Final Solution Research Articles

An Analytical Determination of the Magnetic Field in a System of Finite-Length Ribbon Busbars

Using the analytic method based on the Biot–Savart law for the electromagnetic field, the distribution of the magnetic field of a ribbon busbar of finite length was determined. The analytical formulas describing the magnetic field in all areas of the considered ribbon busbars were obtained. The Mathematica program was used to visualize the solutions obtained. The Mathematica programme is a good and convenient tool for analytical measurements using the integration function and conversion of the analytical solutions, for the determination of field quantities, and for the graphical visualisation of the obtained final solutions. This allowed for quick field analysis to be conducted after changes were made in the geometrical or electrical parameters of the systems under examination.

Patriarchy and Communalism in Mahesh Dattani’s Final Solutions

Mahesh Dattani’s themes highlight the problems that so many urban people face on a daily basis. These individuals may be living in historical transitions, torn between the alluring modernism and globalisation and the firm grasp of tradition and societal values. Skilled playwright and theatre professional Dattani discovers the problem, develops characters, and employs time, place, and the stage all in service of the subject he addresses. Indian urbanites of today make up the characters on stage. They work with socio-cultural tensions as their issues, and expressionism is his method. This article explains the patriarchy of these individuals in Final Solutions of Mahesh Dattani.

FABRICACIÓN ADITIVA DE FÓRCEPS CON FIBRA DE CARBONO CONTINUA PARA EL ENTRENAMIENTO VIRTUAL DEL PARTO

Vaginal delivery assisted with forceps (FAVD) is an obstetric technique that involves the extraction of the fetus by traction and/or expansion of the birth canal with an instrument. Currently, the use of forceps allows for the resolution of pathological situations that would otherwise require a cesarean section with the risks of morbidity and mortality that this surgery entails. For this reason, there is a growing interest in increasing the percentage of FAVD, after obstetricians feel more confident after practicing in simulation workshops with mannequins and acquiring the necessary competencies for their correct use. The forceps is an instrument that requires a precise introduction and placement, so an electromagnetic tracking tool has been developed that knows, with high precision, the position of the instrument both inside the vagina and around the fetal head. With this, childbirth training is complemented by a virtual simulation, in real-time, that allows the internal position of the forceps to be seen, thus facilitating the learning and improvement of the introduction and placement technique. For this, the instrument must not contain any ferrous residue that interferes electromagnetically, so commercial forceps (made of stainless steel) are not valid. In this context, the reproduction of forceps has been proposed through additive manufacturing based on material extrusion (MEX). To obtain forceps that are as realistic as possible in terms of rigidity compared to commercial models, a nylon filament reinforced with carbon microfibers (Onyx) has been used to which continuous carbon fiber is added in successive layers. Given the limitations in the printing volume of the technology used (Mark Two), numerical simulations were carried out by finite element analysis and different proposals for dividing each piece and subsequent union by adhesives were made, finally developing the final solution. Keywords: additive manufacturing; carbon fiber extrusion; vaginal delivery forceps; medical training; electromagnetic tracking.

Assessing battery degradation as a key performance indicator for multi-objective optimization of multi-carrier energy systems

The Pareto frontier is extensively adopted in multi-objective optimization, especially in multi-carrier energy system modeling. Despite the various methodologies available to derive the frontier, it represents different optimal solutions, making the final selection non-trivial. The modeler’s expertise is crucial in determining the weight factors assigned to each objective for selecting the final solution from the Pareto frontier. This study proposes a novel approach to support such decision-making, introducing an additional key performance indicator, the state of health of the battery, evaluated through physical battery modeling. By comparing different scheduling schemes in multi-objective multi-carrier energy systems, each with its distinct battery operational strategy, this newly introduced indicator has deployed to automatically identify the ultimate solution from the Pareto frontier, without additional weighting coefficients. Such an approach, therefore, automates the decision process, which supports easy engineering, especially for the small scale multi-energy systems such as smart homes, like the case study presented in this work that has four distinct energy carriers, adopting the 12 V 128 Ah LFP chemistry Li-ion battery modules, demonstrates the effectiveness of this automated selection process. Furthermore, when compared to the maximum values across the entire frontier, the automatically chosen solution exhibits reductions of 27.96% in CO2 emissions and 3.67% reduction in overall costs. Over long-term operation, this approach has the potential to extend battery lifespan by up to 26.67%, directly impacting the economics of multi-carrier energy systems.

A Preliminary Exploratory Factor Analysis of the BDSM Counselor Competency Scale.

BDSM practitioners represent a large sexual minority group often overlooked, misunderstood, and unnecessarily pathologized by mental health clinicians. Although developing cultural competence for diverse and marginalized populations is widely understood to be a core component of delivering efficacious therapeutic services that can counteract these stigmatizing mental healthcare experiences, no measures currently exist that assess clinicians' self-reported competence to work with BDSM practitioners. Previous measurement work has been done to establish self-report competency scales for clinicians working with other sexual and gender minority groups, but no such scales exist for working with BDSM practitioners. In the current study, we adapted a version of the Sexual Orientation Counselor Competency Scale (SOCCS) to measure clinicians' self-reported competence to work with BDSM practitioners and did a preliminary exploratory factor analysis of the new scale (n = 124). After an initial 24-item administration, principal axis factoring of our final 17-item solution revealed two latent factors (attitudes and skills/knowledge) consistent with the 2013 SOCCS and the theoretical constructs of cultural competency. The BDSM Counselor Competency Scale (BDSM-CCS) can help clinicians, practices, agencies, and training programs track self-reported cultural competence with the BDSM population. Future research directions for scale development and clinical and training applications are discussed.

Psychometric Properties of the Body-Mind-Spirit Wellness Behavior and Characteristic Inventory for the Greek Population.

The Body-Mind-Spirit Wellness Behavior and Characteristic Inventory (BMS-WBCI) is a free-of-charge wellness tool with good psychometric properties, widely used mainly in studies assessing quality of life and healthy lifestyle habits. This certain tool is based on the Hettler's (1980) model and has been validated for use with students aged 18-36. The purpose of this study was to adapt the BMS-WBCI in the Greek language and at the same time to validate it for use in the general population. This study included 520 participants aged 16-75 (M = 39.86, SD = 10.5), who were recruited from the Greek population using the snowball procedure. The BMS-WBCI was adapted into Greek language, following a multiple forward-and-backward translation protocol. Confirmatory Factor Analysis (CFA) was used to validate the overall construct of the Greek BMS-WBCI. The final solution was a three-factor model with 38 items, after removing the items B1, B8, B9, M11, M24, and S43. This final model demonstrated an acceptable to good fit, presenting higher goodness-of-fit indices (CFI = 0.91, TLI = 0.90) and lower badness-of-fit indices (χ2/653 = 2.29, p < 0.001, RMSEA = 0.05, SRMR = 0.06). All items in the hypothesized model exhibited statistically significant standardized factor loadings (p < 0.001), with loadings consistently above 0.40. A very good internal consistency was found using the composite reliability measures (Body 0.86, Mind 0.95, Spirit 0.94). Further analysis indicated a good convergent validity (average variance extracted values: Body 50.5%, Mind 50.7%, Spirit 54.9%). The results indicated adequate discriminant validity, as all square roots of average variance extracted were higher than the correlation between construct items. In conclusion, this psychometric evaluation of the BMS-WBCI adds to the evidence supporting its use in the Greek language, not only in students, but also in the general population.

A hybrid search space reduction algorithm and Newton–Raphson based selective harmonic elimination for an asymmetric cascade H-bridge multi-level inverter

Abstract In this paper, a selective harmonic elimination (SHE) method for an asymmetric cascade H-bridge multi-level inverter (ACHB-MLI) has been performed by using hybrid search space reduction and Newton–Raphson (SSR-NR) algorithm. Three H-bridges are cascaded in a single phase configuration, while each of the bridge is fed with V dc , 3V dc and 6V dc respectively. This particular asymmetric combination of dc sources will produce 10 possible switching combinations and 21-levels in the output voltage ranging from −10V dc to +10V dc . Hence, there will be 10 SHE equations corresponding to fundamental and harmonics, and the solution will be the 10 switching angles which satisfy those SHE equations. While traditional gradient-based techniques like the Newton–Raphson method can yield the global optimal solution for the SHE equations, they necessitate an initial guess. Conversely, meta-heuristic methods often face challenges related to sub-optimal convergence. To address these issues, a novel the hybrid SSR-NR algorithm has been introduced in this paper. This method combines stochastic and deterministic elements, offering a promising solution for solving the SHE equations. In the first step, the evolutionary SSR algorithm will be used and the output of the SSR algorithm is given to the NR method as an initial guess to converge to the final solution, this way the proposed hybrid SSR-NR algorithm is able to address the drawbacks of both the algorithms such as initial guess requirement for NR method and sub-optimal convergence characteristics of evolutionary algorithms. The simulations were carried out in Matlab/simulink environment for a 21-level ACHB-MLI. The results obtained showcase the efficacy of the proposed SSR-NR algorithm in solving the SHE equations. For demonstrating the effectiveness of SSR algorithm the results were also compared with other algorithms such as particle swarm optimization and grey wolf optimizer as well. Further, the experimental results on a laboratory prototype using dSPACE DS1104 R&amp;D controller board were also presented in the paper, to validate the proposed methodology.

Moderately supervised learning: definition, framework and generality

AbstractLearning with supervision has achieved remarkable success in numerous artificial intelligence (AI) applications. In the current literature, by referring to the properties of the labels prepared for the training dataset, learning with supervision is categorized as supervised learning (SL) and weakly supervised learning (WSL). SL concerns the situation where the training dataset is assigned with ideal (complete, exact and accurate) labels, while WSL concerns the situation where the training dataset is assigned with non-ideal (incomplete, inexact or inaccurate) labels. However, various solutions for SL tasks under the era of deep learning have shown that the given labels are not always easy to learn, and the transformation from the given labels to easy-to-learn targets can significantly affect the performance of the final SL solutions. Without considering the properties of the transformation from the given labels to easy-to-learn targets, the definition of SL conceals some details that can be critical to building the appropriate solutions for specific SL tasks. Thus, for practitioners in various AI application fields, it is desirable to reveal these details systematically. This article attempts to achieve this goal by expanding the categorization of SL and investigating the sub-type that plays the central role in SL. More specifically, taking into consideration the properties of the transformation from the given labels to easy-to-learn targets, we firstly categorize SL into three narrower sub-types. Then we focus on the moderately supervised learning (MSL) sub-type that concerns the situation where the given labels are ideal, but due to the simplicity in annotation, careful designs are required to transform the given labels into easy-to-learn targets. From the perspectives of the definition, framework and generality, we conceptualize MSL to present a complete fundamental basis to systematically analyse MSL tasks. At meantime, revealing the relation between the conceptualization of MSL and the mathematicians’ vision, this article as well establishes a tutorial for AI application practitioners to refer to viewing a problem to be solved from the mathematicians’ vision.

Inverse planning of lung radiotherapy with photon and proton beams using a discrete ordinates Boltzmann solver

Objective. A discrete ordinates Boltzmann solver has recently been developed for use as a fast and accurate dose engine for calculation of photon and proton beams. The purpose of this study is to apply the algorithm to the inverse planning process for photons and protons and to evaluate the impact that this has on the quality of the final solution. Approach. The method was implemented into an iterative least-squares inverse planning optimiser, with the Boltzmann solver used every 20 iterations over the total of 100 iterations. Elemental dose distributions for the intensity modulation and the dose changes at the intermediate iterations were calculated by a convolution algorithm for photons and a simple analytical model for protons. The method was evaluated for 12 patients in the heterogeneous tissue environment encountered in radiotherapy of lung tumours. Photon arc and proton arc treatments were considered in this study. The results were compared with those for use of the Boltzmann solver solely at the end of inverse planning or not at all. Main results. Application of the Boltzmann solver at the end of inverse planning shows the dose heterogeneity in the planning target volume to be greater than calculated by convolution and empirical methods, with the median root-mean-square dose deviation increasing from 3.7 to 5.3 for photons and from 1.9 to 3.4 for proton arcs. Use of discrete ordinates throughout inverse planning enables homogeneity of target coverage to be maintained throughout, the median root-mean-square dose deviation being 3.6 for photons and 2.3 for protons. Dose to critical structures is similar with discrete ordinates and conventional methods. Time for inverse planning with discrete ordinates takes around 1–2 h using a contemporary computing environment. Significance. By incorporating the Boltzmann solver into an iterative least squares inverse planning optimiser, accurate dose calculation in a heterogeneous medium is obtained throughout inverse planning, with the result that the final dose distribution is of the highest quality.

Numerical implications of including drifts in SOLPS-ITER simulations of EAST

The inclusion of drifts in plasma edge codes like SOLPS-ITER is required to match simulation data with experimental profiles. However, this remains numerically challenging. In this paper, the effect of some numerical factors on the final plasma solution is investigated. This study is performed on three EAST simulations in the upper single null configuration: an attached purely deuterium case, an attached case with limited Ne-seeding, and a detached Ne-seeded case. The effects of the anomalous conductivity and anomalous thermo-electric coefficient on the plasma potential are investigated. Next, the effect of the employed grids is shown. In order to investigate these effects, accurate drift simulations are needed. Therefore, the employed time step and numerical parameters are discussed for the three studied simulations. For all presented simulations, it is verified that the restriction of the grid to the first flux surface tangent to the main chamber wall is sufficient within the context of non-extended simulations. This means that the main power dissipation takes place inside the simulated domain, and only a small fraction of the power is leaving the B2.5 grid through the grid boundary closest to the first wall. Finally, the effect of drifts on the asymmetry between the inner and outer target for EAST simulations is demonstrated.

The 23 Factorial Design in R

The paper describes the factorial design of the experiment with three input factors that change on two levels. For given values of the input parameters, it is shown how to obtain a variance analysis table and which factors and interactions between factors are significant. The example was done in the software intended for the design of the experiment and in the software R. It is shown how to use the software R to arrive at the final solution of the given example.

A two‐stage, four‐layer robust optimisation model for distributed cooperation in multi‐microgrids

AbstractAs the integration of microgrids (MG) and energy storage continues to grow, the need for efficient distributed cooperation between MGs and common energy storage (CES) becomes paramount. A robust optimisation model for the distributed cooperation of MG‐CES is presented, taking into account distributed generation under uncertainty. The proposed model follows a two‐stage, four‐layer ‘min‐min‐max‐min’ structure. In the first stage, the initial layer ‘min’ addresses the distributed cooperation problem between MG and CES, while the second stage employs ‘min‐max‐min’ to optimise the scheduling of MG. To enhance the solution process and expedite convergence, the authors introduce a column‐constrained generation algorithm with alternating iterations of U and D variables (CCG‐UD) specifically designed for the three‐layer structure in the second stage. This algorithm effectively decouples subproblems, contributing to accelerated solutions. To tackle the convergence challenges posed by the non‐convex MG‐CES model, the authors integrate the Bregman alternating direction method with multipliers (BADMM) with CCG‐UD in the final solution step. Real case tests are conducted using three zone‐level MGs to validate the efficacy of the proposed model and methodology. The results demonstrate the practical utility and efficiency of the developed approach in addressing distributed cooperation challenges in microgrid systems with energy storage.

Research on Optimization Design Based on Heliostat Field

This study aims to deeply explore the mathematical modeling and performance optimization of solar heliostat fields, in order to improve their energy output efficiency. The solar heliostat field is an innovative solar heating system that focuses solar radiation on the absorption tower through a reflector, thereby generating high-temperature heat energy. This article mainly discusses the performance analysis and parameter optimization of the heliostat field to achieve optimal performance while meeting the rated power requirements. A simulated solar model was established using a three-dimensional Cartesian coordinate system, taking into account the effects of solar altitude angle and direct normal irradiance (DNI). The model in this paper comprehensively considers the effects of shadow occlusion loss, cosine efficiency, atmospheric transmittance, collector truncation efficiency, and mirror reflectance on the mirror field. The specific values of each parameter were calculated using model algorithms such as the HFLCAL model, To provide a basis for accurate performance analysis and solve the problem of parameter optimization, this article defines an objective function aimed at improving the energy utilization efficiency of the heliostat field by adjusting the parameters of the reflector, in order to achieve optimal performance. The above parameters are determined by establishing a genetic algorithm to define constants and initial parameters, and initializing the overall layout of the heliostat field to calculate the optical efficiency of the heliostat. In each iteration, the optimal heliostat field layout can be recorded. The final optimal solution includes parameters such as the position coordinates of the absorption tower, heliostat size, installation height, number of heliostats, and heliostat position.

A process capability function approach to multiple response surface optimization based on a posterior procedure

Multiple response surface optimization (MRSO) aims to determine a setting of input variables that simultaneously optimizes multiple responses. The process capability function (PCF) approach is an attractive alternative to MRSO because it considers both the mean and variance of the responses systematically through the process capability index. In MRSO, the responses are often in conflict. Thus, the preference information of a decision maker (DM) on the tradeoffs among the responses should be considered. However, the existing PCF methods are based on an unrealistic assumption that all DM preference information is given in advance. In this article, a posterior preference articulation procedure based on the PCF approach, called P-PCF, is proposed. P-PCF first generates nondominated solutions using the ε-constraint method and then allows the DM to select his/her preferred cluster or final solution using the K-means clustering method. The proposed method is illustrated using a well-known MRSO problem.

Designing a Digital Health Solution: A Platform for Automated Surveillance of Fungal Infection.

Surveillance of invasive fungal infection (IFI) requires laborious review of multiple sources of clinical information, while applying complex criteria to effectively identify relevant infections. These processes can be automated using artificial intelligence (AI) methodologies, including applying natural language processing (NLP) to clinical reports. However, developing a practically useful automated IFI surveillance tool requires consideration of the implementation context. We employed the Design Thinking Framework (DTF) to focus on the needs of end users of the tool to ensure sustained user engagement and enable its prospective validation. DTF allowed iterative generation of ideas and refinement of the final digital health solution. We believe this approach is key to increasing the likelihood that the solution will be implemented in clinical practice.

Optimizing VLIW Instruction Scheduling via a Two-Dimensional Constrained Dynamic Programming

Typical embedded processors, such as Digital Signal Processors (DSPs), usually adopt Very Long Instruction Word (VLIW) architecture to improve computing efficiency. The performance of VLIW processors heavily relies on Instruction-Level Parallelism (ILP). Therefore, it is crucial to develop an efficient instruction scheduling algorithm to explore more ILP. While heuristic algorithms are widely used in modern compilers due to simple implementation and low computational cost, they have limitations in providing accurate solutions and are prone to local optima. On the other hand, exact algorithms can usually find the optimal solution, but their high time overhead makes them less suitable for large-scale problems. This paper proposes a two-dimensional constrained dynamic programming (TDCDP) approach and a quantitative model for instruction scheduling. The TDCDP approach achieves near-optimal solutions within an acceptable time overhead. Furthermore, we integrate our TDCDP approach into mainstream compiler architecture, encompassing Pre- and Post-RA (register allocation) scheduling. We conduct a quantitative evaluation of TDCDP compared to four heuristic algorithms on a typical VLIW processor. Our approach achieves an efficiency improvement of up to 58.34% in final solutions compared to the heuristic algorithms. Additionally, the Post-RA Scheduling enhances programs with an average speedup of 14.04% than solely applying the Pre-RA Scheduling.

Nucleophilic Substitution at Phosphorus Centres - Old and Recent Studies and a Final Solution of Mechanistic and Related Stereochemical Problems.

Among many problems of a fundamental value in the heteroatom chemistry the mechanism and stereochemistry of the nucleophilic substitution reaction at the phosphorus and other heteroatom centres have attracted great attention of phosphorus chemists already in the middle of the last century. This review, which does not have a comprehensive character, summarizes the selected original contributions aimed at solution of the mechanism of SN2-P reaction and its relationship with stereochemistry. The breakthrough in these studies was the Westheimer's concept and his rules which is presented at the beginning of this article. Next, a series of papers is presented where the stereochemistry of the substitution at phosphorus was investigated in cyclic five-, four- and six-membered ring phosphorus compound. The majority of these reactions have been found to occur with retention of the P-configuration. In the third part of this account, the selected examples of substitution reactions at phosphorus in acyclic compounds are discussed. As the results of all the investigations discussed did not allow to undoubtedly ascribe the mechanism (SN2 or A-E) to the investigated reactions, in the last part the SN-P reactions are presented, the mechanism of which has been established by combination of the stereochemical and DFT-studies.

Transient nonlinear responses of a sandwich plate with micro-cellular auxetic core based on modified strain gradient theory under impact loads

This study investigates the nonlinear dynamic response of an auxetic sandwich structure under impact load. The core is made of a micro-cellular auxetic structure. The main point of interest is studying the micro-cellular core properties’ effects on the structure’s nonlinear dynamic response. First, the mechanical properties of the micro-cellular auxetic core are introduced based on modified strain gradient theory (MSGT), followed by experimental and numerical validation. Then, Hamilton’s principle is applied to derive the sandwich structure’s nonlinear governing partial differential equations. Lastly, Galerkin’s method paved the way to achieving the final solution. So as to perform frequency-response analysis, the four-dimensional averaged equations are obtained using the perturbation method. In order to carry out experimental validation, a nanosecond laser system is used for micro-cellular auxetic sample fabrication, and the tensile tests are carried out per ISO 6892 standard. The comparison between the experimental and the theoretical results indicates an excellent agreement among the results. It can be concluded that MSGT plays a significant role in the mechanical properties and the nonlinear dynamic response of the micro-cellular auxetic structures. The present study is applicable in the field of protective panels, sensors, and energy harvesters.

Scheduling Precedence Constraints among Charging Tasks in Wireless Rechargeable Sensor Networks

The development of wireless power transfer (WPT) facilitates wireless rechargeable sensor networks (WRSNs) receiving considerable attention in the sensor network research community. Most existing works mainly focus on general charging patterns and metrics while overlooking the precedence constraints among tasks, resulting in charging inefficiency. In this paper, we are the first to advance the issue of scheduling wireless charging tasks with precedence constraints (SCPC), with the optimization objective of minimizing the completion time of all the charging tasks under the precedence constraints while guaranteeing that the energy capacity of the mobile charger (MC) is not exhausted and the deadlines of charging tasks are not exceeded. In order to address this problem, we first propose a priority-based topological sort scheme to derive a unique feasible sequence on a directed acyclic graph (DAG). Then, we combine the proposed priority-based topological sort scheme with the procedure of a genetic algorithm to obtain the final solution through a series of genetic operators. Finally, we conduct extensive simulations to validate our proposed algorithm under the condition of three different network sizes. The results show that our proposed algorithm outperformed the other comparison algorithms by up to 11.59% in terms of completion time.

Geographical Information System Modeling for Planning Internal Transportation in a Manufacturing Plant’s Outdoor Area

A geographical information system (GIS) is an advanced tool for collecting, managing, and analyzing spatially-referenced data. The contribution of GIS use to process performance indicators can be improved by combining it with multi-criteria decision analysis (MCDA). Combining a GIS and MCDA is, in the scientific literature, rarely discussed for planning an internal transportation system in a manufacturing plant’s outdoor area. The purpose of this article is to clarify what mangers can expect from using a combined approach when deciding on a transport fleet and the operational routing of vehicles. Beside the simulation of MCDA, the computer software ArcGIS Pro 3.0.2 with the Network Analyst extension was used for modelling the transportation system in the form of a case study. The article demonstrates the feasibility and effectiveness of GIS and MCDA use and reveals the extent of the challenge of how decision makers could make the most of ArcGIS functionality. The final solution for an internal transportation system in a manufacturing plant’s outdoor area includes such a vehicle fleet and the set time windows of orders for transport services, so that there are no violations of time windows and the work is completed within the work shift while minimizing costs, time, and distance. Decision makers can use the program without advanced knowledge of optimization approaches, following a procedure that does not differ much from that of learning to use other business software tools. On the contrary, the listed disadvantages can be summarized as the rigidity of setting detailed boundary conditions for a specific simulation scenario.