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Abstract Kinetic energy harvesting has significant potential, but current methods, such as friction and deformation-based systems, require high-frequency inputs and highly durable materials. We report an electrochemical system using a two-phase immiscible liquid electrolyte and Prussian blue analogue electrodes for harvesting low-frequency kinetic energy. This system converts translational kinetic energy from the displacement of electrodes between electrolyte phases into electrical energy, achieving a peak power of 6.4 ± 0.08 μW cm−2, with a peak voltage of 96 mV and peak current density of 183 μA cm−2 using a 300 Ω load. This load is several thousand times smaller than those typically employed in conventional methods. The charge density reaches 2.73 mC cm−2, while the energy density is 116 μJ cm−2 during a harvesting cycle. Also, the system provides a continuous current flow of approximately 5 μA cm−2 at 0.005 Hz for 23 cycles without performance decay. The driving force behind voltage generation is the difference in solvation Gibbs free energy between the two electrolyte phases. Additionally, we demonstrate the system’s functionality in a microfluidic harvester, generating a maximum power density of 200 nW cm−2 by converting the kinetic energy to propel the electrolyte through the microfluidic channel into electricity.

Drought is one of the key drivers of food insecurity in Afghanistan, which is among the most food insecure countries in the world. In this study, we build on previous research and seek to answer the central question: “What is the influence of El Niño-Southern Oscillation (ENSO) on drought outlooks and agricultural yield outcome in Afghanistan, and how do these influences vary spatially?” We do so by utilizing multiple indicators of droughts and available wheat yield reports. We find a clear distinction in the probability of drought (defined as being in the lower tercile) in Afghanistan during La Niña compared to El Niño events since 1981. The probability of drought in Afghanistan increased during La Niña, particularly in the North, Northeast, and West regions. La Niña events are related to an increase in the probability of snow drought, particularly in parts of the Amu Darya basin. It is found that relative to El Niño events, snow water equivalent [total runoff] during La Niña events January–March (March–July total runoff) decreases between 9% and 30% (28%–42%) for the five major basins in the country. The probability of agricultural drought during La Niña events is found to be higher than 70% in the rainfed and irrigated areas of the Northeast, North, and West regions. This result is at least partly supported by reported wheat yield composites related to La Niña events that tend to be lower than for El Niño events across all regions in the case of rainfed wheat (statistically significant in Northeast, West, and South regions) and in some cases for irrigated wheat. The results of this study have direct implications for improving early warning of worsening food insecurity in Afghanistan during La Niña events, given that we now have long-lead and skillful forecasts of ENSO up to 18–24 months in advance, which could potentially be used to provide earlier warning of worsening food insecurity in Afghanistan

Abstract The increased adoption of lithium-iron-phosphate batteries, in response to the need to reduce the battery manufacturing process’s dependence on scarce minerals and create a resilient and ethical supply chain, comes with many challenges. The design of an effective and high-performing battery management system (BMS) for such technology is one of those challenges. In this work, a physics-based model describing the two-phase transition operation of an iron-phosphate positive electrode—in a graphite anode battery—is integrated with a machine-learning model to capture the hysteresis and path-dependent behavior during transient operation. The machine-learning component of the proposed “hybrid” model is built upon the knowledge of the electrochemical internal states of the battery during charge and discharge operation over several driving profiles. The hybrid model is experimentally validated over 15 h of driving, and it is shown that the machine-learning component is responsible for a small percentage of the total battery behavior (i.e., it compensates for voltage hysteresis). The proposed modeling strategy can be used for battery performance analysis, synthetic data generation, and the development of reduced-order models for BMS design.

Free-form concrete panel (FCP) molds require precise manufacturing because each mold demands a unique shape. Therefore, automation technology for producing these molds is being developed. However, when concrete is cast in a free-form mold and subjected to the impact of compaction to distribute it, deformation occurs in the precisely designed form. Consequently, free-form molds are often produced manually, which results in bugholes on the surface of FCPs. These bugholes lead to quality issues in the panels, including deterioration in aesthetics and strength. This study aims to develop a magnetic compaction technology that installs an object which rotates due to magnetic force inside a free-form mold and applies magnetic force from the bottom to perform compaction according to the free form. By comparing a control group using the existing manual FCP production method and an experimental group using magnetic compaction, strength was measured and bughole incidence was verified. As a result, although the experimental group was subjected to rotary motion, no material separation or deterioration in strength occurred. Furthermore, a similar standard deviation of 62.23 mm2 and a mean difference of 187.42 mm2 were observed between the control group and the experimental group. The results of the t-test showed that at a 95% confidence level, the t-value was −16.35 and the p-value was 0.00. This confirms that the incidence of bugholes was reduced in the experimental group where magnetic compaction was applied. This research may contribute to reducing the occurrence of bugholes in existing free-form concrete panels and securing both aesthetics and strength.

FCPs (free-form concrete panels) can be made using reusable and easily customizable silicone molds tailored to the unique shape of each panel. CNC (Computer Numerical Control)-type rods move vertically to press the silicone plate and shape the lower curved surface. Silicone caps are attached to the ends of the rods to facilitate the formation of smooth curves. However, there is currently no fixing method for the silicone caps and the silicone plate, which makes them vulnerable to the lateral pressure exerted during concrete pouring. Therefore, the current study used magnetic force to improve the lower shape of free-form molds. To this end, a neodymium silicone cap was designed by adding a neodymium magnet to the upper surface of the silicone cap. Moreover, two types of silicone plates were developed for fixing: one type is IS-LSM (Iron Sheet–Silicone Mold), which includes an iron sheet, while the other type is IP-LSM (Iron Powder–Silicone Mold), which is made by mixing iron powder. To verify these two techniques, FCP manufacturing experiments were conducted. The experimental results indicated that IS-LSM had a broader error range than existing techniques, thus rendering it unusable, while IP-LSM yielded significant values. Consequently, a t-test was conducted to validate the data for IP-LSM at 30%, 50%, and 70%, and it was confirmed that the difference in error data was significant at a 95% confidence level. Future research in this area should investigate the addition of iron powder to the silicone plate and a side fixing method for the silicone mold. Such research would help advance the production technology of free-form concrete panels.

BackgroundVancomycin pharmacokinetics are highly variable in patients with critical illnesses, and clinicians commonly use population pharmacokinetic (PPK) models based on a Bayesian approach to dose. However, these models are population-dependent, may only sometimes meet the needs of individual patients, and are only used by experienced clinicians as a reference for making treatment decisions. To assist real-world clinicians, we developed a deep learning–based decision-making system that predicts vancomycin therapeutic drug monitoring (TDM) levels in patients in intensive care unit. ObjectiveThis study aimed to establish joint multilayer perceptron (JointMLP), a new deep-learning model for predicting vancomycin TDM levels, and compare its performance with the PPK models, extreme gradient boosting (XGBoost), and TabNet. MethodsWe used a 977-case data set split into training and testing groups in a 9:1 ratio. We performed external validation of the model using 1429 cases from Kangwon National University Hospital and 2394 cases from the Medical Information Mart for Intensive Care–IV (MIMIC-IV). In addition, we performed 10-fold cross-validation on the internal training data set and calculated the 95% CIs using the metric. Finally, we evaluated the generalization ability of the JointMLP model using the MIMIC-IV data set. ResultsOur JointMLP model outperformed other models in predicting vancomycin TDM levels in internal and external data sets. Compared to PPK, the JointMLP model improved predictive power by up to 31% (mean absolute error [MAE] 6.68 vs 5.11) on the internal data set and 81% (MAE 11.87 vs 6.56) on the external data set. In addition, the JointMLP model significantly outperforms XGBoost and TabNet, with a 13% (MAE 5.75 vs 5.11) and 14% (MAE 5.85 vs 5.11) improvement in predictive accuracy on the inner data set, respectively. On both the internal and external data sets, our JointMLP model performed well compared to XGBoost and TabNet, achieving prediction accuracy improvements of 34% and 14%, respectively. Additionally, our JointMLP model showed higher robustness to outlier data than the other models, as evidenced by its higher root mean squared error performance across all data sets. The mean errors and variances of the JointMLP model were close to zero and smaller than those of the PPK model in internal and external data sets. ConclusionsOur JointMLP approach can help optimize treatment outcomes in patients with critical illnesses in an intensive care unit setting, reducing side effects associated with suboptimal vancomycin administration. These include increased risk of bacterial resistance, extended hospital stays, and increased health care costs. In addition, the superior performance of our model compared to existing models highlights its potential to help real-world clinicians.

Sulfite, a widely used food additive, is subject to regulated labeling. The extraction of sulfite as the stable hydroxymethylsulfonate (HMS) form and its quantitative analysis by liquid chromatography-tandem mass spectrometry (LC-MS/MS) has been recognized for their good sensitivity, selectivity, and versatility across various food materials. This study aimed to develop a cost-effective and simpler method for sulfite quantitation, while maintaining the superior sensitivity and selectivity of mass spectrometry (MS). To achieve this, we introduced paper spray ionization (PSI), an ambient desorption ionization technique that could achieve the direct measurement of analytes without employing separation. We also employed a novel internal standard (IS) structurally similar to the analyte, replacing the more expensive isotopically labeled IS. Although the PSI-MS/MS method developed in this study exhibited slightly lower analytical performance compared to the conventional LC-MS/MS, it remained effective for sulfite analysis in dried fruits.

As free-form panel production takes a long time, it extends the construction period and increases construction expenses. This study suggests a method to apply radiant heat to concrete for the purpose of shortening the curing and removal process in free-form panel production. The optimal temperature and time for removal are determined based on the results of constant temperature/humidity curing experiments and quartz tube heater curing experiments. Through an experiment in various time settings, the general time of FCP (Free-form Concrete Panel) production is measured to examine whether the productivity is enhanced. It is expected that findings of this study contribute to shortening the construction period and reducing construction expenses as well as future studies on the FCP manufacturing equipment.

In this study, a big data analysis model was developed to predict the risks associated with overseas projects and a big data analysis technique. The risk analysis model can estimate the probability-cost interval for a planned project’s final cost by forming a probability density function and project costs through comparative analysis of data for a planned project and data for a previous similar project. This study attempted to collect a vast amount of information from the web and social networking services (SNS) in order to verify whether this information is sufficient to support the use of the web data analysis method used in this model. To this end, it was assumed that regional traffic conditions would be indicated on the web and SNS. In addition, two regions with different traffic conditions were selected, and then traffic-related keywords and words to enable assessment of traffic conditions were examined. Text information including these words was collected and the proportions of positive and negative words were analyzed. Results confirmed that two regions with different traffic conditions also had different numbers of negative words exhibited on the web.

Background: A patient's decision to undergo an elective orthopedic procedure is largely based on their symptoms and functional limitations. This point where patients choose to undergo surgery is known as the “tipping point.” The primary aim of this study is to determine the relationship between demographic parameters and the tipping point for elective rotator cuff repair. The secondary aim is to investigate if the tipping point is associated with mental health. The tertiary aim is to determine if the tipping point changes over time. Methods: Retrospective chart review was used to identify all patients who underwent primary arthroscopic rotator cuff repair between January 1, 2015, to January 1, 2020, with 1 of 3 board-certified orthopedic surgeons. Exclusion criteria included age

Objectives This study investigated the association between vaccine stockout and immunisation coverage, and infant/under 5 mortality rates.Design A retrospective cohort study.Setting Low-income and middle-income countries.Participants A cohort of 131 low-income and middle-income countries from 2004 to 2019.Primary outcome measures Main outcomes included immunisation coverages of (1) diphtheria-tetanus-pertussis containing vaccine (DTP), (2) measles containing vaccine (MCV), (3) BCG and (4) oral polio vaccine (OPV). We also included infant and under 5 mortality rates as secondary outcomes.Results The countries that experienced national-level stockouts of DTP and MCV had 3.7 and 4 percentage points lower coverage rates of DTP3 and MCV1, respectively, compared with the countries without the stockout events (p

Chemical exchange saturation transfer with glutamate (GluCEST) imaging is a novel technique for the non-invasive detection and quantification of cerebral Glu levels in neuromolecular processes. Here we used GluCEST imaging and 1H magnetic resonance spectroscopy (1H MRS) to assess in vivo changes in Glu signals within the hippocampus in a rat model of depression induced by a forced swim test. The forced swimming test (FST) group exhibited markedly reduced GluCEST-weighted levels and Glu concentrations when examined using 1H MRS in the hippocampal region compared to the control group (GluCEST-weighted levels: 3.67 ± 0.81% vs. 5.02 ± 0.44%, p < 0.001; and Glu concentrations: 6.560 ± 0.292 μmol/g vs. 7.133 ± 0.397 μmol/g, p = 0.001). Our results indicate that GluCEST imaging is a distinctive approach to detecting and monitoring Glu levels in a rat model of depression. Furthermore, the application of GluCEST imaging may provide a deeper insight into the neurochemical involvement of glutamate in various psychiatric disorders.

Supply chilled water temperature (SCWT) is an important variable for the efficient and stable operation of heating, ventilation, and air conditioning (HVAC) systems. A precisely measured value ensured by the continuous reliability of the temperature sensor is essential for optimal control of an HVAC system because temperature sensor faults can affect the chiller operation and waste energy. Therefore, temperature sensor fault-detection strategies are imperative for maintaining a comfortable indoor thermal environment and ensuring the efficient and stable operation of HVAC systems. This study proposes a fault-detection method for an SCWT sensor using a virtual sensor based on a long short-term memory-autoencoder. The fault-detection performance is evaluated considering a case study under various sensor fault scenarios to evaluate changes in indoor thermal comfort and energy consumption after correcting sensor faults detected by the virtual sensor. The results verify excellent fault-detection performance in various fault scenarios (F-1 scores ranging from 0.9350 to 1.000). After correcting the SCWT fault, indoor thermal comfort is steadily maintained without additional energy consumption (indoor set-point temperature unmet hour reduced by a maximum of 105.7 hours, and energy consumption decreased by up to 1.8%).

Recently, monocular 3D human pose estimation (HPE) methods were used to accurately predict 3D pose by solving the ill-pose problem caused by 3D-2D projection. However, monocular 3D HPE still remains challenging owing to the inherent depth ambiguity and occlusions. To address this issue, previous studies have proposed diffusion model-based approaches (DDPM) that learn to reconstruct a correct 3D pose from a noisy initial 3D pose. In addition, these approaches use 2D keypoints or context encoders that encode spatial and temporal information to inform the model. However, they often fall short of achieving peak performance, or require an extended period to converge to the target pose. In this paper, we proposed HDPose, which can converge rapidly and predict 3D poses accurately. Our approach aggregated spatial and temporal information from the condition into a denoising model in a hierarchical structure. We observed that the post-hierarchical structure achieved the best performance among various condition structures. Further, we evaluated our model on the widely used Human3.6M and MPI-INF-3DHP datasets. The proposed model demonstrated competitive performance with state-of-the-art models, achieving high accuracy with faster convergence while being considerably more lightweight.

Recent studies have been focusing on unmanned aircraft systems (UASs) to inspect safety issues in the construction industry. A UAS can monitor a broad range in real time and identify unsafe situations and objects at the jobsite. The related studies mostly focus on technological development, and there are few studies investigating potential performance that can be obtained by implementing UASs in the construction domain. Hence, the main objective of this research is to evaluate the potential of UAS-based construction safety inspection. To achieve the goal, this study developed a system dynamic (SD) model, and scenario analysis was conducted. When compared to the existing methods, the use of a UAS resulted in improved safety inspection performance, reduced possibility of incidents, reduced worker fatigue, and reduced amount of delayed work. The results of this research verified that UAS-based safety inspections can be more effective than existing methods. The results of this study can contribute to the understanding of UAS-based construction safety inspection technologies and the potential of the technology.

Background: The ultrathin-strut drug-eluting stent (DES) has shown better clinical results than thin- or thick-strut DES. We investigated if re-endothelialization was different among three types of DES: ultrathin-strut abluminal polymer-coated sirolimus-eluting stent (SES), thin-strut circumferential polymer-coated everolimus-eluting stent (EES), and thick-strut polymer-free biolimus-eluting stent (BES) to gain insight into the effect of stent design on promoting vascular healing.Methods: After implanting three types of DES in the coronary arteries of minipigs, we performed optical coherence tomography (OCT) at weeks 2, 4, and 12 (n = 4, each). Afterward, we harvested the coronary arteries and performed immunofluorescence for endothelial cells (ECs), smooth muscle cells (SMCs), and nuclei. We obtained 3D stack images of the vessel wall and reconstructed the en face view of the inner lumen. We compared re-endothelialization and associated factors among the different types of stents at different time points.Results: SES showed significantly faster and denser re-endothelialization than EES and BES at weeks 2 and 12. Especially in week 2, SES elicited the fastest SMC coverage and greater neointimal cross-sectional area (CSA) compared to EES and BES. A strong correlation between re-endothelialization and SMC coverage was observed in week 2. However, the three stents did not show any difference at weeks 4 and 12 in SMC coverage and neointimal CSA. At weeks 2 and 4, SMC layer morphology showed a significant difference between stents. A sparse SMC layer was associated with denser re-endothelialization and was significantly higher in SES. Unlike the sparse SMC layer, the dense SMC layer did not promote re-endothelialization during the study period.Conclusion: Re-endothelialization after stent implantation was related to SMC coverage and SMC layer differentiation, which were faster in SES. Further investigation is needed to characterize the differences among the SMCs and explore methods for increasing the sparse SMC layer in order to improve stent design and enhance safety and efficacy.

The xiamycins are bioactive indolosesquiterpenoids that have been isolated from actinobacterial strains belonging to the Streptomyces genus. The overexpression of orf2011, which encodes the LuxR family regulator in a marine Streptomyces strain (HK18) isolated from a hypersaline saltern, significantly increased the production of xiamycin dimers, namely the previously reported dixiamycins A and C (3 and 4), compared to the wild-type strain. In addition, the engineered strain produced new members of the xiamycin family (lipoxiamycins A and B), which possessed a lipophilic chain linked to the indolosesquiterpenoid core structure by a N–O bond. The transcription analysis of the N-hydroxylase-encoding xiaH by semiquantitative reverse transcription polymerase chain reaction (RT-PCR) revealed that the transcription level of xiaH responsible for the formation of a nitroxyl radical was increased by the overexpression of orf2011, which is located outside the xiamycin biosynthetic gene cluster. The structures of these compounds were determined by full spectroscopic analysis, and the connectivity between the lipophilic chain and the indolosesquiterpenoid moiety was confirmed in both lipoxiamycins A and B (1 and 2) by MS/MS analysis. Moreover, the absolute configurations of these compounds were established using quantum mechanics-based electronic circular dichroism and DP4 calculations. Finally, it was demonstrated that lipoxiamycin A (1) displayed inhibitory activity against lipopolysaccharide-induced NO production at an IC50 of 9.89 ± 0.92 µM in RAW 264.7 cells.

Background: Postoperative pain management continues to be a challenging aspect of patient care. Lidocaine patches have shown efficacy in reducing pain in other surgical specialties and mixed results in orthopedic trials. We sought to determine the effectiveness of nonprescription lidocaine patches in reducing postoperative pain after arthroscopic rotator cuff repair Methods: Patients undergoing primary arthroscopic rotator cuff repair were recruited from 3 surgeons at a single institution. All patients of each surgeon were randomized to a lidocaine patch or control group, with crossover occurring at the midpoint. Experimental group patients received 26 4% lidocaine gel-patches. They were provided written and visual instructions to begin wearing the lidocaine patches during daytime on postoperative day (POD) 2. They were to be switched every 8 hours and removed overnight. Control group patients received normal standard of care but did not receive a placebo control. Exclusion criteria included workmen’s compensation claims, age

Globally, the direct cost of natural disasters stands in the hundreds of billions of USD per year, at a time when water resources are under increasing stress and variability. Much of this burden rests on low- and middle-income countries that, despite their relative lack of wealth, exhibit considerable vulnerability such that losses measurably impact GDP. Within these countries, a growing middle class retains much of its wealth in property that may be increasingly exposed, while the few assets the poor may possess are often highly exposed. Vulnerability to extreme events is thus heterogeneous at both the global and subnational level. Moreover, the distribution and predictability of extreme events is also heterogeneous. Disaster managers and relief organizations are increasingly consulting operational climate information services as a way to mitigate the risks of extreme events, but appropriately targeting vulnerable communities remains a challenge. The advent of forecast-based anticipatory action has added to the suite of opportunities—and complexity—of operationalizing such services given varying prediction skill. Forecasts, including those at the subseasonal-to-seasonal (S2S) scale, may allow disaster managers to shift effort and therefore some risk from post-disaster response to pre-disaster preparedness; however, given the recent emergence of such programs, only a few, specific case studies have been evaluated. We therefore conduct a country-scale analysis pairing S2S forecast skill for monthly and seasonal lead times with flood and drought disaster risk to explore the potential for forecast-based anticipatory action programs broadly. To investigate subnational heterogeneity in risk and predictability, we also evaluate focused outcomes for the Greater Horn of Africa and Peru. Results suggest that forecast skill plays a large part in determining suitability for early action, and that skill itself varies considerably by disaster type, lead time, and location. Moreover, the physical and socioeconomic factors of risk can vary greatly between national and subnational levels, such that finer scale evaluations may considerably improve the effectiveness of early action protocols. By considering vulnerability at multiple spatial scales and forecast skill at multiple temporal scales, this analysis provides a first identification of promising locations for anticipatory action protocol development.

Objective: To conduct a program evaluation of a technology-based intervention for a housing insecure population. Study design: We conduct a quantitative analysis of Samaritan pilot administrative records. Methods: Samaritan conducted an initial single-arm pilot of their technology platform among a housing insecure population (N = 500). Administrative records containing basic demographics and social determinants of health were analyzed as part of this evaluation. Results: Our analysis revealed that among the participants, roughly 60% reported one or more improvements in unmet social determinants of health, showing the greatest improvements in the areas of utilities and nutrition. A gender subgroup analysis also revealed a differential pattern of platform use to address social determinant needs, with women more likely to report improvements in housing and nutrition while men report improvements in income and hope categories. Conclusion: Samaritan, a technology-based intervention targeted at housing insecure individuals, aims to connect users to the financial and social capital necessary to improve their current situations. The results of the pilot demonstrate the potential role the Samaritan platform could play in addressing social determinant needs and insights on potentially useful technology-based intervention features for housing insecure populations.

Free-form molds are used for one-time curve configuration, and because they are produced through manpower, they have issues with reduced precision and the occurrence of errors. In this study, 3D printing technologies were used to ensure precision, and polylactic acid and reusable eco-friendly materials to develop free-form assembly-type side-mold production technologies. In verifying the side mold, a free-form concrete panel was produced to check whether deformation occurs due to lateral pressure. Therefore, in this study, to verify this, a free-form concrete panel was produced and 3D-scanned to analyze the error at the side mold and the cause of the error to confirm the performance of the mold. The results showed that the error at each part was small, with a standard deviation of 1.627 mm, and there was little error at the panel joint part, around 1°. Such research is expected to be used in studies related to mold production technologies using 3D printers and on the production of free-form side molds.

FCP (Free-form Concrete Panel) is used to easily realize the huge and complex curved surfaces of free-form buildings, and research on FCP manufacturing technology is being conducted. However, as the concrete was extruded manually into the manufactured mold, the precision of the FCP was lowered and errors occurred. Therefore, this study developed concrete extrusion equipment that includes a nozzle part, an open/close part, and a control part, according to the required performance derived from previous research analysis. The mixing ratio of concrete was selected at an appropriate value of W/C 38% and extruded uniformly with a width of 60 mm and a thickness of 22 mm. Depending on the opening/closing function, it was possible to open and close at the desired position. The concrete extrusion nozzle for FCP production is the basic equipment, and miniaturization and automation of the nozzle are required in the future. This is expected to contribute to the development of new free-form construction technology and equipment.

Abstract STARR-seq technology has employed progressively more complex genomic libraries and increased sequencing depths. An issue with the increased complexity and depth is that the coverage in STARR-seq experiments is non-uniform, overdispersed, and often confounded by sequencing biases, such as GC content. Furthermore, STARR-seq readout is confounded by RNA secondary structure and thermodynamic stability. To address these potential confounders, we developed a negative binomial regression framework for uniformly processing STARR-seq data, called STARRPeaker. Moreover, to aid our effort, we generated whole-genome STARR-seq data from the HepG2 and K562 human cell lines and applied STARRPeaker to comprehensively and unbiasedly call enhancers in them.

Abstract Background Identifying frequently mutated regions is a key approach to discover DNA elements influencing cancer progression. However, it is challenging to identify these burdened regions due to mutation rate heterogeneity across the genome and across different individuals. Moreover, it is known that this heterogeneity partially stems from genomic confounding factors, such as replication timing and chromatin organization. The increasing availability of cancer whole genome sequences and functional genomics data from the Encyclopedia of DNA Elements (ENCODE) may help address these issues. Results We developed a negative binomial regression-based Integrative Method for mutation Burden analysiS (NIMBus). Our approach addresses the over-dispersion of mutation count statistics by (1) using a Gamma–Poisson mixture model to capture the mutation-rate heterogeneity across different individuals and (2) estimating regional background mutation rates by regressing the varying local mutation counts against genomic features extracted from ENCODE. We applied NIMBus to whole-genome cancer sequences from the PanCancer Analysis of Whole Genomes project (PCAWG) and other cohorts. It successfully identified well-known coding and noncoding drivers, such as TP53 and the TERT promoter. To further characterize the burdening of non-coding regions, we used NIMBus to screen transcription factor binding sites in promoter regions that intersect DNase I hypersensitive sites (DHSs). This analysis identified mutational hotspots that potentially disrupt gene regulatory networks in cancer. We also compare this method to other mutation burden analysis methods. Conclusion NIMBus is a powerful tool to identify mutational hotspots. The NIMBus software and results are available as an online resource at github.gersteinlab.org/nimbus.

ENCODE is a resource comprising thousands of functional genomic datasets. Here, the authors present custom annotation within ENCODE for cancer, highlighting a workflow that can help prioritise key elements in oncogenesis.

Abstract Background During transcription, numerous transcription factors (TFs) bind to targets in a highly coordinated manner to control the gene expression. Alterations in groups of TF-binding profiles (i.e. “co-binding changes”) can affect the co-regulating associations between TFs (i.e. “rewiring the co-regulator network”). This, in turn, can potentially drive downstream expression changes, phenotypic variation, and even disease. However, quantification of co-regulatory network rewiring has not been comprehensively studied. Results To address this, we propose DiNeR, a computational method to directly construct a differential TF co-regulation network from paired disease-to-normal ChIP-seq data. Specifically, DiNeR uses a graphical model to capture the gained and lost edges in the co-regulation network. Then, it adopts a stability-based, sparsity-tuning criterion -- by sub-sampling the complete binding profiles to remove spurious edges -- to report only significant co-regulation alterations. Finally, DiNeR highlights hubs in the resultant differential network as key TFs associated with disease. We assembled genome-wide binding profiles of 104 TFs in the K562 and GM12878 cell lines, which loosely model the transition between normal and cancerous states in chronic myeloid leukemia (CML). In total, we identified 351 significantly altered TF co-regulation pairs. In particular, we found that the co-binding of the tumor suppressor BRCA1 and RNA polymerase II, a well-known transcriptional pair in healthy cells, was disrupted in tumors. Thus, DiNeR successfully extracted hub regulators and discovered well-known risk genes. Conclusions Our method DiNeR makes it possible to quantify changes in co-regulatory networks and identify alterations to TF co-binding patterns, highlighting key disease regulators. Our method DiNeR makes it possible to quantify changes in co-regulatory networks and identify alterations to TF co-binding patterns, highlighting key disease regulators.

Abstract RNA-binding proteins (RBPs) play key roles in post-transcriptional regulation and disease. Their binding sites cover more of the genome than coding exons; nevertheless, most noncoding variant prioritization methods only focus on transcriptional regulation. Here, we integrate the portfolio of ENCODE-RBP experiments to develop RADAR, a variant-scoring framework. RADAR uses conservation, RNA structure, network centrality, and motifs to provide an overall impact score. Then, it further incorporates tissue-specific inputs to highlight disease-specific variants. Our results demonstrate RADAR can successfully pinpoint variants, both somatic and germline, associated with RBP-function dysregulation, which cannot be found by most current prioritization methods, for example, variants affecting splicing.

Abstract Background Cancer cachexia is a multifactorial wasting syndrome that is characterized by the loss of skeletal muscle mass and weakness, which compromises physical function, reduces quality of life, and ultimately can lead to mortality. Experimental models of cancer cachexia have recapitulated this skeletal muscle atrophy and consequent decline in muscle force‐generating capacity. We address these issues in a novel transgenic mouse model Kras, Trp53, and Pdx‐1‐Cre (KPC) of pancreatic ductal adenocarcinoma using multi‐parametric magnetic resonance measures. Methods KPC mice (n = 10) were divided equally into two groups (n = 5 per group) depending on the size of the tumour, that is, tumour size 250 mm3. Using multi‐parametric magnetic resonance measures, we demonstrated the changes in the gastrocnemius muscle at the microstructural level. In addition, we evaluated skeletal muscle contractile function in KPC mice using an in vivo approach. Results Increase in tumour size resulted in decrease in gastrocnemius maximum cross‐sectional area, decrease in T2 relaxation time, increase in magnetization transfer ratio, decrease in mean diffusivity, and decrease in radial diffusivity of water across the muscle fibres. Finally, we detected significant decrease in absolute and specific force production of gastrocnemius muscle with increase in tumour size. Conclusions Our findings indicate that increase in tumour size may cause alterations in structural and functional parameters of skeletal muscles and that MR parameters may be used as sensitive biomarkers to non‐invasively detect structural changes in cachectic muscles.

The Rakhine Basin in the northeastern Bay of Bengal is an active field in hydrocarbon exploration and development. It contains fault structures and steeply sloping stratigraphic reservoirs, both primary features of interest for hydrocarbon exploration that needs to be accurately imaged to improve the interpretation of seismic data and facilitate the accurate identification of features of interest. Although faults are an indicator of possible hydrocarbon traps, they are difficult to identify in seismic images using traditional stack or prestack time migration due to the rather complex behaviors of wave propagation. On the other hand, prestack depth migration (PSDM) can significantly improve the accuracy of seismic images, especially of complex subsurface structures such as faults, folds, overthrusts, and salt domes. Among the various PSDM approaches, reverse time migration (RTM) has been shown to be the most powerful. Here, we show how PSDM-RTM can significantly improve the representation of fault structures and steeply dipping structures in seismic images from field data collected in Rakhine Basin, which is characterized by complex geology including stratigraphic and strati-structural traps as well as complex channel systems. Typically, these structures appear heavily blurred and are difficult to identify using normal stack and prestack time migration. We demonstrate that they become clearer and easier to detect with the PSDM–RTM approach, making this approach particularly suitable for seismic interpretations of geologically complex areas within the context of hydrocarbon prospecting.

Errors that occur on the side shape of Free-form Concrete Panels (FCP) can cause errors in the FCP construction stage. Therefore, the error generated in FCPs must be reduced. Accordingly, side mold development and research are being carried out. However, in the case of studies using side silicone molds, there was no detailed information about the specifications and application methods of molds, and there was no support between molds. When producing FCPs with the method stated above, there is a high possibility for the mold to be pushed due to the side pressure of concrete, which can cause errors on the side shape of FCPs. Therefore, two new types of side silicone molds were developed in this study. In order to verify the performance difference between the newly developed mold and existing molds, FCP production tests and error analysis were performed. In result, the developed mold decreased the average error difference of the FCP side by 3–5 times compared to the existing mold. In addition, the significance of the average error difference with the produced FCP was verified, and results showed that the difference of the average error was significant at a 95% confidence level.

Facility inspections are mainly carried out through manual visual inspections. However, it is difficult to determine the extent of damages to facilities, and it depends on the subjective opinion of the manager in charge of the monitoring. Additionally, when inspectors inspect facilities that cannot be safely accessed, such as high-rise buildings, there are high risks of fatal accidents. For this reason, the construction industry conducts research into unmanned aircraft system (UAS)-based facility inspections. These studies have been focusing on developing the technologies or processes for using UAS in facility condition monitoring, ranging from infrastructure systems to commercial buildings. This study conducted extensive and synthetic reviews of the recent studies in UAS-based facility monitoring using a preferred reporting items for systematic reviews and meta-analysis (PRISMA) method. A total of 32 papers were selected and classified through the types of facilities and the technologies addressed in the studies. This paper analyzes the trends of recent studies by synthesizing the selected papers and consolidates the further directions of UAS applications and studies in facility monitoring domains.

Nanospray desorption electrospray ionization (nano-DESI) is one of the ambient desorption ionization methods for mass spectrometry (MS), and it utilizes a steady-state liquid junction formed between two microcapillaries to directly extract analytes from sample surfaces with minimal sample damage. In this study, we employed nano-DESI MS to perform a metabolite fingerprinting analysis directly from a Hypericum leaf surface. Moreover, we investigated whether changes in metabolite fingerprints with time can be related to metabolite distribution according to depth. From a raw Hypericum leaf, the mass spectral fingerprints of key metabolites, including flavonoids and prenylated phloroglucinols, were successfully obtained using ethanol as a nano-DESI solvent, and the changes in their intensities were observed with time via full mass scan experiments. In addition, the differential extraction patterns of the obtained mass spectral fingerprints were clearly visualized over time through selected ion monitoring and pseudo-selected reaction monitoring experiments. To examine the correlation between the time-dependent changes in the metabolite fingerprints and depth-wise metabolite distribution, we performed a nano-DESI MS analysis against leaves whose surface layers were removed multiple times by forming polymeric gum Arabic films on their surfaces, followed by detaching. The preliminary results showed that the changes in the metabolite fingerprints according to the number of peelings showed a similar pattern with those obtained from the raw leaves over time.

The development of mobile-health technology has the potential to revolutionize personalized medicine. Biomedical sensors (e.g., wearables) can assist with determining treatment plans for individuals, provide quantitative information to healthcare providers, and give objective measurements of health, leading to the goal of precise phenotypic correlates for genotypes. Even though treatments and interventions are becoming more specific and datasets more abundant, measuring the causal impact of health interventions requires careful considerations of complex covariate structures, as well as knowledge of the temporal and spatial properties of the data. Thus, interpreting biomedical sensor data needs to make use of specialized statistical models. Here, we show how the Bayesian structural time series framework, widely used in economics, can be applied to these data. This framework corrects for covariates to provide accurate assessments of the significance of interventions. Furthermore, it allows for a time-dependent confidence interval of impact, which is useful for considering individualized assessments of intervention efficacy. We provide a customized biomedical adaptor tool, MhealthCI, around a specific implementation of the Bayesian structural time series framework that uniformly processes, prepares, and registers diverse biomedical data. We apply the software implementation of MhealthCI to a structured set of examples in biomedicine to showcase the ability of the framework to evaluate interventions with varying levels of data richness and covariate complexity and also compare the performance to other models. Specifically, we show how the framework is able to evaluate an exercise intervention's effect on stabilizing blood glucose in a diabetes dataset. We also provide a future-anticipating illustration from a behavioral dataset showcasing how the framework integrates complex spatial covariates. Overall, we show the robustness of the Bayesian structural time series framework when applied to biomedical sensor data, highlighting its increasing value for current and future datasets.

Many studies have been conducted for the accuracy of free-form concrete panel fabrication, but there still are errors in the process of fabrication. This study developed a connection technology of detachable shape part that can be applied to the existing multi-point Computer Numerical Control (CNC) to enhance the accuracy of fabrication. The detachable type can place a silicone plate on top of the rod without additional fixtures. The accuracy of the technology was verified by curvature test and free-form concrete panel fabrication test. Three curves were created to compare the discrepancies between the designed shapes and the fabricated shapes through quality test. As a result, the detachable type decreased the error by up to 2 mm. In addition, a panel was fabricated to analyze the error to verify the rigidity of the developed molds. The error caused by concrete deflection under load or the error caused by repeated fabrication was about 0.5 mm. The shape error was within 3.5 mm. This small error proved greater accuracy compared to the existing technology.

Patients with repaired unilateral cleft lip with palate (UCLP) often show dysmorphology and distorted facial motion clinically, which can cause psychological issues. However, no report has clarified the details concerning distorted facial motion and the corresponding possible causative factors. In this study, we hypothesized that the physical properties of the scar and surrounding facial soft tissue might affect facial displacement while smiling in patients with UCLP (Cleft group). We thus examined the three-dimensional (3D) facial displacement while smiling in the Cleft and Control groups in order to determine whether or not the physical properties of facial soft tissues differ between the Cleft and Control groups and to examine the relationship between the physical properties of facial soft tissues on 3D facial displacement while smiling. Three-dimensional images at rest and while smiling as well as the facial physical properties (e.g. viscoelasticity) of both groups were recorded. Differences in terms of physical properties and facial displacement while smiling between the two groups were examined. To examine the relationship between facial surface displacement while smiling and physical properties, a canonical correlation analysis (CCA) was conducted. As a result, three typical abnormal features of smiling in the Cleft group compared with the Control group were noted: less upward and backward displacement on the scar area, downward movement of the lower lip, and a greater asymmetric displacement, including greater lateral displacement of the subalar on the cleft side while smiling and greater alar backward displacement on the non-cleft side. The Cleft group also showed greater elastic modulus at the upper lip on the cleft side, suggesting hardened soft tissue at the scar. The CCA showed that this hard scar significantly affected facial displacement, inducing less upward and backward displacement on the scar area and downward movement of the lower lip in patients with UCLP (correlation coefficient = 0.82, p = 0.04); however, there was no significant relationship between greater nasal alar lateral movement and physical properties of the skin at the scar. Based on these results, personalizing treatment options for dysfunction in facial expression generation may require quantification of the 3D facial morphology and physical properties of facial soft tissues.

With the increase of free-form architecture, many studies have been conducted for producing free-form Concrete Panels (FCP), but there are still areas that are lacking in terms of the technological aspect. In particular, as free-form panels are produced by hand, the precision of the shapes is low and cost and time are high. FCP production equipment was developed to resolve this. In this study, the variable side mold for FCP production used in side control equipment among FCP production equipment was developed. Variable side mold is equipment that satisfies five requirements to configure the form of FCPs. The variable side mold is made with steel plates so that it can withstand the side pressure of concrete. As a result, the material has a uniform thickness throughout and is molded to the desired shape. Therefore, in order to verify this, the panel was manufactured as a variable side mold to compare the side form with the designed form through 3D scanning and quality inspection to check for errors. As a result, there was a 0.276 mm mean difference for both ends of the panel and the central part, and it was therefore verified through t-test that errors occurred within the allowed margin of 95% confidence level.

Dairy consumption in adulthood has been demonstrated to influence cancer risk. Although childhood and adolescence represent critical periods of rapid growth, the relationship between milk intake in early life and later cancer risk is unclear. Thus, we examined this relationship by conducting a meta-analysis of the observational studies. PubMed and Embase were searched for relevant articles that were published throughout December 2021. The summary relative risk (RR) and 95% confidence interval (CI) were estimated using the DerSimonian-Laird random-effects model. The summary RR for the highest vs. lowest milk intake was 0.83 (95% CI = 0.69–1.00; p = 0.05; I2 = 60%; seven studies) for breast cancer, 0.98 (95% CI = 0.72–1.32; p = 0.88; I2 = 51%; four studies) for prostate cancer, and 0.90 (95% CI = 0.42–1.93; p = 0.78; I2 = 83%; three studies) for colorectal cancer. No evidence of an association emerged in subgroup analyses of menopausal status, cancer stage, fat content of milk, life stage of milk intake, or study design. Consistent results were observed in the meta-analyses using total dairy intake. In conclusion, milk intake during childhood and adolescence might not be associated with risks of breast, prostate, and colorectal cancer later in life. Given the small number of studies that were included in our meta-analysis, and the high heterogeneity, more studies are warranted for a definitive conclusion.

Alternative RNA splicing provides an important means to expand metazoan transcriptome diversity. Contrary to what was accepted previously, splicing is now thought to predominantly take place during transcription. Motivated by emerging data showing the physical proximity of the spliceosome to Pol II, we surveyed the effect of epigenetic context on co-transcriptional splicing. In particular, we observed that splicing factors were not necessarily enriched at exon junctions and that most epigenetic signatures had a distinctly asymmetric profile around known splice sites. Given this, we tried to build an interpretable model that mimics the physical layout of splicing regulation where the chromatin context progressively changes as the Pol II moves along the guide DNA. We used a recurrent-neural-network architecture to predict the inclusion of a spliced exon based on adjacent epigenetic signals, and we showed that distinct spatio-temporal features of these signals were key determinants of model outcome, in addition to the actual nucleotide sequence of the guide DNA strand. After the model had been trained and tested (with >80% precision-recall curve metric), we explored the derived weights of the latent factors, finding they highlight the importance of the asymmetric time-direction of chromatin context during transcription.

Abstract Background Pancreatic ductal adenocarcinoma (PDA) is a fatal disease with very poor prognosis. Development of sensitive and noninvasive methods to monitor tumor progression in PDA is a critical and unmet need. Magnetic resonance imaging (MRI) can noninvasively provide information regarding underlying pathophysiological processes such as necrosis, inflammatory changes and fibrotic tissue deposition. Methods A genetically engineered KPC mouse model that recapitulates human PDA was used to characterize disease progression. MR measures of T1 and T2 relaxation times, magnetization transfer ratio (MTR), diffusion and chemical exchange saturation transfer were compared in two separate phases i.e. slow and rapid growth phase of tumor. Fibrotic tissue accumulation was assessed histologically using Masson’s trichrome staining. Pearson correlation coefficient (r) was computed to assess the relationship between the fibrotic tissue accumulation and different MR parameters. Results There was a negative correlation between amide proton transfer signal intensity and tumor volume (r = − 0.63, p = 0.003) in the slow growth phase of the tumor development. In the terminal stage of rapid growth phase of the tumor development MTR was strongly correlated with tumor volume (r = 0.62, p = 0.008). Finally, MTR was significantly correlated with % fibrosis (r = 0.87; p

Abstract Metallosphaera sedula is a thermoacidophilic autotrophic archaeon known to utilize the 3-hydroxypropionate/4-hydroxybutyrate cycle (3-HP/4-HB cycle) as carbon fixation pathway. 3-Hydroxypropionyl-CoA dehydratase (3HPCD) is an enzyme involved in the 3-HP/4-HB cycle by converting 3-hydroxypropionyl-CoA to acryloyl-CoA. To elucidate the molecular mechanism of 3HPCD from M. sedula (Ms3HPCD), we determined its crystal structure in complex with Coenzyme A (CoA). Ms3HPCD showed an overall structure and the CoA-binding mode similar to other enoyl-CoA hydratase (ECH) family enzymes. However, compared with the other ECHs, Ms3HPCD has a tightly formed α3 helix near the active site, and bulky aromatic residues are located at the enoyl-group binding site, resulting in the enzyme having an optimal substrate binding site for accepting short-chain 3-hydroxyacyl-CoA as a substrate. Moreover, based on the phylogenetic tree analysis, we propose that the 3HPCD homologues from the phylum Crenarchaeota have an enoyl-group binding pocket similar to that of bacterial short-chain ECHs.

Abstract Background In Korea, hepatitis B virus (HBV) infection accounts for approximately 65–75% of HBV-related diseases, such as chronic hepatitis and liver cancer, and mother-to-child transmission is presumed to be a major source of the infection. To tackle this issue, the Korean government launched the national Perinatal Hepatitis B Prevention Program (PHBPP) in 2002. This study analyzed the cost-effectiveness of the PHBPP with antiviral prophylaxis compared with the current PHBPP and/or universal vaccination, as well as identified the optimal strategy to eliminate mother-to-child transmission of HBV in Korea. Methods A decision tree model with the Markov process was developed and simulated over the lifetime of a birth cohort in Korea during the year 2014. The current PHBPP providing HBV vaccine and hepatitis B immune globulin to neonates born to HBV positive mothers was compared against two other strategies, universal vaccination of HBV and PHBPP with antiviral prophylaxis, with respect to their costs and health outcomes. The Korean National Health Insurance database was investigated to estimate the costs of HBV-related diseases and utilization of health resources. Costs were assessed from the health care system perspective and converted to 2014 US dollars. Health outcome measures were quality-adjusted life years (QALYs) and number of HBV-related diseases and deaths. Both costs and QALYs were discounted at 5%, following the recommendation of the Health Insurance Review & Assessment Service in Korea. The incremental cost-effectiveness ratio (ICER) obtained from the analysis was evaluated using the willingness-to-pay (WTP) in the Korean society. Results PHBPP with antiviral prophylaxis in Korea was cost-effective compared with the current PHBPP. An introduction of antiviral prophylaxis to pregnant women with a high viral load of HBV averted 13 HBV-related deaths per 100,000 people and saved 82 QALYs in total (ICER: $16,159/QALY). Conclusions Considering that WTP in Korea is $29,000, PHBPP with antiviral prophylaxis appears to be a cost-effective strategy. To further decrease the burden of perinatal hepatitis B in Korea, adding antiviral prophylaxis to PHBPP is recommended.

Many studies concerning the precision manufacturing of freeform concrete panels have been conducted, however, this process remains labor intensive taking significant amounts of time and cost. In particular, the precision in the shape of the panels produced tends to be low because of the manual work involved in producing the curves of those panels. This study documents the development of mold production technologies that can be used to produce precise curved surfaces on the upper and lower parts of a mold for freeform concrete panels. A double-sided multipoint press CNC (computer numerical control) produces curved upper and lower surfaces of a mold without the need for manual work, while the operational technology we developed to control this tool enhances the precision of the curves created. The precision of these technologies was verified through experiments. The difference between the shapes designed and those produced were found using 3D scans and quality inspections. Unpredictable errors can occur due to the size of certain curvatures, the elasticity of the silicone plate, and the rotational angle of the joints of the rods supporting the surfaces. To minimize errors, shape compensation was carried out through reverse engineering, leading to a maximum error of 2.887 mm, which is within the allowable error. The results achieved in this study are a significant step toward technologies that will produce increasingly precise freeform concrete panels.

Construction safety climates can reflect organizational safety behavior and commitment, employees’ safety perceptions and attitudes, and the supervisory and support environments. Maintaining a healthy safety climate can help prevent workers from fatal accidents and illnesses. To enhance the safety climate and, consequently, improve safety performance at a construction site, it is very important to analyze the elements that affect the safety climate and are significant for different types of construction work organizations. Therefore, the main goal of this study was to develop a multi-spectra perception model to investigate which factors were considered critical from four key perspectives: managers, superintendents, skilled laborers, and general helpers. To achieve this goal, a survey questionnaire was conducted to collect empirical data from one commercial building construction project. Based on a stepwise regression analysis, it was revealed that the most significant factors enhancing the safety climate are: from the managers’ perspective, a combination of improvement in the support environment and reduction in work pressure; for superintendents and skilled laborers, increasing worker competence; and for general laborers, increasing worker involvement. This research contributes to a better understanding of the significant factors and provides a measure for each important role in enhancing the safety climate at a job site.

The excavation work for constructing urban high-rise buildings is narrow and deep, limiting the mobility of equipment. In addition, the productivity of excavation work varies according to the type and amount of equipment used. Therefore, a combination of excavation and rock breaking equipment is needed to maximize productivity while minimizing the cost of excavation work. Simulation of various cases is needed to draw optimum plans for variables that can greatly affect the productivity of excavation work, but that is not an easy task. Furthermore, a great deal of information and time are necessary for engineers or technicians to review the combination of equipment. For these reasons, most engineers or technicians are unable to accurately predict construction duration or the cost of using a particular equipment combination, based on their experience. The objective of this paper is to propose an efficient equipment combination model for effective excavation work for urban high-rise buildings. In the test case project, applying the proposed model reduced the equipment rental cost about 3% compared with the method relying on experience. The results of this study will be useful in establishing earthwork plans by taking into consideration the productivity of equipment per type and combination.

Although ubiquitination is widely assumed to be the only regulated step in the ubiquitin–proteasome pathway, recent studies have demonstrated several important mechanisms that regulate the activities of the 26S proteasome. Most proteasomes in cells are inactive but, upon binding a ubiquitinated substrate, become activated by a two-step mechanism requiring an association of the ubiquitin chain with Usp14 and then a loosely folded protein domain with the ATPases. The initial activation step is signaled by Usp14’s UBL domain, and many UBL-domain-containing proteins (e.g., Rad23, Parkin) also activate the proteasome. ZFAND5 is a distinct type of activator that binds ubiquitin conjugates and the proteasome and stimulates proteolysis during muscle atrophy. The proteasome’s activities are also regulated through subunit phosphorylation. Agents that raise cAMP and activate PKA stimulate within minutes Rpn6 phosphorylation and enhance the selective degradation of short-lived proteins. Likewise, hormones, fasting, and exercise, which raise cAMP, activate proteasomes and proteolysis in target tissues. Agents that raise cGMP and activate PKG also stimulate 26S activities but modify different subunit(s) and stimulate also the degradation of long-lived cell proteins. Both kinases enhance the selective degradation of aggregation-prone proteins that cause neurodegenerative diseases. These new mechanisms regulating proteolysis thus have clear physiological importance and therapeutic potential.

Free-form concrete panel production requires an increasing amount of manpower because the molds cannot be reused. There are many limitations when it comes to reproducing accurate forms due to the many manual processes. Therefore, the current study developed side mold control equipment that can automatically fabricate molds for free-form concrete panels. The equipment is capable of molding various shapes and sustainable operation. However, there may be errors as it automatically produces various shapes. Therefore, it is necessary to check the errors between manufactured shapes and designed shapes. The shape created using the side mold control equipment showed less than 0.1° error in side angle and ±3 mm error in side length. Therefore, the equipment manufactured a precise shape. Based on the findings of the study, the side mold control equipment will be used to produce accurate shape of free-form concrete panels automatically.

In Korea apartment buildings, most energy is consumed as heating energy. In order to reduce heating energy in apartment buildings, it is required to reduce the amount of energy used in heating systems. Energy saving in heating systems can be achieved through operation and control based on efficient operation plans. The efficient operation plan of the heating system should be based on the predicted heating load. Thus, various methods have been developed for predicting heating loads. Recently, artificial intelligence techniques (e.g., ANN: artificial neural network) have been used to predict heating loads. The process for determination of input data variables is necessary to obtain the accuracy of predicted results using an ANN model. However, there is a lack of studies to evaluate the accuracy level of the predicted results caused by the selection and combination of input variables. There is a need to evaluate the performance of an ANN model for prediction of residential heating loads. Therefore, the purpose of this study is, for a residential building, to evaluate the accuracy levels of predicted heating loads using an ANN model with various combinations of input variables. To achieve the study purpose, each case was classified according to the combination of the input variables and the prediction results were analyzed. Through this, the worst, mean, and best were selected according to the predicted performance. In addition, an actual case was selected consisting of variables that can be measured in an actual building. The derived cv(RMSE) of each case resulted in a percentage value of 38.2% for the worst, 7.3% for the mean, 3.0% for the best, and 5.4% for the actual. The largest difference between the best and worst resulted in 33.2%, and thus the precision of the predicted heating loads was highly affected by the selection and combination of the input variables used for the ANN model.

We examine relationships between the start of rainy season (SOS) and sub-national grain (white maize) market price movements in five African countries. Our work is motivated by three factors: (a) some regions are seeing increasing volatility SOS timing; (b) SOS represents the first observable occurrence in the agricultural season and starts a chain reaction of decisions that influence planting, labor allocation, and harvest—all of which can have direct impacts on local food prices and availability; and (c) pre- and post-harvest price movements provide key insights into supply-and-demand issues related to food insecurity. We start by exploring a number of different SOS definitions using varying reference periods to define whether an SOS is ‘on-time’ or ‘late’. We then compare how those different definitions perform in seasonal price forecasting models. Specifically, we examine if SOS indicators can predict price means over 6 and 9 month periods, or roughly the length of time from planting to market. We use different reference periods for defining ‘early’ versus ‘late’ seasonal starts based on the previous year’s start date, or median start dates over the past 3, 5, and 10 year periods. We then compare the out-of-sample forecast performance of univariate time-series models (autoregressive integrated moving average (ARIMA)) with time-series (ARIMAX) models that include various SOS definitions as exogenous predictors. We find that using some form of SOS indicator (either an SOS anomaly or 1st month’s rainfall anomaly) leads to increased predictive power when examining prices over a 6 months window. However, the results vary considerably by country. We find the strongest performance of SOS indicators in central Ethiopia, southern Kenya, and southern Somalia. We find less evidence in support of the use of SOS indicators for price forecasting in Malawi and Mozambique.

The number of new homes built in China in 2014 doubled compared to 2004, while Korea has built more than 3000 units every year since 2004 and Japan has built more than 6000 new units. Apartments account for 60% of homes in Korea, so it is anticipated that apartment construction projects will not cease in Korea. The current company assumes that the sale rate (pre-sale rate) of apartments may be completely controlled by the pre-sale prices. The study calculated appropriate pre-sale prices to maximize the revenue of companies based on that assumption. For that purpose, the study identified the factors affecting the pre-sale prices and analyzed its correlation with the pre-sale prices based on the apartments located in Seoul, Korea. As a result of the analysis, it was found that the pre-sale prices of apartments are correlated with the number of apartment complexes, local rates, and local development level. The final result of the study suggested a way to calculate the sale prices using the factors that are thought to be correlated with the pre-sale prices. A simulation model was created using the method. When tested, it yielded an average deviation rate of 10.32%. The current study will contribute to preventing the economic losses that may be caused by apartment construction projects.

With the evolution of communication technology and the exponential increase of mobile devices, the ubiquitous networking allows people to use our data and computing resources anytime and everywhere. However, numerous security concerns and complicated requirements arise as these ubiquitous networks are deployed throughout people's lives. To meet the challenge, the user authentication schemes in ubiquitous networks should ensure the essential security properties for the preservation of the privacy with low computational cost. In 2017, Chaudhry et al. proposed a password-based authentication scheme for the roaming in ubiquitous networks to enhance the security. Unfortunately, we found that their scheme remains insecure in its protection of the user privacy. In this paper, we prove that Chaudhry et al.'s scheme is vulnerable to the stolen-mobile device and user impersonation attacks, and its drawbacks comprise the absence of the incorrect login-input detection, the incorrectness of the password change phase, and the absence of the revocation provision. Moreover, we suggest a possible way to fix the security flaw in Chaudhry et al's scheme by using the biometric-based authentication for which the bio-hash is applied in the implementation of a three-factor authentication. We prove the security of the proposed scheme with the random oracle model and formally verify its security properties using a tool named ProVerif, and analyze it in terms of the computational and communication cost. The analysis result shows that the proposed scheme is suitable for resource-constrained ubiquitous environments.