Weak Ones Research Articles

Seismic shocks and financial systems: a topological perspective on Borsa Istanbul after the earthquake

This study examines the impact of the Kahramanmaraş earthquake on the BIST100 index through the application of complex network analysis. The method begins by examining the correlation distance between the logarithmic returns of companies in the BIST100 index, which serves as the foundation for creating filtered weighted networks. This technique prioritizes robust financial connections while downplaying weaker ones, enabling a thorough examination of the network topological structure and identifying noteworthy financial interactions. The study employs a combination of global and local topological metrics, as well as structural entropy, to gain insights on the interconnections among markets in terms of public interest and the roles played by various entities. It demonstrates how exogenous shocks impact the network structure and reaction. Findings indicate initial notable alterations in the network framework, which were subsequently alleviated by the implementation of financial rules and market processes. This demonstrates the ability of financial markets to recover and maintain stability following a disaster. Results offer useful perspectives on the interplay between market dynamics and the resilience of the financial system in the presence of natural disasters.

Enhancing Metaheuristic Algorithm Performance Through Structured Population and Evolutionary Game Theory

Diversity is crucial for metaheuristic algorithms. It prevents early convergence, balances exploration and exploitation, and helps to avoid local optima. Traditional metaheuristic algorithms tend to rely on a single strategy for generating new solutions, often resulting in a lack of diversity. In contrast, employing multiple strategies encourages a variety of search behaviors and a diverse pool of potential solutions, thereby improving the exploration of the search space. Evolutionary Game Theory (EGT) modifies agents’ strategies through competition, promoting successful strategies and eliminating weaker ones. Structured populations, as opposed to unstructured ones, preserve diverse strategies through localized competition, meaning that an individual’s strategy is influenced by only a subset or group of the population and not all elements. This paper presents a novel metaheuristic method based on EGT applied to structured populations. Initially, individuals are positioned near optimal regions using the Metropolis–Hastings algorithm. Subsequently, each individual is endowed with a unique search strategy. Considering a certain number of clusters, the complete population is segmented. Within these clusters, the method enhances search efficiency and solution quality by adapting all strategies through an intra-cluster competition. To assess the effectiveness of the proposed method, it has been compared against several well-known metaheuristic algorithms across a suite of 30 test functions. The results indicated that the new methodology outperformed the established techniques, delivering higher-quality solutions and faster convergence rates.

Emergence of Nigrospora Leaf Blight as Major Biotic Stress in South Eastern Coastal Region of India

Background: Nigrospora is one of the diversified pathogens with wide host range. Endophytic nature of Nigrospora oryzae has been reported previously. Daily variations in the climate led to the emergence of virulent infections from previously expressed weak ones. Nigrospora oryzae infestation comes as a major biotic stress under this present investigation. Methods: A study was conducted to examine the variability of Nigrospora oryzae by collecting isolates from various locations in the south coastal region of Odisha. The level of virulence was assessed and analyzed between 2021 and 2023 to anticipate the emergence and progression of disease. The pathogen was identified using molecular techniques through ITS-r DNA sequencing method. Result: Disease incidence was observed as a critical blast-like appearance, but greater in size, distinguishing it from blast. In the experiment, incomplete filling of grains was highest in MTU-7029 (39.73 plant-1) followed by Niranjana and Gobindobhog; disease incidence was recorded to be highest in Niranjana (60.03%) followed by MTU-7029 and Barsha; and grain yield plant-1 was observed to be highly affected in Gobindobhog (14.58 g plant-1 followed by MTU-7029 and Niranjana. Molecular confirmation was established by ITS-rDNA sequencing, resulted sequence NO 9 (528 bp)- OP592217 and NO 15 (519 bp)- OP592218, exactly matches gene bank accession numbers- MH748173 (India) and ON514037 (India) of Nigrospora database, with 99% accuracy.

Extension of an Eighth-Order Iterative Technique to Address Non-Linear Problems

The convergence order of an iterative method used to solve equations is usually determined by using Taylor series expansions, which in turn require high-order derivatives, which are not necessarily present in the method. Therefore, such convergence analysis cannot guarantee the theoretical convergence of the method to a solution if these derivatives do not exist. However, the method can converge. This indicates that the most sufficient convergence conditions required by the Taylor approach can be replaced by weaker ones. Other drawbacks exist, such as information on the isolation of simple solutions or the number of iterations that must be performed to achieve the desired error tolerance. This paper positively addresses all these issues by considering a technique that uses only the operators on the method and Ω-generalized continuity to control the derivative. Moreover, both local and semi-local convergence analyses are presented for Banach space-valued operators. The technique can be used to extend the applicability of other methods along the same lines. A large number of concrete examples are shown in which the convergence conditions are fulfilled.

The Influence of Corporate Governance on Tax Avoidance and The Role of Independent Auditors in Vietnamese Firms

This research addresses how corporate governance mechanisms influence tax avoidance in Vietnamese firms and the role of independent auditors in detecting or mitigating tax avoidance. The purpose of the research is to explore the relationship between corporate governance structures (e.g., independent boards, audit committees) and tax behavior, as well as to assess how auditors interact with these governance mechanisms to ensure tax compliance. A qualitative case study approach was used to analyze six companies from different sectors and governance models (2 state-owned enterprises; 2 family-owned private companies; and 2 publicly listed companies) through 18 interviews conducted between May 2024 and October 2024 with 3 key individuals from each company, including: Independent auditor; Company CEO; Manager or administrator, along with documentary analysis of financial statements and tax filings. The research found that firms with stronger governance (e.g., independent boards and active audit committees) are less likely to engage in tax avoidance, while weak governance structures often lead to more aggressive tax strategies. Independent auditors were more effective in well-governed firms, but faced ethical challenges in weaker ones. These findings are significant because they highlight the importance of strengthening governance frameworks and ensuring auditor independence to reduce tax avoidance and improve corporate transparency in Vietnam.

Second Life for Recycled Concrete and Other Construction and Demolition Waste in Mortars for Masonry: Full Scope of Material Properties, Performance, and Environmental Aspects

This review presents the scope of current efforts to utilize recycled construction and demolition waste in mortars for masonry. More than 100 articles are divided into groups pertaining to the type of mortar, different binder systems, the type of construction and demolition waste (CDW), and its utilization specifics. Cement-based mortars dominate this research domain, whereas recycled concrete is the main material employed to replace virgin aggregates, followed by recycled masonry and recycled mixed waste aggregates. Such application in cement-based mortars could increase water demand by 20–34% and reduce strength by 11–50%, with recycled concrete aggregates being the most favorable. Natural aggregate substitution is disadvantageous in strong mortars, whereas weaker ones, such as lime-based mortars, could benefit from this incorporation. The extent of this topic also suggests possibilities for different recycled material use cases in mortars for masonry, although the available literature is largely insufficient to infer meaningful trends. Nonetheless, the most relevant knowledge synthesized in this review offers promising and environment-conscious utilization pathways for recycled concrete and other construction and demolition waste, which brings opportunities for further research on their use in mortars for masonry and industrial-scale applications.

PRO-SMOTEBoost: An adaptive SMOTEBoost probabilistic algorithm for rebalancing and improving imbalanced data classification

In the field of data mining and machine learning, dealing with imbalanced datasets is one of the most complex problems. The class imbalance issue significantly affects the classification of minority classes when using common classification algorithms. These algorithms often prioritize improving the performance of the majority class at the expense of the minority class, leading to misclassifying negative instances as positive ones. To address this problem, the Synthetic Minority Over-sampling Technique (SMOTE) has gained popularity to rebalance imbalanced data for classification. However, in this paper, we propose two algorithms to enhance the performance of imbalanced classification further. The first algorithm is PRO-SMOTE, an improvement over SMOTE. PRO-SMOTE relies on conditional probabilities to effectively rebalance imbalanced classes and improve the predictive performance metrics satisfactorily and reliably. By considering conditional probabilities, PRO-SMOTE can reduce the majority classes and optimally increase the minority class. Second, the PRO-SMOTEBoost algorithm, in turn, is based on the PRO-SMOTE to overcome classification anomalies and problems encountered by machine learning algorithms during classification, especially the weak ones. PRO-SMOTEBoost aims to maximize predictive precision to the greatest extent possible by combining the strengths of PRO-SMOTE with boosting techniques. Evaluating these algorithms using traditional machine learning algorithms such as Random Forests, C4.5, Naive Bayes, and Support Vector Machines has demonstrated excellent classification results. The performance metrics, encompassing F1-score, G-means, Precision, Accuracy, Recall, AUC-ROC, and Precision-Recall-curves, achieved by the proposed algorithm demonstrate a range that extends from over 90% to a flawless score of 100%. Compared to using these traditional algorithms individually, the utilization of PRO-SMOTEBoost has shown a significant improvement of 10% to 40% in performance metrics. Overall, the proposed algorithms, PRO-SMOTE and PRO-SMOTEBoost, offer effective solutions to address the challenges posed by imbalanced datasets. They provide improved predictive metrics and demonstrate their superiority when compared to traditional even modern machine learning algorithms.

Abu Eisa At-Tirmidhi and The Science Of Jarh and Ta’dil

This article points out jarh and ta’dil, which are the most important sciences in hadith studies, as well Abu Eisa at-Tirmidhi and his method of examining hadith narrators, his views on “jarh and ta’dil”, and also his teachers in the science of narration. The science of “jarh and ta’dil” discusses the verification of the narrators of hadiths and reports in order to distinguish the acceptable hadith from the rejected ones, and defines reliable hadiths from the weak and matruk ones.

Cytological analysis of flower development, insights into suitable growth area and genomic background: implications for Glehnia littoralis conservation and sustainable utilization

BackgroundGlehnia littoralis F. Schmidt ex Miq., an endangered plant species with significant medicinal, edible, and ecological value, is now a central concern for conservation and sustainable utilization. Investigating the physiological and ecological mechanisms leading to its endangerment and elucidating its genetic background constitutes the foundation for conducting in-depth research on G. littoralis.ResultsOur observations have revealed a significant degree of floral sterility in wild populations of G. littoralis. The inflorescences of G. littoralis are classified into three types: completely fertile, completely sterile, and partially fertile compound umbels. Moreover, the flowers of G. littoralis can be categorized into fertile and sterile types. Sterile flowers exhibited abnormalities in the stigma, ovary, and ovules. This study is the first to discover that the presence or absence of a giant cell at the funiculus during the initiation of ovule primordium determines whether the flower can develop normally, providing cytological evidence for female sterility in G. littoralis. Conversely, both fertile and sterile flowers produced normally developed pollen. Field observations have suggested that robust plants bear more fertile umbels, while weaker ones have fewer or even no fertile umbels, indicating a close relationship between flower fertility and plant nutritional status. Our model correctly predicted that the eastern coastal regions of China, as well as prospective areas in Neimenggu and Sichuan, are suitable environments for its cultivation. Additionally, Using flow cytometry and genome survey, we estimated the genome size of G. littoralis to be 3.06 Gb and the heterozygosity to be 4.58%.ConclusionThe observations and findings presented in this study were expected to provide valuable insights for further conserving its genetic resources and sustainable utilization of G. littoralis.

“Gear-like” process between asymmetric dipole eddies from satellite altimetry

Mesoscale dipoles compose two counter-rotating mesoscale eddies. They are observed in oceans worldwide and significantly impact the biogeochemical cycle, marine ecosystems, and ocean water transport. This study investigates the evolution of asymmetric dipole eddies based on the satellite altimeter data for the period January 1993–December 2020. The coupling and decoupling of eddy dipoles impact various eddy properties. When the movement of eddies has a dipolar structure, a gentle convergence in parameters (rotational speed, amplitude, eddy kinetic energy, propagation speed, and advective nonlinearity) between the asymmetric dipole eddies is observed–this study refers to it as the “gear-like” process. Kinematic characteristics further indicate that the stronger dipole eddies generally drive the weaker ones to revolve during the “gear-like” process. The revolution direction remains consistent with the rotation direction of stronger dipole eddies. Additionally, eddy size remains relatively stable during the “gear-like” process. The composites of sea level anomaly (SLA) demonstrate that dipole eddies are compressed into a kidney-like shape along the line through their centers. This shape is confirmed by the composites of sea surface temperature anomalies, which closely correspond to the structure of SLA. These findings augment the knowledge regarding the evolution of asymmetric dipole eddies and provide a novel perspective for studying dipoles and eddy-eddy interactions.

Seller-Orchestrated Inventory Financing Under Bank Capital Regulation

To help small firms secure bank financing, large sellers often orchestrate joint finance programs, linking their small dealers with major banks that lend to all participating dealers based on the information the seller provides. We examine supply chain decisions (pricing and inventory) and lending terms under such seller-orchestrated financing programs. In loan pricing, we highlight a form of financial friction that is of particular importance under such schemes—bank capital regulation. Banks are globally mandated to maintain regulatory capital to mitigate unforeseen loan losses, using either the standardized approach (where regulatory capital is a fixed percentage of the loan amount) or the internal rating-based (IRB) approach (where it depends on the loan’s value-at-risk). We consider a game-theoretic model consisting of a large seller and multiple capital-constrained newsvendor-type dealers, who obtain financing from banks that are subject to capital regulation. The seller decides the wholesale price and whether to orchestrate a joint finance program for its dealers by collaborating with a bank, and the dealers choose their inventory level and the financing channel. We find that a seller should only orchestrate the joint financing program when the bank adopts the IRB approach and the dealers are of low risk. Such a program is more profitable to the seller when the demand correlation among dealers is low, and there is a large number of dealers. Although always benefiting the seller, these programs may hurt dealers with intermediate risk. Facing dealers with varying financial situations, the terms under the joint finance program should be designed as if the financially strong dealers subsidize the weak ones. Finally, allowing the seller to share part of the loan loss could further enhance the performance of joint financing, but only when the seller’s opportunity cost of capital is low. Our findings provide guidance to large sellers on how to orchestrate joint finance schemes, and to small dealers on making their corresponding operational decisions.

Revisiting the global distribution of the exponent of the power-function complementary relationship of terrestrial evaporation: Insights from an isenthalpic index

The complementary relationship (CR) of evaporation is one of widely-used approaches in estimating terrestrial evapration (E). However, the determination of the parameter values in CR-based models remains a difficult task. This is especially true for the new power-function complementary relationship (PCR) model developed recently, in which the key parameter that accounts for the effect of moisture advection over the drying land, b, must be well calibrated with consideration of an approporiate physical basis. Here, by proposing a new isenthalpic index that can be used for determining b directly without resorting to any a priori information of global E, this study found that the resulting b values (median = 1.64, standard deviation = 0.73) are generally smaller and display a narrower range than those reported recently by Kim et al. (2024). When applied with monthly input variables of 124 FLUXNET stations, the gridded b values improve upon E estimates that employ either i) a standard value of b (=2), or; ii) a calibrated value (same for all stations). The global distribution of the gridded b values –in contrast to those obtained by Kim et al. (2024) –aligns with its physical interpretation: low values (1 < b < 2) where the effect of moisture advection is weak (i.e., permanently/periodically wet, humid regions or regions far away from any significant external moisture source) and high ones (b > 2) when strong (i.e., warm arid/semi arid regions near a sea or high mountains of abundant moisture). The gridded b values improve the calibration-free PCR that employs a constant b (=2) and a Priestley-Taylor coefficient linked to the wet-environment air temperature.

Beyond the Classroom: Investigating the Relationship between Psychomotor Development and Academic Achievement in 4-12-Year-Olds.

The relevance of psychomotor skills in children's growth is being increasingly recognized. The transversal role of psychomotor skills in learning performance is described through a link between cognitive and motor functioning, promoting socio-affective-expressive competencies, but there is a scarcity of evidence from the field. A two-fold goal was defined: to investigate the relationship between psychomotor functions and academic performance and to examine the factors affecting children's academic performance. The Portuguese versions of the Neuropsychomotor Functions Assessment Battery for Children (NPmot.pt), Preschool Diagnostic Tasks (PRE), and School Learning Skills Battery (SLSB) were applied to 350 children (85.72 ± 24.23 months) with and without disabilities attending mainstream schools. Pearson correlations and regression analyses were used. NPmot.pt domains showed moderate to strong correlations with PRE domains (0.30 < r < 0.82) and weak ones with SLSB domains (r < 0.30). Psychomotor development is a stronger predictor (p < 0.001) of pre-academic performance outcomes: (βTonus = 0.67, βGross Motor Skills = 1.04, βSpatial Orientation = -1.44, βRhythm = -1.59 and βAuditory Attention = 3.68) than of academic performance above 7 years old (p > 0.05). Results strengthen the importance of psychomotor skills development from an early age, also at school, with implications for an early psychomotor assessment and intervention for children with and without disabilities. Tailor-fit interventions, including strategies to improve psychomotor skills, should be promoted during the school process of all children for a successful learning process.

Summer Convective Precipitation Changes Over the Great Lakes Region Under a Warming Scenario

AbstractTo understand future summer precipitation changes over the Great Lakes Region (GLR), we performed an ensemble of regional climate simulations through the Pseudo‐Global Warming (PGW) approach. We found that different types of convective precipitation respond differently to the PGW signal. Isolated deep convection (IDC), usually concentrated in the southern domain, shows an increase in precipitation to the north of the GLR. Mesoscale convective systems (MCSs), usually concentrated upwind of the GLR, shift to the downwind region with increased precipitation. Thermodynamic variables such as convective available potential energy (CAPE) and convective inhibition energy (CIN) are found to increase across almost the entire studied domain, creating a potential environment more favorable for stronger convection systems and less favorable for weaker ones. Meanwhile, changes in the lifting condensation level (LCL) and level of free convection (LFC) show a strong correlation with variations in convective precipitation, highlighting the significance of these thermodynamic factors in controlling precipitation over the domain. Our results indicate that the decrease in LCL and LCF in areas with increased convective precipitation is mainly due to increased atmospheric moisture. In response to the prescribed warming perturbation, MCSs occur more frequently downwind, while localized IDCs exhibit more intense rain rates, longer durations, and larger rainfall area.

Navigating the Complex Landscape of Global Economics: The Role of Political Decisions and Institutional Frameworks

This paper investigates the significant role of political decisions and institutional frameworks in shaping global economic dynamics. It critically assesses the interconnections between political stability, regulatory policies, and economic growth, emphasizing how these elements influence investment decisions and market efficiency. By exploring the ramifications of trade agreements and protectionism, the study illustrates their profound impact on international trade and domestic economies. It analyzes how stable political environments contribute to robust economic policies, while political unrest can lead to economic downturns. The paper further examines the role of institutional frameworks in enforcing or undermining economic stability, where strong institutions foster economic growth and weak ones may cause volatility and inefficiency. Drawing on empirical evidence and theoretical models, this comprehensive analysis underscores the delicate balance required in policy-making to enhance economic stability and growth in an interconnected global economy. This study aims to provide policymakers and scholars with deeper insights into how political and institutional mechanisms interact to shape economic landscapes, thus aiding in the formulation of strategies that promote sustainable economic development.

Optimization of resource allocation in 5G networks: A network slicing approach with hybrid NOMA for enhanced uRLLC and eMBB coexistence

SummaryTraditional Orthogonal Multiple Access (OMA) and spectrum sharing methods struggle to provide the diverse quality of service (QoS) demands for enhanced mobile broadband (eMBB), ultra‐reliable low latency communications (uRLLC), and massive machine type communications (mMTC) leading to suboptimal performance and service quality degradation. Single‐carrier‐non‐orthogonal multiple access (SC‐NOMA) appears to be a more optimized solution. It can serve multiple users simultaneously on the same time‐frequency resources. This approach offers both enhanced spectrum efficiency and meets the QoS requirements for the coexistence of eMBB, uRLLC, and mMTC. However, SC‐NOMA has some drawbacks. Decoding a user's signal involves a complex successive interference cancellation (SIC) process that gets harder with more users causing delays and errors. Additionally, strong user signals can interfere with weaker ones, limiting the number of users per channel. In order to overcome the drawbacks associated with OMA and SC‐NOMA, this paper introduces a new method called user‐paired NOMA (hybrid NOMA). Hybrid NOMA adopts a strategic approach, employing two user pairing techniques: near‐far/far‐near (NF‐FN) and near‐near/far‐far (NN‐FF). NF‐FN pairing prioritizes users with similar signal strengths but different distances from the base station. This minimizes interference for the weaker user during SIC. NN‐FF pairing, on the other hand, groups users with similar signal strengths and proximity. This approach further simplifies SIC and minimizes potential interference altogether. The simulation results demonstrate trade‐offs between eMBB and uRLLC performance. OMA suffers with dedicated resource allocation, while SC‐NOMA balances performance but experiences interference. NN‐FF prioritizes eMBB and offers best latency, while NF‐FN prioritizes uRLLC with high spectral efficiency but suffers from higher latency. Finally, by providing a thorough grasp of how hybrid NOMA resource allocation works to improve the performance of various use cases, this research makes a significant contribution to the field of 5G spectrum optimization.

Estimation of human height using ulnar length in the Muslim community of Golbazar Municipality, Siraha, Nepal

Calculating an individual's height based on bone measurements is critical in identifying unknown bodies, body parts, or skeletal remains. Anthropologists, anatomists, and forensic experts have used a variety of anthropometric approaches to forecast stature from skeletal remains. Although few studies have been conducted to understand ulnar length and height, it has not been conducted in a few ethnic communities of Nepal. This study aimed to analyze the relationship between human height and ulnar length in Muslim communities of Golbazar Municipality, Siraha, Nepal. Two hundred people (male 100 and female 100) aged 20 to 40 years were purposively selected from Golbazaar, Siraha, Nepal. Ulnar length was measured as the distance between the tip of the Olecranon process and the styloid process with the elbow fully flexed. Height was measured from the top of the head to the floor while the participants stood upright in the anatomical position with their heads in the Frankfort plane. Statistical techniques, such as linear regression analysis and correlation coefficients, were used to determine a correlation between ulna length and height. The population's mean ulna length was 26.53 cm (SD = 2.06), and the mean height was 159.59 cm (SD = 9.72). The correlation between ulna length and height was stronger in males (0.46) than in females (0.36). The study established regression equations for height estimation based on ulna length for different age groups, with the strongest correlations found in the 31–35 years age group and weaker ones in the 20–25 years and 26–30 years age groups. The study suggests that ulna length could be a reliable predictor of height, especially when direct measurement is challenging or impossible. However, additional study is required to explore potential differences based on age, ethnicity, and gender and to validate the findings in more extensive and more diverse populations.

Diffusive‐Convection Staircase Merger Events Mediated by Subsurface Eddies in the Canada Basin

AbstractDiffusive‐convection (DC) staircases, characterized by interleaving well‐mixed layers and high‐gradient interfaces of thermohaline properties, are commonly observed in the Canada Basin, Arctic. Previous studies suggested a quasi‐steady state of the thermohaline structures of these staircases. In this work, we employ mooring and Ice‐Tethered Profiler (ITP) data to show emergent merger events of staircases mediated by subsurface eddies with warm cores located at 300–500 m depths in 2005, 2007 and 2009. Two types of merger events, the B‐merger (resulting from strengthening of stronger interfaces at the expense of weaker ones) and the H‐merger (produced by vertical drift and collision of staircase interfaces), are documented during the passage of subsurface eddies. The merging staircases are located at 320–450 m depths, with layers of up to 70 m thick and high‐gradient interfaces thinner than 5 m. Mooring measurements reveal the emergence of staircase mergers associated with a background Richardson number dropping from a typical magnitude of – to , indicating the persistence of DC staircases in the presence of strong shears. In parallel, ITP data suggest the formation of B‐mergers (H‐mergers) to be favored within the eddy core (flank). The vertical heat fluxes associated with the B‐merger (H‐merger) staircases are estimated to be ∼3 W/m2 (∼1.5 W/m2), which substantially exceed the typical staircase‐driven heat fluxes of W/m2. These observations imply the significance of subsurface eddies in shaping the halocline structures and thus the vertical heat fluxes in the Arctic Ocean, particularly in a warming climate.

Research and application of Rayleigh wave imaging based on the Born–Jordan time‐frequency distribution

AbstractCurrently, the horizontal resolution of Rayleigh wave exploration is low. In this study, we propose the Born–Jordan time‐frequency distribution to analyse Rayleigh waves. The seismic signal was filtered with a wavelet transform for denoising, and the Rayleigh wave was separated in the time domain. Using the Born–Jordan time‐frequency distribution, the time waveform of each frequency comprising the Rayleigh wave from every seismic channel was obtained, and the time difference of the Rayleigh wave with the same frequency was calculated, based on which the dispersion curve between the two channels was obtained. Combined with the multichannel Rayleigh wave dispersion curve, phase velocity and frequency imaging under the seismic arrangement were obtained. Applying this method to detect abnormal geological bodies in engineering investigations showed that hard geologic bodies, such as comcrete rocks, have high velocity and frequency, whereas weak ones have low velocity and frequency. This strategy facilitated the detection of fractured zones, underground goafs and obstacles during pipe‐jacking construction near the surface.

Mining in Russia. Cryptocurrencies as investment

In modern conditions, when parity international financial and monetary relations are turning from traditional instruments of economic interaction into instruments of neocolonial political pressure and blackmail of economically strong countries against weaker ones, the need for a new global financial order and monetary standard, that would be independent of the will of any central bank, but with a value as reliable as that of modern fiat currencies, is increasingly realized. Today, the global economy feels the urgent need for fundamentally new opportunities for market interaction and monetary exchange. Cryptocurrencies have become such tools — electronic decentralized money issued by specific individuals in specialized open computer networks through the transformation of electricity into fixed computer codes. The process of such transformation is called mining, which uses ASIC miners as computer equipment to obtain cryptocurrency. The concept of cryptocurrencies and the specifics of investing in them are considered. The characteristic is presented of the current state and dynamics of the development of the crypto market. The possibilities and methods of forecasting the development of the cryptocurrency market are shown both using classical methods of economic forecasting and a fundamentally new methodology based on the current assessment of the psychological state of participants in the crypto market and their readiness to commit certain economic actions. An assessment is given of the possibilities of investing in mining as a technology for mining cryptocurrencies. Requirements to special mining equipment — ASIC miners — are described. An assessment is given of the prospects for the development of the crypto economy.