intricacies inherent in the Mizo language.

Zero-Shot Segmentation of Eye Features Using the Segment Anything Model (SAM)

The advent of foundation models signals a new era in artificial intelligence. The Segment Anything Model (SAM) is the first foundation model for image segmentation. In this study, we evaluate SAM's ability to segment features from eye images recorded in virtual reality setups. The increasing requirement for annotated eye-image datasets presents a significant opportunity for SAM to redefine the landscape of data annotation in gaze estimation. Our investigation centers on SAM's zero-shot learning abilities and the effectiveness of prompts like bounding boxes or point clicks. Our results are consistent with studies in other domains, demonstrating that SAM's segmentation effectiveness can be on-par with specialized models depending on the feature, with prompts improving its performance, evidenced by an IoU of 93.34% for pupil segmentation in one dataset. Foundation models like SAM could revolutionize gaze estimation by enabling quick and easy image segmentation, reducing reliance on specialized models and extensive manual annotation.

Interoperability Structure of Smart Water Conservancy Based on Internet of Things

Massive smart water conservancy object (WCO) need to be connected for real-time monitoring and control, which produces massive data. Unfortunately, heterogeneous data structures and semantics lead to low interoperability between WCO and management systems. To address this challenge, we propose a novel interoperability structure for a smart water conservancy system based on the Internet of Things (IoT), and the key design includes a smart WCO terminal, interoperability network, special interoperability protocol, WCO information model, and cloud platform. Universal terminal and network are the base of interoperability hardware, and special interoperability protocol and information model for interconnection of WCO are designed for smart water conservancy management system. WCO can be connected to a water conservancy Big Data processing cloud platform for interoperability applications. The application results demonstrate that our proposed WCO’s interoperability structure has obvious advantages than the general IoT at WCO interoperability. The interoperability protocol is reliable, the information model can ease interoperability and security, and the semantic dictionary is very rich and covers all semantic services of WCO.

CP factor model for dynamic tensors

Abstract Observations in various applications are frequently represented as a time series of multidimensional arrays, called tensor time series, preserving the inherent multidimensional structure. In this paper, we present a factor model approach, in a form similar to tensor CANDECOMP/PARAFAC (CP) decomposition, to the analysis of high-dimensional dynamic tensor time series. As the loading vectors are uniquely defined but not necessarily orthogonal, it is significantly different from the existing tensor factor models based on Tucker-type tensor decomposition. The model structure allows for a set of uncorrelated one-dimensional latent dynamic factor processes, making it much more convenient to study the underlying dynamics of the time series. A new high-order projection estimator is proposed for such a factor model, utilizing the special structure and the idea of the higher order orthogonal iteration procedures commonly used in Tucker-type tensor factor model and general tensor CP decomposition procedures. Theoretical investigation provides statistical error bounds for the proposed methods, which shows the significant advantage of utilizing the special model structure. Simulation study is conducted to further demonstrate the finite sample properties of the estimators. Real data application is used to illustrate the model and its interpretations.

Parametric Production of Prostheses Using the Additive Polymer Manufacturing Technology Multi Jet Fusion.

This study aims to develop a procedure for the production of 3D-printed forearm prostheses (especially hard outer sockets). The production procedure is designed in the form of a parametric workflow (CAD model), which significantly speeds up the designing process of the prosthesis. This procedure is not fixedly dependent on the software (SW) equipment and is fully transferable into another SW environment. The use of these prostheses will significantly increase the comfort of their patients' lives. It is possible to produce prostheses faster and in larger amounts and variants by the usage of additive technology. The input for the own production of the prosthesis is a model of the internal soft socket of the patient. This soft socket (soft bed) is made by a qualified prosthetist. A 3D-scanned CAD model is obtained afterward using the scanning method by an automatic laser projector. An editable, parametric external socket (modifiable in any CAD format) is generated from the obtained 3D scan using a special algorithmic model. This socket, after the necessary individual modifications, is transferred to 3D printing technology and produced using powder technology Multi Jet Fusion (HP MJF). The result of the designed and tested procedure is a quickly editable 3D-printed outer socket (main part of prosthesis), which is able to fully replace the current long-fiber composite solution. Production of current solutions is relatively time-consuming, and only one piece is produced in a given time. The newly designed technology eliminates this. This study summarized the possibilities of speeding up the production of forearm prostheses (but not only these) by creating a parametric CAD model that is applicable to different patients.

Legal security of mediation in Ukraine: shortcomings and prospects for improvement through the prism of the experience of the countries of the European Union

In the content of the article, the author investigated the peculiarities of the legal provision of mediation in Ukraine and the countries of the European Union. Despite the fact that today in legal science, a significant body of scientific works has been formed, which relate to certain aspects of mediation, it is worth noting that the issue of legal support for mediation in Ukraine has not been properly studied scientifically. Modern studies of the legal provision of mediation are especially relevant, given the high degree of its variability in Ukraine. On the basis of a critical analysis of the provisions of Ukrainian legislation in the field of mediation, its shortcomings and prospects for improvement were determined. The following are noted as shortcomings of the legal provision of mediation in Ukraine: a) the absence of provisions that would regulate the issues of mediation procedures, in particular before submitting an appeal to the court, during court proceedings, as well as during the execution of a court decision, etc.; b) inconsistency of its categorical and conceptual apparatus with established legislative terminology (for example, the terms "conflict” and "dispute”); c) inaccuracy of the legal provision of mediation principles; d) superficial, too general regulation of the mediation process itself; e) failure to take into account the European standard of mediation transparency, etc. The analysis of the peculiarities of the legal provision of the EU countries (France, Germany, Italy, Austria, etc.) made it possible to substantiate the prospects for improving the legal provision of mediation in Ukraine. It is summarized that the Ukrainian legislation has experienced legal transformations in the field of mediation, moving from the least effective and least reliable informal model to a specialized model by adopting the framework Law of Ukraine "On Mediation”. In our opinion, the most effective model of legal support for mediation is the specialized model chosen by Ukraine, which, among other things, should be complemented by effective mechanisms of self-regulation of this legal activity through the development and application of acts of the so-called "unofficial law” adopted by professional associations of mediators. The combination of the legal regulation of mediation in Ukraine was noted: a) the typicality of the law "On Mediation”, which established the legal foundation of mediation and regulated the most general and important issues in this area; b) self-regulating mechanisms of mediation, designed to ensure a quick response to the dynamic development of mediation and relations where it should be applied.

Treatment of phenolic wastewater in an anaerobic fluidized bed microbial fuel cell filled with graphene oxide-macroporous adsorption resin as multifunctional carrier

ABSTRACT A novel graphene oxide-modified resin (graphene oxide-macroporous adsorption resin) was prepared and used as a multifunctional carrier in an anaerobic fluidized bed microbial fuel cell (AFB-MFC) to treat phenolic wastewater (PW). The macroporous adsorption resin (MAR) was used as the carrier, graphene oxide was used as the modified material, the conductive modified resin was prepared by loading graphene oxide (GO) on the resin through chemical reduction. The modified resin particles were characterized by scanning electron microscopy (SEM), Raman spectroscopy (RS), specific surface area and pore structure analysis. Graphene oxide-macroporous adsorption resin special model was established using the Amorphous Cell module in Materials Studio (MS), and the formation mechanism of graphene oxide-macroporous adsorption resin was studied using mean square displacement (MSD) of the force module. Molecular dynamics simulation was used to study the motion law of molecular and atomic dynamics at the interface of graphene oxide-macroporous adsorption resin composites. The strong covalent bond between GO and MAR ensures the stability of GO/MAR. When the modified resin prepared in 3.0 mg/mL GO mixture was used in the AFB-MFC, the COD removal of wastewater was increased by 9.1% to 72.44%, the voltage was increased by 84.04% to 405.8 mV, and power density was increased by 765.44% to 242.67 mW/m2.

A Critical Review of Large Language Models: Sensitivity, Bias, and the Path Toward Specialized AI

Abstract This paper examines the comparative effectiveness of a specialized compiled language model and a general-purpose model like OpenAI’s GPT-3.5 in detecting SDGs within text data. It presents a critical review of Large Language Models (LLMs), addressing challenges related to bias and sensitivity. The necessity of specialized training for precise, unbiased analysis is underlined. A case study using a company descriptions dataset offers insight into the differences between the GPT-3.5 and the specialized SDG detection model. While GPT-3.5 boasts broader coverage, it may identify SDGs with limited relevance to the companies’ activities. In contrast, the specialized model zeroes in on highly pertinent SDGs. The importance of thoughtful model selection is emphasized, taking into account task requirements, cost, complexity, and transparency. Despite the versatility of LLMs, the use of specialized models is suggested for tasks demanding precision and accuracy. The study concludes by encouraging further research to find a balance between the capabilities of LLMs and the need for domain-specific expertise and interpretability. Peer Review https://www.webofscience.com/api/gateway/wos/peer-review/10.1162/qss_a_00310

Statistical approaches enabling technology-specific assay interference prediction from large screening data sets

High throughput screening (HTS) technologies allow the biological testing of hundreds of thousands of compounds per day. Typically, a substantial proportion of the initial hits obtained by HTS are artifacts caused by assay interference. Therefore, global and technology-specific in silico models for identifying and predicting compounds interfering with biological assays have been developed. The global models benefit from training on large screening data sets, while the specialized models benefit from training on assay technology-specific experimental data. In this work, we develop and explore strategies for generating better predictors of technology-specific assay interference by utilizing the large bioactivity data matrices global models are trained on and employing partially new compound labeling approaches to maintain the assay technology awareness of specialized models. We demonstrate the utility of the statistically derived interference labels in machine learning using fluorescence-based assay interference as a representative example. Our random forest and multi-layer perceptron classifiers showed improved performance compared to existing models, achieving Matthews correlation coefficients (MCCs) of up to 0.47 on holdout data and up to 0.45 on an external test set. These results demonstrate that accurate assay-specific interference labels can be derived from large bioactivity data matrices, enabling the development of new machine-learning models without the need for further experimental data.

Wheat bread partially replaced with fermented cowpea flour: optimizing the formulation and storage study at 25 °C

Incorporating legume flours, like fermented cowpea flour (FCF), into wheat bread presents an opportunity to enhance protein intake. Fermenting cowpea flour through solid-state methods led to 79 % decrease in phytate levels, a 46 % reduction in tannins, and a 160 % increase in free amino acids. This study further aimed to optimize wheat bread by substituting wheat flour with FCF using a d-optimal mixture design. Varied proportions of wheat flour (66.3–93.7 %), FCF (5–30 %), xanthan gum (0.5–3 %), and lecithin (0.3–1 %) were tested to assess bread qualities such as hardness, color changes in crust and crumb, and specific loaf volume. A special cubic model was fitted to each response. The optimized blend comprised 75.2 % wheat flour, 22.9 % FCF, 1.6 % xanthan gum, and 0.3 % lecithin, scoring a desirability value of 0.63. The corresponding bread crumb hardness was 1981.3 N, and the loaf volume was 1.84 cm3/g. Shelf-life evaluation at 25 °C revealed microbial stability (aerobic mesophile count < 5 log10 cfu/g) of 6 days, with a decline in overall acceptability (9-point scale) ratings from 7.1 to 5.3 after this period. Over time, the bread's crumb experienced increased hardness and retrogradation enthalpy while moisture migration from crumb to crust was observed.

Incorporating Adaptive Sparse Graph Convolutional Neural Networks for Segmentation of Organs at Risk in Radiotherapy

Precisely segmenting the organs at risk (OARs) in computed tomography (CT) plays an important role in radiotherapy’s treatment planning, aiding in the protection of critical tissues during irradiation. Renowned deep convolutional neural networks (DCNNs) and prevailing transformer-based architectures are widely utilized to accomplish the segmentation task, showcasing advantages in capturing local and contextual characteristics. Graph convolutional networks (GCNs) are another specialized model designed for processing the nongrid dataset, e.g., citation relationship. The DCNNs and GCNs are considered as two distinct models applicable to the grid and nongrid datasets, respectively. Motivated by the recently developed dynamic-channel GCN (DCGCN) that attempts to leverage the graph structure to enhance the feature extracted by the DCNNs, this paper proposes a novel architecture termed adaptive sparse GCN (ASGCN) to mitigate the inherent limitations in DCGCN from the aspect of node’s representation and adjacency matrix’s construction. For the node’s representation, the global average pooling used in the DCGCN is replaced by the learning mechanism to accommodate the segmentation task. For the adjacency matrix, an adaptive regularization strategy is leveraged to penalize the coefficient in the adjacency matrix, resulting in a sparse one that can better exploit the relationships between nodes. Rigorous experiments on multiple OARs’ segmentation tasks of the head and neck demonstrate that the proposed ASGCN can effectively improve the segmentation accuracy. Comparison between the proposed method and other prevalent architectures further confirms the superiority of the ASGCN.

MODELLING OF MASS TRANSFER IN WASTEWATER FACILITIES

Problem statement. The design of wastewater treatment systems is a complex process and requires the use of special mathematical models. As a rule, empirical models are used at the stage of designing structures of water drainage systems, which allow obtaining only an “integral” characteristic of the efficiency of wastewater treatment. But in a number of cases, it is important to have information about the spatial distribution of the impurity concentration in the structure. To solve this problem, you need to have three-dimensional mathematical models. In the future, there is a shortage of such models, so the creation of three-dimensional multifactorial models for the analysis of the efficiency of drainage system structures is an urgent task. The purpose of the article. Development of a three-dimensional numerical model for the analysis of the mass transfer process to determine the impurity concentration in the clarifier. Methodology. The analysis of impurity concentration fields in the clarifier is carried out by numerical integration of the three-dimensional equation for the velocity potential and the three-dimensional equation of the convective-diffusion transport of the impurity. For the numerical integration of the Laplace equation for the velocity potential, the variable-triangular method and the Liebmann method are used. Finite-difference splitting schemes are used for numerical integration of the three-dimensional equation of convective-diffusion transport of impurities. Scientific novelty. A dynamic multifactorial numerical model was created for the analysis of the process of mass transfer of impurities in a settling tank by conducting a computational experiment. Practical value. The built multifactorial numerical model makes it possible to analyze the efficiency of wastewater treatment in clarifiers that have a complex geometric shape and cannot be calculated on the basis of existing engineering methods. Conclusions. On the basis of the developed three-dimensional numerical model, a computer code was created, which allows you to quickly obtain information about the distribution of the impurity concentration in the settling tank.

Assessing the applicability of a special model of plutonium behavior in presence of chelates in cases of wound intake of industrial compounds of plutonium in the FSUE Mayak PA workers

In cases of intake of higher levels of plutonium, a chelate diethylenetriaminepentaacetic acid is used to enhance the elimination and reduce deposition rate in the organs of major deposit in the form of calcium compound Ca-DTPA (pentacinum). To interpret the results of the nuclide measurements in excreta while estimating the doses of internal exposure to plutonium, the biokinetic and dosimetry models recommended by ICRP are applied; and in case of the actinide intake through damaged skin it is recommended to use the models of NCRP of the USA. Since the application of chelates significantly changes the picture of plutonium urinary excretion, special models of complex Pu-DTPA behavior are developed to describe the plutonium biokinetics in presence of chelates. The purpose of this work is to assess the suitability of a special model of plutonium behavior in presence of chelates elaborated by K. Konzen to describe the plutonium biokinetics in the body by the cases of wound intake of industrial plutonium compounds in the FSUE Mayak PA workers. This study used the measurements of plutonium activity in daily amount of urine for three cases with the actinide intake through damaged skin and Ca-DTPA treatment, the duration of pentacinum application and observation periods for workers depending on the levels of intake. For each case, the most likely type of an entered compound was selected using Akaike information criterion, then the daily urinary activity of plutonium was calculated by the model (estimated value) on the same days when it was determined actually (actual values). The analysis of the compliance of calculated data with actual data was conducted using the statistic tests of Foster-Stewart,Breusch-Godfrey, Mann-Whitney, Shapiro-Wilk, and the determination coefficient. It was found that for all cases considered, the adjusted determination coefficient and Foster-Stewart test indicated the compliance of actual data with calculated ones. The statistical analysis of the compliance of calculated data with actual measurements of plutonium urinary activity showed that the special model of plutonium behavior proposed by K. Konzen was able to predict the urinary activity of plutonium during both the radionuclide decorporation measures and the period of pentacinum aftereffect.

Establishment of a humanized mouse model using steady‐state peripheral blood‐derived hematopoietic stem and progenitor cells facilitates screening of cancer‐targeted T‐cell repertoires

AbstractBackgroundCancer‐targeted T‐cell receptor T (TCR‐T) cells hold promise in treating cancers such as hematological malignancies and breast cancers. However, approaches to obtain cancer‐reactive TCR‐T cells have been unsuccessful.MethodsHere, we developed a novel strategy to screen for cancer‐targeted TCR‐T cells using a special humanized mouse model with person‐specific immune fingerprints. Rare steady‐state circulating hematopoietic stem and progenitor cells were expanded via three‐dimensional culture of steady‐state peripheral blood mononuclear cells, and then the expanded cells were applied to establish humanized mice. The human immune system was evaluated according to the kinetics of dendritic cells, monocytes, T‐cell subsets, and cytokines. To fully stimulate the immune response and to obtain B‐cell precursor NAML‐6‐ and triple‐negative breast cancer MDA‐MB‐231‐targeted TCR‐T cells, we used the inactivated cells above to treat humanized mice twice a day every 7 days. Then, human T cells were processed for TCR β‐chain (TRB) sequencing analysis. After the repertoires had been constructed, features such as the fraction, diversity, and immune signature were investigated.ResultsThe results demonstrated an increase in diversity and clonality of T cells after treatment. The preferential usage and features of TRBV, TRBJ, and the V–J combination were also changed. The stress also induced highly clonal expansion. Tumor burden and survival analysis demonstrated that stress induction could significantly inhibit the growth of subsequently transfused live tumor cells and prolong the survival of the humanized mice.ConclusionsWe constructed a personalized humanized mouse model to screen cancer‐targeted TCR‐T pools. Our platform provides an effective source of cancer‐targeted TCR‐T cells and allows for the design of patient‐specific engineered T cells. It therefore has the potential to greatly benefit cancer treatment.

Spatial Trueness Evaluation of 3D-Printed Dental Model Made of Photopolymer Resin: Use of Special Structurized Dental Model.

(1) Background: Various 3D printers are available for dental practice; however, a comprehensive accuracy evaluation method to effectively guide practitioners is lacking. This in vitro study aimed to propose an optimized method to evaluate the spatial trueness of a 3D-printed dental model made of photopolymer resin based on a special structurized dental model, and provide the preliminary evaluation results of six 3D printers. (2) Methods: A structurized dental model comprising several geometrical configurations was designed based on dental crown and arch measurement data reported in previous studies. Ninety-six feature sizes can be directly measured on this original model with minimized manual measurement errors. Six types of photo-curing 3D printers, including Objet30 Pro using the Polyjet technique, Projet 3510 HD Plus using the Multijet technique, Perfactory DDP and DLP 800d using the DLP technique, Form2 and Form3 using the SLA technique, and each printer's respective 3D-printable dental model materials, were used to fabricate one set of physical models each. Regarding the feature sizes of the simulated dental crowns and dental arches, linear measurements were recorded. The scanned digital models were compared with the design data, and 3D form errors (including overall 3D deviation; flatness, parallelism, and perpendicularity errors) were measured. (3) Results: The lowest overall 3D deviation, flatness, parallelism, and perpendicularity errors were noted for the models printed using the Objet30 Pro (overall value: 45 μm), Form3 (0.061 ± 0.019 mm), Objet30 Pro (0.138 ± 0.068°), and Projet 3510 HD Plus (0.095 ± 0.070°), respectively. In color difference maps, different deformation patterns were observed in the printed models. The feature size proved most accurate for the Objet30 Pro fabricated models (occlusal plane error: 0.02 ± 0.36%, occlusogingival direction error: -0.06 ± 0.09%). (4) Conclusions: The authors investigated a novel evaluation approach for the spatial trueness of a 3D-printed dental model made of photopolymer resin based on a structurized dental model. This method can objectively and comprehensively evaluate the spatial trueness of 3D-printed dental models and has a good repeatability and generalizability.

LEARNING STRATEGIES USED BY GRADE ONE ELEMENTARY TEACHERS IN TEACHING STUDENTS WITH SPECIAL NEEDS: REFLECTIONS AND DISPOSITIONS IN FOCUS

General Education teachers dont have exceedingly specialized capabilities in special education. We dont have much encounters in an inclusive classroom. Not continuously, but regularly, it appears that teachers feel unequal to the errand of educating students with special needs. It may be a characteristic of the calling that General Education teachers need certainty with such understudies, and feel incapable since we think we lack the skills. This can be something of a myth to be refuted. But teachers know more than they think they know. The challenge is getting these students to appreciate, engage, and keep them on task as much as possible without ruining the usual flow of the classes. Ten participants who are all Elementary teachers in selected public schools in Davao City are the focused in this study. The said participants are chosen through random sampling. All of them are licensed teachers and has experience teaching for almost two years to learners in grade one with disabilities. Many professionals in the literature express concerns about whether full inclusion is appropriate for all students with disabilities and emphasize the importance of maintaining a continuum of services. Supporters for a continuum of services for students with disabilities cite two main reasons. First, they believe there is an inadequate research base to advocate such a drastic change to the current educational system believes, the research evidence on the relative efficacy of one special education service delivery model over another is scarce, methodologically flawed, and inconclusive. Some studies support positive trends with inclusion programs; however, others have reported disappointing or unsatisfactory academic and social achievement through inclusion models.Learning in a remote setting may differ from mainstream, classroom-based environments. This includes expectations for students and course methodology. Curricula must often be adjusted. For example, homework can be simplified, allowing students to dictate rather than type, and audio materials can be provided for reading assignments during online class. KEY WORDS: learning strategies, grade one elementary teacher, teaching students with special needs, reflections

Optimizing the heat capacities of sphalerite phases as single system, or how nuclear physics can help physical chemistry

The correct mathematical description of heat capacities Cp in a wide range of temperatures is still unsolved problem. A fragmental description of some phases is like a vision of one part of a large mosaic picture. A single description of Cp or other property of a phase of any isostructural series does not allow one to see the integrity of the entire ensemble. We propose a special mathematical model to describe Cp in a wide temperature range for a whole large class of isostructural sphalerite phases. In the proposed model, it is believed that an ideal crystal does not have any foreign inclusions, defects, or dislocations. The group IV elements (Si, Ge, α-Sn and diamond-like Pb) were taken as the basis, with flerovium (114Fl) closing this group. There should be no other elements in this group according to the fine structure constant (α) (or the Sommerfeld constant). As a consequence, the limiting value of the heat capacities of phases with a sphalerite structure falls on the element 114 (114Fl) and has a value of Cp = 30.5 ± 0.3 J · mol-at−1 · K−1. This value was obtained as a maximal virtual point Cp of the last element (114Fl) of group IV and corresponds to Ln (Cp/R) = 1.30 ± 0.01 for the isotherms ln (Cp/R) vs Ln(N), where N is an atomic number of an element of group IV or the sum of the atomic numbers of AIIIBV or AIIBVI compounds per mole-atom. The common point of heat capacity attributable to flerovium is obtained from the linear equations Ср/R vs Ln(N) at low temperatures from 25 to 35K. For only pure elements of group IV (Si, Ge, α-Sn and diamond-like Pb), flerovium closes this group, and there are no other elements behind it, according to α. The maximum heat capacity of flerovium can be taken as 30.5 J·mol-at−1·K−1 with an accuracy of 1%. As the temperature decreases, this value slowly decreases (within 1%), and then, when it approaches 0 K, it drops sharply to 0 J·mol-at−1·K−1. To describe the set of the isostructural experimental data Cp(T) for diamond-like phases in solid state as a whole system, here we used a special multi-parameter family of functions. For each substance, the parameters are found by minimizing the discrepancy between the theoretical dependence Cp(T) and corresponding experimental data. The dependence of the heat capacities for elements of group IV (Si, Ge, α-Sn, diamond-like Pb, Fl) at fixed temperatures on Ln(N), where N is the atomic number or the demi sum of the atomic numbers of phases AIIBVI or AIIIBV. In this case, either a break point or an inflection point attributable to germanium is observed for parameter dependencies on Ln(N).

Activity of light industry in Russia in the conditions of mobilization events in the field of economy

The purpose of the article is to establish the principles of organizing the light industry and adjacent sectors of the Russian economy in the context of mobilization activities in the economy, in order to ensure the uninterrupted supply of products to meet the needs of the troops within the framework of the state order. Methods. The industry analysis is carried out under two main assumptions: a) the industry should be reformed, on the terms of public-private mobilization partnership (PPMP); b) relations between the light industry and related economic sectors should be built on the terms of an inter-industry syndicate (cluster, strategic alliance). The assessment of the stability of companies in the light industry sector is carried out on the basis of a matrix aggregate calculator (MAC). The return on equity of companies is analyzed using the DuPont formula. Results. Based on the simulation results, it is shown that the industry maintains economic stability provided that three basic conditions are met: a) it maintains an irreducible net profitability at the level of 5–7 %; b) forms an irreducible turnover of its assets at the level of 1.5 times a year; c) is credited by the state under the supplier’s factoring scheme, which gives a level of financial leverage of about 1.6. All this together should lead to a return on industry equity (ROE) of about 20 % per annum, which guarantees a rapid attraction of private capital to the industry, with the issuance of additional state guarantees for capital protection (a real option for business). Discussion. A more detailed modeling of sectoral economic stability requires the correct selection of modeling factors by groups: Threats, Opportunities, BSC, Risks, Chances, Decisions. A special modeling of the industry corporate culture is needed, using the approaches of related specialties. Conclusion. The GFMP principle guarantees the sustainable functioning of the Russian economy in the conditions of a period of maximum and medium levels of response and high readiness. The conventional wisdom that it is possible to ensure the sovereignty of the country by keeping the peacetime economy in the rear is untenable.

Identifying deoxyribonucleic acids of individuals based on their chromosomes by proposing a special deep learning model

One of the most significant physiological biometrics is the deoxyribonucleic acid (DNA). It can be found in every human cell as in hair, blood, and skin. In this paper, a special DNA deep learning (SDDL) is proposed as a novel machine learning (ML) model to identify persons depending on their DNAs. The proposed model is designed to collect DNA chromosomes of parents for an individual. It is flexible (can be enlarged or reduced) and it can identify one or both parents of a person, based on the provided chromosomes. The SDDL is so fast in training compared to other traditional deep learning models. Two real datasets from Iraq are utilized called: Real Iraqi Dataset for Kurd (RIDK) and Real Iraqi Dataset for Arab (RIDA). The results yield that the suggested SDDL model achieves 100% testing accuracy for each of the employed datasets.

Explain the Explainer: Interpreting Model-Agnostic Counterfactual Explanations of a Deep Reinforcement Learning Agent

Counterfactual examples (CFs) are one of the most popular methods for attaching post-hoc explanations to machine learning (ML) models. However, existing CF generation methods either exploit the internals of specific models or depend on each sample's neighborhood; thus, they are hard to generalize for complex models and inefficient for large datasets. This work aims to overcome these limitations and introduces RELAX, a model-agnostic algorithm to generate optimal counterfactual explanations. Specifically, we formulate the problem of crafting CFs as a sequential decision-making task. We then find the optimal CFs via deep reinforcement learning (DRL) with discretecontinuous hybrid action space. In addition, we develop a distillation algorithm to extract decision rules from the DRL agent's policy in the form of a decision tree to make the process of generating CFs itself interpretable. Extensive experiments conducted on six tabular datasets have shown that RELAX outperforms existing CF generation baselines, as it produces sparser counterfactuals, is more scalable to complex target models to explain, and generalizes to both classification and regression tasks. Finally, we show the ability of our method to provide actionable recommendations and distill interpretable policy explanations in two practical, real-world use cases.