Scale Problems Research Articles

Рotential threats of Kakhovka catastrophe to bird populations of different ecological groups

Any military action involves a number of risks for the existence of animals. In the event of the destruction of hydraulic structures, not only aquatic ecosystems usually suffer, but also the ecosystems of islands and banks. Undermining the dam of the Kakhovska hydroelectric power station had a wide range of negative impact on the life of animals. A number of its consequences manifested themselves immediately after the destruction of the dam connected with drainage of bottom sediments in some regions and flooding of the coast in others. But one should also expect the transformation of faunal complexes associated with long-term changes in the water regime, and in some regions with drying and moisture deficiency. The purpose of this work is to assess the species composition of birds whose populations will suffer population losses as a result of the Kakhovka catastrophe in the short and long term on a regional and global scale. The published lists of bird species for the territories affected by the Kakhovka catastrophe an analysis was made: for the Kakhovka reservoir, the 'Kinburn Kosa Regional Landscape Park, the Black Sea Biosphere Reserve; and for the registration of some rare species in the region. Questionable data was checked against an electronic database https://uabirds.org/. We assessed the species composition of birds whose populations may decrease in number as a result of the Kakhovka catastrophe in the near and long term. Problems of regional and global scale are considered. The status of residence and ecology of birds were taken into account. Birds affected as a result of the Kakhovka catastrophe were divided into 3 groups: І — 82 species — birds nesting in the region of the disaster and lost part of their populations this year. These are birds that build nests in aquatic vegetation, not high in trees, shrubs, on the ground on the islands; ІІ — 69 species — birds that will lose their habitats in the coming nesting seasons due to changes in the Dnieper channel, water regime and the development of devastation processes. These are limnophilous that inhabited the thickets of the coastline and the islands of the Dnieper; ІІІ — 121 species — birds that will lose habitats for feeding and shelter during migration and wintering. The number of not only populations that nest in Ukraine will suffer, but also those migrating from Europe and Asia. And a group of birds (33 species) that are unlikely to be affected. The catastrophe of the undermining of the dam of the Kakhovska hydroelectric power station harms the biodiversity and integrity of habitats on a large area of the banks of the lower reaches of the Dnieper. The effects of the disaster on natural communities will have a lasting impact. We predict a change in the biotopes of the existence of birds in the direction of degradation of wetlands and drainage of terrestrial ones, which will lead to a change in the species composition of birds in the region. Relevant in the future will be the study of the succession processes of ecosystems on the banks of the lower reaches of the Dnieper and changes in the communities of birds that inhabit them. The consequences of the catastrophe we have an impact on migratory bird species on a global scale.

Electrolysis-induced hardness variation for quantitatively assessing the scaling tendency of circulating water

Abstract The scaling problem exists universally in circulating water systems. Efficient and accurate assessment of scaling tendency is a prerequisite for solving the problem. In this work, it proposes electrolysis to induce hardness variation, which connects with the water stability evaluation index for quantitatively assessing the scaling tendency of water. The quantitative assessment is further realized on the basis of qualitative assessment. Then, the dynamic simulation experiments of circulating water are carried out to provide support for the electrolysis-induced hardness variation which clarifies the criteria that can meet the actual water requirements. This work can achieve an efficient and accurate quantitative assessment of the scaling tendency, which is of great significance for solving the scaling problem of circulating water systems.

Agricultural Soils Using TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) Method

Agricultural soil science is a branch of soil science; it is food and fiber related to production regarding edaphic conditions deals with research. In this context, it is a department of agriculture component, thus soil agriculture also described as 19 yenfor the development of pedology in the century formerly, agricultural soil science (or edaphology) of soil science it was a single branch. 2006 yes as per the year, the soil of their agricultural potentialfor viewing purposes only bias of early soil science,as of 2006, pedagogy of the soil science professionand in popular settings defines. And nutrients. Soils have their chemical and heavily in physical properties differs. Leakage, weather and microbial activity such processes are various create different soil types. complete agricultural soil science follows the pattern. Terrain integrating ecosystems partly soil is examined, but manageable nature also recognized as resource. Agricultural soil science, soils chemistry, physics, biology and mineralogical composition as related to agriculture examines. Agricultural soil scientists use soil improve and diet and increases productivity of fiber crops develop methods. Soil for the importance of sustainability continued emphasis is given. Soil erosion, compaction, fertility such as depletion and contamination soil erosion continue to be intense are concerns. They are irrigation and drainage, tillage, soil classification, plant nutrition, soil fertility and other areas conducting research. Plant (and thus animals) produce maximization is the right goal however, sometimes it isit can come at a high cost, though is immediately apparent(produced by monoculture massive crop disease) or long periodic (eg chemical fertilizers and effects of pesticides on humans in health). A farmer soil scientist, sustainable methods and using solutions productivity can be increased come up with a plan, they are farmers to do that science, physics, chemistry, biology, meteorology and geography many sciences including have to look at the fields. ideal solution (TOPSIS) is prioritized through unity is a technique that provides, this is a multi-criteria decision analytical method. TOPSIS stands for (PIS). Short geometric distance alternative to select is the positive ideal solution; basically distance to have ideal solution of thought (nis) negative too long from is geometry. Of TOPSIS the assumption is even greater is, is coming or going the criteria are the same are increasing. Many parameters in scaling problems or criteria often improper dimensions normalizations due to having are generally required. Alternative taken as clay, sandy, silty, peaty, chalky and loamy. Evaluation preference taken as arsenic (As),cadmium (Cd),copper (Cu), mercury (Hg). From the result it is seen that peaty is got the first rank where as is the silty is having the lowest rank.

Evaluation of Soil Management Practices for Agriculture using TOPSIS Method

Agricultural soil science is a subfield of soil science that focuses on the production of food and fiber in relation to soil conditions. It encompasses research in the realm of edaphic conditions, making it an integral part of agricultural departments. Historically, it was considered a distinct branch of soil science, known as edaphology. However, by 2006, it had merged with the broader field of soil science, particularly pedology, in both professional and popular contexts. Agricultural soil science delves into the chemical, physical, and biological aspects of soils as they pertain to agriculture. Soils exhibit variations in their chemical and physical properties due to factors such as climate, weather patterns, and microbial activities, leading to different soil types. Agricultural soil science follows a comprehensive approach that not only investigates the characteristics of soil but also considers the broader ecosystem and its sustainable management. This field scrutinizes soil chemistry, physics, biology, and mineral composition concerning agricultural applications, with a focus on enhancing crop productivity and dietary quality. Agricultural soil scientists address various concerns related to soil sustainability, such as soil erosion, compaction, fertility depletion, and contamination. They conduct research in areas like irrigation, drainage, tillage practices, soil classification, plant nutrition, and soil fertility. While maximizing crop and animal production is a fundamental goal, it is essential to be mindful of potential negative consequences, such as the impact of monoculture on crop diseases and the long-term effects of chemical fertilizers and pesticides on human health. To address these challenges, farmer-scientists employ an interdisciplinary approach, drawing from fields such as physics, chemistry, biology, meteorology, and geography to develop sustainable solutions. Techniques like TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) are used for multi-criteria decision analysis. In TOPSIS, the positive ideal solution (PIS) is determined by selecting the alternative with the shortest geometric distance from the ideal solution. This method assumes that an increase in criteria values is preferable. Normalization of parameters is often required in scaling problems, ensuring that criteria with varying dimensions are properly considered. Alternatives are evaluated based on characteristics such as clay content, sand content, silt content, peat content, chalk content, and loaminess. These criteria are then applied to evaluate preferences for elements like arsenic (As), cadmium (Cd), copper (Cu), and mercury (Hg). The results of the evaluation indicate that peaty soil ranks the highest, while silty soil receives the lowest ranking.

BLOCKCHAIN TECHNOLOGY IN ACCOUNTING: PROSPECTS AND CONSEQUENCES OF IMPLEMENTATION

In the article was substantiated the essence of blockchain technology, examines its role, significance and directions of use in various spheres of public life. It has been established that the reason for the widespread use of blockchain to optimize business processes in various fields of activity lies in the advantages of this innovative technology, in particular, decentralization, transparency and security of data storage through the use of cryptographic information security mechanisms. At the same time, it was found that blockchain technology, despite its advantages, has disadvantages associated with the high network energy consumption, insufficient data storage capacity, scalability problems, lack of standardization and legal regulation of its implementation in various sectors of the economy. It is substantiated that one of the most acceptable business processes for using blockchain is accounting, since this technology is based on the double entry method, known in accounting practice, in which each transaction is recorded twice in the same amount for each of the parties to the transaction. It was found that the use of blockchain technology leads to the emergence of the so-called triple-entry accounting method. It is substantiated that the introduction of blockchain technology into accounting practice will create the prerequisites for optimizing a number of routine accounting processes, save resources on accounting, ensure accuracy, transparency and security of data, reduce the risk of distortion of accounting information and will build trust in the accounting institution. Taking into account the functionality of blockchain technology, possible directions for its use in the field of accounting are proposed, in particular for maintaining operational accounting in real time, accounting for accounts payable and receivable, accounting for the movement of the assets within an enterprise, carrying out a multilateral audit of transactions by all participants with access to blockchain networks.

The Influence of Leaders on the Quality of Citizen Deliberation: An Exploratory Assessment of Online Deliberation in New Zealand

Leaders can be essential in setting the tone of deliberation in the public sphere, but can their discursive style influence the wider public sphere? Mass communication usually mediates leader-citizen interactions, and the proliferation of social media has presented new, large-scale opportunities to support deliberation. Further, leaders using these platforms have widespread reach. Using deliberative discourse analysis, this exploratory research studies whether leaders influence the quality of citizen deliberation and whether this is dependent on the online arena. Two leaders with contrasting communication styles were chosen: New Zealand prime minister Jacinda Ardern and opposition leader Judith Collins. Two online arenas were included: a national news media Facebook page and the leaders’ respective Facebook pages. The results found that deliberative quality was variable within the news mass media arena; however, citizens displayed higher deliberative quality when the leader did so in the leaders-led online media arena. This study suggests that a leader’s use of deliberative dialogue to foster more deliberative discussion among citizens when they engage as both participants and facilitators in arenas with greater access to directly support deliberation. It presents theoretical arguments for leaders to participate in legitimation processes as part of the response ot the problem of scale and introduces a communication model for leaders to support deliberation in the public sphere. The model suggests that the leader's ability to affect the deliberative quality of citizens' discussions is mediated by their level of influence within that space. 

Measurement bias in caregiver-report of early childhood behavior problems across demographic factors in an ECHO-wide diverse sample.

Research and clinical practice rely heavily on caregiver-report measures, such as the Child Behavior Checklist 1.5-5 (CBCL/1.5-5), to gather information about early childhood behavior problems and to screen for child psychopathology. While studies have shown that demographic variables influence caregiver ratings of behavior problems, the extent to which the CBCL/1.5-5 functions equivalently at the item level across diverse samples is unknown. Item-level data of CBCL/1.5-5 from a large sample of young children (N=9087) were drawn from 26 cohorts in the Environmental influences on Child Health Outcomes program. Factor analyses and the alignment method were applied to examine measurement invariance (MI) and differential item functioning (DIF) across child (age, sex, bilingual status, and neurodevelopmental disorders), and caregiver (sex, education level, household income level, depression, and language version administered) characteristics. Child race was examined in sensitivity analyses. Items with the most impactful DIF across child and caregiver groupings were identified for Internalizing, Externalizing, and Total Problems. The robust item sets, excluding the high DIF items, showed good reliability and high correlation with the original Internalizing and Total Problems scales, with lower reliability for Externalizing. Language version of CBCL administration, education level and sex of the caregiver respondent showed the most significant impact on MI, followed by child age. Sensitivity analyses revealed that child race has a unique impact on DIF over and above socioeconomic status. The CBCL/1.5-5, a caregiver-report measure of early childhood behavior problems, showed bias across demographic groups. Robust item sets with less DIF can measure Internalizing and Total Problems equally as well as the full item sets, with slightly lower reliability for Externalizing, and can be crosswalked to the metric of the full item set, enabling calculation of normed T scores based on more robust item sets.

High Electric Fields on Water Microdroplets Catalyze Spontaneous and Fast Reactions in Halogen-Bond Complexes.

The use of external electric fields as green and efficient catalysts in synthetic chemistry has recently received significant attention for their ability to deliver remarkable control of reaction selectivity and acceleration of reaction rates. Technically, methods of generating high electric fields in the range of 1-10 V/nm are limited, as in-vacuo techniques have obvious scalability issues. The spontaneous high fields at various interfaces promise to solve this problem. In this study, we take advantage of the spontaneous high electric field at the air-water interface of sprayed water microdroplets in the reactions of several halogen bond systems: Nu:--X-X, where Nu: is pyridine or quinuclidine and X is bromine or iodine. The field facilitates ultrafast electron transfer from Nu:, yielding a Nu-X covalent bond and causing the X-X bond to cleave. This reaction occurs in microseconds in microdroplets but takes days to weeks in bulk solution. Density functional theory calculations predict that the reaction becomes barrier-free in the presence of oriented external electric fields, supporting the notion that the electric fields in the water droplets are responsible for the catalysis. We anticipate that microdroplet chemistry will be an avenue rich in opportunities in the reactions facilitated by high electric fields and provides an alternative way to tackle the scalability problem.

Reactive transport model predicting calcite precipitation: Case of a dynamically operated standing column well

The sanding column well is a promising ground heat exchanger. Nevertheless, hard water can lead to scaling problems. The aim of this study is to calibrate a thermo-hydro-chemical model on experimental data to quantify the factors influencing the scaling. Groundwater samples were collected during 70 days on a standing column well operated jointly with a groundwater treatment unit. A coupled thermo-hydro-chemical model integrating nine aqueous species, calcite mineral reactions, CO2 degassing, and temperature dependent reactions was developed and calibrated on the experimental measurements. Calibration results indicated a precipitated calcite mass of a few nanograms per day. It was observed numerically that when bleed is not active, treating only 2% of the flowrate avoids calcite precipitation. If sealing the wellhead and limiting CO2 degassing are possible, the simulation results indicate a reduction in precipitated calcite masses of 36%.

Stage‐parallel preconditioners for implicit Runge–Kutta methods of arbitrarily high order, linear problems

AbstractFully implicit Runge–Kutta methods offer the possibility to use high order accurate time discretization to match space discretization accuracy, an issue of significant importance for many large scale problems of current interest, where we may have fine space resolution with many millions of spatial degrees of freedom and long time intervals. In this work, we consider strongly A‐stable implicit Runge–Kutta methods of arbitrary order of accuracy, based on Radau quadratures. For the arising large algebraic systems we introduce efficient preconditioners, that (1) use only real arithmetic, (2) demonstrate robustness with respect to problem and discretization parameters, and (3) allow for fully stage‐parallel solution. The preconditioners are based on the observation that the lower‐triangular part of the coefficient matrices in the Butcher tableau has larger in magnitude values, compared to the corresponding strictly upper‐triangular part. We analyze the spectrum of the corresponding preconditioned systems and illustrate their performance with numerical experiments. Even though the observation has been made some time ago, its impact on constructing stage‐parallel preconditioners has not yet been done and its systematic study constitutes the novelty of this article.

Improving collaborative recommender system using hybrid clustering and optimized singular value decomposition

In the era of internet and big data, recommender systems are necessary to filter out useless information. Collaborative filtering (CF) is one of the most successful technique used in recommender systems, this technique suffers from a large number of users and items, or the scalability issue. In this paper, a new hybrid method based on K-means clustering (KM) and Singular Value Decomposition (SVD) which uses evolutionary algorithms is proposed to deal with scalability issue. On the one hand, KM optimized by Particle Swarm Optimization (PSO) and denoising by Density-Based Spatial Clustering of Applications with Noise (DBSCAN) is used to cluster users and reduce the number of comparisons between the target user and other users. On the other hand, SVD optimized by Genetic Algorithm (GA) is used to reduce the number of items. The proposed method is assessed on three standard datasets and the results are compared with basic CF and other extended versions that use clustering algorithms, evolutionary algorithms and dimensionality reduction techniques. The results show that our proposed method performed better than other methods in terms of precision, recall and MAE, and the scalability problem was improved by reducing the time complexity. Also, the combined clustering method was optimized in terms of Davies-Bouldin and the Dunn's index compared to the basic clustering methods.

Социально-психологические предикторы адаптации студентов колледжей – представителей коренных и малочисленных народов Севера, Сибири и Дальнего Востока к образовательной организации (на примере организаций среднего профессионального образования)

<p>The paper presents the results of studying the resources of academic, socio-cultural and psychological adaptation of students representing indigenous peoples of the North, Siberia and the Far East to college. The sample consisted of 720 students, of which 304 people (58% girls) identify themselves as representatives of the indigenous peoples of the North and the Far East. The collection of empirical data was carried out using questionnaires (Scale of academic motivation; Questionnaire of online and offline coping; The Child and Youth Resilience Measure; Types of ethnic identity questionnaire; Scales of Psychological problems of adolescents; Methodology psychological safety of the educational environment). The adaptability of students was studied using the Scale of Socio-Cultural Adaptation; Self-assessment scales of satisfaction with various aspects of life; author's questionnaire. Regression analysis revealed invariant predictors (emotional attitude towards college, satisfaction with the educational environment of the college, adaptation to the amount of homework in the educational process) only for academic adaptation. Indicators of introjected learning motivation are also significant for predicting the success of academic adaptation of students representing the Indigenous Minorities and the Far East. The predictor of socio-cultural adaptation for the students representing the North, Siberia and the Far East nations is the level of ethno-egoism. Coping strategies and the size of the settlement predict the psychological adaptation this group students. The results obtained can contribute to the optimization of the process of socio-psychological adaptation of students-representatives the Indigenous Minorities to study in colleges.</p>

Noise-robust ground state energy estimates from deep quantum circuits

In the lead up to fault tolerance, the utility of quantum computing will be determined by how adequately the effects of noise can be circumvented in quantum algorithms. Hybrid quantum-classical algorithms such as the variational quantum eigensolver (VQE) have been designed for the short-term regime. However, as problems scale, VQE results are generally scrambled by noise on present-day hardware. While error mitigation techniques alleviate these issues to some extent, there is a pressing need to develop algorithmic approaches with higher robustness to noise. Here, we explore the robustness properties of the recently introduced quantum computed moments (QCM) approach to ground state energy problems, and show through an analytic example how the underlying energy estimate explicitly filters out incoherent noise. Motivated by this observation, we implement QCM for a model of quantum magnetism on IBM Quantum hardware to examine the noise-filtering effect with increasing circuit depth. We find that QCM maintains a remarkably high degree of error robustness where VQE completely fails. On instances of the quantum magnetism model up to 20 qubits for ultra-deep trial state circuits of up to 500 CNOTs, QCM is still able to extract reasonable energy estimates. The observation is bolstered by an extensive set of experimental results. To match these results, VQE would need hardware improvement by some 2 orders of magnitude on error rates.

Diagnostic Utility of Parent Ratings on the Behavior Assessment System for Children-Third Edition in Children who are Deaf and Hard of Hearing and Diagnosed with Autism Spectrum Disorder.

Between 1 to 2 of every 1,000 children are born deaf or hard of hearing (DHH) and, of those, 30-50% have additional disabilities, including Autism Spectrum Disorder (ASD). Most measures assessing ASD characteristics rely on some degree of behavioral response to sound (e.g., responding to name, listening response), and may not be appropriate for use with children who are DHH. Further, ASD specific measures do not provide information on a child's functional abilities across developmental domains. We conducted a cross-sectional analysis comparing mean T-scores on a standardized multidimensional measure, the Behavior Assessment System for Children, Third Edition, Parent Rating Scale (BASC-3 PRS), across three groups matched for age and sex: children who are DHH and diagnosed with ASD (DHH + ASD; n = 16); children who are DHH without ASD (DHH-ASD; n = 16); and children who are typically hearing with ASD (H + ASD; n = 16). Analyses revealed statistically significant differences across scales of Attention Problems, Atypicality, Withdrawal, Behavioral Symptoms Index, Social Skills, Leadership, Functional Communication, Activities of Daily Living, Adaptive Skills, Autism Probability Indices, and Developmental Social Disorders. Pairwise comparisons showed DHH + ASD and H + ASD mean T-scores were statistically similar and distinct from DHH-ASD mean T-scores on all these scales except for Withdrawal, Leadership, Functional Communication, and Activities of Daily Living, where pairwise comparisons varied. The findings add to the literature on ASD and DHH childrenand call for further exploration of the BASC-3 as a tool for both evaluation of ASD and the development of individualized treatment plans in this unique population.

Tightly coupled IMU-Laser-RTK odometry algorithm for underground multi-layer and large-scale environment

PurposeThe purpose of this study is to designed a robot odometry based on three dimensional (3D) laser point cloud data, inertial measurement unit (IMU) data and real-time kinematic (RTK) data in underground spatial features and gravity fluctuations environment. This method improves the mapping accuracy in two types of underground space: multi-layer space and large-scale scenarios.Design/methodology/approachAn IMU–Laser–RTK fusion mapping algorithm based on Iterative Kalman Filter was proposed, and the observation equation and Jacobian matrix were derived. Aiming at the problem of inaccurate gravity estimation, the optimization of gravity is transformed into the optimization of SO(3), which avoids the problem of gravity over-parameterization.FindingsCompared with the optimization method, the computational cost is reduced. Without relying on the wheel speed odometer, the robot synchronization localization and 3D environment modeling for multi-layer space are realized. The performance of the proposed algorithm is tested and compared in two types of underground space, and the robustness and accuracy in multi-layer space and large-scale scenarios are verified. The results show that the root mean square error of the proposed algorithm is 0.061 m, which achieves higher accuracy than other algorithms.Originality/valueBased on the problem of large loop and low feature scale, this algorithm can better complete the map loop and self-positioning, and its root mean square error is more than double compared with other methods. The method proposed in this paper can better complete the autonomous positioning of the robot in the underground space with hierarchical feature degradation, and at the same time, an accurate 3D map can be constructed for subsequent research.

Розробка інтелектуальних інформаційно-аналітичних вебпорталів на основі семантичних вікітехнологій: проблеми та перспективи

We analyse the main functions of modern web portals that differ them from other types of sites. In this work we consider intelligent information-analytical web portals as a specific subset of intelligent information-analytical systems, and their features and development trends that have an influence on their semanticization are analysed. As a part of such analysis we considered knowledge models and technologies of their processing that can be used for this aim. We determine standards and tools of the Semantic Web that can be used for the semanticization of web portals and describe the ways of such use on practical examples. Some basic requirements for technologies and instrumental tools used for development of semantic portals are considered. We analyse and classify typical problems related to the processing, structuring, and analysis of large volumes of data that arise in the process of creating and deploying such portals, and the criteria for evaluating the development opportunities of existing web portals are considered. On example of web portal based on semantic Wiki technologies we investigate practical problems of implementation and scaling of portal knowledge processing tools and services and propose some ways of their prevention and solution.

Forecast-Assisted Service Function Chain Dynamic Deployment for SDN/NFV-Enabled Cloud Management Systems

Software-defined network (SDN) and network function virtualization (NFV) are acknowledged as the most promising technologies to effectively allocate resource for network service. A service function chain (SFC), which can deploy virtualized network functions (VNFs) and chain them with associated flows allocation, can be used to represent each network service owing to the introduction of the SDN/NFV technology. Co-hosted applications on multiple Internet of Things terminals have dynamic and time-varying service requirements, in order to allocate network resources optimally and meet the end-to-end delay requirements of services, sufficient strategies are required to satisfy the continuously changing service demands. In this article, a deep learning model that combines the multitask regression layer above the graph neural networks is first presented to predict the future resource requirements of each VNF instance. The SFC deployment problem is then solved using the integer nonlinear programming (INLP) approach, and a novel prediction-assisted Viterbi algorithm is presented to overcome the scalability problem of the INLP approach. According to the simulation findings, the proposed deep model provides a minimum of a 6.2% improvement in prediction accuracy over baseline prediction models, and the proposed SFC deployment strategy has been demonstrated to deliver better performance in terms of acceptance ratio and revenue, compared to the current passive deployment algorithms.

Design of Energy-Efficient RFET-Based Exact and Approximate 4:2 Compressors and Multipliers

The ever-increasing demand for low-power and area-efficient circuits for use in battery-powered devices and the CMOS scaling problems have attracted the attention of VLSI designers to beyond-CMOS technologies like Reconfigurable Field-Effect Transistors (RFETs). Improving the efficiency of multipliers is critical as the core component of many applications such as image processing and Machine Learning (ML). This paper proposes a compact and energy-efficient RFET-based architecture for the 4:2 compressor and Dadda multiplier, leveraging transistor-level reconfigurability and multi-input support of the RFET. Moreover, we propose a novel approximate 4:2 compressor based on efficient RFET logic cells to cater to the needs of error-resilient applications. Extensive circuit-level simulations with 14nm germanium nanowire (GeNW) RFET technology show that the proposed RFET-based exact multiplier improves the power consumption and power-delay product (PDP) by 65% and 45%, respectively, compared to the conventional CMOS-based counterpart in 14nm FinFET technology. Besides, we show that utilizing the proposed approximate compressor, the area and PDP of the multiplier reduce by 46% and 42%. The effectiveness of the approximate multiplier is evaluated in the image multiplication, and the average PSNR and SSIM values are 31.39 and 0.87, respectively.

Real-time air quality monitoring in Bull Trench Kiln-based Brick industry by calibrating sensor readings and utilizing the Serverless Computing

Bull Trench Kiln-based Brick industries are major contributors of Particulate Matter Pollutants (PM2.5 and PM10), which leads to deterioration of air quality, and hence needs to be monitored. As the static air monitoring stations require huge infrastructure and are sparsely located, it is necessary to find alternate ways using recent technologies like IoT, Machine Learning, and Serverless Computing. IoT devices being low cost and portable could be deployed near brick industries, however, they are sensitive to fluctuating ambient air temperature or humidity and hence have to be calibrated and there is a need for powerful back-end infrastructures for storage and analyses of data. The existing approach of transmitting IoT-based data to the cloud is time-consuming, energy inefficient, prone to scaling problems, has poor resource utilization, and is expensive. In this study, the IoT data are calibrated using ML Classifier algorithms and an Automatic Function Triggerer - Function-as-a-Service (AFT-FaaS) method is proposed, which is based on event-driven, Serverless method using AWS Lambda along with the Three-Tier Serverless architecture (Edge Tier, Fog Tire, and Cloud Tier) to deal with the challenges of delay, under-resource allocation, and to reduce the expenditures. The data transmitted to the cloud using the proposed AFT-FaaS method are indexed in ElasticSearch and Kibana is used for the analysis and visualization of data. While performing calibration, Random Forest Classifier was chosen as it had accuracy of above 98%. Finally, an in-depth pricing analysis is performed to better understand the difference between different configurations deployed for Serverless and non-Serverless Computing methods, which shows there is a significant reduction (7 times) of expenses incurred while using Serverless Computing than the non-Serverless Computing methods.

Development of the Workplace Interpersonal Problems Scale for Care Workers (WIPS) and examination of its reliability and validity

IntroductionThe turnover rate of care workers has remained high by global standards, with previous studies showing an association between workplace interpersonal relations and care worker turnover and turnover intentions. This study details the development of the Workplace Interpersonal Problems Scale for Care Workers (WIPS) and examines its reliability and validity according to the COSMIN guidelines. MethodsA total of 476 care workers employed by elder care facilities participated in the study. This study examined the reliability and validity of the WIPS after its development. Reliability was evaluated using Cronbach's α, test reliability with the standard error of measurement, and test–retest reliability with the intraclass correlation coefficient. Content validity, construct validity, and structural validity were examined to evaluate validity. ResultsBoth total and subscale scores of the WIPS had a Cronbach's α coefficient >0.75 and high test–retest reliability (intraclass correlation coefficient, 0.75). Content validity analysis showed the item-content validity index of ≥0.90 for all WIPS items, confirming 100% of the hypotheses for testing construct validity. Confirmatory factor analysis showed an acceptable fit for the hypothesized six-factor construct (CFI = 0.92, TLI = 0.91, RMSEA = 0.07, SRMR = 0.05). ConclusionsThe WIPS was found to be a valid and reliable instrument. With the growth of the elderly population worldwide, we believe that the WIPS will be a useful quantitative measure to assess workplace interpersonal problems affecting care workers in various aspects.