Maximum Performance Research Articles

Exploring Synergies, Differences, and Impacts of Agile and DevOps on Software Development Efficiency

Agile and DevOps are now the preferred methods for satisfying the ever-changing demands for better software. Improvements in efficiency, adaptability, and collaboration are the aims of newly proposed solutions. It examines the impact of these factors on software development efficiency, focussing special attention to the ways in which they differ and complement one another. The focus of DevOps is continuous integration and delivery with operational automation, whereas Agile is on iterative improvement, consumer cooperation, and adaptation. Acquire an understanding of how these methods can be combined or utilised independently with the goal to achieve maximum performance in software tasks. One method that ranks the importance of factors including improvement velocity, quality, cooperation, and automation in relation to their impact on efficiency is the Multivocal Software Factor Prioritisation Development Approach (MSFPDA). By giving these considerations priority, this method integrates the most beneficial features of both methodologies. With the goal to maximise the stability, adaptability, automation, and scalability of both Agile and DevOps, MSFPDA employs comment looping structures, iterative workflows, and continuous monitoring. The simulation analysis is used by the granting studies to evaluate MSFPDA's performance in exceptional improvement circumstances. By identifying and prioritising critical components, MSFPDA can improve software program efficiency, according to this assessment. Blending Agile and DevOps presents a variety of issues, such as managing automation complexity, assuring non-stop delivering without sacrificing agility, and harmonising the team's tradition. This methodology for software enhancement has enormous ability, and the results of the simulations demonstrate how MSFPDA can be utilised in real-world scenarios, improving multiple processes.

Enhancing Nursing Excellence: Exploring the Relationship between Nurse Deployment and Performance.

Proper nurse placement is crucial for enhancing the performance and quality of health services. This study aims to explore in-depth the relationship between nurse placement and performance in order to promote nursing excellence. A quantitative analysis was conducted using a descriptive correlational methodology. The population in this study consisted of 214 executive nurses at Medan Government Hospital, with a sample size of 139. The study's findings revealed that nurses performed exceptionally well in providing nursing care, scoring 94.2%, with those well-placed scoring 90.6%. The results from the Spearman rho correlation test showed that nursing qualifications, experience, work environments, and team dynamics have significant relationships with nurse performance. Meanwhile, the nurse's rho factor towards patients and the policy or regulation component showed low significance and relationship. The novelty of this study lies in its indication that nursing performance can be enhanced by aligning the placement of nurses with their abilities and experience, and fostering a work environment and positive team dynamics that encourage collaboration and efficiency. These findings provide vital insights for nursing staff management in order to enhance nursing care quality and patient health outcomes. This study highlights the need for suitable placement and professional development for nurses in order to attain maximum performance.

A study of powertrain parameter optimization based on performance requirements of a vehicle platform

Powertrain top down design is a vital process for vehicle platformization and performance development. To obtain the optimal component size of a plug-in hybrid electric vehicle (PHEV) platform, this study proposes an optimization framework with the goal of minimizing performance redundancy. First, we analyze the characteristics of the powertrain architecture, and propose the indicators through performance gradient planning of the vehicle platform. Then we establish the mathematical model of the powertrain, and formulate the optimization problem. Particle Swarm Optimization (PSO) is used to solve the problem and a platform optimization problem with eight vehicle models is performed. Optimization results show that compared to traditional single vehicle powertrain optimization, platform based powertrain optimization can greatly reduce the number of components and save development costs through the sharing of components. With only three electric machines, two engines, two generators, and two sets of transmissions, the performance requirements of the entire platform can be met, and the maximum performance redundancy of different vehicle models is only 0.205. It can be seen that platform based powertrain optimization can greatly reduce vehicle costs and improve profit margins.

HAETAE on ARMv8

In this work, we present the highly optimized implementation of the HAETAE algorithm, submitted to the second round of the Korean Post-Quantum Cryptography (KpqC) competition and to the first round of NIST’s additional post-quantum standardization for digital signatures on 64-bit ARMv8 embedded processors. To the best of our knowledge, this is the first optimized implementation of the HAETAE algorithm on 64-bit ARMv8 embedded processors. We apply various optimization techniques to enhance the multiplication operations in the HAETAE algorithm. We utilize parallel operation techniques involving vector registers and NEON (Advanced SIMD technology used in ARM processors) instructions of ARMv8 embedded processors. In particular, we achieved the best performance of the HAETAE algorithm on ARMv8 embedded processors by applying all the state-of-the-art NTT (Number Theoretic Transform) implementation techniques. Performance improvements of up to 3.07×, 3.63×, and 9.15× were confirmed for NTT, Inverse-NTT, and pointwise Montgomery operations (Montgomery multiplication used in modular arithmetic), respectively, by applying the state-of-the-art implementation techniques, including the proposed techniques. As a result, we achieved a maximum performance improvement of up to 1.16× for the key generation algorithm, up to 1.14× for the signature algorithm, and up to 1.25× for the verification algorithm.

Heat recovery integration in a hybrid geothermal-based system producing power and heating using machine learning approach to maximize outputs

This study offers an in-depth thermodynamic analysis and optimization of an integrated renewable energy system that merges a double-flash geothermal system with a transcritical carbon dioxide Rankine cycle, utilizing machine learning algorithms. The innovative design aims to maximize the concurrent generation of heat and electricity, ultimately benefiting environmental sustainability and energy security. By employing regression machine learning algorithms, the research evaluates and enhances system performance, achieving remarkable R-squared accuracy levels of 98.86 % for heating output and 99.89 % for power output predictions. The thermodynamic modeling, which has been validated against recognized benchmarks, confirms the accuracy of the system's design. Optimization findings indicate that operating pressures between 840 and 870 kPa and pressure ratios of 1.56–1.60 deliver optimal outputs, with power production between 2582 and 2585 kW and heating output ranging from 12260 to 12280 kW. The system reaches its maximum performance at a pressure of 850 kPa and a pressure ratio of 1.57, resulting in a power output of 2583.97 kW and a heating output of 12279.3 kW. These results highlight the potential of combining advanced thermodynamic systems with machine learning methodologies to improve the efficiency and effectiveness of renewable energy sources.

RP squeeze U-SegNet model for lesion segmentation and optimization enabled ShuffleNet based multi-level severity diabetic retinopathy classification

ABSTRACT In Diabetic Retinopathy (DR), the retina is harmed due to the high blood pressure in small blood vessels. Manual screening is time-consuming, which can be overcome by using automated techniques. Hence, this paper proposed a new method for classifying the multi-level severity of DR. Initially, the input fundus image is pre-processed by Non-local means Denoising (NLMD). Then, lesion segmentation is carried out by the Recurrent Prototypical-squeeze U-SegNet (RP-squeeze U-SegNet). Next, feature extraction is effectuated to mine image-level features. DR is categorized as abnormal or normal by ShuffleNet and it is tuned by Fractional War Royale Optimization (FrWRO), and later, if DR is detected, severity classification is performed. Furthermore, the FrWRO-SqueezeNet obtained the maximum performance with sensitivity of 97%, accuracy of 93.8%, specificity of 95.1%, precision of 91.8%, and F-Measure of 94.3%. The devised scheme accurately visualizes abnormal regions in the fundus images. Also, it has the ability to identify the severity levels of DR effectively, which avoids the progression risk to vision loss and proliferative disease.

How optimal is the minimum-time manoeuvre of an artificial race driver?

Minimum-lap-time optimal control problems (MLT-OCPs) are a popular tool to assess the best lap time of a vehicle on a racetrack. However, MLT-OCPs with high-fidelity dynamic vehicle models are computationally expensive, which limits them to offline use. When using autonomous agents in place of an MLT-OCP for online trajectory planning and control, the question arises of how far the resulting manoeuvre is from the maximum performance. In this paper, we improve a recently proposed artificial race driver (ARD) for online trajectory planning and control, and we compare it with a benchmark MLT-OCP. The novel challenge of our study is that ARD controls the same high-fidelity vehicle model used by the benchmark MLT-OCP, which enables a direct comparison of ARD and MLT-OCP. Leveraging its physics-driven structure and a new formulation of the g-g-v performance constraint, ARD achieves lap times comparable to the offline MLT-OCP (few milliseconds difference). We analyse the different trajectories resulting from the ARD and MLT-OCP solutions, to understand how ARD minimises the effect of local execution errors in search of the minimum-lap-time. Finally, we show that ARD consistently maintains its performance when tested on unseen circuits, even with unmodelled changes in the vehicle's mass.

Experimental study on enhancing the performance of hydrokinetic integrated Darrieus-Savonius rotor in open water-channel

The kinetic energy stored in water streams could be used to generate electricity via hydrokinetic rotors, due to the availability of kinetic energy from flowing water. Darrieus and Savonius are vertical axis kinetic rotors, they are promising for generating power in low speed flows. The integrated Darrieus- Savonius hydrokinetic rotor works on the principles of integrated forces including lift and drag forces. An experimental investigation has been conducted to get the optimum configuration for Darrieus-Savonius integrated rotor in order to attain improved self-starting and high-power-factor in open channel water flow, under different inflow conditions. This study investigated the effects of the radius ratio, add-on angle, water heights, and flow velocity on performance of hydrokinetic integrated Darrieus-Savonius rotor with three standard NACA 020 airfoil shape as Darrieus rotor and single/double stage semi-circular blades as Savonius rotor on the power factor. Three radius ratios (R.R) namely (0.8, 0.6 and 0.4) and add-on angles of (0o, 30o, 45o, 60o and 90o) were studied. The experimental channel was (26 m) long water flume with a (1m wide ×1.2 m depth). The horizontal walls of the flume were made of glass with a strengthened frame, to permit visual examination. The flume entrance consisted of a receiving tank (3 m long, 1.5 m wide, and 0.75 m depth), a honeycomb pattern pipe box, and a screen box field with a large stone to dissipate the water turbulence and energy at the entrance. Two centrifugal pumps were used to supply water to the channel. Darrieus-Savonius integrated rotor was placed at distance equal to ten times width of the experimental channel, to avoid any turbulence and to make sure that the flow was steady.Based on the experimental results, it was observed that the optimum Cp values at λ (1.84), add-on angle of (45o) and R.R (0.4, 0.6 and 0.8) were (0.335, 0.29 and 0.299) respectively. The maximum hybrid performance was attained at R.R (0.4), add-on angle (45o) and tip-speed ratio (1.8) and Re (240x106) with value of (0.339). Decrease in the Re values by 25 % enhanced the performance by 1.19 %. Accordingly, it is not necessary to rely on Re values in calculating the power-factor values compared to the influence of the rest of the variables.

SpEpistasis: A sparse approach for three-way epistasis detection

Epistasis detection is a fundamental application in the areas of bioinformatics and biomedicine, providing important insights regarding the relationship between the human genome and the occurrence of certain diseases. Exhaustive epistasis detection approaches are employed to achieve an accurate and deterministic solution, at the cost of high computational complexity, especially when targeting high-order epistasis. While recent works employ vectorization and cache-blocking techniques to alleviate this burden, these solutions are now limited by the maximum performance of the functional units of computing systems. Thus, to further improve the performance of epistasis detection it is necessary to reduce its number of memory transfers and computations. To tackle this issue, this work proposes SpEpistasis, which performs three-way epistasis detection by relying on sparse features, which by only storing the non-zero elements of the dataset, allows for reducing the number of operations needed for epistasis detection. To achieve this goal, a new hybrid format to represent the input dataset is proposed, which stores a subset of the data in the compressed sparse row format. Moreover, new sparse-aware algorithmic approaches are also proposed in order to leverage both the hybrid format and the vector capabilities of current CPUs from Intel, AMD, and ARM. The experimental results show that SpEpistasis provides a speedup up to 3.7× and average speedups of around 1.8× and 1.33× when compared with other state-of-the-art works.

Bioelectrochemical performance on constructed wetland-microbial fuel cells operated under diffuse and direct solar radiation using root exudates as endogenous substrate to feed an electroactive biofilm

Exploiting fossil fuels through conventional technologies focused on energy production has led to severe environmental adversities, mainly affecting global warming and climate change. Constructed wetland-microbial fuel cells (CW-MFC) have emerged as an innovative technology that could mitigate these effects. One of the main limitations of the CW-MFC system is the low power density generated. This study evaluated the impact of diffuse (CW-MFC1) and direct (CW-MFC2) solar radiation intensity to enhance bioelectrochemical performance. To do this, the CW-MFC was inoculated with the Geobacter sulfurreducens bacteria as a bioelectrocatalyst, which was fed with the root exudates of the macrophyte Juncus effusus. The change from diffuse to direct solar radiation increased the electroactivity of the system by 43% in terms of power density, cathodic potential, anodic potential, and internal resistance, resulting in a maximum performance of 128.7 mW/m2, 172 mV, −290 mV, and 204 Ω respectively. This was obtained from a concentration of 35.3 mg/L of root exudates (total organic carbon). The analysis of results determined that solar radiation is a predominant factor that directly affects the photosynthetic efficiency and coulombic efficiency of CW-MFC by controlling the rhizodeposition process of the macrophytes, where the amount of bioelectricity produced depends on the amount of exudates discharged by the root system towards the rhizosphere.

Spectrally efficient DWDM system using DQPSK modulation

Abstract DWDM technology is gaining popularity in fiber-optic communication due to high-speed transmission channel capacity demands. The adoption of this system has been driven by this demand. Researchers focus on optimizing data transmission parameters within channel characteristics for maximum performance. The signal’s form typically influences the optical channel capacity, the guiding medium’s dispersive and non-linear characteristics, and interference from multiple sources. This study investigates how efficient modulated systems control linear and non-linear impairments in optical channel capacity due to high data rates transmitted over optical communication networks, aiming to circumvent current issues. The selection of pulse shape and modulation format is crucial for an effective DWDM optical communication system due to the rigorous specifications of the optical channel. Modulation formats like intensity and phase have become viable network design technologies, allowing for network creation that operates to the designer’s satisfaction. This academic study examines the theoretical properties of various models used to analyze and recreate high-speed optical communication links, aiming to understand the impact of system components and modulation techniques.

Exploring Role of Organizational Learning, Job Satisfaction and Organizational Commitment on Employee Performance at National Rice Milling Industry

Improving employee performance is the main goal for every organization in achieving sustainable success and growth. Organizational learning, work satisfaction, and organizational commitment are examples of internal elements that must be taken into account in order to attain maximum performance. The purpose of this study is to ascertain how employee performance is affected by organizational learning, job satisfaction, and organizational commitment. The researcher used quantitative research methods, using descriptive research. Employees will be the target population for researchers. Researchers used non-probability sampling, with a sample size of 100 respondents. Data collection was carried out through two main techniques, namely interviews and questionnaires. With the use of smartPLS 3.0 software, the Partial Least Squares (PLS) approach is used for data analysis in this study. Numerous findings were obtained for this study based on the outcomes of the hypothesis testing. Work satisfaction and organizational learning have a strong positive correlation. Organizational learning and organizational commitment have a strong positive correlation. There is no discernible connection between worker performance and organizational learning. There is no discernible link between worker performance and job satisfaction. Employee performance and organizational commitment have a strong positive correlation. As a result, the study's findings help to clarify the variables influencing worker performance, organizational commitment, and job happiness.

A novel magnetic S/N co-doped tea residue biochar applied to tetracycline adsorption in water environment

The issue of tetracycline (TC) contamination has attracted widespread attention. Adsorption technology has great potential for application in the field of TC remediation. This study achieved the production of nitrogen self-doped biochar using waste tea leaves as raw materials, and further improved its TC removal effect by doping sulfur elements. A recyclable high-performance TC adsorbent magnetic S/N co-doped tea residue biochar (MSNB) was obtained through ferromagnetic modification, which could be used as an effective TC adsorbent. The effects of MSNB dosage and initial solution pH on TC removal were evaluated. The recovery performance of MSNB adsorbent on TC was studied. The maximum adsorption performance at 298 K calculated using the Langmuir model was 140.76 mg g−1. The fitting results of adsorption kinetics indicated that chemical adsorption was the main factor affecting the adsorption reaction. After 5 cycles of adsorption, the removal of TC by MSNB remained above 88.30 %. This study could provide theoretical support for the treatment of tetracycline wastewater by biochar adsorption.

Seismic Performance Evaluation of Irregular Auditorium Building based on ASCE 41-23

This study discusses the seismic performance evaluation of an irregular auditorium building. The building is an educational facility which has roof span up to 46 meters, column height up to 10 meters, beam span up to 15 meters, cantilever up to 6.50 meters, 2 inclined columns with the angle up to 54.72°, and 1 transfer column with 1 transfer beam. The evaluation process was carried out using Tier 3 method with linear dynamic procedure which consisted of Response Spectrum Analysis (RSA) and Linear Time History Analysis (LTHA) according to ASCE 41-23 using SAP2000 subjected to short (S s ) and long (S ) period of earthquakes. The building is designed to Risk Category IV, which the target performance levels for structural components are Immediate Occupancy (IO) for Basic Safety Earthquake 1 for New Building (BSE-1N) and Life Safety (LS) for Basic Safety Earthquake 2 for New Building (BSE-2N). Even though the building had a torsional strength irregularity, the percentage of components with a Demand-Capacity Ratio value that did not meet the requirements was 9.87% of the total components; hence the linear procedure was assumed to be still applicable. Analyses showed that the average acceptance criteria ratio of the components with the RSA method was lower than with the LTHA method but the percentage of the components with acceptance criteria ratio exceeding 1 using RSA method was higher than using the LTHA method. In addition, the results indicated that the average performance level of the components was IO for BSE-1N and LS for BSE-2N, which both results had met the expected performance level targets. However, the maximum performance level of the components did not meet the IO performance level target for BSE-1N and did not meet the LS performance level target for BSE-2N.

Outdoor Performance Analysis of Semitransparent Photovoltaic Windows with Bifacial Cells and Integrated Blinds

The stricter requirements for the energy performance of buildings are creating a market for several building‐integrated photovoltaic (BIPV) technologies, including photovoltaic (PV) windows. Herein, an innovative multifunctional PV window concept designed to enhance energy generation while providing overheating protection for better indoor thermal and visual comfort is presented. This concept utilizes bifacial c‐Si solar cell strips combined with venetian blinds, all embedded in a unique insulating glazing unit. The bifacial technology increases the energy yield by using the blinds as reflectors, directing more irradiance to the cells’ rear side. The goal of this study is to analyze the outdoor performance of this concept under real operating conditions. Twelve demonstrators are installed and monitored. Various measurement campaigns are conducted, examining the impact of different blind types, tilt angles, sun positions and sky conditions. The highest energy boosts occur when the blinds are fully closed at a 75° angle with their convex side facing outward. Blinds with the highest specular reflectance achieve a maximum performance increase of 25% on sunny days and a daily average increase of 12% compared to the case of no blinds.

PSXI-19 Effects of different forage levels on performance and feeding behavior of feedlot cattle

Abstract Body weight (BW) gain is highly correlated with profitability and positively associated with feed efficiency and reduced environmental impact of beef production. Fiber stimulates rumination which is essential for digestion, nutrient absorption, and maintenance of animal health. The quantity and quality of fiber in the diet affect BW gain and feeding and rumination patterns. Forage neutral detergent fiber (fNDF) has a key role in regulating intake and health. This study tested two levels of forage inclusion NDF on performance and feeding behavior of Nellore bulls. Corn silage was the source of forage fiber, diets had 12% or 7% fNDF [23.3% or 13.6% in the total dry matter (DM). Diets had 64% and 73.5% ground corn, and 0.2 and 0.5% potassium chloride respectively for 12% and 7% fNDF. Both treatments included soybean meal (1.5%); cottonseed (7%), urea (1.2%), mineral mixture (2.7%) and monensin (25 ppm). Diets were formulated to meet protein and mineral requirements with RLM model (rlm.app.br). Nellore bulls (n = 40; BW = 460 ± 38 kg and an average age of 20 mo) were fed for 50 d after 21 d adaptation. Each animal was considered as experimental unit in a completely randomized design. On d 21 and 42 behavior was evaluated every 10 min for 24 h. Feed intake was measured using Growsafe feeders. Average daily gain (ADG) was estimated by linear regression based on BW (without fast) obtained in real time scales at the water trough. Data were evaluated by SAS PROC GLM. Daily gain (1.71 vs. 1.43 kg/d, P = 0.0002), DM intake (10.97 vs. 10.00 kg/d, P < 0.001) and feed efficiency (0.154 vs. 0.139, P = 0.04) were greater for the 12% fNDF treatment. Likewise, rumination time was greater for the 12% fNDF compared with the 7% fNDF treatment; 356 and 275 min/d respectively (P = 0.0005). Animals fed 12% fNDF had longer rumination times and improved performance compared with those fed 7%. There were no differences in the number of visits to the troughs or in total feeding time (P > 0.05), with mean visits of 15.17 and 15.43, and feeding times of 145.6 and 146.8 minutes for the 12% and 7% fNDF diets, respectively. Linear correlation between ADG and rumination time was positive r = 0.49 (P < 0.03) and r = 0.28 (P < 0.23), indicating that rumination is associated with an increase in ADG. Coefficient of variation for ADG was 13.4 and 27.2% for 12 and 7% fNDF diets, with greater variability for the low fiber diets. The results highlight the importance of providing adequate forage to feedlot cattle. Results are consistent with previous work from our group demonstrating that the maximum performance is achieved at around 11 to 13% fNDF. Bulls fed 12% fNDF diets had greater BW gain, higher rumination and reduced ADG variation compared with those fed 7% fNDF diets.

Quantifying the Training Loads and Corresponding Changes in Physical Qualities among Adolescent, Schoolboy Rugby League Players.

The adolescent development period is critical for rugby league athletes, given the physical growth, neuromuscular adaptation, and skill acquisition that occurs. Secondary schools play an important role in the development of adolescent rugby league players; however, players may be selected into rugby league academies and development programs outside of school, as well as participating in additional sports. In turn, the training loads these young athletes accrue and the implications of these loads are currently unknown. Our aim was to quantify the training loads and concomitant changes in physical qualities of schoolboy and adolescent rugby league players during mesocycles within the pre-season and in-season phases. This is a prospective experimental study. Twenty-one schoolboy rugby league players (16.2 ± 1.3 years) were monitored across separate 4-week mesocycles in the pre-season and in-season. Session frequency, duration, and the session rating of perceived exertion (sRPE) load were reported for all examples of training and match participation in the school rugby league program, as well as club and representative teams for any sport and personal strength and conditioning. Various physical qualities were assessed before and after each 4-week mesocycle. The sRPE load that accumulated across the 4-week mesocycles was higher in the pre-season than the in-season (8260 ± 2021 arbitrary units [AU] vs. 6148 ± 980 AU, p < 0.001), with non-significant differences in accumulated session frequency and duration between phases. Session frequency, duration, and sRPE load differed (p < 0.05) between some weeks in an inconsistent manner during the pre-season and in-season mesocycles. Regarding physical qualities, improvements (p < 0.05) in the 10 m sprint test, Multistage Fitness Test, medicine ball throw, and 1-repetition maximum back squat and bench press performances were evident across the pre-season mesocycle, with declines (p < 0.05) in the 505-Agility Test, L-run Test, and 1-repetition maximum back squat performances across the in-season mesocycle. Conclusions: These novel training load data show schoolboy rugby league players experience considerable demands that may be suitable in developing several physical qualities during the pre-season but detrimental to maintaining such qualities across the in-season.

Accurately identifying positive and negative regulation of apoptosis using fusion features and machine learning methods

Apoptotic proteins play a crucial role in the apoptosis process, ensuring a balance between cell proliferation and death. Thus, further elucidating the regulatory mechanisms of apoptosis will enhance our understanding of their functions. However, the development of computational methods to accurately identify positive and negative regulation of apoptosis remains a significant challenge. This work proposes a machine learning model based on multi-feature fusion to effectively identify the roles of positive and negative regulation of apoptosis. Initially, we constructed a reliable benchmark dataset containing 200 positive regulation of apoptosis and 241 negative regulation of apoptosis proteins. Subsequently, we developed a classifier that combines the support vector machine (SVM) with pseudo composition of k-spaced amino acid pairs (PseCKSAAP), composition transition distribution (CTD), dipeptide deviation from expected mean (DDE), and PSSM-composition to identify these proteins. Analysis of variance (ANOVA) was employed to select optimized features that could yield the maximum prediction performance. Evaluating the proposed model on independent data revealed and achieved an accuracy of 0.781 with an AUROC of 0.837, demonstrating our model's potent capabilities.

Device Simulation of 25.9% Efficient ZnOxNy${\rm ZnO}_x{\rm N}_y$/Si Tandem Solar Cell

AbstractThe novel, high electron mobility material has been investigated theoretically as an absorber in a two‐terminal tandem solar cell. In addition to its high mobility, can attain sufficiently low carrier concentration to enable ‐junctions, and has a tunable bandgap around the 1.7 eV range. It is therefore suitable for pairing with a Si‐based bottom cell. In addition to the layer, the tandem cell consists of a emitter and a Si heterojunction bottom cell. A buffer layer is introduced between the emitter and absorber in the top cell to mediate a large valence band offset that resulted in a poor fill factor, . A buffer layer bandgap of 1.5 eV gave the highest power conversion efficiency (PCE). The objective is to estimate the optimal performance of in a tandem solar cell. The dependence of current–voltage (–) characteristics on thickness, mobility and carrier concentration in the layer is evaluated, and found to yield maximum performance with 0.35 , 250 Vs–1 and , respectively. Using these conditions, the – parameters of the device under AM1.5 illumination are short circuit current density, mA , open circuit voltage, V, and . With this, it is reported on, to the best of the knowledge, the first device simulation based on .

Considerations for identifying subtypes of speech sound disorder.

Speech sound disorders (SSDs) in children are heterogeneous. Differentiating children with SSDs into distinct subtypes is important so that each child receives a treatment approach well suited to the particular difficulties they are experiencing. To study the distinct underlying processes that differentiate phonological processing, phonological planning or motor planning deficits. The literature on the nature of SSDs is reviewed to reveal diagnostic signs at the level of distal causes, proximal factors and surface characteristics. Subtypes of SSDs may be identified by linking the surface characteristics of the children's speech to underlying explanatory proximal factors. The proximal factors may be revealed by measures of speech perception skills, phonological memory and speech-motor control. The evidence suggests that consistent phonological disorder (CPD) can be identified by predictable patterns of speech error associated with speech perception errors. Inconsistent phonological disorder (IPD) is associated with a deficit in the selection and sequencing of phonemes, that is, revealed as within-word inconsistency and poor phonological memory. The motor planning deficit that is specific to childhood apraxia of speech (CAS) is revealed by transcoding errors on the syllable repetition task and an inability to produce [pətəkə] accurately and rapidly. Children with SSDs form a heterogeneous population. Surface characteristics overlap considerably among those with severe disorders, but certain signs are unique to particular subtypes. Careful attention to underlying causal factors will support the accurate diagnosis and selection of personalized treatment options. What is already known on the subject SSD in children are heterogenous, with numerous subtypes of primary SSD proposed. Diagnosing the specific subtype of SSD is important in order to assign the most efficacious treatment approach for each child. Identifying the distinct subtype for each child is difficult because the surface characteristics of certain subtypes overlap among categories (e.g., CPD or IPD; CAS). What this paper adds to the existing knowledge The diagnostic challenge might be eased by systematic attention to explanatory factors in relation to the surface characteristics, using specific tests for this purpose. Word identification tasks tap speech perception skills; repetition of short versus long strings of nonsense syllables permits observation of phonological memory and phonological planning skills; and standard maximum performance tests provide considerable information about speech motor control. What are the potential or actual clinical implications of this work? Children with SSDs should receive comprehensive assessments of their phonological processing, phonological planning and motor planning skills frequently, alongside examinations of their error patterns in connected speech. Such assessments will serve to identify the child's primary challenges currently and as they change over developmental time.