Miracle-dz/UniFAR-FTUs huggingface dataset:
Half and full solar cell efficiency binning by deep learning on electroluminescence images

End‐of‐line characterization of solar cells is necessary to filter out defective cells and bin cells to avoid power mismatch loss in photovoltaic modules. Current–voltage testers, used by almost any photovoltaic company and research laboratory, are costly to maintain and to adapt to recent and predicted morphological changes in solar cells: larger and thinner wafers, half or shingled cells, a wide range of busbar layouts, and more. In this study, we challenge this fundamental technique and propose to bin solar cells and detect defective cells based on a deep learning analysis of their electroluminescence images. The use of electroluminescence imaging addresses the above‐mentioned limitations of the current–voltage technique, as well as allowing faster measurements as it avoids any capacitance effects. By introducing LumiNet, a convolutional neural network end‐to‐end framework, solar cell efficiency bins can be accurately predicted from electroluminescence imaging with a mean error similar to that obtained by current–voltage measurements. The proposed framework is validated on several state‐of‐the‐art mono‐crystalline silicon solar cell structures. We show that photovoltaic modules fabricated using the proposed method would have similar mismatch loss as the traditional current–voltage binning. We then demonstrate the method on half‐cut silicon solar cells. Predicting the half‐cut cell efficiencies, from the deep learning framework, enables manufacturers to assess post‐cutting damages and reassess their binning strategy before module assembly. Furthermore, the deep learning framework is shown to work well even on datasets that have not been previously seen. The trained deep learning LumiNet models' structure and weight are shared with the community to accelerate the adaptation of deep learning for luminescence image analysis in the photovoltaic industry.
Equilibria of an anaerobic wastewater treatment process and their stability

This paper deals with the analysis of the equilibria and their stability of the so-called AMOCO model developed by Bernard and ali (2001) to describe the dynamical behavior of an anareobic fixed-bed wastewater treatment process. Depending on the model parameters, the steady states are analytically characterized and their stability is analyzed. Following this study, it is shown that the overloading tolerance (denoted herein OT), a parameter proposed in Hess and Bernard (2007) to on-line monitoring anaerobic processes, may be not adapted under certain operating conditions and even lead to bad operating decisions.
Fatigue Damage Diagnostics of Composites Using Data Fusion and Data Augmentation With Deep Neural Networks

Composite materials can be modified according to the requirements of applications, and hence, their applications are increasing significantly with time. Due to the complex nature of the aging of composites, it is equally challenging to establish structural health monitoring techniques. One of the most applied non-destructive techniques for this class of materials is using Lamb waves to quantify the damage. Another important advancement in damage detection is the application of deep neural networks. The data-driven methods have proven to be most efficient for damage detection in composites. For both of these advanced methods, the burning question always has been the requirement of data and quality of data. In this paper, these measurements were used to create a framework based on a deep neural network for efficient fault diagnostics. The research question developed for this paper was as follows: Can data fusion techniques used along with data augmentation improve the damage diagnostics using the convolutional neural network? The specific aims developed to answer this research question were: (1) highlighting the importance of data fusion methods, (2) underlining the importance of data augmentation techniques, (3) generalization abilities of the proposed framework, and (4) sensitivity of the size of the dataset. The results obtained through the analysis concluded that the artificial intelligence techniques along with the Lamb wave measurements can efficiently improve the fault diagnostics of complex materials such as composites.
Imbalanced Sensory Eye Dominance of Surgically Aligned Late-onset Acute Acquired Concomitant Esotropes with normal stereopsis

Background: Adults with late-onset acute acquired concomitant esotropia (AACE) have chance to develop normal binocular functions including a balanced ocular dominance before the onset of esotropia. For most patients, strabismus surgery re-establishing the ocular alignment indeed effectively restore stereopsis and visual acuity to the normal level. However, it is unclear whether they have already acquired balanced two eyes.Methods: 11 surgically aligned patients with AACE (24.3 ± 1.5 years; mean ± SE) and 14 adults with normal vision (26.1±1.2 years) participated in our experiments. All patients had normal binocularity and stereopsis. Using binocular phase combination paradigm, sensory eye dominance was quantified as the interocular contrast ratio, termed balance point, at which the contribution of each eye to the perception of cyclopean grating were equal.Results: Normal controls had a mean balance point value close to unity (0.95±0.01), while AACE group exhibited evident binocular imbalance (0.76±0.05), which was significantly different from control group (t (10.45) = -3.485, p = 0.006) . The balance point value didn’t depend on the interval from AACE onset to strabismus surgery (r = -0.357, p = 0.281) or the interval from the surgery to examination of sensory eye dominance (r = -0.105, p = 0.759). Conclusions: Although strabismus surgery effectively straightened AACE patients’ ocular alignment and even conferred them normal stereopsis, late-onset AACE patients’ two eyes were still not balanced. These results indicated that binocular imbalance might be a risk factor for adult AACE.
Economics of Manufacturing Structural Lumber from Low- to Medium-Density Hardwoods.

Abstract : An economic assessment and comparison are given of two hypothetical sawmills that process structural lumber from low- to medium-density hardwoods using innovative processing techniques developed at FPL. One mill uses the Saw-Dry-Rip (SDR) process for manufacturing straight structural lumber from low- to medium-density hardwoods. The second mill uses SDR combined with the Edge-Glue-and-Rip (EGAR) process that enables the manufacture of any width of structural lumber even when using small logs. Both processes appear to be viable investment opportunities based on returns on investment. (Author)
Registration and matching of large geometric datasets for cultural heritage applications

The last decade has seen an increasing number of projects to acquire detailed 3-D representations of cultural heritage objects at museums and archaeological excavations, with a goal of improving preservation, understanding, restoration, and dissemination. However, careful study and virtual reassembly of cultural heritage objects often requires sub-millimeter precision to faithfully capture fine details, irrespective of the size and number of objects. Existing 3-D scanning technologies can produce such detail for small models with a modest amount of manual labor but do not scale to the tens of thousands of fragments that may be present at an excavation. High-precision scanners also have limited viewing volumes, making it very difficult to acquire large objects such as statues. Most scanning technologies used in cultural heritage acquire many raw 3-D scans, each from a single viewpoint. This data does not become readily usable until the relative viewpoints of each scan have been recovered, and the data is merged into a final model. Alignment, or registration, is the process of recovering these viewpoints, and is the focus of this thesis. Assembling a large, fragmented object from its pieces involves recovering the pose of each fragment. We therefore examine the virtual reassembly problem as one of alignment. We examine the alignment and assembly problems in cultural heritage scanning using data from the Digital Michelangelo and Theran Fresco projects. In the context of the Digital Michelangelo Project, which scanned many Michelangelo statues in Florence at approximately 0.25mm precision, we address the challenges of aligning large, detailed range scans. Because of the statues’ size, deformations due to calibration error are inevitable. We present an algorithm which accommodates warp in many large scans, thereby preserving the raw detail in the final model. We also consider the case of many small range scans, in the context of the Theran Fresco project, which is using 3-D models of fresco fragments to aid in reconstruction. Although fragments contain few range scans, they lack the detail required for stable, automatic alignment using traditional techniques. We show how to exploit the properties of fresco fragments to obtain robust, automatic alignments, and to manually correct any misalignment in only a few seconds. Finally, we present a method for matching fresco fragments based only on geometry. Many fragments contain no decoration or distinctive edge features, so exhaustively matching edge geometry between all pairs of fragments is essential. We show how this problem relates to range scan alignment, and present a new convolution-like algorithm for efficiently computing all possible alignment of each fragment pair.
Process optimization for the production of ready-to-cook carrot halwa

Increasing prevalence of nuclear family among the population as well as changing lifestyles have subjected consumers towards the adoption of instant food at the global level. Increasing popularity of ready-to-cook (RTC) foods amongst the consumers owing to convenience in their preparation, handling as well as their longer shelf life had encouraged the development of a variety of instant foods by various food processors. The term ‘ready-to-cook’ refers to simple, quick, and convenient food, which is easy and quicker to cook apart from being hygienic and free from contamination. The present study aims at the optimization of the recipe for the development of ready-to-cook carrot halwa based on color, texture, and sensory characteristics such as appearance, odor, taste, and overall acceptability. The sample (H 4 ) with desirable red color, best organoleptic properties, and least hardness was selected for the proximate analysis.
: “Comparing Groups of Time Dependent Data Using Locally Weighted Scatterplot Smoothing Alpha-Adjusted Serial T-tests” by Niiler (2020)

The main contribution in Niiler (2020) is a new α level correction 2 method for multiple testing in a functional data context. A secondary 3 contribution is the proposal to use the nonparametric LOESS smoothing 4 method of Cleveland & Devlin (1988) to overcome problems with irregu5 larly sampled functional data. Unfortunately, both contributions generally 6 lead to invalid statistical inferences and I will explain these issues in the 7 following. 8 In Section 2, I briefly explain the α level correction method of Niiler 9 (2020) and introduce necessary notation. In Section 3, I explain the sta10 tistical shortcomings in Niiler (2020) and demonstrate that the α level 11 correction of Niiler (2020) leads to invalid inferences. A short conclusion 12 is given in Section 4. 13
The Design and fabrication of Capacitive Humidity Sensor Having Interdigital Electrodes and Its Signal Processing Circuit

For the purpose of developing capacitive humidity sensor having interdigital electrodes, interdigital electrode was modeled and simulated to obtain capacitance and sensitivity as a function of geometric parameters like the structural gap and thickness. For the development of ASIC, switched capacitor signal processing circuits for capacitive humidity sensor were designed and simulated by Cadence using CMOS process parameters. The signal processing circuits are composed of amplifier for voltage gain control, and clock generator for sensor driving and switch control. The characteristics of the fabricated sensors are; 1) sensitivity is 9fF/%R.H. , 2) temperature coefficient of offset(TCO) is , 3) nonlinearity is 1.2%FS, 4) hysteresis is 1.5%FS in humidity range of . The response time is 50 seconds in adsorption and 70 seconds in desorption. Fabricated process used in this capacitive humidity sensor having interdigital electrode are just as similar as conventional IC process technology. Therefore this can be easily mass produced with low cost, simple circuit and utilized in many applications for both industrial and environmental measurement and control system, such as monitoring system of environment, automobile, displayer, IC process room, and laboratory etc.
Novel fused quinazolinones: further studies on the anticonvulsant activity of 1,2,9,11-tetrasubstituted-7H-thieno[2´,3´:4,5]pyrimido[6,1-b]-quinazolin-7-one and 1,3,10,12-tetrasubstituted-8H-pyrido[2´,3´:4,5]pyrimido[6,1-b]quinazolin-8-one.

BACKGROUND Epilepsy is one of the most common neurological disorders, affecting approximately 1% of the world's population. The currently available anticonvulsants are effective in reducing the severity and number of seizures in less than 70% of patients. Moreover, their use is associated with undesirable side effects, ranging from cosmetic (gingival hyperplasia) to life-threatening (hepatotoxicity or megaloblastic anemia). Therefore, the continued search for safer and more effective anti-epileptic drugs is both urgent and necessary. A survey of the literature reveals that various derivatives of quinazolinone, thienopyrimidine and pyridopyrimidine have shown very promising anticonvulsant activity, along with other pharmacological activities. We therefore decided to concentrate our efforts on screening novel quinazolinone fused with thienopyrimidine/pyridopyrimidine. RESULTS A novel series of 1,2,9,11-tetrasubstituted-7H-thieno[2´,3´:4,5]pyrimido[6,1-b]-quinazolin-7-ones and 1,3,10,12-tetrasubstituted-8H-pyrido[2´,3´:4,5]pyrimido[6,1-b]quinazolin-8-ones were synthesized by the method reported. The anticonvulsant activity of all the new compounds was evaluated against maximum electroshock-induced seizures and against subcutaneous pentylenetetrazole (scPTZ)-induced seizure models in mice. The neurotoxicity was assessed using the Rotorod procedure. All the compounds tested were administered intraperitoneally at a various dose levels ranging from 15 to 175 mg/kg body weight, and the median toxic dose (TD(50)) and the protection index values were determined. All compounds tested exhibited good activity. The structure-activity relationships based on the results obtained for these series were also studied. CONCLUSION This study indicates that fused quinazolinones display very good anticonvulsant activity. In both series, electron-withdrawing substitutions showed more activity. Among all the compounds tested, 10,12-dibromo-1- (4-chloro-phenyl)-3- (4-tolyl)-8H-pyrido[2´,3´:4,5]pyrimido[6,1-b]quinazolin-8-one and 10,12-dibromo-3- (4-chloro-phenyl)-1-phenyl-8H-pyrido[2´,3´:4,5]pyrimido[6,1-b]quinazolin-8-one were found to be the most potent.
Machine learning in the prediction of depression treatment outcomes: a systematic review and meta-analysis

Abstract Background Multiple treatments are effective for major depressive disorder (MDD), but the outcomes of each treatment vary broadly among individuals. Accurate prediction of outcomes is needed to help select a treatment that is likely to work for a given person. We aim to examine the performance of machine learning methods in delivering replicable predictions of treatment outcomes. Methods Of 7732 non-duplicate records identified through literature search, we retained 59 eligible reports and extracted data on sample, treatment, predictors, machine learning method, and treatment outcome prediction. A minimum sample size of 100 and an adequate validation method were used to identify adequate-quality studies. The effects of study features on prediction accuracy were tested with mixed-effects models. Fifty-four of the studies provided accuracy estimates or other estimates that allowed calculation of balanced accuracy of predicting outcomes of treatment. Results Eight adequate-quality studies reported a mean accuracy of 0.63 [95% confidence interval (CI) 0.56–0.71], which was significantly lower than a mean accuracy of 0.75 (95% CI 0.72–0.78) in the other 46 studies. Among the adequate-quality studies, accuracies were higher when predicting treatment resistance (0.69) and lower when predicting remission (0.60) or response (0.56). The choice of machine learning method, feature selection, and the ratio of features to individuals were not associated with reported accuracy. Conclusions The negative relationship between study quality and prediction accuracy, combined with a lack of independent replication, invites caution when evaluating the potential of machine learning applications for personalizing the treatment of depression.
Modified setting of overcurrent protection for distribution feeders with distributed generation

Connecting Distributed Generators (DGs) into conventional distribution systems leads the protection design assumptions to become incorrect. These generators are commonly utilized with renewable energy resources including solar photovoltaic (PV) and wind electric conversion systems (WECS). As a result, miss-coordination or improper operation of overcurrent relays in distribution networks can be expected. This paper presents a practical and versatile setting profile for overcurrent protective elements in such networks that have considerable amounts of DG units. For this target, a typical 11 kV underground distribution feeder is modeled while a considerable DG unit is inserted. Different locations and sizes are considered for the utilized DG. For simulation purposes, Matlab/Simulink package is employed with its massive simulation library and broad programming facilities. Different fault type scenarios are constructed and analyzed. The results corroborate a better understanding of the essence of DG problems in distribution systems and assist to realize a better protection coordination, lower fault clearing times as well as optimized selectivity for overcurrent relays with the existence of DG units. (6 pages)
Daylighting Strategies for U.S. Air Force Office Facilities: Economic Analysis of Building Energy Performance and Life-Cycle Cost Modeling with Monte Carlo Method

Abstract : The U.S. federal government maintains more than 500,000 facilities in the United States and around the world, most of which are heavily dependent on fossil fuels to produce electricity. Within the federal government, the Department of Defense (DOD) spends over $2.5 billion per year on facility energy consumption which makes them the largest single energy consumer in the United States. Therefore, federal energy conservation goals focus on aggressively reducing energy consumption by reducing the energy demand at the facility level within the next 20 years. Daylighting is a passive solar energy strategy at the facility level that leverages load avoidance by relying on windows and skylights to reduce building electrical lighting load; which accounts for approximately $15-23 billion annually in energy consumption. Our research findings show that electrochromic windows have the lowest energy consumption compared with other daylighting strategies appropriate for building retrofit. However, the prohibitive initial investment cost of electrochomic windows do not make them economically viable; therefore, the only daylighting strategy currently viable for Air Force facilities, based on our simulations, is the advanced daylighting control system. We found that economic incentive policies currently available for other passive solar technology could make emerging daylighting technology, such as electrochromic windows, viable. Finally, we demonstrate the robustness of probabilistic life-cycle cost model using Monte Carlo simulation that could provide significantly more information compared to the current deterministic tool, BLCC 5, used for federal energy projects.
Biomedical sensor design using analog compressed sensing

The main drawback of current healthcare systems is the location-specific nature of the system due to the use of fixed/wired biomedical sensors. Since biomedical sensors are usually driven by a battery, power consumption is the most important factor determining the life of a biomedical sensor. They are also restricted by size, cost, and transmission capacity. Therefore, it is important to reduce the load of sampling by merging the sampling and compression steps to reduce the storage usage, transmission times, and power consumption in order to expand the current healthcare systems to Wireless Healthcare Systems (WHSs). In this work, we present an implementation of a low-power biomedical sensor using analog Compressed Sensing (CS) framework for sparse biomedical signals that addresses both the energy and telemetry bandwidth constraints of wearable and wireless Body-Area Networks (BANs). This architecture enables continuous data acquisition and compression of biomedical signals that are suitable for a variety of diagnostic and treatment purposes. At the transmitter side, an analog-CS framework is applied at the sensing step before Analog to Digital Converter (ADC) in order to generate the compressed version of the input analog bio-signal. At the receiver side, a reconstruction algorithm based on Restricted Isometry Property (RIP) condition is applied in order to reconstruct the original bio-signals form the compressed bio-signals with high probability and enough accuracy. We examine the proposed algorithm with healthy and neuropathy surface Electromyography (sEMG) signals. The proposed algorithm achieves a good level for Average Recognition Rate (ARR) at 93% and reconstruction accuracy at 98.9%. In addition, The proposed architecture reduces total computation time from 32 to 11.5 seconds at sampling-rate=29 % of Nyquist rate, Percentage Residual Difference (PRD)=26 %, Root Mean Squared Error (RMSE)=3 %.
Graphene nanoplatelets/epoxy nanocomposites: A review on functionalization, characterization techniques, properties, and applications

Graphene nanoplatelets (GNPs) have received immense attention from the global scientific research community in the 21st century due to their two-dimensional planar structure, high surface area, functionalization abilities, and notable thermal-mechanical-electrical properties. When appropriately integrated into polymer matrices, graphene nanosheets improve the mechanical performance of polymer under static and high-strain rate loading. On the other hand, surface modification of GNPs through functionalization enhances dispersibility and interfacial strength of GNPs/polymer composites. Computational methods for GNPs-based nanocomposites considering micromechanical and multiscale modeling were also developed to predict their thermo-mechanical and electrical properties. These nanocomposite materials have been identified as having a wide range of applications in aerospace, automotive, construction, biomedical, energy storage, sensor, and textiles. In this review paper, recent advances of GNPs/epoxy nanocomposites, including their functionalization processes, characterization techniques, production methods, properties, and potential applications, have been comprehensively explained. Furthermore, it attempts to provide a complete framework for researchers by summarizing and evaluating the extensive literature on these nanocomposite materials.