Evaluation of the Cost-Optimal Method Applied to Existing Schools Considering PV System Optimization

In Italy in 2020, only 15.5% of school building heritage was retrofitted from an energy and environmental point of view. In this paper, the cost-optimal method was applied to two different school buildings belonging to the same Italian cold climate zone but characterized by different structural and technological solutions. The research aims at defining the cost-effective redevelopment solution among several ones proposed to apply to this building type. At the same time, this paper provides a critical analysis of the methodology applied, highlighting deficiencies related to a not proper evaluation of environmentally friendly retrofitting measures. In a cost-effective context, the main results show that the intervention on the heating system is more convenient than the retrofitting of the envelope. The energy saving is equal to about 35% for both considered schools. Among the different proposed requalification configurations, the adoption of PV (photovoltaic) electric generation is included. In this regard, an optimization procedure was implemented in a generative design environment to maximize energy production with reference to different design parameters. As a result, a solution with south oriented PV modules with a tilt angle of 42° and arranged in 0.7 m spaced rows proved to be the most effective.

The Cost of Improving the Precision of the Variational Quantum Eigensolver for Quantum Chemistry

New approaches into computational quantum chemistry can be developed through the use of quantum computing. While universal, fault-tolerant quantum computers are still not available, and we want to utilize today’s noisy quantum processors. One of their flagship applications is the variational quantum eigensolver (VQE)—an algorithm for calculating the minimum energy of a physical Hamiltonian. In this study, we investigate how various types of errors affect the VQE and how to efficiently use the available resources to produce precise computational results. We utilize a simulator of a noisy quantum device, an exact statevector simulator, and physical quantum hardware to study the VQE algorithm for molecular hydrogen. We find that the optimal method of running the hybrid classical-quantum optimization is to: (i) allow some noise in intermediate energy evaluations, using fewer shots per step and fewer optimization iterations, but ensure a high final readout precision; (ii) emphasize efficient problem encoding and ansatz parametrization; and (iii) run all experiments within a short time-frame, avoiding parameter drift with time. Nevertheless, current publicly available quantum resources are still very noisy and scarce/expensive, and even when using them efficiently, it is quite difficult to perform trustworthy calculations of molecular energies.

Comparison of different feature extraction methods for applicable automated ICD coding

Background Automated ICD coding on medical texts via machine learning has been a hot topic. Related studies from medical field heavily relies on conventional bag-of-words (BoW) as the feature extraction method, and do not commonly use more complicated methods, such as word2vec (W2V) and large pretrained models like BERT. This study aimed at uncovering the most effective feature extraction methods for coding models by comparing BoW, W2V and BERT variants. Methods We experimented with a Chinese dataset from Fuwai Hospital, which contains 6947 records and 1532 unique ICD codes, and a public Spanish dataset, which contains 1000 records and 2557 unique ICD codes. We designed coding tasks with different code frequency thresholds (denoted as $$f_s$$ f s ), with a lower threshold indicating a more complex task. Using traditional classifiers, we compared BoW, W2V and BERT variants on accomplishing these coding tasks. Results When $$f_s$$ f s was equal to or greater than 140 for Fuwai dataset, and 60 for the Spanish dataset, the BERT variants with the whole network fine-tuned was the best method, leading to a Micro-F1 of 93.9% for Fuwai data when $$f_s=200$$ f s = 200 , and a Micro-F1 of 85.41% for the Spanish dataset when $$f_s=180$$ f s = 180 . When $$f_s$$ f s fell below 140 for Fuwai dataset, and 60 for the Spanish dataset, BoW turned out to be the best, leading to a Micro-F1 of 83% for Fuwai dataset when $$f_s=20$$ f s = 20 , and a Micro-F1 of 39.1% for the Spanish dataset when $$f_s=20$$ f s = 20 . Our experiments also showed that both the BERT variants and BoW possessed good interpretability, which is important for medical applications of coding models. Conclusions This study shed light on building promising machine learning models for automated ICD coding by revealing the most effective feature extraction methods. Concretely, our results indicated that fine-tuning the whole network of the BERT variants was the optimal method for tasks covering only frequent codes, especially codes that represented unspecified diseases, while BoW was the best for tasks involving both frequent and infrequent codes. The frequency threshold where the best-performing method varied differed between different datasets due to factors like language and codeset.

Precise Monitoring of Soil Salinity in China’s Yellow River Delta Using UAV-Borne Multispectral Imagery and a Soil Salinity Retrieval Index

Monitoring salinity information of salinized soil efficiently and precisely using the unmanned aerial vehicle (UAV) is critical for the rational use and sustainable development of arable land resources. The sensitive parameter and a precise retrieval method of soil salinity, however, remain unknown. This study strived to explore the sensitive parameter and construct an optimal method for retrieving soil salinity. The UAV-borne multispectral image in China’s Yellow River Delta was acquired to extract band reflectance, compute vegetation indexes and soil salinity indexes. Soil samples collected from 120 different study sites were used for laboratory salt content measurements. Grey correlation analysis and Pearson correlation coefficient methods were employed to screen sensitive band reflectance and indexes. A new soil salinity retrieval index (SSRI) was then proposed based on the screened sensitive reflectance. The Partial Least Squares Regression (PLSR), Multivariable Linear Regression (MLR), Back Propagation Neural Network (BPNN), Support Vector Machine (SVM), and Random Forest (RF) methods were employed to construct retrieval models based on the sensitive indexes. The results found that green, red, and near-infrared (NIR) bands were sensitive to soil salinity, which can be used to build SSRI. The SSRI-based RF method was the optimal method for accurately retrieving the soil salinity. Its modeling determination coefficient (R2) and Root Mean Square Error (RMSE) were 0.724 and 1.764, respectively; and the validation R2, RMSE, and Residual Predictive Deviation (RPD) were 0.745, 1.879, and 2.211.

Gel Textural Characteristics of Hair Gel with Cocoa Shell Extract by Using Mixture D-optimal Method

This study was used a mixture design to optimize the spreadability and viscosity of topical hair gel incorporates cocoa shell extract. The factor of the hair gel ingredient was thickener (0.2 – 0.8%), styling polymer A (2-5%), styling polymer B (2-6%), and solvent (84.63-91.63%) were studied on two responses selected spreadability and viscosity. The data collected were fitted to the model with high coefficient determination (R2= 0.994 for the spreadability and 0.9937 for the viscosity). The model can be predicted by showing the good lack of fit test result not significant with the p-value bigger than 0.05. From the ramp function simulation, the optimized formulation was selected and established at thickener (0.55%), styling polymer A (3.61%), styling polymer B (3.72%), and solvent (88.55%) with the spreadability and viscosity at 353.77 g.s and 39.91 pa.s respectively. The benefit of using mixture design in this experiment, it can help a formulator to understand the complex interaction between factors and can easily modify the formulation through ramp function simulation to obtain the desired result. The predicted validation test shows that both values were comparable. Under this condition showed that the model development could be used to predict future observations within the design range thickener (0.2 – 0.8%), styling polymer A (2-5%), styling polymer B (2-6%), and solvent (84.63-91.63%).

Locating Faults in Thyristor-Based LCC-HVDC Transmission Lines Using Single End Measurements and Boosting Ensemble

Most of the fault location methods in high voltage direct current (HVDC) transmission lines usemethods which require signals from both ends. It will be difficult to estimate fault location if the signal recorded is not correct due to communication problems.Hence a robust method is required which can locate fault with minimum error. In this work, faults are located using boosting ensembles in HVDC transmission lines based on single terminal direct current (DC) signals. The signals are processed to obtain input features that vary with the fault distance. These input features are obtained by taking maximum of half cycle current signals after fault and minimum of half cycle voltage signals after fault from the root mean square of DC signals. The input features are input to a boosting ensemble for estimating the location of fault. Boosting ensemble method attempts to correct the errors from the previous models and find outputs by combining all models. The boosting ensemble method has been also compared with the decision tree method and thebagging-based ensemble method. Fault locations are estimated using three methods and compared to obtain an optimal method. The boosting ensemble method has better performance than all the other methods in locating the faults. It also validated varying fault resistance, smoothing reactors, boundary faults, pole to ground faults and pole to pole faults. The advantage of the method is that no communication link is needed. Another advantage is that it allowsreach setting up to 99.9% and does not exhibitthe problem of over-fitting. Another advantage is that the percentage error in locating faults is within 1% and has a low realization cost. The proposed method can be implemented in HVDC transmission lines effectively as an alternative to overcome the drawbacks of traveling wave methods.

A Novel Approach of Tracing in Vivo Bioluminescence Imaging Expression of Vitrified Immature Testicular Tissue Grafts Until Adulthood: A Translational Transgenic Mouse Model

Background: The optimal method for cryopreserving immature testicular tissue (ITT) remains unknown and there is no standardized protocol. Controlled slow freezing remains the mainstream method of choice in human prepubertal male fertility preservation. Currently, the outcomes for ITT vitrification are conflicting, and most data are limited to in vitro animal studies.Methods: A total of 12 pairs of donor and recipient mice were included in our experiments. The donors were immature transgenic mice, and the recipients were wild-type male mice. In the vitrification group, ITT was vitrified and thawed before transplantation. In the control group, ITT was transplanted to the recipients immediately. After thawing, we measured the expression of apoptosis-related mRNA caspase-3. More importantly, we monitored to adulthood all the transplanted grafts in vivo using noninvasive bioluminescence imaging (BLI) technology. On day 31, we removed the grafts for evaluation via hematoxylin and eosin staining and immunohistochemistry (IHC).Results: We traced the survival of the grafts by in vivo BLI on days 1, 2, 5, 7, and 31 after transplantation. In both the vitrification and the control groups, bioluminescence decreased between days 2 and 5. Subsequently, the bioluminescence showed an upward trend until day 31. Compared with day 1, the bioluminescence was significantly stronger on day 31 after transplantation (P = 0.009). The differences between the two groups were constantly insignificant after analysis. These results indicate that both fresh and frozen–thawed testicular tissues can survive for at least 31 days after transplantation. Moreover, the vitrification group showed BLI signals comparable with those of fresh tissues. Compared with the control group, expression of the caspase-3 gene was significantly increased after vitrification (P = 0.04). Histology and IHC showed that both tissue structure and protein expression were intact in both groups.Conclusions: Transplanted vitrified ITT grafts could survive till adulthood with BLI intensity comparable to that of the fresh control. Intact cells and structures for spermatogenesis in vitrified ITT grafts were as well-preserved as those in the control group. This translational model of self-repairing vitrified ITT grafts in vivo, lends weight to the role of vitrification in prepubertal male fertility preservation.

Negative renal angiography with subsequent denied angioembolization: findings in a series of 180 patients at a tertiary interventional uroradiology unit

Background Renal arterial embolization (RAE) is considered to be a safe and effective method for treating a variety of renal lesions and pathology. It is the optimal method not only to stop bleeding, but to preserve renal parenchyma and renal function. Patients who are scheduled to RAE who showed negative catheter angiography with the procedure subsequently denied have a special concern because they are subjected to unnecessary procedure with its complications and didn’t get its benefits. This circumstance is infrequently reported in the literature, and that compelled us to identify the predictors of negative renal angiography findings that would result in a failure to undertake RAE. Results The study included 180 patients (126 males; 70%) with a mean ± SD age of 44 ± 14 years. Iatrogenic causes were the most common indication for RAE (108 of 180; 60%), while spontaneous unknown reasons constituted (17 of 180 patients; 9%). Angiography showed various lesions in 148 patients: pseudoaneurysm (80 of 148; 54%), tumours (28 of 148; 19%), arteriovenous (AV) fistulas (22 of 148; 15%) and both pseudoaneurysm and AV fistulas (18 of 148; 12%). However, in the remaining 32 of 180 patients (18%) no lesions were identified on renal angiography and RAE procedures were not undertaken. On bivariate analysis, neither gender, side of the lesions, haematuria prior to RAE, or renal artery anatomy were predictors for negative angiography. However, the indication for RAE (spontaneous unknown reasons) of renal haemorrhage was the only predictor for negative angiography (9/17 (53%), P = 0.001). Conclusion Patients scheduled for RAE may show negative findings with no lesions on renal angiography. Among the different indications for RAE, patients with spontaneous (unknown) have the highest probability (53%) of being associated with negative renal angiography findings, however, those with renal tumours and post-traumatic causes have a low probability. In those patients with spontaneous (unknown), conservative management should be the initial treatment of choice in order to avoid unnecessary RAE and its associated complications.

Multi-Objective Bee Swarm Optimization Algorithm with Minimum Manhattan Distance for Passive Power Filter Optimization Problems

Harmonic distortion in power systems is a significant problem, and it is thus necessary to mitigate critical harmonics. This study proposes an optimal method for designing passive power filters (PPFs) to suppress these harmonics. The design of a PPF involves multi-objective optimization. A multi-objective bee swarm optimization (MOBSO) with Pareto optimality is implemented, and an external archive is used to store the non-dominated solutions obtained. The minimum Manhattan distance strategy was used to select the most balanced solution in the Pareto solution set. A series of case studies are presented to demonstrate the efficiency and superiority of the proposed method. Therefore, the proposed method has a very promising future not only in filter design but also in solving other multi-objective optimization problems.