High Potential For Improvement Research Articles

The Current State and Legal Issues of Online Crimes Related to Children and Adolescents.

There are two categories of online crimes related to children and adolescents: those committed by adolescents and those committed against children and adolescents. While recent trends in criminal law show consensus on strengthening punishment in cases of crimes against children and adolescents, there are mixed stances in cases of juvenile delinquency. One perspective emphasizes strict punishment, whereas the other emphasizes dispositions aligned with human rights. While various forms of online crime share the commonality in that the main part of the criminal act occurs online, they can be categorized into three types: those seeking financial gain, those driven by sexual motives, and those engaged in bullying. Among these, crimes driven by sexual motives are the most serious. Second-hand trading fraud and conditional (sexual) meeting fraud fall under the category of seeking financial gain and occur frequently. Crimes driven by sexual motives include obscenity via telecommunication, filming with discrete cameras, child and adolescent sexual exploitation material, fake video distribution, and blackmail/coercion using intimate images/videos ("sextortion"). These crimes lead to various legal issues such as whether to view vulgar acronyms or body cams that teenagers frequently use as simple subcultures or crimes, what criteria should be applied to judge whether a recorded material induces sexual desire or shame, and at what stage sexual grooming becomes punishable. For example, sniping posts, KakaoTalk prisons, and chat room explosions are tricky issues, as they may or may not be punished depending on the case. Particular caution should be exercised against the indiscriminate application of a strict punishment-oriented approach to the juvenile justice system, which is being discussed in relation to online sexual offenses. In the punishment case of online crime, juvenile offenders with a high potential for future improvement and reform must be treated with special consideration.

Social media adoption and export intensity: the moderating role of firm size

PurposeSocial media (SM) networks offer a golden opportunity for firms that particularly engage in international activities to set up sustainable customer relationships and improve competitiveness. The purpose of this study is to examine the influence of SM adoption on the export intensity (EI) of firms listed on Borsa Istanbul (BIST) for the years 2010–2020. The authors use social media index (SMI) to measure SM adoption and firm size (FSize) as a moderator on exploring the interaction of SM and EI.Design/methodology/approachUsing a sample of 150 firms listed on the BIST Industrials Index, this study explores how the adoption of SM affects EI by using panel data analysis over the period of 2010–2020.FindingsThe results indicate that the SMI has a positive and significant effect on the EI. FSize positively moderates the interaction of SMI and EI, indicating that large firms benefit more from the SM in increasing export performance. The findings reflect high potential of EI improvement through adopting right SM policies in emerging markets.Research limitations/implicationsThe sample covers only public companies listed on the BIST Industrials Index. Future studies may extend the coverage and include multiple emerging markets to draw generalized results for the export-oriented firms. This research also analyzes solely four SM networks, i.e. Facebook, Instagram, Twitter and YouTube. However, there are many other SM networks that firms use in online marketing in foreign markets. Finally, this research did not discuss the potential factors that could influence the use of SM in emerging market firms.Practical implicationsThis study denotes the significant role of SM adoption on the EI of firms in an emerging market setting from the perspective of resource-based view. It presents an insightful approach in understanding the mission played by SM networks in enhancing the EI of Turkish firms. Policymakers may use the findings to develop public support programs to promote the adoption and implementation of the SM among exporting firms in emerging markets.Originality/valueThe study provides evidence on the effects of SM adoption on the EI from the perspective of emerging countries. It also helps to gain a deeper understanding of how different SM platforms contribute to the internationalization of firms.

Does olive cultivation sequester carbon?: Carbon balance along a C input gradient

Currently, there are some initiatives aimed at transforming agriculture from being a source to a sink of greenhouse gases, mainly by encouraging combination of management practices that drive nature-based climate processes (NbCS) resulting in an increase in the stocks of soil and biomass organic carbon. Olive grove cultivation represents a key socio-economic and environmental asset for Mediterranean rural regions. Being a woody crop planted in an orchard fashion, the potential for organic carbon accumulation in the biomass and the soil is very high. In this study, farm, trees and soil carbon balances were analysed in 12 pairs of olive groves located in Southern Spain with different tree densities, age, varieties and irrigation regimens. One from each pair of the olive groves has applied (> 8 years) a combination of management practices that promote NbCS resulting in an increase in the entry of carbon, whereas the other comparable nearby olive grove has not implemented management practices that promote NbCS or C entries (non-NbCS). C balance at the farm level was mainly neutral or positive and averaged + 1.20 Mg C ha−1 y−1. However, C balance in the NbCS olive groves was 5 times higher than that of the non-NbCS. The mean soil C balances were negative (−0.18 Mg C ha−1 y−1; losing soil organic C) in the non-NbCS and positive (+1.48 Mg C ha−1y−1) in the NbCS olive groves thanks to the increase in the carbon entries due to the management practices which boost NbCS. This study highlights the important contribution of olive farming in mitigating climate change, which in turn would be an economic incentive for olive growers. Nonetheless, there is a high potential for improvement by implementing management practices which enhance nature-based processes such as the cultivation of temporary spontaneous cover crops and the application of shredded tree pruning and composted olive mill pomace and/or manure to the soil.

Exergy destruction analysis of a power generation system utilizing the cold energy of LNG

The purpose of this research is in-depth understanding of the internal causes of exergy destruction in various parts of the system and to identify potential improvements for the components. The focus is on a combined cycle power generation system that utilizes the organic Rankine cycle (ORC) and direct expansion cycle (DEC). To investigate the primary sources of exergy destruction in each component, advanced exergy analysis (AEA) is utilized. The result demonstrates that the net out power of the proposed system can reach 106.64 kW with energy efficiency of 11.22%, and exergy efficiency of 21.40%. The heat exchanger is identified as the primary contributor to exergy destruction, constituting 81.70% of the total ratio. Specifically, the condenser exhibits the highest exergy destruction ratio at 59.82%, indicating a need for prioritized optimization efforts. The findings of AEA reveal that the primary source of component irreversibility stems from the endogenous part. This shows that, while most exergy destruction is unavoidable, there remains room for system improvement. Regarding the turbine, its exergy destruction is primarily attributed to inefficiencies, leading to irreversibility. Nevertheless, there is exergy destruction that may be avoidable and can be reduced by 25.93 kW, which is 2.5 times greater than that of the heat exchanger. This finding underscores the high potential for improvement in ORC and DEC turbines, making them a priority for optimization efforts.

Advanced exergoenvironmental and thermo-sustainability evaluation of cement plant, splitting the environmental impact into endogenous and exogenous parts: a case study.

The paper presents an advanced exergoenvironmental and thermo-sustainability analysis of a dry process, 5-million-ton capacity cement plant with real-time operational data. The analysis was based on component-wise modelling by separating the exergy destruction (ED) into endogenous, exogenous, avoidable and unavoidable parts for the two production lines, L1 and L2. The result shows an exergy efficiency ([Formula: see text]) of 55.58% for L1 and 58.22% for L2. Additionally, the overall exergoenvironmental factor (EEF) was calculated at 6.47% for (L1) and 5.89% for (L2), with the rotary kiln (RK) having the highest (EEF) of 53.7%. Conversely, the advanced exergy analysis showed that the plant's mainstream ED rates are endogenous and unavoidable. The component sustainability index (SI) for L1 and L2 ranged between [Formula: see text] and [Formula: see text], respectively. Similarly, the sustainability results show that the environmental destruction coefficient (EDC) and the environmental destruction index (EDI) were high in L1. Nonetheless, the study identified components with high potential for improvement, making system design and optimization suggestions possible.

Dual-Energy CT in Cardiothoracic Imaging: Current Developments.

This article describes the technical principles and clinical applications of dual-energy computed tomography (DECT) in the context of cardiothoracic imaging with a focus on current developments and techniques. Since the introduction of DECT, different vendors developed distinct hard and software approaches for generating multi-energy datasets and multiple DECT applications that were developed and clinically investigated for different fields of interest. Benefits for various clinical settings, such as oncology, trauma and emergency radiology, as well as musculoskeletal and cardiovascular imaging, were recently reported in the literature. State-of-the-art applications, such as virtual monoenergetic imaging (VMI), material decomposition, perfused blood volume imaging, virtual non-contrast imaging (VNC), plaque removal, and virtual non-calcium (VNCa) imaging, can significantly improve cardiothoracic CT image workflows and have a high potential for improvement of diagnostic accuracy and patient safety.

DIGITAL TWIN VALUE IN INTELLIGENT BUILDING DEVELOPMENT

The aim of the research. This article discusses the use of the Digital Twin in automation and its impact on the resulting solution. The research aims to illuminate the Digital Twin concept explanation and systematise the knowledge base and fulfill information gaps. Research results. The paper overviews the history of the concept and determines the main phases of Digital Twin development. The significant attention was paid to the classification issue to show the huge variation depending on Digital Twin‘s purpose, lifecycle phase, the scale of the physical twins and data amount in order to explain the twin‘s relation and the hierarchy of complex system. The defined capabilities and values of the concept identify the possible use cases and explain the potential benefits of Digital Twin implementation. Also, this paper takes a look at the use of Digital Twin in the area of building automation. This concept potentially may act as the integration platform for building management systems (BMS) and building information modelling (BIM) technologies with IoT solutions. The discussion of Digital Twin implementation for the building automation complex is presented. We conclude that the Digital Twin can integrate human factor to the control system by using the indexes of climate satisfaction, the feedback functionality and human-machine interfaces. As a result, the improvement of system efficiency depends on the coordination and orchestration of equipment operating mode. Conclusion. The Digital Twin has a high potential for energy efficiency improvements, as it considers many factors, integrates a huge amount of data and continuously improves themselves with real-world data.

Total Factor Efficiency of Energy and Carbon Emissions in 18 Asian Countries: An Empirical Assessment

Energy efficiency is crucial for reducing energy consumption and mitigating the negative impact of carbon dioxide emissions on the environment. The present study aims to evaluate the energy efficiency and emissions efficiency of 18 Asian countries using the undesirable slack-based data envelopment model and a total factor framework for the period 2001–2020. It has been observed that none of the countries had significantly improved energy efficiency. The total factor energy efficiency (TFEE) index shows that Asian countries have varying levels of energy efficiency, with some countries having high potential for improvement and others already performing well. Kazakhstan and Iran have the most room for improvement, while China and the UAE have seen fluctuations in their TFEE scores. The category with the highest number of countries includes Singapore, Hong Kong, Pakistan, India, Japan, Saudi Arabia, Israel, the Philippines, Bangladesh, and Indonesia, with some experiencing significant drops in TFEE scores over the years. Similar patterns can be seen in the total factor carbon emissions efficiency (TFCEE) Index, suggesting that no country is entirely efficient in reducing carbon emissions. Kazakhstan maintains its position at the bottom of the ranking for lowering carbon emissions, while Bangladesh has shown more success. Overall, there is a need for continued efforts to improve energy efficiency in these nations to reduce their carbon footprint and contribute to global sustainability goals.

Damage Assessment of Similar Martensitic Welds Under Creep, Fatigue, and Creep-Fatigue Loading

Abstract For the assessment of welds under high-temperature conditions in the creep or creep-fatigue regimes, the knowledge of the damage location and its temporal evolution is of high importance. The local failure behavior of weld joints is not reflected in design guidelines using weld reduction factors or in typical assessment approaches. The evaluation of local strains and stresses in the heat affected zone (HAZ) is essential for a more detailed consideration of weld behavior and has a high potential for improvement of design and inspection guidelines. In this paper, an overview of current developments in the assessment of weld joints is given. Uni-axial creep, component tests, low cycle fatigue (LCF), and creep-fatigue experiments with base material, weld joints and microstructure simulated HAZ material are presented. The use of test results of microstructure simulated HAZ material allows the parameter identification of numerical material models for the HAZ and improves the simulation of the local stress and strain behavior of weld joints. Two assessment methods, one for creep and one for fatigue/creep-fatigue were presented, based on the local behavior determined by the numerical simulations. The assessment approach for pure creep loads was validated using several uni-axial creep tests and two component tests. The approach for the fatigue/creep-fatigue loads is still in the developmental stage but the first results were presented and further areas for improvement were identified.

Ni-single atom decorated mesoporous carbon electrocatalysts for hydrogen evolution reaction

A single atom catalyst (SAC) is a type of heterogeneous catalyst in which individual metal atoms are dispersed on a support material, such as carbon, metal oxides, or zeolites, rather than forming nanoparticles or clusters. SACs have unique catalytic properties, such as high selectivity and activity, due to their high surface area and the exposed active metal sites, allowing them to interact more efficiently with the reactants. SACs also have the advantage of being more environmentally friendly and cost-effective than traditional catalysts, as they require smaller amounts of metal and can be easily recycled. In this study, we report the synthetic method of mesoporous single atom catalysts (MSACs) containing nickel (Ni), platinum (Pt), iridium (Ir), cobalt (Co), and iron (Fe) as metal-centered atoms for hydrogen evolution reaction (HER). MSACs are successfully synthesized with metal-chelation and soft-templating methods. The results show that highly uniform MSACs can be fabricated with high reproducibility. The metal contents in MSACs can be reduced to under 1wt%, keeping good HER reactivity. For HER, the Ni mesoporous single atom catalysts (Ni-MSACs) show 270 mV overpotential at 10 mA/cm2 at the initial polarization curve and reduces to 190 mV after 2,000 cycles, which outperforms Ni bulk catalyst (Ni plate). In contrast, porous pure carbon without Ni atoms shows inferior performance, indicating that Ni metal center plays an important role in the catalytic activity. The computational calculation using density functional theory (DFT) tells that Volmer-Heyrovsky pathway is dominant for HER using the Ni-MSACs. The performance of MSACs has a high potential for improvement of performance and extreme reduction of metal usage by doping nitrogen, phosphorous, and two or more metal atoms into MSACs.

Review of Characteristics and Analytical Methods for Determination of Thiabendazole.

Thiabendazole (TBZ) is a fungicide and anthelmintic drug commonly found in food products. Due to its toxicity and potential carcinogenicity, its determination in various samples is important for public health. Different analytical methods can be used to determine the presence and concentration of TBZ in samples. Liquid chromatography (LC) and its subtypes, high-performance liquid chromatography (HPLC) and ultra-high-performance liquid chromatography (UHPLC), are the most commonly used methods for TBZ determination representing 19%, 18%, and 18% of the described methods, respectively. Surface-enhanced Raman spectroscopy (SERS) and fluorimetry are two more methods widely used for TBZ determination, representing 13% and 12% of the described methods, respectively. In this review, a number of methods for TBZ determination are described, but due to their limitations, there is a high potential for the further improvement and development of each method in order to obtain a simple, precise, and accurate method that can be used for routine analysis.

Utilizing hydrogen pressure energy by expansion machines – PEM fuel cells in mobile and other potential applications

Hydrogen is an important energy carrier for a future without fossil fuels, but PEM fuel cell systems have to utilize the entire available energy as efficiently as possible. Hydrogen is typically stored at a maximum pressure of up to 700 bar and is supplied to the anode system at about 15 bar. Normally, a pressure reduction valve expands the hydrogen between 700 and 15 bar, but the pressure energy is unused during expansion. In this paper, different expansion machines are analyzed and the most attractive concepts are studied in detail to regain the expansion energy. Finally, a piston expansion system concept is designed with an estimated power output of 10 kW and a driving range increase of 2%, reflecting the high potential for efficiency improvement.

Artificial intelligence and education governance

Artificial intelligence (AI) and big data methodologies have provided a vast possibility of applications in different public policy sectors. AI and big data provide essential innovations in school teaching and learning practices, curriculum, and management in education, transforming the entire formulation and implementation of education policy. The possibilities of AI in education present a high potential for improvement but a series of new risks and threats that question the benefits of adopting AI in education. This article discusses the possibilities and limits of artificial intelligence in education, observing the institutionalization of new governance practices in education that emerge with digital technologies. To discuss these new governance practices, we deal with the Beijing Consensus on artificial intelligence in education and the challenges of building new education governance. In addition, we discuss potential impacts of AI on education, with a special focus on social justice.

Isolation of Rhizobia From the Nodules of Bambara Groundnuts for Inoculant Production

Rhizobia symbiotic interactions with legumes fix atmospheric nitrogen into the soil, which is essential in amending the characteristically low-nitrogen soils in most farming communities in northern Ghana. A high potential for improvement of Bambara groundnuts production in low-nitrogen soils is by the exploitation of colonization of the plant roots with rhizobial inoculation. This experiment sought to isolate Legume Nodulating Bacteria (LNB) obtained from root nodules of Bambara groundnut (Vigna subterranea) plants and to identify effective strains for improved production of the crop. Roots nodules of Bambara plants used in this study were obtained from preserved plants and the isolates were authenticated for their symbiotic effectiveness under screen house conditions. Nodulation of the isolates was examined in plastic pots containing sterile river sand and test crop (Bambara seeds). The experiment included reference strains, a positive nitrogen control and an un-inoculated control. The results were obtained after two months of data collection. The difference in results was explained via nodulation capacity. Out of the two isolates obtained, 2CL showed a high nodulation capability, rating it as highly effective. The outcome of this study provides stakeholders with the prospect for the use of effective isolates as inoculants to improve Bambara groundnut yield in general and in northern Ghana in particular.

Mitochondrial DNA D-loop Genetic Relatedness and Characterization of Nile Tilapia (Oreochromis niloticus Linnaeus, 1758) from Lakes Alau and Bako, Northeast, Nigeria

This study investigated the genetic relatedness and characterization of Nile tilapia (Oreochromis niloticus) populations from Lake Bako, Mambilla Plateau and Lake Alau, Maiduguri using Mitochondrial DNA (mtDNA) D-loop sequences. Blood samples were collected from each individual of and preserved on an FTA card. DNA was extracted from the card using Aqualex extraction kits. Polymerase Chain Reaction was performed using a commercially available decamer primer: FISH COMUM D-LOOP F (5’-GGATTYTAACCCYTRCCC -3’) and (R:) as the forward primer and C. zilli_rev (5′ – AGTAAAGTCAGGACCAAGCC – 3′) as reverse primer. The results showed that, in total 117 variable sites were observed and Hap_1_Hap_40 was detected. Lake Alau population has 65.50 % A+T and 34.5% G+C, while 65.5% and 34.9% were the nucleotide composition of Lake Bako population. Both populations have 1.00 Haplotypes diversity. The nucleotide diversity observed in Lake Bako population is 0.0342, while 0.0285 were recorded in Lake Bako population. The average number of nucleotide differences of 26.61053 and 31.98421 were observed in Lakes Bako and Alau populations, respectively with and overall value of 29.996. The Neighbour-Joining phylogenetic tree revealed two major clusters. The genetic variation among population is 95.31%and within population is 4.69%. Populations of O. niloticus in Lakes Bako and Alau are genetically different. Lake Bako population seemed to have intact genetic structure. Both populations have high genetic diversity, hence have high potential for genetic improvement and conservation.

Safer cycling in older age (SiFAr): effects of a multi-component cycle training. a randomized controlled trial

BackgroundThe risk of older adults being injured or killed in a bicycle accident increases significantly due to the age-related decline of physical function. Therefore, targeted interventions for older adults to improve safe cycling competence (CC) are urgently needed.MethodsThe “Safer Cycling in Older Age” (SiFAr) randomized controlled trial investigated if a progressive multi-component training program related to cycling improves CC of older adults. Between June 2020 and May 2022, 127 community-dwelling persons living in the area Nürnberg-Fürth-Erlangen, Germany aged 65 years and older were recruited, who are either (1) beginners with the e-bike or (2) feeling self-reported unsteadiness when cycling or (3) uptaking cycling after a longer break. Participants were either randomized 1:1 to an intervention group (IG; cycling exercise program, 8 sessions within 3 months) or an active control group (aCG; health recommendations). The CC as primary outcome was tested not blinded in a standardized cycle course prior and after the intervention period and after 6–9 months, which consists of variant tasks requiring skills related to daily traffic situations. Regression analyses with difference of errors in the cycling course as dependent variable and group as independent variable adjusted for covariates (gender, number of errors at baseline, bicycle type, age and cycled distance) were performed.Results96 participants (73.4 ± 5.1 years; 59.4% female) were analyzed for primary outcome. Compared to the aCG (n = 49), the IG (n = 47) made an average of 2.37 fewer errors in the cycle course after the 3 months intervention period (p = 0.004). People with more errors at baseline had higher potential for improvement (B=-0.38; p < 0.001). Women on average made 2.31 (p = 0.016) more errors than men, even after intervention. All other confounders had no significant effect on the difference in errors. The intervention effect was very stable until 6–9 months after the intervention (B=-3.07, p = 0.003), but decreased with a higher age at baseline in the adjusted model (B = 0.21, p = 0.0499).ConclusionThe SiFAr program increases cycling skills among older adults with self-perceived needs for improvement in CC and could easily be made available to a broad public due to its standardized structure and a train-the-trainer approach.Trial RegistrationThis study was registered with clinicaltrials.gov: NCT04362514 (27/04/2020), https://clinicaltrials.gov/ct2/show/NCT04362514.

Hospitalizations, emergency room visits and causes of death in 198 patients with permanent hypoparathyroidism: a retrospective Austrian study (2005-2022).

Chronic hypoparathyroidism (HP) is associated with acute and chronic complications, especially those related to hypocalcemia. We aimed to analyze details on hospital admissions and the reported deaths in affected patients. In a retrospective analysis, we reviewed the medical history of 198 patients diagnosed with chronic HP over a continuous period of up to 17 years at the Medical University Graz. The mean age in our mostly female cohort (70.2%) was 62.6 ± 18.7 years. The etiology was predominantly postsurgical (84.8%). About 87.4% of patients received standard medication (oral calcium/vitamin D), 15 patients (7.6%) used rhPTH1-84/Natpar® and 10 patients (4.5%) had no/unknown medication. Two hundred and nineteen emergency room (ER) visits and 627 hospitalizations were documented among 149 patients, and 49 patients (24.7%) did not record any hospital admissions. According to symptoms and decreased serum calcium levels, 12% of ER (n = 26) visits and 7% of hospitalizations (n = 44) were likely attributable to HP. A subgroup of 13 patients (6.5%) received kidney transplants prior to the HP diagnosis. In eight of these patients, parathyroidectomy for tertiary renal hyperparathyroidism was the cause of permanent HP. The mortality was 7.8% (n = 12), and the causes of death appeared to be unrelated to HP. Although the awareness for HP was low, calcium levels were documented in 71% (n = 447) of hospitalizations. Acute symptoms directly related to HP did not represent the primary cause of ER visits. However, comorbidities (e.g. renal/cardiovascular diseases) associated with HP played a key role in hospitalizations and deaths. Hypoparathyroidism (HP) is the most common complication after anterior neck surgery. Yet, it remains underdiagnosed as well as undertreated, and the burden of disease and long-term complications are usually underestimated. There are few detailed data on emergency room (ER) visits hospitalizations and death in patients with chronic HP, although acute symptoms due to hypo-/hypercalcemia are easily detectable. We show that HP is not the primary cause for presentation but that hypocalcemia is a typical laboratory finding (when ordered) and thus may contribute to subjective symptoms. Patients often present with renal/cardiovascular/oncologic illness for which HP is known to be a contributing factor. A small but very special group (n = 13, 6.5%) are patients after kidney transplantations who showed a high ER hospitalization rate. Surprisingly, HP was never the cause for their frequent hospitalizations but rather the result of chronic kidney disease. The most frequent cause for HP in these patients was parathyroidectomy due to tertiary hyperparathyroidism. The causes of death in 12 patients appeared to be unrelated to HP, but we found a high prevalence of chronic organ damages/comorbidities related to it in this group. Less than 25% documented HP correctly in the discharge letters, which indicates a high potential for improvement.

Technical Analysis Based on Exergoeconomic, Advanced Exergetic Evaluation of An In-Service Cement Production Plant With Instantaneous Process Data

An all-inclusive advanced exergy and exergoeconomic, analysis of a dry process, 5 million MTPA cement plant with real-time operational data located in Mfamosing, Cross River State, Nigeria was performed. The analysis was based on component-wise modelling by separating the exergy destruction (ED) rates into endogenous, exogenous, avoidable and unavoidable portions for the two production lines, which makes the study novel as available studies in a cement plant was based on conventional applications. The result shows an exergy efficiency (EE) of 55.58 % for L1 and 58.22 % for L2. The exergoeconomic analysis revealed that the cost of exergy destruction (COED) was higher in L2 at $14,786.40/hr with L1 calculated at $12,977.34/hr. The preheaters (PH) and Clinker Coolers (CC) contributed about 72.39 % and 59.67% for L1 and 11.09 % and 18.20 % for L2, respectively, to the overall COED. The advanced exergy analysis showed that the mainstream of the ED rates in the plant is endogenous and unavoidable. Components with high potential for improvement were identified and possible system design and optimization suggestions.

Exergoeconomic and sensitivity analyses of a gas turbine power plant using MINI-REFPROP and MATLAB simulink

In this paper, exergy, exergoeconomic, and exergoenvironmental analysis of a gas turbine cycle and its optimization have been carried out by MINI-Reference Fluid Properties (MINI-REFPROP) and Matrix Laboratory (MATLAB) SIMULINK. The parametric study was carried out based on the Specific Exergy Costing approach. The mathematical models were developed and presented regarding mass, energy, and economy. The Excel and MATLAB LIBRARY TOOLS BOX are used to perform thermodynamic properties and research analyses. The analyses lead to valuable economic status benchmarks. The exergoeconomic factor, relative cost, total cost of energy loss, and energy destruction for the combustion chamber and work output were determined. The parametric study was conducted, considering the effects of the gas turbine inlet temperature, air compressor inlet temperature, and compressor pressure ratio. For the considered case study status, the combustion chamber in the plant revealed the highest amount of energy destruction (85%), leading to the recommendation that more attention be paid to boilers in terms of design, selection, operation, and maintenance, while the combustion chamber has a high improvement potential (91%).

Standardizing and Centralizing Datasets for Efficient Training of Agricultural Deep Learning Models.

In recent years, deep learning models have become the standard for agricultural computer vision. Such models are typically fine-tuned to agricultural tasks using model weights that were originally fit to more general, non-agricultural datasets. This lack of agriculture-specific fine-tuning potentially increases training time and resource use, and decreases model performance, leading to an overall decrease in data efficiency. To overcome this limitation, we collect a wide range of existing public datasets for 3 distinct tasks, standardize them, and construct standard training and evaluation pipelines, providing us with a set of benchmarks and pretrained models. We then conduct a number of experiments using methods that are commonly used in deep learning tasks but unexplored in their domain-specific applications for agriculture. Our experiments guide us in developing a number of approaches to improve data efficiency when training agricultural deep learning models, without large-scale modifications to existing pipelines. Our results demonstrate that even slight training modifications, such as using agricultural pretrained model weights, or adopting specific spatial augmentations into data processing pipelines, can considerably boost model performance and result in shorter convergence time, saving training resources. Furthermore, we find that even models trained on low-quality annotations can produce comparable levels of performance to their high-quality equivalents, suggesting that datasets with poor annotations can still be used for training, expanding the pool of currently available datasets. Our methods are broadly applicable throughout agricultural deep learning and present high potential for substantial data efficiency improvements.