Cost Efficiency Research Articles

Techno-economic optimization and working fluid selection of a biogas-based dual-loop bi-evaporator ejector cooling cycle involving power-to-hydrogen and water facilities

Biogas-fueled decentralized energy systems featuring gas turbines emerge as pivotal players in the quest for more adaptable and eco-friendly energy solutions. This study presents the design of a cutting-edge multigeneration system that harnesses the potential of a gas turbine coupled with ejector-driven dual-loop bi-evaporator technology, reverse osmosis desalination, proton exchange membrane electrolysis, and an organic Rankine cycle. The research encompasses an extensive sensitivity analysis and deploys a genetic algorithm-based optimization technique. Through meticulous Optimization, the system achieves remarkable outcomes, including electricity generation, cooling capacity, heating capacity, desalinated water production, and hydrogen yield, measured at 957.3 kW, 231.4 kW, 272.3 kW, 7.336 kg/s, and 0.99 kg/h, respectively. Notably, this performance surpasses the base case by 2% and 8.9%, yielding exergy efficiency and total unit cost improvements of 33.3% and 16.4 $/GJ. Among the critical decisions made was selecting an organic working fluid for the organic Rankine cycle. Through rigorous evaluation, isobutene emerged as the optimal choice, demonstrating significantly improved output power compared to alternatives. Isobutane as the working fluid led to a substantial increase in overall exergy efficiency and a resultant total unit cost of 18.06 $/GJ, accompanied by an impressive 23.48% exergy efficiency.

AI Applications in Pediatrics: Past, Present, and Future

In pediatric medicine, Artificial Intelligence (AI) currently remains as a novel area for research and development. Most AI applications created for adults are often complicated to fully transfer over to pediatrics due to several limiting factors. These factors include physiological differences, lack of data, and unknown implications. Nevertheless, applications in adult medicine have influenced extensive reviews for algorithms that show significant potential in pediatrics. As pediatric data gradually increases, several AI algorithms for adults can be fine-tuned to provide similar accuracies and sensitivities for diagnosing specific pediatric diseases. This chapter will discuss the pioneering applications of AI in medicine as well as current and arising applications or subspecialties. Areas of discussion encompass precision medicine, oncology, cardiology, radiology, chatbots, neurology, and ophthalmology. AI algorithms within these domains range from assistance with diagnoses, shorter waiting times, to continuous monitoring. Common benefits exhibited by many applications include increased accuracy, time and cost efficiency, patient compliance, and flexibility. For example, medical wearable technology and chatbots are two AI applications that can provide certain patient care in both outpatient and inpatient settings. These algorithms are convenient to use and offer continuous monitoring when a provider may not be readily available. Furthermore, other topics explored in this chapter relate to common concerns for using AI in pediatric medicine and education for health professionals, parents, and children. Most apprehension pertains to the misuse of data and quality of care. Reviewing these concerns is crucial for AI to exponentially flourish in medicine. Without established comprehension, hesitance and distrust may prevail over the promising potential AI can offer. Education and training can help alleviate these common misunderstandings by explaining how AI can affect both medicine and health care. Moreover, learning environments can allow health professionals, parents, and children to openly communicate their concerns that could guide ethical frameworks for developing AI applications. Analyzing these past, present, future applications, and related concerns can help demonstrate how AI will transform pediatric medicine.

Performance evaluation of a modified axial flow water Lifting pump for small to medium irrigation in Nigeria.

Most irrigation pumps used in Nigeria are imported. Consequently, the challenges farmers faced in using these pumps include but not limited to the following; priming before operation, relatively low-capacity delivery at low head and high price which is often time not affordable by peasant farmers. The objective of this research was to develop an axial flow pump with locally sourced materials and affordable by farmers for small to medium scale irrigation in Nigeria. The design of the pump for the study was adopted from an existing and functioning axial pumping machine, developed by Agricultural Machinery and Mechatronics Department, Kasetsart University, Thailand. The pump was fabricated at the workshop of National Centre for Agricultural Mechanization (NCAM), Ilorin, Nigeria. Diesel fuel was used in powering the pump because of higher torque deliverable. The developed pump based on its capacity can be used to irrigate one hectare in one hour at 16.2 mm depth of water using 2000 rpm at 1m head and 9.6 mm depth of water using 1000 rpm at 3 m head. The maximum water discharge of 162 m3/h (45 l/s) was at 1m using a speed of 2000 rpm with water to fuel delivery of 124.29 l/s. The maximum water discharge of 162 m3/h (45 l/s) was at 1m using a speed of 2000 rpm with water to fuel delivery of 119.57m3/L. And the least water discharge of 59.30 m3/h (28 l/s) and water delivery per fuel was 41.0m3/L at 3 m head at 1000 rpm. Therefore, the developed pump operates best at lower head of 1m featuring a 3kW prime mover. The developed pump based on its capacity can be used to irrigate one hectare in one hour at 16.2 mm depth of water using 2000rpm at 1m head and 9.6 mm depth of water using 1000rpm at 3m head at a moisture content of 45% on a sand-loamy soil. Statistical analysis reveals relationships between pump speed, head, fuel consumption, and various performance indicators. The centrifugal pumps were found to produce lower discharge than axial flow pumps at different water heads evaluated. Comparative economic analysis demonstrates the pump's superiority over conventional centrifugal pumps in terms of water-fuel delivery efficiency and operational cost. This study introduced a locally modified axial flow pump as a practical, cost-effective, and efficient solution to irrigation challenges faced by small and medium-scale farmers in Nigeria. This pump has higher capacity delivery, spare parts are readily available at local market, it is appropriate for use where the irrigation head requirements are low and large volumes of water is required to be lifted at low pressure, it is appropriate where water can be lifted from rivers and canals and then directed to farmers' fields for irrigation. It is also applicable in rice farm flooding and flood drainage. This type of pump is good for flood (surface) irrigation system, which include furrow, border, basin irrigation and uncontrolled flooding in loamy and clay soils for rice production and sugarcane productions. The pump's adoption and economic viability positions it as a potential catalyst for increased food production, income, and food security in the country.

Technical Efficiency and Allocative Efficiency of Beef Cattle Fattening in The Content of Digital Economy: An Empirical Study Based on Survey in China

This study applies data envelopment analysis (DEA) to gauge technical efficiency and allocative efficiency in China’s beef cattle-fattening industry using survey data. The Tobit model considers the salient determinants that drive these efficiencies. The results indicate that (1) large-scale farms exhibit robust TE and pure technical efficiencies (PTE), whereas scale efficiencies (SE) diverge significantly between large and medium-sized operations. The cost efficiency (CE) of smaller farms lags behind their larger counterparts, with the latter displaying greater revenue efficiencies (RE) and profit efficiency (PE). (2) The influence of identical factors on the efficiency of beef cattle fattening production can vary, sometimes antithetically, across different scales. Local policy interventions must be differentiated according to farm type and size. (3) The unique context of China’s national conditions and the status quo of livestock farming render the dual implementation of environmental regulations and technological subsidies less viable for Chinese beef cattle farms. These entities should prioritize production over technological innovation and advancement. Policymakers should adopt strategies such as targeted skill/technological training for farm managers at particular scales of operation. This could represent a critical trajectory to augment the efficiency of beef cattle production and increase beef yield in China.

Practice efficiency and total cost of care with bispecifics and CAR-T in relapsed/refractory diffuse large B-cell lymphoma: an institutional perspective1.

Aim: Novel treatment options for relapsed/refractory diffuse large B-cell lymphoma include T-cell targeting therapies. Practice efficiency and cost are important for informed treatment decisions. Materials/methods: An institutional decision-maker cost model was developed for 6-month, 1-year and median cycles of treatment time horizons comparing practice efficiency and costs of epcoritamab vsglofitamab and axicabtagene ciloleucel (axi-cel). Results: Overall, epcoritamab required the shortest personnel and chair time, except over 1year (second shortest chair time). Across all time horizons, epcoritamab was cost-saving vs axi-cel and had similar costs to glofitamab on a per-month basis. Conclusion: Epcoritamab reduced personnel and chair time. Additionally, epcoritamab was cost-saving vsaxi-cel and had similar costs to glofitamab on a per-month basis.

Considerations for reporting variants in novel candidate genes identified during clinical genomic testing.

Since the first novel gene discovery for a Mendelian condition was made via exome sequencing (ES), the rapid increase in the number of genes known to underlie Mendelian conditions coupled with the adoption of exome (and more recently, genome) sequencing by diagnostic testing labs has changed the landscape of genomic testing for rare disease. Specifically, many individuals suspected to have a Mendelian condition are now routinely offered clinical ES. This commonly results in a precise genetic diagnosis but frequently overlooks the identification of novel candidate genes. Such candidates are also less likely to be identified in the absence of large-scale gene discovery research programs. Accordingly, clinical laboratories have both the opportunity, and some might argue a responsibility, to contribute to novel gene discovery which should in turn increase the diagnostic yield for many conditions. However, clinical diagnostic laboratories must necessarily balance priorities for throughput, turnaround time, cost efficiency, clinician preferences, and regulatory constraints, and often do not have the infrastructure or resources to effectively participate in either clinical translational or basic genome science research efforts. For these and other reasons, many laboratories have historically refrained from broadly sharing potentially pathogenic variants in novel genes via networks like Matchmaker Exchange, much less reporting such results to ordering providers. Efforts to report such results are further complicated by a lack of guidelines for clinical reporting and interpretation of variants in novel candidate genes. Nevertheless, there are myriad benefits for many stakeholders, including patients/families, clinicians, researchers, if clinical laboratories systematically and routinely identify, share, and report novel candidate genes. To facilitate this change in practice, we developed criteria for triaging, sharing, and reporting novel candidate genes that are most likely to be promptly validated as underlying a Mendelian condition and translated to use in clinical settings.

Accuracy calibration of the multi-light screen measurement system using array optical signal

Abstract The multi-light screen measurement systems must be calibrated before being come into use, and a calibration method using live-fire practice is generally adopted. However, this method has many shortcomings, such as high cost, high risk and poor efficiency. To effectively improve the measurement accuracy of a multi-light screen measurement system, we propose a calibration method based on array optical signal in the paper. According to the measurement principle of the system, the proposed calibration theory is systematically described. The aim is to map the given measurement results in the form of vector into the specified time sequence. Furthermore, we develop a calibration platform whose core components are an arbitrary waveform generator and a tunable laser, and thus it can generate a set of six optical signals with an exact sequential relationship. After receiving the optical signals, some measurement results with errors are obtained by the system. Many calibration operations are performed to obtain the systematic errors, which are the statistical average of the deviation between the given measurement results and those with errors. Finally, we use the proposed calibration method combined with the error tensor and the traditional one to carry out comparison experiments. The results show that the proposed calibration method is superior to the traditional method in terms of comprehensive performance. In addition, the proposed calibration method relative to the traditional ones has some advantages, such as high efficiency, safety and low cost.

Enhanced broadband sound absorption of multi-layer composite material based on three-dimensional warp-knitted spacer fabric

Compared with other sound-absorbing materials, spacer fabric with a “sandwich” structure has many advantages, such as light weight, an environmentally friendly production process, recyclability, versatility, low production cost and high efficiency, and strong designability of the organizational structure. However, as a porous sound-absorbing material, the effective sound-absorbing frequency band of the spacer fabric is concentrated at high frequencies, so the sound-absorbing performance needs to be improved. This paper establishes a Johnson–Champoux–Allard theoretical model by using a genetic algorithm to characterize the sound absorption performance of the three-dimensional (3D) warp-knitted spacer fabric. The theoretical model is validated through finite element analysis and experimental methods. Based on this model, two composite materials are designed to improve the sound absorption performance: (1) multi-layer spacer fabric composite material and (2) multi-layer spacer fabric-aluminum foil composite material. Numerical simulation and experimental results show that the sound absorption performance is significantly improved by widening the effective frequency band and increasing the sound absorption coefficient. The contribution of this paper is twofold. Firstly, the theoretical acoustic model of 3D warp-knitted spacer fabric is established. Secondly, the superior sound absorption performance is achieved by proposing multi-layer and multi-material composite material. The spacer fabric-based composite material has broad application prospects in the field of noise reduction and sound insulation. The paper provides a theoretical basis and more strategies for the acoustic and multifunctional design of spacer fabric in the future.

A novel multi-model estimation of phosphorus in coal and its ash using FTIR spectroscopy

The level of phosphorus must be carefully monitored for proper and effective utilization of coal and coal ash. The phosphorus content needs to be assessed to optimize combustion efficiency and maintenance costs of power plants, ensure quality, and minimize the environmental impact of coal and coal ash. The detection of low levels of phosphorus in coal and coal ash is a significant challenge due to its complex chemical composition and low concentration levels. Effective monitoring requires accurate and sensitive equipment for the detection of phosphorus in coal and coal ash. X-ray fluorescence (XRF) is a commonly used analytical technique for the determination of phosphorus content in coal and coal ash samples but proves challenging due to their comparatively weak fluorescence intensity. Fourier Transform Infrared spectroscopy (FTIR) emerges as a promising alternative that is simple, rapid, and cost-effective. However, research in this area has been limited. Until now, only a limited number of research studies have outlined the estimation of major elements in coal, predominantly relying on FTIR spectroscopy. In this article, we explore the potential of FTIR spectroscopy combined with machine learning models (piecewise linear regression—PLR, partial least square regression—PLSR, random forest—RF, and support vector regression—SVR) for quantifying the phosphorus content in coal and coal ash. For model development, the methodology employs the mid-infrared absorption peak intensity levels of phosphorus-specific functional groups and anionic groups of phosphate minerals at various working concentration ranges of coal and coal ash. This paper proposes a multi-model estimation (using PLR, PLSR, and RF) approach based on FTIR spectral data to detect and rapidly estimate low levels of phosphorus in coal and its ash (R2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$^2$$\\end{document} of 0.836, RMSE of 0.735 ppm, RMSE (%) of 34.801, MBE of − 0.077 ppm, MBE (%) of 5.499, and MAE of 0.528 ppm in coal samples and R2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$^2$$\\end{document} of 0.803, RMSE of 0.676 ppm, RMSE (%) of 38.050, MBE of − 0.118 ppm, MBE (%) of 4.501, and MAE of 0.474 ppm in coal ash samples). Our findings suggest that FTIR combined with the multi-model approach combining PLR, PLSR, and RF regression models is a reliable tool for rapid and near-real-time measurement of phosphorus in coal and coal ash and can be suitably modified to model phosphorus content in other natural samples such as soil, shale, etc.

Empowering Sustainable Industrial and Service Systems through AI-Enhanced Cloud Resource Optimization

This study focuses on examining the shift of an application system from a traditional monolithic architecture to a cloud-native microservice architecture (MSA), with a specific emphasis on the impact of this transition on resource efficiency and cost reduction. In order to evaluate whether artificial intelligence (AI) and application performance management (APM) tools can surpass traditional resource management methods in enhancing cost efficiency and operational performance, these advanced technologies are integrated. The research employs the refactor/rearchitect methodology to transition the system to a cloud-native framework, aiming to validate the enhanced capabilities of AI tools in optimizing cloud resources. The main objective of the study is to demonstrate how AI-driven strategies can facilitate more sustainable and economically efficient cloud computing environments, particularly in terms of managing and scaling resources. Moreover, the study aligns with model-based approaches that are prevalent in sustainable systems engineering by structuring cloud transformation through simulation-supported frameworks. It focuses on the synergy between endogenous AI integration within cloud management processes and the overarching goals of Industry 5.0, which emphasize sustainability and efficiency that not only benefit technological advancements but also enhance stakeholder engagement in a human-centric operational environment. This integration exemplifies how AI and cloud technology can contribute to more resilient and adaptive industrial and service systems, furthering the objectives of AI and sustainability initiatives.

Research on a multiobjective cooperative operation scheduling method for agricultural machinery across regions with time windows

This article aims to solve the problems of high cost, low efficiency, and task delay caused by the lack of scientific scheduling strategies during the agricultural harvest season. Considering the constraints of operation time windows, a multimachine multiobjective cross-regional collaborative operation scheduling model is constructed with the goal of minimizing the transfer distance of agricultural machinery and nonoperational scheduling costs. A multiobjective genetic algorithm (HTSMOGA) based on a hybrid time window priority rule and a tabu search strategy is designed. The initial population is generated by utilizing time window priority rules, genes with earlier operation start times are preferentially retained, and a mixed search strategy is introduced to avoid local optimal solutions. Experiments were conducted among 24 farms in a region of Hebei Province, and the results showed that, compared with other algorithms, the HTSMOGA achieved an average reduction of 17.18 % in the transfer distance of agricultural machine operations and 19.36 % in the nonoperational waiting time. Therefore, this study provides a rational and feasible solution for the cross-regional operation scheduling of agricultural machinery cooperatives and provides theoretical support for ensuring the timely completion of harvesting tasks and the cost savings and efficiency of agricultural machinery operations.

Deep learning and saliency-based parking IoT classification under different weather conditions

The world’s population is growing at an exponential rate, which has created a slew of new issues in our daily lives. Among these challenges is the difficulty of finding available parking spaces. To overcome this obstacle, an effective system for detecting vacant parking spaces is crucial, particularly in densely populated areas. Even though there are numerous parking detecting systems on the market today, they all have their own set of restrictions. There are primarily two types of parking systems available. The first is a hardware-based system that uses hardware to detect whether a space is vacant or occupied, and the second is a smart parking system that uses cameras to classify parking status. Vision-based parking systems are becoming increasingly popular due to benefits such as flexibility and cost efficiency. However, due to challenging weather conditions and various occlusions caused by objects, such systems are challenging to develop. The limitations of a vision-based parking system must be addressed for it to become more accurate. To date, many researchers have attempted to incorporate deep learning-based parking systems with various algorithms but none of them has focused on reducing the complexity of Convolutional Neural Network (CNN) training through unsupervised pre-processing steps. In this study, an attempt is made to reduce the complexity of CNN by integrating salient features as a pre-processing step for normalizing weather conditions. It has been observed that salient features-based pre-processing makes the system more robust in terms of accuracy and time efficiency.

An improved neural operator framework for large-scale CO2 storage operations

Geologic carbon storage (GCS) is a practical solution to mitigate the impact of climate change and achieve net-zero carbon emissions. However, due to complex subsurface characteristics and operational constraints, GCS can potentially induce seismic events and leakage to groundwater resources. Moreover, predicting subsurface response in pressure and saturation changes during GCS requires high-fidelity models with high computational times. Machine learning (ML) offers a promising solution to alleviate these challenges. Yet, ML-driven approaches in subsurface physics face significant obstacles, such as the substantial computational resources required for training large-scale models and the accuracy requirement as a reliable surrogate model. To tackle these issues, we propose an improved neural operator (INO) that has improved a DeepONet architecture to handle a realistic reservoir model efficiently, with computational efficiency and high accuracy.Our proposed INO framework has three key benefits: precision, adaptability, and cost-efficiency. For precision, it achieves an average root mean square error (RMSE) of 0.05% (1.5 psi) compared to the average reservoir pressure (3,150 psi) and a RMSE value of 0.016 in void fraction for CO2 saturation among testing cases. Although it is sensitive to high-error areas with steep state variable gradients, proper domain decomposition, ensemble methods, and/or integrating domain-specific knowledge could improve this limitation. For adaptability, the INO framework can be trained with a subset of the computational domain during each backpropagation to achieve better training adaptability, even for problems with high-dimensional inputs (i.e., heterogeneous fields). For example, it can be trained with random subsampling of only 0.2% of the full domain but can predict pressure and saturation in any space and time within the entire domain. For cost efficiency, training procedures with a subset of the whole computational domain significantly improve computational time. For example, for 90 training cases with 1.73 million cells and 50-time steps, the overall training time was about 2.5 h using a single GPU. A trained INO model takes 1 s per evaluation over 1.73 million and 50 time stamps, much faster than a high-fidelity simulator. Overall, the proposed INO approach with these improved benefits will enable us to perform large-scale GCS applications, enhancing the potential of ML models in the subsurface and other multiphysics problems.

Utilization of jarosite and manganite mineral as raw material to fabricate metal-oxide composite film for gas sensor application

This study explores the utilization of Indonesia’s abundant geological resources, specifically jarosite and manganite minerals, to develop composite films for ethanol gas sensing applications. With jarosite’s Fe2O3 composition and manganite’s diverse applications in manganese oxide minerals, these minerals serve as promising raw materials. Building on prior research that employed Fe2O3 and manganese oxide composites, our focus lies in creating a cost-effective composite film comprising iron, manganese, and zinc oxides to enhance ethanol gas sensing capabilities. The fabrication process involves mineral extraction through a precipitation method, followed by composite synthesis and film formation using a screen-printing technique. Analysis of the resulting composite film’s morphological and crystal structure through SEM and XRD machines provides porosity and crystallinity characteristics. Examining electrical characteristics reveals the gas sensor behavior, emphasizing sensitivity and response in an ethanol environment. This study aims to contribute to gas sensor technology advancements, leveraging Indonesia’s geological wealth, promoting cost efficiency, and addressing critical aspects of sensitivity in gas sensor applications.

A systematic theoretical study of CO2 hydrogenation towards methanol on Cu-based bimetallic catalysts: role of the CHO&CH3OH descriptor in thermodynamic analysis.

The application of density functional theory (DFT) has enriched our understanding of methanol synthesis through CO2 hydrogenation on Cu-based catalysts. However, variations in catalytic performance under different metal doping conditions have hindered the development of universal catalytic principles. To address these challenges, we systematically investigated the scaling relationships of adsorption energy among different reaction intermediates on pure Cu, Au-Cu, Ni-Cu, Pt-Cu, Pd-Cu and Zn-Cu models. Additionally, by summing the respective adsorption energies of two separate species, we have developed a dual intermediate descriptor of CHO&CH3OH, capable of achieving computational accuracy on par with DFT results using the multiple linear regression method, all the while enabling the rapid prediction of thermodynamic properties at various stages of methanol synthesis. This method facilitates a better understanding of the coupling mechanisms between energy and linear expressions on copper-based substrates, and the universal linear criterion can be applied to other catalytic systems, with the aim of pursuing potential catalysts having both high efficiency and low cost.

Unveiling the potential of TPA-based molecules to tune the optoelectronic properties and enhance the efficiency of dye-sensitized solar cells.

The world's energy and environmental requirements are changing due to rapid population growth and industrial growth, and solar cells can be used to meet these demands. Dye-sensitized solar cells (DSSCs) are solar cells in which energy conversion occurs via a process similar to photosynthesis in plants. DSSC development is still in its infancy. DSSCs can operate under cloudy conditions and indirect sunlight and have attracted considerable attention due to their low cost and high efficiency. We designed two metal-free TPA-based dyes (Dye2 and Dye3) based on the reference dye Mg207 (Dye1) by increasing the donor strength of the molecule, as such dyes have shown enhanced efficiency in DSSCs. Moreover, the triphenylamine (TPA) moiety has been demonstrated to be a good donor that prevents charge recombination. Intramolecular charge transfer (ICT) from the donor to acceptor moiety was found in the sensitizers, and electrons were promoted to the conduction band (CB) of the TiO2 semiconductor. The negative binding energy of the dye@TiO2 clusters indicated that dye adsorption on the semiconductor surface was stable. The double donor increased the electron injection and electronic coupling constants in Dye2 and Dye3, indicating that these newly designed dyes have superior charge injection capacity. Accordingly, the efficiencies of DSSCs with Dye2 and Dye3 were 9.77% and 9.62%, respectively, and substitution with the TPA unit at the -R1 and -R2 positions in Dye1 resulted in better power conversion compared to the parent compound (9.09%). Increased donor strength improved photovoltaic performance by increasing current density and light-harvesting efficiency. This is a good molecular design approach for preparing targeted donor- -acceptor (D- -A) organic dyes with high DSSC efficiency. To predict the charge transport and optoelectronic characteristics of the TPA dyes, quantum chemical calculations were carried out using Gaussian16. The ground-state (S0) optimized geometries of the sensitizers were computed by utilizing DFT at the B3LYP/6-31G** level. The absorption spectra ( max) were computed by employing TD-DFT with various functionals (B3LYP, PBE1PBE, CAM-B3LYP, and BHandHLYP) in the gas and solvent (DCM) phases. Among the studied functionals, BHandHLYP was found to be best at successfully reproducing the experimental data. Thus, the absorption spectra of the newly designed dyes and dye@TiO2 were calculated at the BHandHLYP/6-31G** level. The dye@TiO2 cluster optimizations were carried out at the B3LYP/6-31G**(LANL2DZ) level.

Adaptive designs were primarily used but inadequately reported in early phase drug trials

BackgroundFaced with the high cost and limited efficiency of classical randomized controlled trials, researchers are increasingly applying adaptive designs to speed up the development of new drugs. However, the application of adaptive design to drug randomized controlled trials (RCTs) and whether the reporting is adequate are unclear. Thus, this study aimed to summarize the epidemiological characteristics of the relevant trials and assess their reporting quality by the Adaptive designs CONSORT Extension (ACE) checklist.MethodsWe searched MEDLINE, EMBASE, Cochrane Central Register of Controlled Trials (CENTRAL) and ClinicalTrials.gov from inception to January 2020. We included drug RCTs that explicitly claimed to be adaptive trials or used any type of adaptative design. We extracted the epidemiological characteristics of included studies to summarize their adaptive design application. We assessed the reporting quality of the trials by Adaptive designs CONSORT Extension (ACE) checklist. Univariable and multivariable linear regression models were used to the association of four prespecified factors with the quality of reporting.ResultsOur survey included 108 adaptive trials. We found that adaptive design has been increasingly applied over the years, and was commonly used in phase II trials (n = 45, 41.7%). The primary reasons for using adaptive design were to speed the trial and facilitate decision-making (n = 24, 22.2%), maximize the benefit of participants (n = 21, 19.4%), and reduce the total sample size (n = 15, 13.9%). Group sequential design (n = 63, 58.3%) was the most frequently applied method, followed by adaptive randomization design (n = 26, 24.1%), and adaptive dose-finding design (n = 24, 22.2%). The proportion of adherence to the ACE checklist of 26 topics ranged from 7.4 to 99.1%, with eight topics being adequately reported (i.e., level of adherence ≥ 80%), and eight others being poorly reported (i.e., level of adherence ≤ 30%). In addition, among the seven items specific for adaptive trials, three were poorly reported: accessibility to statistical analysis plan (n = 8, 7.4%), measures for confidentiality (n = 14, 13.0%), and assessments of similarity between interim stages (n = 25, 23.1%). The mean score of the ACE checklist was 13.9 (standard deviation [SD], 3.5) out of 26. According to our multivariable regression analysis, later published trials (estimated β = 0.14, p < 0.01) and the multicenter trials (estimated β = 2.22, p < 0.01) were associated with better reporting.ConclusionAdaptive design has shown an increasing use over the years, and was primarily applied to early phase drug trials. However, the reporting quality of adaptive trials is suboptimal, and substantial efforts are needed to improve the reporting.

Integration of field measurements with unmanned aerial vehicle to predict forest inventory metrics at tree and stand scales in natural pure Crimean pine forests

ABSTRACT Inventorying forest ecosystems is an essential part of forest management planning. However, it is quite costly and time-consuming, particularly for larger areas. Recently, significant developments have been made in unmanned aerial vehicle (UAV) technology to improve the cost and time efficiency in forest inventory. Therefore, UAV images have become one of the inventory tools that produces data with high spatial resolution in determining forest resources. This study aims to investigate the contribution of UAV data to forest inventory in a case study area with a total of 30 sample plots located in pure and natural Crimean pine (Pinus nigra J.F. Arnold ssp. pallasiana (Lamb.) Holmboe) stands in the Black Sea backward region of Türkiye. Total tree height (h) and stem volume (v) were recorded at individual tree level (n = 367), and the number of trees (N), mean height (hmean), top height (htop), stand basal area (BA) and stand volume (V) were calculated at sample plot level (n = 30) from both the field and UAV-based data. Pearson’s correlation coefficients (r) for h and v were 0.96 and 0.72, respectively, the highest correlation at the sample plot level was observed for the hmean - htop (r = 0.96), while the lowest correlation was found for BA (r = 0.54). The suitability of the observation and prediction values was assessed using a t-test at both individual tree and sample plot levels. According to the t-test results, the observation and prediction values for h, v, hmean, htop, BA and V metrics were found to be compatible (p > 0.05), but not for N (p < 0.05). Overall results indicated that UAV technology has a potential to be used in forest inventory and can contribute to the determination of individual tree and stand metrics. Thereby, it saves cost and time in forest inventory studies and helps monitoring the dynamic structure of the forest ecosystem with an effective approach in forest inventory.

Economic efficiency of small-scale wheat production in Jigawa state, Nigeria

Africa’s most populous country has failed to grow more food for its fast-rising population. With 420,000 metric tons of wheat produced in Nigeria in 2020, the country is still far from bridging its 4.6 million metric tons annual wheat gap. Increasing wheat production is a challenge for the nation to fulfill the food requirements of its growing population. So far literature has shown a rise in research on wheat in different parts of Nigeria, with a paucity of information on the economic efficiency of wheat production in the study area, and the country in general. To bridge these knowledge and empirical gaps, this research investigates the economic efficiency of wheat production in Jigawa State of Nigeria using information gathered from a survey elicited by a well-structured questionnaire coupled with an interview schedule from 341 active wheat farmers selected through a multi-stage sampling technique. Both descriptive and inferential statistics were used to analyze the data collected. Empirically, despite the wheat enterprise being profitable in the study area, farmers didn’t achieve the targeted goals of output maximization, cost minimization, and profit maximization as evident respectively by the technical, cost, and economic efficiency indexes. However, these goals were challenged by induced human risks viz. gender discrimination that affects women’s access to productive resources, poor labor productivity due to diminishing marginal returns associated with old age, capital consumption triggered by increased income, poor prioritization of wheat enterprise as a business, and vulnerable household size. Furthermore, the identified constraints mediating the links that affected the economic efficiency of wheat farmers were price/marketing, technological, managerial, and infrastructural risks. Therefore, for long-run sustainable wheat production, the responsibility lies on policymakers to concentrate more on marketing and technological risks challenging wheat production in the study area as empirically established by this research.

Review of Fault Diagnosis Methods for Induction Machines in Railway Traction Applications

Induction motors make up approximately 80% of the electric motors in the railway sector due to their robustness, high efficiency, and low maintenance cost. Nevertheless, these motors are subject to failures which can lead to costly downtime and service interruptions. In recent years, there has been a growing interest in developing fault diagnosis systems for railway traction motors using advanced non-invasive detection and data analysis techniques. Implementing these methods in railway applications can prove challenging due to variable speed and low-load operating conditions, as well as the use of inverter-fed motor drives. This comprehensive review paper summarizes general methods of fault diagnosis for induction machines. It details the faults seen in induction motors, the most relevant signals measured for fault detection, the signal processing techniques for fault extraction as well as some classification algorithms for diagnosis purposes. By giving the advantages and drawbacks of each technique, it helps select the appropriate method that could address the challenges of railway applications.