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Abstract Background Systemic inflammation is believed to contribute to small cell lung cancer (SCLC) progression, but the underlying relationship remains unclear. Lipocalin‐2, a potential biomarker of inflammation, has been implicated in various cancers but its prognostic value in SCLC is underexplored. Methods We retrospectively analyzed 191 patients with SCLC (72 with limited‐stage [LD] and 119 with extensive‐stage) treated using platinum‐based chemotherapy. Lipocalin‐2 expression was evaluated using immunohistochemistry. Optimal cutoff values for lipocalin‐2 and neutrophil‐to‐lymphocyte ratio (NLR) were determined using time‐dependent receiver operating characteristic curve analysis. The pectoralis muscle index was used to assess sarcopenia. Results In LD‐SCLC, high lipocalin‐2 expression was associated with worse progression‐free survival (PFS; median: 7.0 vs. 15.9 months, p = 0.015) and overall survival (OS; median: 12.9 vs. 30.3 months, p = 0.035) compared with low lipocalin‐2 expression. Patients were stratified into three prognostic groups by combining lipocalin‐2 with NLR: low lipocalin‐2/low NLR, high lipocalin‐2/low NLR or low lipocalin‐2/high NLR, and high lipocalin‐2/high NLR (median PFS: 17.3 vs. 11.0 vs. 6.3 months, p = 0.004; median OS: 30.5 vs. 17.3 vs. 8.6 months, p = 0.002). Similar trends were observed when combining lipocalin‐2 with the pectoralis muscle index. High lipocalin‐2 expression was also associated with lower complete response rates (18.9% vs. 34.3%, p = 0.035). No significant prognostic implications were found for lipocalin‐2 in extensive‐stage SCLC. Conclusions High lipocalin‐2 expression is potentially associated with poorer survival in LD‐SCLC. Combining lipocalin‐2 with other inflammation‐related markers could improve prognostic stratification.

Abstract Muscle strength assessments are vital in rehabilitation, orthopedics, and sports medicine. However, current methods used in clinical settings, such as manual muscle testing and hand-held dynamometers, often lack reliability, and isokinetic dynamometers (IKD), while reliable, are not easily portable. The aim of this study was to design and validate a wearable dynamometry system with high accessibility, accuracy, and reliability, and to validate the device. Therefore, we designed a wearable dynamometry system (WDS) equipped with knee joint torque sensors. To validate this WDS, we measured knee extension and flexion strength in 39 healthy adults using both the IKD and WDS. Comparing maximal isometric torque measurements, WDS and IKD showed strong correlation and good reliability for extension (Pearson’s r: 0.900; intraclass correlation coefficient [ICC]: 0.893; standard error of measurement [SEM]: 9.85%; minimal detectable change [MDC]: 27.31%) and flexion (Pearson’s r: 0.870; ICC: 0.857; SEM: 11.93%; MDC: 33.07%). WDS demonstrated excellent inter-rater (Pearson’s r: 0.990; ICC: 0.993; SEM: 4.05%) and test–retest (Pearson’s r: 0.970; ICC: 0.984; SEM: 6.15%) reliability during extension/flexion. User feedback from 35 participants, including healthcare professionals, underscores WDS's positive user experience and clinical potential. The proposed WDS is a suitable alternative to IKD, providing high accuracy, reliability, and potentially greater accessibility.

Shape-memory polyurethanes (SMPUs) are promising materials that change shape in response to external heat. These polymers have a dual-segment structure: a hard segment for netpoint and a soft segment for molecular switch. Understanding the molecular behavior of each segment and microphase-separated morphology is crucial for comprehending the shape-memory mechanism. This study aimed to understand the shape-memory behavior by observing the phase separation of SMPU using mesoscale models based on dissipative particle dynamics (DPD) simulations. The SMPU copolymer was modeled using 4,4′-diphenylmethane diisocyanate (MDI, hard segment) and poly(ethylene oxide) (PEO, soft segment). By calculating segment solubility and repulsion parameters, we found that the hard-segment domain changes from isolated form to a lamellar and interconnected structure and eventually to a continuous form as its content increases. Combining these insights with shape-memory performance models can enhance our understanding of better SMPU design and contribute significantly to the optimization of smart stimuli-responsive materials.

Abstract Monitoring ankle strength is crucial for assessing daily activities, functional ability, and preventing lower extremity injuries. However, the current methods for measuring ankle strength are often unreliable or not easily portable to be used in clinical settings. Therefore, this study proposes a portable dynamometer with high reliability capable of measuring ankle dorsiflexion and plantar flexion. The proposed portable dynamometer comprised plates made of aluminum alloy 6061 and a miniature tension–compression load cell. A total of 41 healthy adult participants applied maximal isometric dorsiflexor and plantar flexor forces on a Lafayette Handheld Dynamometer (HHD) and the portable dynamometer. The results were cross-validated, using change in mean, and two independent examiners evaluated the inter-rater and test–retest reliabilities in separate sessions using intraclass correlation coefficients, standard error of measurement, and minimal detectable change. Both dorsiflexion and plantar flexion measurements demonstrated a strong correlation with the HHD (r = 0.827; r = 0.973) and showed high inter-rater and test–retest reliabilities. Additionally, the participant responses to the user experience questionnaire survey indicated vastly superior positive experiences with the portable dynamometer. The study findings suggest that the designed portable dynamometer can provide accurate and reliable measurements of ankle strengths, making it a potential alternative to current methods in clinical settings.

Abstract Background Although the prognostic value of the Controlling Nutritional Status (CONUT) score in diffuse large B-cell lymphoma (DLBCL) has been reported in several previous studies, its clinical relevance for the presence of sarcopenia has not been assessed. Methods In this study, 305 DLBCL patients were reviewed. They were categorized into normal/mild (n = 219) and moderate/severe (n = 86) CONUT groups. Sarcopenia was assessed using the L3-skeletal muscle index measured by baseline computed tomography imaging. Based on CONUT score and sarcopenia, patients were grouped: A (normal/mild CONUT and no sarcopenia), B (either moderate/severe CONUT or sarcopenia, but not both), and C (both moderate/severe CONUT and sarcopenia). Results The moderate/severe CONUT group showed higher rates of ≥ grade 3 febrile neutropenia, thrombocytopenia, non-hematologic toxicities, and early treatment discontinuation not related to disease progression, compared to the normal/mild CONUT group. The moderate/severe CONUT group had a lower complete response rate (58.1% vs. 80.8%) and shorter median overall survival (18.5 vs. 162.6 months) than the normal/mild group. Group C had the poorest prognosis with a median survival of 8.6 months, while groups A and B showed better outcomes (not reached and 60.1 months, respectively). Combining CONUT score and sarcopenia improved the predictive accuracy of the Cox regression model (C-index: 0.763), compared to the performance of using either CONUT score (C-index: 0.754) or sarcopenia alone (C-index: 0.755). Conclusions In conclusion, the moderate/severe CONUT group exhibited treatment intolerance, lower response, and poor prognosis. Additionally, combining CONUT score and sarcopenia enhanced predictive accuracy for survival outcomes compared to individual variables.

Recent studies have increasingly focused on integrating detonation processes into engine technologies, advancing beyond the fundamental research phase of detonation research. The present study investigates the detonability and combustion characteristics of liquid fuels, specifically ethanol, with an emphasis on the effects of atomization properties facilitated by different atomizer designs to implement pulse detonation combustion engines. Oxygen was used as the oxidizer. We employed internal injectors (I45, I90, IB4) and atomizer venturis (VA, VB, VR) to examine how variations in liquid fuel atomization and atomizer configurations influence detonation. The occurrence of detonation was assessed using predicted Sauter mean diameters (SMDs) and exit velocities for different atomizer setups. Additionally, we evaluated the effects of nitrogen dilution at concentrations of 0%, 25%, and 50% on velocity variations and changes in detonation characteristics. The findings suggest that while higher exit velocities decrease SMD, facilitating detonation, excessively high velocities hinder detonation initiation. Conversely, lower exit velocities emphasize the role of SMD in initiating detonation. However, the introduction of nitrogen, which reduces the SMD, was found to decrease reactivity and impede detonation.

In the current study, two Salmonella Typhimurium strains, JOL 912 and JOL 1800, were engineered from the wild-type JOL 401 strain through in-frame deletions of the lon and cpxR genes, with JOL 1800 also lacking rfaL. These deletions significantly attenuated the strains, impairing their intracellular survival and creating unique immunological profiles. This study investigates the response of these strains to various abiotic stress conditions commonly experienced in vivo, including temperature, acidity, osmotic, and oxidative stress. Notably, cold stress induced a non-significant trend towards increased invasion by Salmonella compared to other stressors. Despite the observed attenuation, no significant alterations in entry mechanisms (trigger vs. zipper) were noted between these strains, although variations were evident depending on the host cell type. Both strains effectively localized within the cytoplasm, demonstrating their ability to invade and interact with the intracellular environment. Immunologically, JOL 912 elicited a robust response, marked by substantial activation of nuclear factor kappa B (NF-kB), and chemokines, interleukin 8 (CXCL 8) and interleukin 10 (CXCL 10), comparable to the wild-type JOL 401 (over a fourfold increase compared to JOL 1800). In contrast, JOL 1800 exhibited a minimal immune response. Additionally, these attenuations influenced the expression of cyclins D1 and B1 and caspases 3 and 7, indicating cell cycle arrest at the G2/M phase and promotion of the G0/G1 to S phase transition, alongside apoptosis in infected cells. These findings provide valuable insights into the mechanisms governing the association, internalization, and survival of Salmonella mutants, enhancing our understanding of their regulatory effects on host cell physiology.

Detonation engines are gaining prominence as next-generation propulsion systems that can significantly enhance the efficiency of existing engines. This study focuses on developing an injector utilizing liquid fuel and a gas oxidizer for application in detonation engines. In order to better understand the spray characteristics suitable for the pulse detonation engine (PDE) system, an injector was fabricated by varying the Venturi nozzle exit diameter ratio and the geometric features of the fuel injection hole. Analysis of high-speed camera images revealed that the Venturi nozzle exit diameter ratio plays a crucial role in determining the characteristics of air-assist or air-blast atomization. Under the conditions of an exit diameter ratio of Re/Ri = 1.0, the formation of a liquid film at the exit was observed, and it was identified that the film’s length is influenced by the geometric characteristics of the fuel injection hole. The effect of the fuel injection hole and Venturi nozzle exit diameter ratio on SMD was analyzed by using droplet diameter measurement. The derived empirical correlation indicates that the atomization mechanism varies depending on the Venturi nozzle exit diameter ratio, and it also affects the distribution of SMD. The characteristics of the proposed injector, its influence on SMD, and its velocity, provide essential groundwork and data for the design of detonation engines employing liquid fuel.

Abstract Muscle strength assessment is important in predicting clinical and functional outcomes in many disorders. Manual muscle testing, although commonly used, offers suboptimal accuracy and reliability. Isokinetic dynamometers (IKDs) have excellent accuracy and reliability; but are bulky and expensive, offering limited accessibility. This study aimed to design a portable dynamometer that is accessible, accurate and reliable, and to validate the device in a general population. The portable articulated dynamometry system (PADS) is a portable device with an embedded high-precision load cell, designed to measure muscle strength with optimal accuracy. Seventy-two participants underwent maximal isometric and isokinetic knee extensor torque measurement with the PADS and IKD, respectively. The PADS results were cross-validated against IKD results using change in mean (CIM). Interrater and intra-rater reliabilities were assessed using intraclass correlation coefficients, standard error of measurement, and minimal detectable change. The PADS maximal knee extensor strength results were not significantly different from those by IKD (CIM: − 2.13 Nm; 95% CI − 4.74, 0.49 Nm). The PADS showed interrater reliability (Pearson’s r: 0.958; ICC: 0.979; SEM: 5.51%) and excellent intra-rater reliability (Pearson’s r: 0.912; ICC: 0.954; SEM: 8.38%). The proposed PADS may be an effective alternative to IKD, offering good accuracy, reliability, and potentially better accessibility.

Abstract Background Individualized, targeted, and intense training is the hallmark of successful gait rehabilitation in people post-stroke. Specifically, increasing use of the impaired ankle to increase propulsion during the stance phase of gait has been linked to higher walking speeds and symmetry. Conventional progressive resistance training is one method used for individualized and intense rehabilitation, but often fails to target paretic ankle plantarflexion during walking. Wearable assistive robots have successfully assisted ankle-specific mechanisms to increase paretic propulsion in people post-stroke, suggesting their potential to provide targeted resistance to increase propulsion, but this application remains underexamined in this population. This work investigates the effects of targeted stance-phase plantarflexion resistance training with a soft ankle exosuit on propulsion mechanics in people post-stroke. Methods We conducted this study in nine individuals with chronic stroke and tested the effects of three resistive force magnitudes on peak paretic propulsion, ankle torque, and ankle power while participants walked on a treadmill at their comfortable walking speeds. For each force magnitude, participants walked for 1 min while the exosuit was inactive, 2 min with active resistance, and 1 min with the exosuit inactive, in sequence. We evaluated changes in gait biomechanics during the active resistance and post-resistance sections relative to the initial inactive section. Results Walking with active resistance increased paretic propulsion by more than the minimal detectable change of 0.8 %body weight at all tested force magnitudes, with an average increase of 1.29 ± 0.37 %body weight at the highest force magnitude. This improvement corresponded to changes of 0.13 ± 0.03 N m kg− 1 in peak biological ankle torque and 0.26 ± 0.04 W kg− 1 in peak biological ankle power. Upon removal of resistance, propulsion changes persisted for 30 seconds with an improvement of 1.49 ± 0.58 %body weight after the highest resistance level and without compensatory involvement of the unresisted joints or limb. Conclusions Targeted exosuit-applied functional resistance of paretic ankle plantarflexors can elicit the latent propulsion reserve in people post-stroke. After-effects observed in propulsion highlight the potential for learning and restoration of propulsion mechanics. Thus, this exosuit-based resistive approach may offer new opportunities for individualized and progressive gait rehabilitation.

Objective To explore the effects of tactile stimulation using air pressure at the auricular branch of the vagus nerve on autonomic activity in healthy individuals. Methods Three types of tactile stimulation were used in this study: continuous low-amplitude, continuous high-amplitude, and pulsed airflow. The tactile stimulations were provided to the cymba concha to investigate autonomic activity in 22 healthy participants. The mean heart rate (HR) and parameters of HR variability, including the standard deviation of R-R intervals (SDNN) and root mean square of successive R-R interval differences (RMSSD) were compared at baseline, stimulation, and recovery periods. Results Two-way repeated measures ANOVA indicated a significant main effect of time on HR (p=0.001), SDNN (p=0.003), and RMSSD (p

Specific humidity (SH) which means the amount of water vapor in 1 kg of air, is used as an indicator of energy exchange between the atmosphere and the Earth’s surface. SH is typically computed using microwave satellites. However, the spatial resolution of data for microwave satellite is too low. To overcome this disadvantage, we introduced new methods that applied data collected by the Landsat-8 satellite with high spatial resolution (30 m), a meteorological model, and observation data for South Korea in 2016–2017 to 4 machine learning techniques to develop an optimized technique for computing SH. Among the 4 machine learning techniques, the random forest-based method had the highest accuracy, with a coefficient of determination (R) of 0.98, Root Mean Square Error (RMSE) of 0.001, bias of 0, and Relative Root Mean Square Error (RRMSE) of 11.16%. We applied this model to compute land surface SH using data from 2018 to 2019 and found that it had high accuracy (R = 0.927, RMSE = 0.002, bias = 0, RRMSE = 28.35%). Although the data used in this study were limited, the model was able to accurately represent a small region based on an ensemble of satellite and model data, demonstrating its potential to address important issues related to SH measurements from satellites.

Deep learning models perform well when there is enough data available for training, but otherwise the performance deteriorates rapidly owing to the so-called data shortage problem. Recently, model-agnostic meta-learning (MAML) was proposed to alleviate this problem by embedding common prior knowledge from different tasks into the initial parameters of the target model. Data shortages are very common in regional influenza predictions, and MAML also often struggles with regional influenza forecasting, especially when region-specific knowledge, such as peak timing or intensity, varies. In this paper, we propose a novel MAML-based parameter adjustment scheme for influenza forecasting, called MARAPAS. The fundamental idea of our scheme is to adjust the initial parameters obtained from common knowledge to a target region by using adjustment variables. We experimentally show that MARAPAS outperforms other MAML-based methods, in terms of root mean square error and Pearson correlation coefficient. Particularly, this scheme improves the forecasting performance by up to 34 % compared with that of the state-of-the-art schemes. We also show the robust forecasting accuracy of our scheme and demonstrate its applicability by performing zero-shot COVID-19 forecasting.

Abstract The amendment of cementitious binders with waste materials aids as a path to reduce the volume of waste and carbon emission. This review summarizes the current state of practice for cementitious binder fabrication in favor to the utilization of waste materials such as waste concrete powder (WCP), coal bottom ash (CBA) and steel slags. These materials have the potential to be employed as cementitious material, however much of the application is still up to the laboratory scale. This manuscript will serve as the support to understand the utilization of mentioned waste as nontraditional cementitious products. The highlighted areas likely need more refinement and research with indication on possible negative impact on application of wastes. The use of the aforementioned wastes for blending with OPC (ordinary Portland cement) can reduce carbon emissions from cement manufacturing. Additionally, it can also reduce the use of natural resources during clinker production.

The growing demand for artificial intelligence has faced challenges for traditional computing architectures. As a result, neuromorphic computing systems have emerged as possible candidates for next-generation computing systems. Two-dimensional (2D) materials-based neuromorphic devices that emulate biological synapses and neurons play a key role in neuromorphic computing hardware due to their unique properties such as high strength, thermal conductivity, and flexibility. Although several studies have shown the simulations of individual devices, experimental implementation of large-scale crossbar arrays is still unclear. In this review, we explore the working principles and mechanisms of memristive devices. Then, we overview the development of neuromorphic devices based on 2D materials including transition metal dichalcogenides, graphene, hexagonal boron nitride, and layered halide perovskites. We also highlight the requirement and recent progress for building crossbar arrays by utilizing the advantageous properties of 2D materials. Lastly, we address the challenges that hardware implementation of neuromorphic computing systems currently face and propose a path towards system-level applications of neuromorphic computing.

All-solid-state lithium-ion batteries (ASSLBs) represent a promising breakthrough in battery technology owing to their high energy density and exceptional stability. When crafting cathode electrodes for ASSLBs, the solid electrolyte/cathode material interface is physically hindered by the specific morphology of carbon additive materials. In this paper, we examine the distribution of conductive additives within the electrode and its impact on the electrochemical performance of composites incorporating either nano-sized carbon black (CB) or micron-sized carbon nanofibers (CNF) into Ni-rich (LiNi0.8Co0.1Mn0.1O2) cathode material based composites. When nano-sized CB is employed as a conductive additive, it enhances the electrical conductivity of the composite by adopting a uniform distribution. However, its positioning between the solid electrolyte and cathode material leads to an increase in interfacial resistance during charge and discharge cycles, resulting in decreased electrochemical performance. In contrast, using micron-sized CNF as a conductive additive results in a reduction in the composite’s electrical conductivity compared to CB. Nevertheless, due to the comparatively uninterrupted interfaces between the solid electrolyte and cathode materials, it exhibits superior electrochemical characteristics. Our findings are expected to aid the fabrication of electrochemical-enhanced cathode composite electrodes for ASSLBs.

All-solid-state lithium-ion batteries (ASSLBs) have recently received significant attention due to their exceptional energy/power densities, inherent safety, and long-term electrochemical stability. However, to achieve energy- and power-dense ASSLBs, the cathode composite electrodes require optimum ionic and electrical pathways and hence the development of electrode designs that facilitate such requirements is necessary. Among the various available conductive materials, carbon black (CB) is typically considered as a suitable carbon additive for enhancing electrode conductivity due to its affordable price and electrical-network-enhancing properties. In this study, we examined the effect of different weight percentages (wt%) of nano-sized CB as a conductive additive within a cathode composite made up of Ni-rich cathode material (LiNi0.8Co0.1Mn0.1O2) and solid electrolyte (Li6PS5Cl). Composites including 3 wt%, 5 wt%, and 7 wt% CB were produced, achieving capacity retentions of 66.1%, 65.4%, and 44.6% over 50 cycles at 0.5 C. Despite an increase in electrical conductivity of the 7 wt% CB sample, a significantly lower capacity retention was observed. This was attributed to the increased resistance at the solid electrolyte/cathode material interface, resulting from the presence of excessive CB. This study confirms that an excessive amount of nano-sized conductive material can affect the interfacial resistance between the solid electrolyte and the cathode active material, which is ultimately more important to the electrochemical performance than the electrical pathways.

Sulfide electrolyte all-solid-state lithium-ion batteries (ASSLBs) that have inherently nonflammable properties have improved greatly over the past decade. However, determining both the stable and functional electrode components to pair with these solid electrolytes requires significant investigation. Solid electrolyte comprises 20–40% of the composite cathode electrode, which improves the ionic conductivity. However, this results in thick electrolyte that blocks the electron pathways in the electrode, significantly lowering the electrochemical performance. The application of conductive carbon material is required to overcome this issue, and, hence, determining the carbon properties that result in the most stable performance in the sulfide solid electrolyte is vital. This study analyzes the effect of the cathode conductive additive’s morphology on the electrochemical performance of sulfide electrolyte-based ASSLBs. Carbon black (CB) and carbon nanotubes (CNTs), which provide electron pathways at the nanoscale and sub-micron scale, and carbon nanofiber (CNF), which provides electron pathways at the tens-of-microns scale, are all tested individually as potential conductive additives. When the CNF, with its high crystallinity, is used as a conductive material, the electrochemical performance shows an excellent initial discharge capacity of 191.78 mAh/g and a 50-cycle capacity retention of 83.9%. Conversely, the CB and the CNTs, with their shorter pathways and significantly increased surface area, show a relatively low electrochemical performance. By using the CNF to provide excellent electrical conductivity to the electrode, the polarization is suppressed. Furthermore, the interfacial impedance across the charge transfer region is also reduced over 50 cycles compared with the CB and CNT composite cells. These findings stringently analyze and emphasize the importance of the morphology of the carbon conductive additives in the ASSLB cathode electrodes, with improvements in the electrochemical performance being realized through the application of long-form two-dimensional crystalline CNFs.

Abstract Background The endothelial activation and stress index (EASIX) score has been reported to predict overall survival (OS) in hematological cancers. However, it has not been validated as a prognostic marker for diffuse large B-cell lymphoma (DLBCL) to date. Methods The records of 265 patients who presented with DLBCL in the Republic of Korea between January 07, 2004, and March 05, 2020 were retrospectively reviewed. For all included patients, EASIX scores were calculated using serum lactate dehydrogenase (LDH) and creatinine levels and the platelet count measured at diagnosis as follows: LDH (U/L) × creatinine (mg/dL)/platelet count (109/L). Results The median age of the patients was 64 years. The optimal cutoff value of EASIX according to the receiver operating characteristic analysis for OS was 1.33. All the patients were treated with cyclophosphamide, doxorubicin, vincristine, and prednisone combined with rituximab. The 1-year OS and progression-free survival (PFS) rates were lower in the high-EASIX group than in the low EASIX group (63.8% vs. 84.4%, p

Abstract Background Owing to increasing concerns about climate change and the depletion of fossil fuels, the development of efficient microbial processes for biochemical production from lignocellulosic biomass has been a key issue. Because process efficiency is greatly affected by the inherent metabolic activities of host microorganisms, it is essential to utilize a microorganism that can rapidly convert biomass-derived sugars. Here, we report a novel Vibrio-based microbial platform that can rapidly and simultaneously consume three major lignocellulosic sugars (i.e., glucose, xylose, and arabinose) faster than any previously reported microorganisms. Results The xylose isomerase pathway was constructed in Vibrio sp. dhg, which naturally displays high metabolic activities on glucose and arabinose but lacks xylose catabolism. Subsequent adaptive laboratory evolution significantly improved xylose catabolism of initial strain and led to unprecedently high growth and sugar uptake rate (0.67 h−1 and 2.15 g gdry cell weight −1 h−1, respectively). Furthermore, we achieved co-consumption of the three sugars by deletion of PtsG and introduction of GalP. We validated its superior performance and applicability by demonstrating efficient lactate production with high productivity (1.15 g/L/h) and titer (83 g/L). Conclusions In this study, we developed a Vibrio-based microbial platform with rapid and simultaneous utilization of the three major sugars from lignocellulosic biomass by applying an integrated approach of rational and evolutionary engineering. We believe that the developed strain can be broadly utilized to accelerate the production of diverse biochemicals from lignocellulosic biomass.

Abstract This paper investigates the effect of silica fume on the mechanical properties of metakaolin-based geopolymers with different silicon-to-aluminum molar (S/A) ratios. Geopolymer has been extensively studied as an alternative to traditional cementitious material because of its low CO2 emissions. Previous studies revealed that the application of silica fume can improve the compressive strength of geopolymer, however, the optimum dosages are different. To examine the reason for the different optimum dosages of silica fume, this study prepares geopolymer specimens of which variables are the S/A ratio and silica fume dosage, and conducts compressive strength and initial setting time tests. To examine whether the strength degradation is caused by the expansion due to the added silica fume in geopolymer, the volume and dynamic modulus are also measured. The results show that a part of silica fume dissolves and changes the S/A ratio of geopolymer, and that a part of silica fume remains in the geopolymer matrix. These combined effects of silica fume result in an irregular compressive strength trend, and, thus, an optimum dosage of silica fume can vary depending on the S/A ratio. Furthermore, the volume expansion of geopolymer with silica fume is observed, however, no sign of damage on the compressive strength is found.

This paper investigates the effect of the rheological properties of fresh binder on the compressive strength of pervious concrete. Pervious concrete can be used to reduce stormwater runoff, urban heat island effect, or noise. Because of a limited amount of binder, the compressive strength of pervious concrete is one of the most critical properties for its structural applications. Although researchers have revealed the important factors contributing to the compressive strength of pervious concrete, still the effect of rheological properties of fresh binder have been rarely investigated. This study measured the plastic viscosity, yield stress, adhesion energy of fresh binder using a rotational rheometer; and the data were compared with the compressive strength of the pervious concrete. The experimental variables were flowability and water-to-cement (w/c) ratios of binder materials, target porosity, and the adoption of silica fume and fibers. The test results indicate that the adhesive energy results in the best correlation with compressive strength among various rheological properties. Three-dimensional computed tomography (CT) scanning test also demonstrates the effect of the rheological conditions of the binder on the homogeneity of pervious concrete.

This paper explores hybrid alkaline cement composites with high cement content over 50%. Calcium (Ca) accelerates the strength development and improves the compressive strength of geopolymer. However, the mixing proportion of Ca in geopolymer has been limited because Ca significantly decreases the flow of fresh geopolymer. This study resolves the flow decline issue by mixing a part of the initial mixing water with cement before adding it into a metakaolin-based geopolymer, which is named hybrid alkaline cement composites (HACC) in this study. The cement/(metakaolin + cement) ratio investigated is up to 85%. Based on the initial setting time and compressive strength results, some unique characteristics of the HACC are observed: i) the material is set within 10 min and the 28-day compressive strength is over 80 MPa with 1:1 mixing ratio of geopolymer and cement paste, and ii) the compressive strength of HACC is not always higher with a lower w/c ratio of cement paste.

The membrane process has a limit to the decay of various pollutants in water. To improve the problem, the roles of backwashing media and titanium dioxide (TiO2) photocatalyst-immobilized-polyethersulfone (PES) beads’ concentration were investigated in a combined system of tubular alumina MF and the PES beads for advanced drinking water treatment. The space between the outside of the MF membrane and the module inside was filled with the PES beads. UV at a wavelength of 352 nm was irradiated from outside of the acryl module. A quantity of humic acid and kaolin was dissolved in distilled water for synthetic water. Water or air intermittent backwashing was performed outside to inside. The membrane fouling resistance after 3 h process (Rf,180) was minimum at 30 g/L of the PES beads for water backwashing, and at 40 g/L for air backwashing when increasing the PES beads from 0 to 50 g/L. The irreversible membrane fouling resistance after physical cleaning (Rif) was at the bottom at 5 g/L of the PES beads for water backwashing, which was 3.43 times higher than minimal at 40 g/L of the PES beads for air backwashing. The treatment effectiveness of turbidity increased when increasing the PES beads’ concentration from 0 to 50 g/L; however, it reached a maximum at 98.1% at 40 g/L and 99.2% at 50 g/L for water and air backwashing, respectively. The treatment effectiveness of UV254 absorbance, which was dissolved organic matter (DOM), increased dramatically when increasing the PES beads; however, it reached a peak of 83.0% at 40 g/L and 86.0% at 50 g/L for water and air backwashing, respectively. Finally, the best PES beads’ concentration was 20~30 g/L to minimize the membrane fouling; however, it was 50 g/L to remove pollutants effectively. The water backwashing was better than the air at treating DOM; however, the air backwashing was more effective than the water at removing turbid matter and reducing membrane fouling.

Carbon nanotubes (CNTs) are being used as a conductive material to achieve fast charging/discharging properties in high current density LIB systems. However, it is difficult to form a well-distributed electron transfer network within the electrode using CNTs due to their high aggregational properties. In this paper, we propose a strategy to fabricate the positive electrode with well-dispersed CNTs, controlling the solid content and sonication process of the slurry using LiNi0.8Co0.1Mn0.1O2 as the cathode materials. The CNTs are typically not well-mixed within the active material composite and aggregation occurs on the electrode surface in low solid content of under 50 wt%. When the solids content is as high as 70 %, CNTs are more evenly distributed in the electrode as bundled particles, however, there was no performance improvement using the ultra-sonication process. The CNTs at a mass ratio of CNTs at 4 wt% are well distributed in the electrode in 60 wt% of solid content, resulting in improved capacity retentions of 99.1 % after 100 cycles at a current density of 1C. When the electrochemical performance with CNTs was compared with Super P according to 1,2,3 and 4 wt% at a solid content of 60 %, the electrode using CNTs has a generally improved electrochemical performance due to the stable H1-M phase transition and stable average voltage during charging/discharging. Additionally, the excellent cycle ability at 5C with CNTs (92.8 % of capacity retention after 100 cycles) is helped by the fact that little polarization was observed compared with the Super P cells.

Abstract Background Cancer cachexia is known to adversely affect the clinical course in patients with malignant lymphoma. The cachexia index (CXI) is a potential biomarker of cancer cachexia, and its implications for the prognosis and treatment outcome of lung cancer and aggressive lymphoma has been assessed in previous studies. Methods A total of 267 patients diagnosed with diffuse large B‐cell lymphoma who were treated with rituximab plus cyclophosphamide, doxorubicin, vincristine, and prednisone (R‐CHOP) immunochemotherapy were retrospectively reviewed. The CXI was calculated as the skeletal muscle index (SMI) × serum albumin/neutrophil–lymphocyte ratio (NLR). Although previous studies measured the SMI using the muscles of the L3 vertebral level, the present study used both the L3 vertebral muscles and the pectoralis muscles (PM) at the T4 vertebral level to measure the SMI. Depending on the type of muscles used, the CXI was termed the L3‐CXI or PM‐CXI. Using sex‐specific cutoff values for CXI, the patients were categorized as follows: (i) high‐CXI group (high L3‐CXI and high PM‐CXI), (ii) intermediate‐CXI group (high L3‐CXI and low PM‐CXI), and (iii) low‐CXI group (low L3‐CXI and low PM‐CXI). Results Complete responses to R‐CHOP were obtained in 145/173 (83.8%), 25/36 (69.4%), and 27/57 (47.4%) patients in the high‐CXI, intermediate‐CXI, and low‐CXI groups, respectively (P

Abstract Background Ankle-targeting resistance training for improving plantarflexion function during walking increases rehabilitation intensity, an important factor for motor recovery after stroke. However, understanding of the effects of resisting plantarflexion during stance on joint kinetics and muscle activity—key outcomes in evaluating its potential value in rehabilitation—remains limited. This initial study uses a unilateral exosuit that resists plantarflexion during mid-late stance in unimpaired individuals to test the hypotheses that when plantarflexion is resisted, individuals would (1) increase plantarflexor ankle torque and muscle activity locally at the resisted ipsilateral ankle, but (2) at higher forces, exhibit a generalized response that also uses the unresisted joints and limb. Further, we expected (3) short-term retention into gait immediately after removal of resistance. Methods Ten healthy young adults walked at 1.25 m s−1 for four 10-min discrete bouts, each comprising baseline, exposure to active exosuit-applied resistance, and post-active sections. In each bout, a different force magnitude was applied based on individual baseline ankle torques. The peak resistance torque applied by the exosuit was 0.13 ± 0.01, 0.19 ± 0.01, 0.26 ± 0.02, and 0.32 ± 0.02 N m kg−1, in the LOW, MED, HIGH, and MAX bouts, respectively. Results (1) Across all bouts, participants increased peak ipsilateral biological ankle torque by 0.13–0.25 N m kg−1 (p 0.10). Conclusions Targeted resistance of ankle plantarflexion during stance by an exosuit consistently increased local ipsilateral plantarflexor effort during active resistance, but force magnitude will be an important parameter to tune for minimizing the involvement of the unresisted joints and limb during training.

Background Measures of whole-body angular momentum in the sagittal plane are commonly used to characterize dynamic balance during human walking. To compute angular momentum, one must specify a reference point about which momentum is calculated. Although biomechanists primarily compute angular momentum about the center of mass (CoM), momentum-based controllers for humanoid robots often use the center of pressure. Here, we asked if the choice of the reference point influences interpretations of how dynamic balance is controlled in the sagittal plane during perturbed walking. Methods Eleven healthy young individuals walked on a dual-belt treadmill at their self-selected speed. Balance disturbances were generated by treadmill accelerations of varying magnitudes and directions. We computed angular momentum about two reference points: (1) the CoM or (2) the leading edge of the base of support and then projected it along the mediolateral axes that pass through either of the reference points as the sagittal plane angular momentum. We also performed principal component analysis to determine if the choice of reference point influences our interpretations of how intersegmental coordination patterns contribute to perturbation recovery. Results We found that the peak angular momentum was correlated with perturbation amplitude and the slope of this relationship did not differ between reference points. One advantage of using a reference point at the CoM is that one can easily determine how the momenta from contralateral limbs, such as the left and right legs, offset one another to regulate the whole-body angular momentum. Alternatively, analysis of coordination patterns referenced to the leading edge of the base of support may provide more insight into the inverted-pendulum dynamics of walking during responses to sudden losses of balance.

Abstract A detailed analysis of the systematic uncertainties in the calculation of the isovector momentum fraction, 〈x〉 u − d , helicity moment, 〈x〉Δu − Δd , and the transversity moment, 〈x〉 δu − δd , of the nucleon is presented using high-statistics data on seven ensembles of gauge configurations generated by the JLab/W&M/LANL/MIT collaborations using 2 + 1-flavors of dynamical Wilson-clover quarks. The much higher statistics have facilitated better control over all systematics compared to previous lattice calculations. The least understood systematic — excited-state contamination — is quantified by studying the variation of the results as a function of different estimates of the mass gap of the first excited state, obtained from two- and three-point correlation functions, and as a function of the pion mass M π . The final results are obtained using a simultaneous fit in the lattice spacing a, pion mass M π and the finite volume parameter M π L keeping leading order corrections. The data show no significant dependence on the lattice spacing and some evidence for finite-volume corrections. Our final results, in the MS ¯ $$ \overline{\mathrm{MS}} $$ scheme at 2 GeV, are 〈x〉 u − d = 0.155(17)(20), 〈x〉Δu − Δd = 0.183(14)(20) and 〈x〉 δu − δd = 0.220(18)(20), where the first error is the overall analysis uncertainty assuming excited-state contributions have been removed, and the second is an additional systematic uncertainty due to possible residual excited-state contributions. These results are consistent with phenomenological global fit values.

Recently, energy procurement by renewable energy sources has increased. In particular, as solar power generation has a high penetration rate among them, solar radiation predictions at the site are attracting much attention for efficient operation. Various approaches have been proposed to forecast solar radiation accurately. Recently, hybrid models have been proposed to improve performance through forecasting in the frequency domain using past solar radiation. Since solar radiation data have a pattern, forecasting in the frequency domain can be effective. However, forecasting performance deteriorates on days when the weather suddenly changes. In this paper, we propose a domain hybrid forecasting model that can respond to weather changes and exhibit improved performance. The proposed model consists of two stages. In the first stage, forecasting is performed in the frequency domain using wavelet transform, complete ensemble empirical mode decomposition, and multilayer perceptron, while forecasting in the sequence domain is accomplished using light gradient boosting machine. In the second stage, a multilayer perceptron-based domain hybrid model is constructed using the forecast values of the first stage as the input. Compared with the frequency-domain model, our proposed model exhibits an improvement of up to 36.38% in the normalized root-mean-square error.

Macroalgae is regarded as a promising third-generation marine biomass that can be utilized as a sustainable feedstock for bio-industry due to the high sugar level and absence of lignin. Alginate, composed of 1,4-linked D-mannuronate (M) and L-guluronate (G), is one of the major carbohydrates in brown macroalgae. It is difficult to be assimilated by most industrial microorganisms. Therefore, developing engineered microorganisms that can utilize alginate as a feedstock in order to produce natural products from marine biomass is critical. In this study, we isolated, characterized, and sequenced Vibrio sp. SP1 which rapidly grows using alginate as a sole carbon source. We further engineered this strain by introducing genes encoding enzymes under the control of synthetic expression cassettes to produce lycopene and β-carotene which are attractive phytochemicals owing to the antioxidant property. We confirmed that the engineered Vibrio sp. SP1 could successfully produce 2.13 ± 0.37 mg L−1 of lycopene, 2.98 ± 0.43 mg L−1 of β -carotene, respectively, from 10 g L−1 of alginate as a sole carbon source. Furthermore, our engineered strain could directly convert 60 g L−1 of brown macroalgae Sargassum fusiforme into 1.23 ± 0.21 mg L−1 of lycopene without any pretreatment which had been vitally required for the previous productions. As the first demonstrated strain to produce high-value product from Sargassum, Vibrio sp. SP1 is evaluated to be a desirable platform for the brown macroalgae-based biorefinery.

In the present study, we characterized the involvement of Lon protease in bacterial virulence and intracellular survival in Salmonella under abiotic stress conditions resembling the conditions of a natural infection. Wild type (JOL401) and the lon mutant (JOL909) Salmonella Typhimurium were exposed to low temperature, pH, osmotic, and oxidative stress conditions and changes in gene expression profiles related to virulence and metal ion uptake were investigated. Expression of candidate genes invF and hilC of Salmonella Pathogenicity Island (SPI)-1 and sifA and sseJ of SPI-2 revealed that Lon protease controls SPI-1 genes and not SPI-2 genes under all stress conditions tested. The lon mutant exhibited increased accumulation of hydroxyl (OH·) ions that lead to cell damage due to oxidative stress. This oxidative damage can also be linked to an unregulated influx of iron due to the upregulation of ion channel genes such as fepA in the lon mutant. The deletion of lon from the Salmonella genome causes oxidative damage and increased expression of virulence genes. It also prompts the secretion of host pro-inflammatory cytokines leading to early clearance of the bacteria from host cells. We conclude that poor bacterial recovery from mice infected with the lon mutant is a result of disrupted bacterial intracellular equilibrium and rapid activation of cytokine expression leading to bacterial lysis.

Abstract Background Systemic inflammation and cachexia are associated with adverse clinical outcomes in diffuse large B-cell lymphoma (DLBCL). The Geriatric Nutritional Risk Index (GNRI) is one of the main parameters used to assess these conditions, but its efficacy in DLBCL is inconclusive. Methods We retrospectively reviewed 228 DLBCL patients who were treated with R-CHOP immunochemotherapy (rituximab plus cyclophosphamide, doxorubicin, vincristine, and prednisone). The patients were stratified according to GNRI score (> 98, 92 to 98, 82 to

Load forecasting is one of the critical tasks for enhancing the energy efficiency of smart grids. Even though recent deep learning-based load forecasting models have shown excellent forecasting performance, one of the common problems they faced was that their forecasting accuracy was highly dependent on the data quality and quantity available for the model training. Collecting a sufficient amount of high-quality data is expensive and time-consuming. Recently, a generative adversarial network (GAN) has shown its potential as a solution to the data shortage problem by generating virtual data based on a small amount of real data, and several studies have used GAN to generate electric load data for training forecasting models. However, due to the noise data problem of GANs, their predictive performance also deteriorated. To solve this problem, in this study, we propose a two-stage data generation scheme that more effectively generates input and output variables for short-term load forecasting. In the first stage, we generate virtual calendar and temperature data used as input variables using a conditional tabular GAN (CTGAN). In the second stage, we generate electric load data corresponding to the input variables using a deep learning-based regression model. Lastly, we construct our forecasting model by training another regression model using a mixture of generated data and real data. To verify the effectiveness of our scheme, we conducted extensive experiments using various datasets and data generation models. We report some of the results.

Lithium-ion batteries (LIBs) continue to dominate the battery market with their efficient energy storage abilities and their ongoing development. However, at high charge/discharge C-rates their electrochemical performance decreases significantly. To improve the power density properties of LIBs, it is important to form a uniform electron transfer network in the cathode electrode via the addition of conductive additives. Carbon nanotubes (CNTs) with high crystallinity, high electrical conductivity, and high aspect ratio properties have gathered significant interest as cathode electrode conductive additives. However, due to the high aggregational properties of CNTs, it is difficult to form a uniform network for electron transfer within the electrode. In this study, to help fabricate electrodes with well-dispersed CNTs, various electrodes were prepared by controlling (i) the mixing order of the conductive material, binder, and active material, and (ii) the sonication process of the CNTs/NMP solution before the electrode slurry preparation. When the binder was mixed with a well sonicated CNTs/NMP solution, the CNTs uniformly adsorbed to the then added cathode material of LiNi0.6Co0.2Mn0.2O2 and were well-dispersed to form a flowing uniform network. This electrode fabrication process achieved > 98.74% capacity retention after 50 cycles at 5C via suppressed polarization at high current densities and a more reversible H1-M phase transition of the active material. Our study presents a novel design benchmark for the fabricating of electrodes applying well-dispersed CNTs, which can facilitate the application of LIBs in high current density applications.

Brown macroalgae is a good candidate feedstock for biorefinery, but the major carbohydrate alginate cannot be digested by current industrial microbes. Here, the authors isolate Vibrio sp. dhg and engineer it to produce value-added biochemicals from alginate using newly developed genetic tools.

The vibration-reducing ability of construction materials is generally described by the damping ratio of the materials. Previously, many studies on the damping ratio of concrete have been done, such as the addition of rubber, polymer, fiber, and recycled aggregates in the concrete. However, the application of these materials in construction is limited due to their drawbacks. This paper investigated the effect of the replacement ratio and the size of the hollow glass microspheres (HGM), cenospheres (CS), and graphite flakes (GF) on the damping ratio of mortar. Furthermore, rubber particles (RP), aluminum powder (AP), and natural fiber (NF) were investigated to find if they have a combination effect with HGM. The half-power bandwidth method was conducted to obtain the damping ratio at 28 days of curing, and the compressive and flexural strength tests were also conducted to study the mechanical properties of mortar that contained HGM, CS, and GF. The results show that increases in the size of HGM and the replacement ratio of sand with HGM lead to an increase in the damping ratio. Moreover, RP and NF do not provide a combination effect with HGM on the damping ratio, whereas the application of AP results in a drastic compressive strength decrease even with an increase in damping ratio when incorporated with HGM. Besides, an increase in the replacement percentage of CS also leads to an improvement in the damping ratio, and a smaller size and higher replacement ratio of GFs can improve the damping ratio compared to other additives. As a result, CS and GF are more effective than HGM. 50% replacement ratio of CS slightly reduced the compressive strength by 6.4 MPa while improving the damping ratio by 15%, and 10% replacement ratio of samller GF can enhance the flexural strength by over 4.55% while increasing the damping ratio by 20.83%.

Abstract The theory that the sensorimotor system minimizes energetic cost during locomotion has long been supported by both computational models and empirical studies. However, it has yet to be determined if the behavior to which people converge when exposed to a novel perturbation during locomotion is also energetically optimal. We address this issue in the context of adaptation to walking on a split-belt treadmill, which can impose a left-right asymmetry in step lengths. In response to this asymmetry, participants gradually adjust their foot placement to adopt steps of equal length. Here, we characterized metabolic, mechanical, and perceptual estimates of energetic cost associated with a range of asymmetries to determine whether symmetry is the energetically optimal strategy for walking on a split-belt treadmill. We found that taking steps of equal length did not minimize metabolic cost or mechanical cost. In addition, perceptual estimates of cost were not sensitive to changes in asymmetry. However, symmetry was identified as the optimal strategy when energetic cost was estimated from a composite metric that combined both metabolic and mechanical costs. These results suggest that adaptation may arise from optimization of a composite estimate of effort derived from feedback about the interaction between the body and environment.

Smart grids have recently attracted increasing attention because of their reliability, flexibility, sustainability, and efficiency. A typical smart grid consists of diverse components such as smart meters, energy management systems, energy storage systems, and renewable energy resources. In particular, to make an effective energy management strategy for the energy management system, accurate load forecasting is necessary. Recently, artificial neural network–based load forecasting models with good performance have been proposed. For accurate load forecasting, it is critical to determine effective hyperparameters of neural networks, which is a complex and time-consuming task. Among these parameters, the type of activation function and the number of hidden layers are critical in the performance of neural networks. In this study, we construct diverse artificial neural network–based building electric energy consumption forecasting models using different combinations of the two hyperparameters and compare their performance. Experimental results indicate that neural networks with scaled exponential linear units and five hidden layers exhibit better performance, on average than other forecasting models.

Although ultra high-performance concrete (UHPC) has great performance in strength and durability, it has a disadvantage in the environmental aspect; it contains a large amount of cement that is responsible for a high amount of CO2 emissions from UHPC. Supplementary cementitious materials (SCMs), industrial by-products or naturally occurring materials can help relieve the environmental burden by reducing the amount of cement in UHPC. This paper reviews the effect of SCMs on the properties of UHPC in the aspects of material properties and environmental impacts. It was found that various kinds of SCMs have been used in UHPC in the literature and they can be classified as slag, fly ash, limestone powder, metakaolin, and others. The effects of each SCM are discussed mainly on the early age compressive strength, the late age compressive strength, the workability, and the shrinkage of UHPC. It can be concluded that various forms of SCMs were successfully applied to UHPC possessing the material requirement of UHPC such as compressive strength. Finally, the analysis on the environmental impact of the UHPC mix designs with the SCMs is provided using embodied CO2 generated during the material production.

Recently, multistep-ahead prediction has attracted much attention in electric load forecasting because it can deal with sudden changes in power consumption caused by various events such as fire and heat wave for a day from the present time. On the other hand, recurrent neural networks (RNNs), including long short-term memory and gated recurrent unit (GRU) networks, can reflect the previous point well to predict the current point. Due to this property, they have been widely used for multistep-ahead prediction. The GRU model is simple and easy to implement; however, its prediction performance is limited because it considers all input variables equally. In this paper, we propose a short-term load forecasting model using an attention based GRU to focus more on the crucial variables and demonstrate that this can achieve significant performance improvements, especially when the input sequence of RNN is long. Through extensive experiments, we show that the proposed model outperforms other recent multistep-ahead prediction models in the building-level power consumption forecasting.

Long-term space missions affect the gut microbiome of astronauts, especially the viability of some pathogens. Probiotics may be an effective solution for the management of gut microbiomes, but there is a lack of studies regarding the physiology of probiotics in microgravity. Here, we investigated the effects of microgravity on the probiotic Escherichia coli Nissle 1917 (EcN) by comparing transcriptomic data during exponential and stationary growth phases under simulated microgravity and normal gravity. Microgravity conditions affected several physiological features of EcN, including its growth profile, biofilm formation, stress responses, metal ion transport/utilization, and response to carbon starvation. We found that some changes, such as decreased adhesion ability and acid resistance, may be disadvantageous to EcN relative to gut pathogens under microgravity, indicating the need to develop probiotics optimized for space flight.

For efficient and effective energy management, accurate energy consumption forecasting is required in energy management systems (EMSs). Recently, several artificial intelligence-based techniques have been proposed for accurate electric load forecasting; moreover, perfect energy consumption data are critical for the prediction. However, owing to diverse reasons, such as device malfunctions and signal transmission errors, missing data are frequently observed in the actual data. Previously, many imputation methods have been proposed to compensate for missing values; however, these methods have achieved limited success in imputing electric energy consumption data because the period of data missing is long and the dependency on historical data is high. In this study, we propose a novel missing-value imputation scheme for electricity consumption data. The proposed scheme uses a bagging ensemble of multilayer perceptrons (MLPs), called softmax ensemble network, wherein the ensemble weight of each MLP is determined by a softmax function. This ensemble network learns electric energy consumption data with explanatory variables and imputes missing values in this data. To evaluate the performance of our scheme, we performed diverse experiments on real electric energy consumption data and confirmed that the proposed scheme can deliver superior performance compared to other imputation methods.

Smart grid systems, which have gained much attention due to its ability to reduce operation and management costs of power systems, consist of diverse components including energy storage, renewable energy, and combined cooling, heating and power (CCHP) systems. The CCHP has been investigated to reduce energy costs by using the thermal energy generated during the power generation process. For efficient utilization of CCHP and numerous power generation systems, accurate short-term load forecasting (STLF) is necessary. So far, even though many single algorithm-based STLF models have been proposed, they showed limited success in terms of applicability and coverage. This problem can be alleviated by combining such single algorithm-based models in ways that take advantage of their strengths. In this paper, we propose a novel two-stage STLF scheme; extreme gradient boosting and random forest models are executed in the first stage, and deep neural networks are executed in the second stage to combine them. To show the effectiveness of our proposed scheme, we compare our model with other popular single algorithm-based forecasting models and then show how much electric charges can be saved by operating CCHP based on the schedules made by the economic analysis on the predicted electric loads.

The 5q35.2-q35.3 duplication phenotype is characterized by growth delay, microcephaly, mental retardation and delayed bone aging. However, there has been no reports on the occurrence of pancytopenia as a consequence of 5q35.2-q35.3 duplication. A 42-year-old male visited the emergency room due to multiple trauma. He had been diagnosed with mental retardation in the past. No previous history of severe bleeding symptom was also reported . Complete blood cell counts were leukocyte 3.51×109/L, neutrophil 0.19×109/L, hemoglobin 8.3 g/dL, hematocrit 25.0%, and platelet 4.0×109/L. There was no relevant history of any medication intake and there were no other haematological parameters leading to the persistent pancytopenia. A bone marrow biopsy revealed hypercellular marrow with increased trilineage hematopoiesis. The uptake of fluorodeoxyglucose was increased in multiple lymph nodes, bone and spleen in positron emission tomography–computed tomography. A biopsy of the right axillary lymph node was performed and histologic findings were unremarkable. The chromosomal microarray revealed a 3.46 Mb microduplication at the 5q35.2-q35.3 site including NSD1. The patient had distinctive features related to atypical pancytopenia. Various managements for pancytopenia had no effect on the patient. However, there were no complications such as massive bleeding or serious infection compared to the severity of pancytopenia during a follow-up of 3 months. In addition, periodic patterns of deterioration and improvement in pancytopenia appeared spontaneously. Since it is rare for these distinctive features of pancytopenia and chromosomal abnormality to coexist, it is important to investigate the association. In the current study, we describe the first case of 5q35.2-q35.3 microduplication encompassing NSD1 with unexplained pancytopenia.

Observing animal movements enables us to understand animal behavior changes, such as migration, interaction, foraging, and nesting. Based on spatiotemporal changes in weather and season, animals instinctively change their position for foraging, nesting, or breeding. It is known that moving patterns are closely related to their traits. Analyzing and predicting animals’ movement patterns according to spatiotemporal change offers an opportunity to understand their unique traits and acquire ecological insights into animals. Hence, in this paper, we propose an animal movement prediction scheme using a predictive recurrent neural network architecture. To do that, we first collect and investigate geo records of animals and conduct pattern refinement by using random forest interpolation. Then, we generate animal movement patterns using the kernel density estimation and build a predictive recurrent neural network model to consider the spatiotemporal changes. In the experiment, we perform various predictions using 14 K long-billed curlew locations that contain their five-year movements of the breeding, non-breeding, pre-breeding, and post-breeding seasons. The experimental results confirm that our predictive model based on recurrent neural networks can be effectively used to predict animal movement.