Control Purposes Research Articles

Association of a novel restrictive housing diversion program with rates of mental health and self-injury in prison.

Restrictive housing for control purposes (RHCP, a form of solitary confinement) is used in prisons in response to disruptive behaviors, including violence. North Carolina prisons introduced the rehabilitative diversion unit (RDU) in 2016 as an alternative to and step-down from RHCP. We compared rates of psychiatric treatment and self-injury among men enrolled in the RDU and men eligible for the RDU but placed in RHCP. We used Poisson regression to calculate rate ratios (RRs) and 95% confidence intervals (CIs) for these outcomes, using inverse probability of treatment weights to adjust for confounders. The cohort included 1,225 men in the RDU group and 3,059 in the RHCP group. In RHCP, the adjusted psychiatric treatment rate was 2.6 times (95% CI: 1.8, 3.8) and the adjusted self-injury incident rate was 1.2 times (95% CI: 0.6, 2.8) that in RDU. Nearly all self-injury incidents in RDU occurred during "non-participating time" (i.e., in a restrictive housing setting and not actively participating in RDU). After excluding non-participating time from the RDU group's person time, the adjusted RR for self-injury incidents was 23.5 (95% CI: 8.6, 64.2). These results further knowledge of potential benefits of diversion from restrictive housing. Continued development, implementation, and evaluation is needed.

Perencanaan Sumber Daya Material pada Pekerjaan Arsitektur dengan Material Requerement Planning (MRP) pada Proyek Gedung Bertingkat

Construction in Indonesia is one of the government's top priorities in an effort to improve the welfare of the society. Construction planning must be carefully planned in order to achieve efficiency in project implementation, in terms of time and cost. A construction project requires planning and controlling material inventory. The purpose of material planning and control is so that construction projects can run smoothly, effectively and efficiently. Material planning and control can be analyzed using the material requirements planning (MRP) method. In this study, using economic order quantity (EOQ) and period order quantity (POQ) techniques. Cost budget design data, s-curves, work unit price analysis, and work progress reports are the data components to be processed. The results showed the cost of procurement of materials needed with the EOQ technique, namely: purchase cost of IDR 8,466,623,718.49, ordering cost of IDR 1,598,050.00, and storage cost of IDR 8,942,979.62. The total cost required is IDR 8,477,164,748.11. Furthermore, for the POQ technique, namely: purchase cost of IDR 8,466,623,718.49, ordering cost of IDR 1,598,050.00 and storage cost of IDR 9,674,554.05. The total cost required is IDR 8,477,896,322.53 Comparison of the costs required from the EOQ and POQ techniques can be concluded that Material Requirement Planning (MRP) using the Economic Order Quality (EOQ) technique is less costly than the Period Order Quantity (POQ) technique. Material procurement using Economic Order Quality (EOQ) is more effective. This is because the EOQ technique requires less costly / economic procurement compared to the POQ technique. In addition, with the application of the EOQ technique, there is a clearer material ordering schedule and the amount of material ordered is in accordance with the needs so that the material storage is not too much.

Literature Review on Analytical Method Development and Validation of Alfuzosin Hydrochloride

Alfuzosin hydrochloride, an alpha-adrenergic blocker, is extensively used in the treatment of benign prostatic hyperplasia. Robust and reliable analytical methods are indispensable for quantifying alfuzosin hydrochloride in various matrices, ensuring the quality, efficacy, and safety of the drug. Recent advancements in analytical method development and validation for alfuzosin hydrochloride encompass a spectrum of techniques, including UV spectrometry, RP-HPLC, HPTLC, UPLC, and voltammetry. Optimization of chromatographic conditions, such as mobile phase composition, column selection, and detection wavelength, is crucial for achieving optimal separation and quantification of the drug. Method validation parameters, including accuracy, precision, linearity, and robustness, are assessed in accordance with ICH guidelines. These analytical methods find application in the determination of alfuzosin hydrochloride in bulk drugs, pharmaceutical formulations, and biological matrices. Stability-indicating methods are developed to evaluate drug stability under various stress conditions. The current state of analytical method development and validation for alfuzosin hydrochloride facilitates the selection of suitable analytical techniques for routine quality control and research purposes

ИСПОЛЬЗОВАНИЕ ПОПРОЦЕССНОГО БЮДЖЕТИРОВАНИЯ В ЦЕЛЯХ КОНТРОЛЯ ЗАТРАТ И ПОВЫШЕНИЯ ЭФФЕКТИВНОСТИ БИЗНЕС-ПРОЦЕССОВ КОМПАНИИ

The article discusses the issues of organizing sound budget planning of company expenses. Particular attention is paid to the problem of forming budgets for indirect costs. As a tool for determining their value, the ABB method (Activity based budgeting) is proposed, based on a functional cost analysis of costs and an assessment of the planned amount of resource consumption, depending on the level of each type of activity necessary to implement the production program and achieve a given level of income. Recommendations are given for the implementation of process-based budgeting techniques. The advantages of using the ABB method for the purposes of effective control of resources and financial results of activities are highlighted.

A parallelized environmental-sensing and multi-tasks model for intelligent marine structure control in ocean waves coupling deep reinforcement learning and computational fluid dynamics

In addressing the active control challenges of marine structures in ocean waves, a coupling model is proposed combining computational fluid dynamics (CFD) and deep reinforcement learning (DRL). Following the Markov decision process (MDP), the proposed DRL-CFD model treats the wave fields and simplified marine structures as the environment and the agent, respectively. The CFD component utilizes the PIMPLE algorithm to solve the Navier–Stokes equations, in which the free surface is reconstructed using the volume of fluid method. The DRL component utilizes the Soft Actor-Critic algorithm to realize the MDP between marine structures and the wave fields. Three simulation cases with different control purposes are conducted to show the effectiveness of the DRL–CFD coupling model, including the active controls for wave energy absorption, attenuation, and structure heave compensation. Comparative analyses with passive (resistive) control are performed, demonstrating the advantages of the DRL–CFD coupling model. The results confirm that the proposed coupling model enables the marine structure to observe the wave environment and generate effective active control strategies for different purposes. This suggests that the model has the potential to address various active control challenges of marine structures in ocean waves, while being capable of environmental sensing and handling multiple tasks simultaneously.

Вопросы применения медианного значения рыночного интервала в регулировании трансфертных цен

Material changes in the tax regulation of transfer pricing have occurred in the early 2024, such changes include the introduction of the median value of the market price interval (financial parameters). This is a new instrument in the regulation of transfer pricing, its introduction was not justified in the bill discussion period, but its effect is significant for tax obligations of Russian companies. The new instrument in the transfer pricing system creates additional risks for taxpayers, as well as for the state in terms of economic relations with regard to timely and full calculation and payment of taxes, as well as for parties to international economic activities and internal relations. This predetermines the research subject: the conditions of and procedure for applying the median value for the purposes of tax control of intracompany transaction prices. The research methodology is based on an analysis of transfer pricing recommendations of the ОECD in АННОТАЦИИ Налоги № 2-2024 47 АННОТАЦИИ terms of applying statistical tools to determine the price of the transaction under analysis, provisions of foreign regulations that determine the pricing procedure in transactions between related entities. Special attention is paid to an analysis of foreign court judgments on determination of the market price of intracompany transactions. An analysis of the ОECD recommendations has given an opportunity to single out obligatory conditions for the application of the median value. The author has identified a tendency towards harmonization of approaches to using the median value in foreign countries and the general trend to clarifying regulations in 2022-2024. The judicial practice shows that irrespective of the activity type or type of transactions performed by group subdivisions, courts award uniform judgments in terms of applying the median value and take into account the ОECD recommendations when passing a judgment. The paper concludes that Russian taxpayers face an additional tax risk rooted in the differences in the approach towards the application of the median value in the Tax Code of the Russian Federation and the OECD Transfer Pricing Guidelines for transnational corporations and tax administrations used to regulate taxation of transactions between related entities in explanations of tax authorities and the judicial practice. The scientific novelty of the study lies in the determination of tendencies towards applying the median value for transfer pricing purposes, evaluation of tax consequences of the novelty for Russian taxpayers.

Soft Sensors for Industrial Processes Using Multi-Step-Ahead Hankel Dynamic Mode Decomposition with Control

In this paper, a novel data-driven approach for the development of soft sensors (SSs) for multi-step-ahead prediction of industrial process variables is proposed. This method is based on the recent developments in Koopman operator theory and dynamic mode decomposition (DMD). It is derived from Hankel DMD with control (HDMDc) to deal with highly nonlinear dynamics using augmented linear models, exploiting input and output regressors. The proposed multi-step-ahead HDMDc (MSA-HDMDc) is designed to perform multi-step prediction and capture complex dynamics with a linear approximation for a highly nonlinear system. This enables the construction of SSs capable of estimating the output of a process over a long period of time and/or using the developed SSs for model predictive control purposes. Hyperparameter tuning and model order reduction are specifically designed to perform multi-step-ahead predictions. Two real-world case studies consisting of a sulfur recovery unit and a debutanizer column, which are widely used as benchmarks in the SS field, are used to validate the proposed methodology. Data covering multiple system operating points are used for identification. The proposed MSA-HDMDc outperforms currently adopted methods in the SSs domain, such as autoregressive models with exogenous inputs and finite impulse response models, and proves to be robust to the variability of systems operating points.

Prediction of saccharides concentration in intact and ground Codonopsis root using near-infrared reflectance spectroscopy

Codonopsis root, a traditional Chinese medicine, produces saccharides that serve as key indicators for assessing its quality. In this study, we employ near-infrared reflectance spectroscopy to predict total polysaccharide concentration as well as the major individual saccharides of intact (surface and cross-section) and ground samples of Codonopsis roots. We developed regression relationships between the spectra and the concentration of fructose, glucose and polysaccharides for the ground spectra set (Rp2 > 0.8, RPD>2.0) based on partial least squares regression (PLSR) model. However, for the intact cross-section spectra set, only the dominant component of the fructose, could be quantitatively detected (Rp2 > 0.8, RPD>2.0). All models based on the intact surface spectra set were of unacceptable performance. This discrepancy could be attributed to the low concentration levels of different saccharides and the distinct chemical profiles reflected by the spectra sets from samples with varying homogeneity. The findings provide an approach for efficient and sustainable analysis for the purposes of quality control and standardization of Codonopsis roots.

A STUDY TO ASSESS THE EFFECTIVENESS OF STRUCTURED TEACHING PROGRAM ON KNOWLEDGE REGARDING HEALTH HAZARDS OF ELECTRONIC DEVICES AMONG ANTENATAL MOTHERS AT SELECTED MATERNITY HOSPITALS, BANGALORE

Background of the Study:Electronic devices are components for controlling the flow of electrical currents for the purpose of information processing and system control. Prominent examples include transistors and diodes. Electronic devices are usually small and can be grouped together into packages called integrated circuits. Electronic devices are now an integral part of modern life. In many countries over half the population use electronic devices and the market is growing very rapidly. The electromagnetic field radiations impart an adverse effect on physical, mental and the emotional state of a pregnant mother and deeply affect the brain cells of mother and her unborn child. It affects level of melatonin, a sleep- controlling hormone which causes sleep disturbances in mothers, that pass to unborn babies through the placenta resulting in growth retardation. Objectives: 1. To assess the level of knowledge regarding health hazards of electronic devices during pregnancy among antenatal mothers in selected maternity Hospitals, Bangalore. 2. To evaluate the effectiveness of structured teaching program on knowledge regarding health hazards of electronic devices during pregnancy among antenatal mothers attending in selected maternity Hospitals, Bangalore. 3. To determine the association between knowledge score with selected demographic variables among antenatal mothers attending in selected maternity Hospitals, Bangalore. Methods:The research approach adopted was an evaluative research approach and research design adopted for this study was Pre experimental one group pretest and post-test research design. Simple random sampling technique was used to collect 60 antenatal mother sample. Data was collected over a period of one month. Structured knowledge questionnaire used as a tool to assess knowledge regarding the health hazards of electronic devices. Data obtained was analysed in terms of objectives which was calculated and tabulated. Results:In pretest, out of 60 antenatal mothers, 33.3% had inadequate knowledge and 66.7% had moderately adequate knowledge and none of the antenatal mothers had adequate knowledge. In post-test, out of 60 antenatal mothers, 73.3% had adequate knowledge, 26.7% had moderately adequate knowledge and none of the antenatal mother had inadequate knowledge. Over all knowledge the paired mean difference was 11.08 and SD of difference was 0.263 and enhancement mean percentage was 37 It was found significant (t=24.464, df=59) (i.e, p<0.05). The paired t-test was also worked out and it was found to be invariably significant (i.e p<0.05). The present study found the significant association between knowledge regarding health hazards of electronic devices at selected demographic variables of antenatal mothers. Interpretation and Conclusion: The study concluded that the post-test knowledge of antenatal mothers was improved after undergone Structured teaching programme regarding health hazards of electronic devices. The overall findings of the study clearly showed that the structured teaching programme was effective in improving the knowledge level of antenatal mothers regarding health hazards of electronic devices.

Surveillance of SARS-CoV-2 Infection in Rodent Populations in Tokyo, Japan.

The maintenance of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) among wildlife populations poses a potential risk for the emergence of novel variants. Therefore, monitoring SARS-CoV-2 infection among animals is crucial. As urban rodents live in close proximity to human habitats, there is concern that they may be a potential source of zoonoses. To examine the prevalence of SARS-CoV-2 in rodent populations, we analyzed 128 serum samples and 129 oral swabs collected from 128 brown rats (Rattus norvegicus) and 2 black rats (Rattus rattus) captured for pest control purposes in Tokyo, Japan, between May and December 2023. A virus-neutralizing test using the Omicron variant revealed no evidence of SARS-CoV-2 infection in these populations. Real-time RT-PCR from oral swabs did not detect any SARS-CoV-2 RNA-positive rats. These results indicate the low probability of SARS-CoV-2 circulation among rat populations in Tokyo.

Augmented Reality for Mathematics Learning: A Study for Enhancing Mathematical Comprehension in High School Students

Augmented Reality for Mathematics Learning: A Study for Enhancing Mathematical Comprehension in High School Students

Contour error control for the biaxial system based on active disturbance rejection generalised prediction

ABSTRACT For the purpose of contour error control in two-axis systems, a cross-coupled controller utilising auto-disturbance rejection generalised predictive control and nonlinear proportional differentiation is proposed. The contour error components along each axis are estimated by utilising the information from the nearest reference point and its adjacent reference points. The combination of ADRC and GPC is utilised for uniaxial tracking control. The LESO is utilised for estimating and compensating for the total disturbance of the system while simplifying it into two cascaded integrators. Because GPC is designed only for the series form of the integrator, the dependence on mathematical models is reduced. A nonlinear cross-coupled controller is designed utilising the fal function, with compensation control quantities for each axis directly obtained from their respective contour error components. Finally, the results obtained from the simulation demonstrate that the control strategy can availably enhance the performance of single-axis tracking and contour processing.

Biosynthesized gold nanoparticles for enhanced determination of nicotine in heated tobacco products

An electrochemical sensor for nicotine determination in heated tobacco products (HTPs) was developed using biosynthesized AuNPs from Ceiba pentandra leaf extract. The biosynthesis process was optimized by investigating the effects of HauCl4 concentration, extract volume, reaction temperature, and time on the formation of gold nanoparticles (AuNPs). The biosynthesized AuNPs were characterized using various techniques, confirming their crystallinity, spherical morphology, and uniform size distribution with an average diameter of 19.8 ± 3.6 nm. The AuNP modified glassy carbon electrode (AuNP/GCE) exhibited enhanced electrochemical performance compared to the bare GCE, facilitating efficient oxidation of nicotine. The AuNP/GCE sensor demonstrated a wide linear range of 0.02–280 µmol L−1 and a low limit of detection of 0.01 µmol L−1 for nicotine determination. The sensor showed excellent selectivity towards nicotine in the presence of common interfering substances, with negligible responses (<5 % change) to uric acid, paracetamol, glucose, melamine, L-cysteine, and dopamine. The AuNP/GCE sensor's applicability was validated by analyzing nicotine levels in real HTP samples, with excellent recovery rates (97.6–102.4 %) and good agreement with the HPLC (R2 = 0.998). The biosynthesized AuNP-based electrochemical sensor offers a rapid, sensitive, eco-friendly, and cost-effective approach for nicotine determination in HTPs, providing a valuable tool for quality control and regulatory purposes.

Hospital environment microbiome

The aim of the review is to give a brief description of the biodiversity and structure of the hospital environment microbiome based on molecular genetic research methods. Until a certain time, studies of the hospital environment microbiota for the purposes of epidemiological surveillance and control of healthcare-associated infections (HAIs) were based on routine microbiological identification of clinically relevant bacterial taxa. Discovery of DNA, the development of sequencing technologies, PCR and cloning techniques enabled the investigation of microbial communities using cultivation-independent, DNA and RNA-based approaches. At the current level of knowledge, the hospital environment can be considered as a superorganism with its own microbiome. Multiomic technologies, including meta-transcriptomic, meta-proteomic and metabolomic approaches, provide detailed information about microbial activity in the environment. Now it has been established that there is a stable core of the hospital microbiome where the vast majority of microorganisms are necessary for the functioning of the hospital ecosystem and are not classified as human pathogens. The hospital microbiome has a homogeneous structure composed by a massive dominance of a few taxa and microbial network with low connectivity forming a clustered topology. A keystone species is a taxon whose importance for maintaining community structure is relatively higher than others and its identification is of paramount importance. Due to the lack of knowledge of the hospital environment microbiome by molecular genetic technologies, there is no single shared point of view on the microbial diversity in different healthcare facilities. But there is no doubt that molecular genetic technologies will shed light on the evolution of hospital strains and determine which indicators are the most informative for monitoring and prognosis of HAIs.

Evaluation of a cost-effective photoelectric sensor for daylight harvesting in smart public lighting systems

Global warming concerns, along with international agreements and regulations, reflect a broader effort to change the public's high energy demand in recent years. Smart public lighting systems are gaining popularity due to their energy-saving capabilities, reduction in carbon dioxide emissions, and improved public comfort. However, transitioning to smart public lighting requires careful planning and multiple stages. This is not only to accommodate public behavior, revise scenarios, and test citizen acceptance but also due to the necessary infrastructure investments. Smart public lighting incorporates new technologies, often with a breakeven point that takes several years to reach. To promote the widespread adoption of smart public lighting, it is essential to produce relatively expensive components in large quantities and explore cost-effective solutions. This research focuses on investigating a cost-effective photoelectric sensor for smart public purposes. The primary originality of this study lies in identifying a cost-effective photoelectric sensor that can replace technically equivalent but more expensive sensor solutions for indoor and outdoor lighting control purposes.

Optimization-based disjoint and overlapping epsilon decompositions of large-scale dynamical systems via graph theory

To address the complexity challenge of a large-scale system, the decomposition into smaller subsystems is very crucial and demanding for distributed estimation and control purposes. This paper proposes novel optimization-based approaches to decompose a large-scale system into subsystems that are either weakly coupled or weakly coupled with overlapping components. To achieve this goal, first, the epsilon decomposition of large-scale systems is examined. Then, optimization frameworks are presented for disjoint and overlapping decompositions utilizing bipartite graphs. Next, the proposed decomposition algorithms are represented for particular cases of large-scale systems using directed graphs. In contrast to the existing user-based techniques, the proposed optimization-based methods can reach the solution rapidly and systematically. At last, the capability and efficiency of the proposed algorithms are investigated by conducting simulations on three case studies, which include a practical distillation column, a modified benchmark model, and the IEEE 118-bus power system.

Experimental study on migration characteristics of LNAPL in the aquitard under pumping conditions.

Research on the migration behaviors of contaminants in the aquitard has been deficient for an extended period. Clay is commonly employed as an impermeable layer or barrier to stop the migration of contaminants. However, under certain conditions, the clay layer may exhibit permeability to water, thereby allowing contaminants to infiltrate and potentially contaminate adjacent aquifers. Consequently, it holds immense importance to scrutinize and investigate the migration characteristics of light non-aqueous phase liquid (LNAPL) within the aquitard for the purposes of groundwater pollution control and remediation. To evaluate the environmental risk posed by organic contaminants in the aquitard, an experimental model was formulated and devised to monitor the LNAPL concentration in the aquitard under pumping conditions. The correlation between pumping rate and LNAPL concentration was investigated. A self-developed plexiglass sandbox model was used to simulate the migration characteristics of LNAPL in the aquitard under pumping conditions. Four experimental scenarios were designed, varying pumping rates, aquitard thicknesses, and groundwater level changes. The LNAPL concentration curve was derived by systematically tracking and analyzing LNAPL levels at various locations within the aquitard. The results indicated that higher pumping rates corresponded to increased migration of LNAPL, resulting in greater LNAPL ingress into the pumping well during extraction. A thicker aquitard demonstrated a more pronounced inhibitory effect on LNAPL, leading to an extended penetration time of LNAPL within the aquitard. The drawdown within the aquitard exerted a discernible influence on LNAPL migration, with the LNAPL concentration continuing to decrease in tandem with declining water levels during pumping. These research findings can establish a scientific foundation for the control and remediation of contaminants within aquitards.

Design, Characterization and Biopharmaceutical Applications of Novel Green Boron-Carbon Quantum Dots for Quantification of Apalutamide; Greenness Evaluations.

The diagnosis of prostate cancer has been evolving in the current decade, with expected mortality rates of 499,000 death by the year 2030. Apalutamide (APL) has been approved in 2018 as the first drug for the controlling of prostate cancer. APL significant success warrantied its high global sales, which are expected to surpass 58% of segment market sales (together with another drug; enzalutamide). Therefore, new, fast and environmentally friendly analytical methods are required for its determination for the quality control and biological monitoring purposes. The proposed research designs and evaluates the first fluorimetric approach based on novel porous green boron-doped carbon quantum dots (B@CDs) for the determination of APL in biopharmaceutical matrices. The synthetic approach has high quantum yield (31.15%). B@CDs were characterized using several tools, including transmission electron microscopy (TEM), dynamic light scattering (DLS), FTIR and Energy dispersive X-ray spectroscopy (EDX) which proved their improved surface properties with an average nano-diameter of 3.0nm. The interaction between B@CDs and APL led to enhancement their fluorescence at 441nm (excitation at 372nm). The approach was validated for the determination of APL within concentration range of 15.0-700.0 ng mL- 1 with quantification limit LOQ 4.37 ng mL- 1 and detection limit LOD 1.44 ng mL- 1. The approach was successfully applied for the determination of APL in human plasma and pharmaceutical monitoring of its marketed tablet form. Then, the approach was assessed for its environmental impact using different metrics and proved its ecological greenness.

Resilient distributed model predictive control of state‐delayed linear parameter varying systems with quantitative communication against denial of service attacks

AbstractThis work presents a resilient distributed model predictive control (MPC) method for linear parameter varying (LPV) systems with state delays and attacks in communication networks. Coordinations are required for distributed MPC (DMPC) to achieve the global performance of centralized MPC (CMPC). However, control performance can be severely degraded by unreliable communication networks, for example, with denial of service (DoS) attacks. A resilient control framework is derived to address the unreliable communications in DMPC. A global system is divided into subsystems for the distributed control purpose. To deal with the model uncertainties and state delays, a “min‐max” DMPC algorithm is presented with a buffer to ensure resilience against DoS attacks. A quantization scheme is introduced to quantize the control information exchanged between subsystems. An iterative interaction scheme is proposed to exchange feedback control laws among subsystems. The stability of the closed‐loop system under the proposed algorithm is ensured by using a Lyapunov function method. The effectiveness of the proposed DMPC is demonstrated through two simulation examples.

Multiple exponentially weighted moving average control schemes for monitoring and diagnostics of correlated quality variables of different types with individual observations

In the era of big data analytics, more data, in terms of quantity and variety, are being collected for quality control purposes, thus leading to new challenges, especially for multivariate statistical process control (MSPC). One such challenge, which has received very little attention, is multivariate control chart (MCC) for monitoring correlated quality variables of different types. A recent study by Huang et al. (2023), which develops a Shewhart type MCC, is the first attempt to tackle such a challenge. The proposed chart of Huang et al. (2023), which utilizes the step-down multiple testing procedure of Holm (1979), not only can monitor correlated variables of different types, but also can provide instantaneous diagnostics of which parameters are out of control when the chart signals. In this study, we adapt and extend the methodology of Huang et al. (2023) to develop an exponentially weighted moving average (EWMA) chart specifically for the case when the sample size is one. The proposed chart is shown to be effective in detecting parameter changes as well as diagnosing which parameters are out of control when the chart signals.