Low Resource Utilization Research Articles

Assessment for the influence of processing methods on chemical compositions and pharmacodynamics of Epimedii Folium and revealing the component transformation mechanism

Epimedii Folium is a traditional medicinal plant commonly used in Northeast Asia. The nonuniform drying methods during processing is an important factor leading to the uneven quality of commercial Epimedii Folium, which further results in low resource utilization. Our previous studies have proved that the content of icariin and baohuoside I, the quality control markers in Epimedii Folium, was significantly increased during thermal-processing of Epimedium koreanum Nakai, but the quality and component transformation mechanisms remained unclear. Therefore, in this study, chemical composition analysis, combined with pharmacodynamics were carried out for the first time to comprehensively evaluate the influence of three processing methods, including traditional shade-drying, oven-drying and classical suet stir-frying, on Epimedii Folium, and reveal the component transformation mechanism. A total of 50 components were identified using UHPLC-ESI-Q/TOF-MS, of which 3′′′-carbonyl-2′′-β-L-quinovosyl baohuoside I was identified to be a new prenylflavonoid diglycoside. To clarify the types of components that can convert into icariin and baohuoside I, their content in different elution fractions of E. koreanum aqueous extract before and after baking were compared by HPLC-DAD for the first time. The source of increased icariin and baohuoside I was determined to be prenylflavonoid triglycosides and diglycosides, respectively. In order to explore the component transformation mechanism, the content of 15 main components, including five major prenylflavonoid triglycosides and three diglycosides in shade-drying, oven-drying and suet stir-frying E. koreanum were analyzed using UPLC-PDA. The result was demonstrated for the first time that icariin and baohuoside I were possibly mainly derived from the heat-labile prenylflavonoid glycosides, such as 3′′′-carbonyl-2′′-β-L-quinovosyl icariin and 3′′′-carbonyl-2′′-β-L-quinovosyl baohuoside I, rather than the previously reported common prenylflavonoid glycosides with stable structures. The pharmacodynamic comparison results indicated for the first time that both shade-drying and oven-drying E. koreanum were less potent than suet stir-frying E. koreanum in ameliorating renal function. However, based on the comprehensive evaluation of the content and efficacy in this study, oven-drying was still considered to have advantages over the traditional drying methods such as shade-drying, in elevating drying efficiency, content of bioactive components and the resource utilization rate of Epimedii Folium and worth popularizing.

A Branch-and-Price Algorithm for an Integrated Online and Offline Retailing Distribution System with Product Return

This study identifies critical inefficiencies within a dual-channel operation model employed by a fast fashion company, particularly the independent operation of three logistics distribution systems. These systems result in high operational costs and low resource utilization, primarily due to redundant vehicle dispatches to meet the distinct demands of retail store replenishment, online customer orders, and customer return demands, as well as random and scattered return requests leading to vehicle underutilization. To address these challenges, we propose a novel integrated logistics distribution system design and management method tailored for dual-channel sales and distribution businesses. The approach consolidates the three distribution systems into one cohesive framework, thus streamlining the delivery process and reducing vehicle trips by combining retail and customer visits. An optimization algorithm is introduced to factor in inventory and distribution distance, aiming to achieve global optimization in pairing retail store inventory with online customer orders and unifying the distribution of replenishment products, online products, and returned products. The paper contributes to the field by introducing a new variation of the Vehicle Routing Problem (VRP) that arises from an integrated distribution system, combining common VRP issues with more complex challenges. A custom Branch-and-Price (B&P) algorithm is developed to efficiently find optimal routes. Furthermore, we demonstrate the benefits of the integrated system over traditional, segregated systems through real-world data analysis and assess various factors including return rates and inventory conditions. The study also enhances the model by allowing inventory transfers between retail stores, improving inventory distribution balance, and offering solutions for scenarios with critically low inventory levels. Our findings highlight a significant reduction in total operating cost savings of up to 49.9% and vehicle usage when using the integrated distribution system compared to independent two-stage and three-stage systems. The integrated approach enables the utilization of vacant vehicle space and the dynamic selection and combination of tasks, preventing unnecessary mileage and space wastage. Notably, the integration of inventory sharing among retail stores has proven to be a key factor in generating feasible solutions under tight inventory conditions and reducing operational costs and vehicle numbers, with the benefits amplified in large-scale problem instances.

Algorithm for community security risk assessment and influencing factors analysis by back propagation neural network

This paper aims to accurately assess and effectively manage various security risks in the community and overcome the challenges faced by traditional models in handling large amounts of features and high-dimensional data. Hence, this paper utilizes the back propagation neural network (BPNN) to optimize the security risk assessment model. A key challenge of researching community security risk assessment lies in accurately identifying and predicting a range of potential security threats. These threats may encompass natural disasters, public health crises, accidents, and social security issues. The intricate interplay of these risk factors, combined with the dynamic nature of community environments, presents difficulties for traditional risk assessment methodologies to address effectively. Initially, this paper delves into the factors influencing safety incidents within communities and establishes a comprehensive system of safety risk assessment indicators. Leveraging the adaptable and generalizable nature of the BPNN model, the paper proceeds to optimize the BPNN model, enhancing the security risk assessment model through this optimization. Subsequent comparison experiments with traditional models validate the rationality and effectiveness of the proposed model, with hidden layer nodes set at various levels like 10, 15, 20, 25, 30, and 35. These traditional models include Convolutional Neural Network (CNN), Long Short-Term Memory Network (LSTM), Bidirectional Encoder Representations from Transformers (BERT), Generative Pre-trained Transformer (GPT), and eXtreme Gradient Boosting (XGBOOST). Experimental findings demonstrate that with 20 hidden layer nodes, the optimized model achieves a remarkable final recognition accuracy of 99.1 %. Moreover, the optimized model exhibits significantly lower final function loss compared to models with different node numbers. Increasing the number of hidden layer nodes may diminish the optimized model's fit and accuracy. Comparison with traditional models reveals that the average accuracy of the optimized model in community risk identification reaches 98.5 %, with a maximum accuracy of 99.6 %. This marks an improvement of 9%–11 % in recognition accuracy across various risk factors compared to traditional models. Regarding system response time and resource utilization, the optimized model exhibits a response time ranging from 100 ms to 120 ms and consistently lower resource utilization rates across all scenarios, underscoring its efficiency in community security risk assessment. In conclusion, this experiment sheds light on the underlying mechanisms and patterns of community safety risk formation, offering novel perspectives and methodologies for researching community safety risk assessment. The paper concludes by presenting recommendations and strategies for addressing community safety risks based on experimental analysis.

Alfalfa Cultivation Patterns in the Yellow River Irrigation Area on Soil Water and Nitrogen Use Efficiency

Establishing lucerne field is an efficient way to protect natural steppes, alleviate conflicts between meadows and livestock, and promote the development of animal husbandry. However, problems such as extensive field management, valuing yield over quality, and low resource utilization are endemic in production. Exploring reasonable cultivation patterns can contribute to improving the current situation of artificial grassland production and promoting the high-quality development of husbandry and prataculture. Lucerne the field experiment was carried out in Jingtai, Gansu Province, China in 2021–2022; this study compared and analyzed the effects of three cultivation patterns—ridge tillage with plastic film mulching (PM), ridge tillage with biodegradable film mulching (BM), and traditional flat planting (FP)—on soil water, heat, and fertilizer, as well as lucerne growth, yield, quality, and water and nitrogen use efficiency. The results show that: (1) during the growth period of lucerne, PM and BM treatments augment the average moisture content of the soil layer of 0–120 cm by 31.19% and 24.03% compared to the FP treatment, respectively. In the soil layer of 0–40 cm, PM and BM treatments abate the soil moisture content of the ridges by an average of 19.29% and 7.89% compared to that in the ditches, respectively. In the soil layer of 40–120 cm, PM and BM treatments elevate the soil moisture content of the ridges by 4.40% and 4.65% on average compared to that in the ditches, respectively. The average soil temperature in a soil layer of 5–25 cm shows PM > BM > FP. In contrast with the FP treatment, PM and BM treatments increase the soil temperature of the ridges by an average of 1.87 °C and 0.96 °C and decrease that of the ditches by an average of 0.47 °C and 0.46 °C, respectively. After two years of planting, the three cultivation patterns all promote the soil nutrient content. Compared to the FP treatment, PM and BM treatments increase the organic matter content by 9.94% and 19.94%, respectively. (2) Ridge tillage with film mulching can evidently stimulate the growth of lucerne and enhance yield and quality. Compared to the FP treatment, PM and BM treatments enhance plant height by an average of 15.37% and 4.04%, stem diameter by an average of 34.14% and 14.58%, yield by an average of 21.20% and 14.77%, crude protein content by an average of 13.47% and 7.68%, and relative feed value by an average of 8.71% and 4.41%, respectively. (3) During the two-year growing period, the irrigation amount of lucerne was 508.60–615.30 mm, and the evapotranspiration was 563.70–761.80 mm. Compared to the FP treatment, PM and BM treatments hoist water use efficiency by an average of 43.50% and 17.56%, nitrogen partial factor productivity by an average of 21.20% and 15.22%, and net income by an average of 14.78% and 11.05%, respectively. In summary, in ridge tillage, both ordinary film mulching and biodegradable film mulching can create a favorable soil environment for lucerne growth and heighten production effect. The former has a better effect on advancing the lucerne production effect, and the latter exhibits superior performance in improving soil fertility.

Secure Virtual Machine Migration and Host Overload Detection Using Modified Pelican Optimization with Variable Load Mean Function

Low-resource utilization and high-energy consumption have become progressively protuberant issues in cloud data centers. Virtual Machine (VM) migration is the key objective to resolve this issue. Moreover, extreme VM migration might empower Service-Level Agreement (SLA) violations. Few works are considered for optimizing throughput and energy consumption. An efficient VM migration must consider different parameters like network communication overhead, migration overhead, resource utilization, energy consumption and quality of service which is a multi-objective issue. Hence, in this paper, a Modified Pelican Optimization-based Variable Load Mean Function (MPO-VLMF)-based host overload detection is presented and security enhancement is developed. The main motive of this study is to achieve host overload detection and security enhancement. To obtain host overload detection, the variable load mean function is developed. In this mean function, the weight parameter is selected by considering Modified Pelican Optimization (MPO). The Levy flight (LF) is considered for enhancing the updating process of Pelican Optimization (PO). To enhance the security of the system, the Digital Signature-based Encryption (DSE) is developed. Based on this proposed approach, security and host overload detection are obtained. The proposed technique is implemented and evaluated by performance measures. It is compared with the conventional approaches to justify the performance of the system.

Multi-Agent Deep Reinforcement Learning-Based Fine-Grained Traffic Scheduling in Data Center Networks

In data center networks, when facing challenges such as traffic volatility, low resource utilization, and the difficulty of a single traffic scheduling strategy to meet demands, it is necessary to introduce intelligent traffic scheduling mechanisms to improve network resource utilization, optimize network performance, and adapt to the traffic scheduling requirements in a dynamic environment. This paper proposes a fine-grained traffic scheduling scheme based on multi-agent deep reinforcement learning (MAFS). This approach utilizes In-Band Network Telemetry to collect real-time network states on the programmable data plane, establishes the mapping relationship between real-time network state information and the forwarding efficiency on the control plane, and designs a multi-agent deep reinforcement learning algorithm to calculate the optimal routing strategy under the current network state. The experimental results demonstrate that compared to other traffic scheduling methods, MAFS can effectively enhance network throughput. It achieves a 1.2× better average throughput and achieves a 1.4–1.7× lower packet loss rate.

Applying augmented reality multimedia technology to construct a platform for translation and teaching system

This paper investigates the integration of augmented reality (AR) technology into English translation teaching for college students, emphasizing the pivotal role of innovative teaching methods in enhancing students' translation skills and learning experiences. To address the issue of insufficient interest in English translation teaching, the paper initially assesses the purpose and significance of learning English translation through a questionnaire survey, elucidating challenges encountered in English language acquisition. Subsequently, adhering to AR principles, a teaching demonstration platform rooted in AR technology is conceived and developed, intricately aligned with English translation instruction. The platform serves as a solution to issues in English learning, such as inadequate course comprehension, low utilization of teaching resources, and instructors' lack of experience. The research culminates in the analysis of survey results, wherein the quantitative disparities in translation ability between students utilizing the research platform and those subjected to traditional teaching methods are examined. The findings underscore the positive impact of the AR-based research platform on improving students' translation proficiency. The AR platform heightens learners' engagement in the learning process, contributes to constructing a robust knowledge framework, and enhances overall learning outcomes. The platform offers educators opportunities to optimize experimental courses and elevate teaching standards. The paper's outcomes present novel pedagogical scenarios for learners, propose technical solutions for other technical disciplines and furnish a theoretical foundation and application model for a new generation of experimental demonstration platforms.

Energy efficiency in 5G systems: A systematic literature review

To ensure Energy Efficiency (EE) and better Quality of service (QoS), it is necessary to analyze the energy saving possibilities for low resource utilization in the current networks caused by rigorous QoS requirements and implementing EE approach in the planned model for performance improvement. Distributed Denial of Service (DDoS) attacks aim to exhaust the network’s processing and communication capacity by saturating it with packets and generating malicious traffic. There are numerous advantages that make Digital Twin (DT) and Intrusion Detection technique (ID) an effective remedy for a range of (fifth generation) 5G problems. A DDoS attack must be immediately detected and stopped before a legitimate user can access the target of the attacker for the 5G network to provide an efficient energy service. Although they clearly show promise in assisting with the creation and implementation of the challenging 5G environment, Digital Twins is still a relatively new technology for 5G networks but will increase EE. In this research, a thorough examination of the materials was carried out to identify the most cutting-edge DT and ID methods. The purpose of this study was to comprehend the problems with Energy Efficiency, the need for DT, and the methods for dealing with large-scale attack by DDoS on Energy Efficient networks. Only 94 of the 1555 articles produced by the procedure were determined to be relevant using inclusion and exclusion criteria. The outcome demonstrates that in 5G networks, DT, and its fundamental approaches, like QoS and DDoS attack mitigation, can be used to regulate the network’s Energy Efficiency. Numerous practical applications focusing on 5G Systems use their own principles. The effectiveness of these strategies was evaluated using several assessment criteria, including DT, Intrusion Detection, QoS, Energy Efficiency, and 5G Systems. Each study issue is thoroughly explained, along with typical methods, advantages, disadvantages, and performance metrics. Energy economy, network reliability, privacy, and cost reduction are all considerably increased by the implementation of intrusion detection technology in 5G systems. The decision is supported by the technology’s demonstrated efficacy, scalability, real-time detection capacities, low error level, and personalized learning attributes, all of which contribute to the long-term viability of 5G networks as an entire system.

An improved resource allocation method for mapping service function chains based on A3C

AbstractNetwork function virtualization (NFV) technology deploys network functions as software functions on a generalised hardware platform and provides customised network services in the form of service function chain (SFC), which improves the flexibility and scalability of network services and reduces network service costs. However, irrational resource allocation during service function chain mapping will cause problems such as low resource utilisation, long service request processing time and low mapping rate. To address the unreasonable problem of service mapping resource allocation, an improved service function chain mapping resource allocation method (SA3C) based on the Asynchronous advantageous action evaluation algorithm (A3C) is proposed. This study proposes an SFC mapping model and a mathematical model for joint allocation, which modeled the minimization of processing time as a Markov process. The main network was trained and multiple sub‐networks were generated in parallel using the ternary and deep reinforcement learning algorithm A3C, with the goal of identifying the optimal resource allocation strategy. The experimental simulation results show that compared with the Actor‐Critic (AC) and Policy Gradient (PG) methods, SA3C algorithm can improve the resource utilisation by 9.85%, reduce the total processing time by 10.72%, and improve the mapping rate by 6.72%, by reasonably allocating node computational resources and link bandwidth communication resources.

Leveraging neuro-inspired AI accelerator for high-speed computing in 6G networks.

The field of wireless communication is currently being pushed to new boundaries with the emergence of 6G technology. This advanced technology requires substantially increased data rates and processing speeds while simultaneously requiring energy-efficient solutions for real-world practicality. In this work, we apply a neuroscience-inspired machine learning model called echo state network (ESN) to the critical task of symbol detection in massive MIMO-OFDM systems, a key technology for 6G networks. Our work encompasses the design of a hardware-accelerated reservoir neuron architecture to speed up the ESN-based symbol detector. The design is then validated through a proof of concept on the Xilinx Virtex-7 FPGA board in real-world scenarios. The experiment results show the great performance and scalability of our symbol detector design across a range of MIMO configurations, compared with traditional MIMO symbol detection methods like linear minimum mean square error. Our findings also confirm the performance and feasibility of our entire system, reflected in low bit error rates, low resource utilization, and high throughput.

Primary headaches prevalence, characteristics, and healthcare utilization in Italian medical students.

Among university students, migraine is notably prevalent and is linked to compromised academic performance and daily functioning. Medical students are a particularly vulnerable category due to the demanding nature of their training, as they are often exposed to headache trigger factors. We therefore aimed to determine the prevalence, characteristics, and healthcare-seeking practices of primary headaches among Italian medical students. We conducted a cross-sectional study among medical students attending the Università Cattolica del Sacro Cuore in Rome who completed a self-administered questionnaire designed following the International Classification of Headache Disorders-3 criteria. The questionnaire assessed sociodemographic and headache features, healthcare utilization, the use of symptomatic andpreventive treatment, and headache trigger factors. Five hundred thirty-six students filled out the questionnaire. The lifetime and last-year prevalence of headache in this cohort was 76.7% (n = 411). Among the students surveyed, migraine had a prevalence of 26.9%, probable migraine of 12.9%, and tension-type headache (TTH)/probable TTH of 36.9%. Two hundred and forty-six students (59.8%) reported that their headache worsened after starting university. All students reporting headache had at least one trigger factor. In students fulfilling the criteria for migraine (n = 144), 137 (95.1%) had previously used acute non-prescription treatments, and eight concurrently useda preventive treatment. Thirty-five students fulfilling the criteria for migraine underwent a brain MRI scan (24.3%), 43 performed a neurological evaluation (29.9%), 36 received a diagnosis of migraine (25%), and 20 (13.9%) accessed the emergency room. Migraine and TTH are common among medical students in Italy despite low healthcare resource utilization. These results support the need to promote public health policies and strategies in order to reduce the disability and burden associated with primary headaches among medical students.

Multiscale analysis of fine slag from pulverized coal gasification in entrained-flow bed

Fine slag (FS) is an unavoidable by-product of coal gasification. FS, which is a simple heap of solid waste left in the open air, easily causes environmental pollution and has a low resource utilization rate, thereby restricting the development of energy-saving coal gasification technologies. The multiscale analysis of FS performed in this study indicates typical grain size distribution, composition, crystalline structure, and chemical bonding characteristics. The FS primarily contained inorganic and carbon components (dry bases) and exhibited a "three-peak distribution" of the grain size and regular spheroidal as well as irregular shapes. The irregular particles were mainly adsorbed onto the structure and had a dense distribution and multiple pores and folds. The carbon constituents were primarily amorphous in structure, with a certain degree of order and active sites. C 1s XPS spectrum indicated the presence of C–C and C–H bonds and numerous aromatic structures. The inorganic components, constituting 90% of the total sample, were primarily silicon, aluminum, iron, and calcium. The inorganic components contained Si–O-Si, Si–O–Al, Si–O, SO42−, and Fe–O bonds. Fe 2p XPS spectrum could be deconvoluted into Fe 2p1/2 and Fe 2p3/2 peaks and satellite peaks, while Fe existed mainly in the form of Fe(III). The findings of this study will be beneficial in resource utilization and formation mechanism of fine slag in future.

Eventually-consistent federated scheduling for data center workloads

Data center schedulers operate at unprecedented scales today to accommodate the growing demand for computing and storage power. The challenge that schedulers face is meeting the requirements of scheduling speeds despite the scale. To do so, most scheduler architectures use parallelism. However, these architectures consist of multiple parallel scheduling entities that can only utilize partial knowledge of the data center’s state, as maintaining consistent global knowledge or state would involve considerable communication overhead. The disadvantage of scheduling without global knowledge is sub-optimal placements—tasks may be made to wait in queues even though there are resources available in zones outside the scope of the scheduling entity’s state. This leads to unnecessary queuing overheads and lower resource utilization of the data center.In this paper, we extend our previous work on Thiyyakat et al. (2023), a federated decentralized data center scheduling architecture that uses eventual consistency. The architecture utilizes both parallelism and an eventually consistent global state in each of its scheduling entities to make fast decisions in a scalable manner. In our work, we compare Megha with 3 scheduling architectures — Sparrow, Eagle, and Pigeon, using simulation. We also evaluate Megha’s prototype on a 123-node cluster and compare its performance with Pigeon’s prototype using cluster traces. The results of our experiments show that Megha consistently reduces delays in job completion time when compared to other architectures.

A resource competition-based truthful mechanism for IoV edge computing resource allocation with a lowest revenue limit

Resource allocation in Internet of Vehicles (IoV) edge computing is currently a research hotspot. Existing studies focus on social welfare or revenue maximization. However, there is little research on lowest revenue guarantees, which is a problem of great concern to resource providers. This paper presents the innovative concept of the lowest revenue limit, which enables service providers to preset the revenue B and calculate whether the preset revenue can be achieved under the current supply and demand of resources through mechanism design. This approach is very friendly to service providers and can prevent low revenue and waste of resources. Specifically, we improved the ascending price auction mechanism so that it can be used for multi-resource allocation, the unit prices of different resources are calculated according to the intensity of competition among users, and the winning users and the payment are determined by eliminating users with low cost performance. Our mechanism is not sensitive to resource capacity, works well under deployment constraints in edge computing, and satisfies economic characteristics such as individual rationality and truthfulness. Compared with existing algorithms, our approach is shown to enable the service provider to obtain a higher revenue under a lower resource utilization.

"The Critical Services Are Out of Reach": Diabetes Management and the Experiences of South Asian Immigrants in Ontario.

Type 2 diabetes is a serious chronic condition affecting millions of people worldwide. South Asians (individuals originating from Pakistan, India, Bangladesh, Sri Lanka, and Nepal) represent a high-risk ethnicity for developing type 2 diabetes (T2D) and experience a high prevalence of the disease, even in migrant populations. The objective of this study was to investigate perceptions and experiences of South Asians living with T2D in Ontario, and their utilization of diabetes related services within the provincial healthcare system. Data were obtained from 20 in-depth interviews with South Asian participants diagnosed with T2D and living in the Greater Toronto Area. Our findings indicate a dissatisfaction with Ontario's coverage for diabetes services; varying uptake of recommended health tests, exams, and monitoring equipment; low utilization of additional resources (diabetes centers); and a need for primary care physicians to better facilitate awareness and utilization of available coverages and resources in the community. This study provides support for the fact that even in Canada's universal healthcare system, disparities exist, particularly for ethnic minorities, and that a universal prescription drug coverage component is a crucial step forward to ensure equitable access to health services utilization for all.

Intensive care utilisation after elective surgery in Australia and New Zealand: A point prevalence study

ObjectiveWe aimed to describe the characteristics, outcomes and resource utilisation of patients being cared for in an ICU after undergoing elective surgery in Australia and New Zealand (ANZ). MethodsThis was a point prevalence study involving 51 adult ICUs in ANZ in June 2021. Patients met inclusion criteria if they were being treated in a participating ICU on he study dates. Patients were categorised according to whether they had undergone elective surgery, admitted directly from theatre or unplanned from the ward. Descriptive and comparative analysis was performed according to the source of ICU admission. Resource utilisation was measured by Length of stay, organ support and occupied bed days. Results712 patients met inclusion criteria, with 172 (24%) have undergone elective surgery. Of these, 136 (19%) were admitted directly to the ICU and 36 (5.1%) were an unplanned admission from the ward. Elective surgical patients occupied 15.8% of the total ICU patient bed days, of which 44.3% were following unplanned admissions. Elective surgical patients who were an unplanned admission from the ward, compared to those admitted directly from theatre, had a higher severity of illness (AP2 17 vs 13, p<0.01), require respiratory or vasopressor support (75% vs 44%, p<0.01) and hospital mortality (16.7% vs 2.2%, p < 0.01). ConclusionsICU resource utilisation of patients who have undergone elective surgery is substantial. Those patients admitted directly from theatre have good outcomes and low resource utilisation. Patient admitted unplanned from the ward, although fewer, were sicker, more resource intensive and had significantly worse outcomes.

Non-covalent crosslinked hydrogels to realize renewable and re-moldable strain sensors with excellent sensing behavior

Ion hydrogels have garnered significant attention owing to outstanding softness and tissue similarity. However, disposable mechanically available hydrogels result in low resource utilization and environmental pollution. Although self-healing hydrogels are serviceable when slightly damaged, they fail to recover after experiencing extreme conditions. Therefore, exploring renewable hydrogels effectively recycles the materials and contributes to preparing functional materials. Herein, a hydrogen-bond crosslinked polyvinyl alcohol/gelatin/tannic acid hydrogel (PGTH) was fabricated, allowing complete remolding without any additives. After the Fe3+ ions were introduced into PGTH hydrogels, the strain sensors with adhesion, freezing-resistance, biocompatibility and antibacterial properties were produced. The strain sensors based on the PGTFH showed excellent strain-sensing performance, including considerable sensitivity (GF = 1.93), exceptional linearity (R2 = 0.996), fast response (102 ms) and signal repeatability (>250 cycles). Minor and major motions (the action “saying”, “drinking” and joint bending) were detected and distinguished, which demonstrated their promising application prospects in next-generation green flexible devices for health care, motion monitoring and speech recognition.

Nitrogen-doped coal-based microporous carbon material co-activated by HCOOK and urea for high performance supercapacitors

The doping of heteroatoms can adjust the surface chemical state of porous carbons, which is conducive to improving the surface activity, and enhancing the hydrophicity and conductivity of carbon electrodes. Herein, we developed a facile and green strategy to obtain nitrogen-doped coal-derived porous carbon dominated by micropores for high performance supercapacitor. The hydrothermal treatment of coal and urea can not only achieve the doping of nitrogen atoms, but also conducive to the subsequent activation process. Benefiting from the large micropore volume and specific surface area (0.68 cm3 g−1 and 1563 m2 g−1), suitable micropore size, and high content of surface CO, N-Q and N-5, the optimal sample exhibits a large capacitance of 371 F g−1 (1 A g−1), high capacitance retention of 67% (50 A g−1), and outstanding cyclic durability in a three-electrode system (aqueous electrolyte). Furthermore, the symmetrical supercapacitor in neat EMIM BF4 shows a high energy density of 40 Wh kg−1 (625 W kg−1). This work proposes a facile and potential strategy for preparing nitrogen-doped porous carbon materials with outstanding electrochemical property for supercapacitors and provides a novel insight for the efficient and low carbonization utilization of coal resources.

Healthcare resource utilization for chronic low back pain among high-utilizers

BACKGROUNDChronic low back pain is a leading cause of morbidity and is among the largest cost drivers for the healthcare system. Research on healthcare resource utilization of patients with low back pain who are not surgical candidates is limited, and few studies follow individuals who generate high healthcare costs over time. PURPOSEThis claims study aimed to identify patients with high-impact mechanical, chronic low back pain (CLBP), quantify their low back pain-related health resource utilization, and explore associated patient characteristics. We hypothesize that patients in the top quartile of healthcare resource utilization in the second year after initial diagnosis will continue to generate considerable back pain-related costs in subsequent years. STUDY DESIGN/SETTINGIBM MarketScan Research Databases from 2009-2019 were retrospectively analyzed. PATIENT SAMPLEAdults in the United States with an initial diagnosis of low back pain between 2010 and 2014 who did not have cancer, spine surgery, recent pregnancy, or inflammatory spine conditions, were identified using the International Classification of Diseases (ICD) and Current Procedural Terminology (CPT) codes. To ensure patients had chronic low back pain, it was required that individuals had additional claims with a low back pain diagnosis 6 to 12 and 12 to 24 months after initial diagnosis. OUTCOME MEASURESCost and utilization of inpatient visits, outpatient visits, emergency room visits, pharmacologic and nonpharmacologic treatment options and imaging for chronic low back pain. METHODSAnnual back pain-related costs and the use of pharmacologic and nonpharmacologic treatments for 5 years were analyzed. Logistic regression was utilized to identify factors associated with persistent high spending. RESULTSOf 16,917 individuals who met the criteria for chronic low back pain, 4,229 met the criteria for having high healthcare utilization, defined as being in the top quartile of back pain-related costs in the 12 to 24 months after their initial diagnosis. The mean and median back pain-related cost in the first year after an initial diagnosis was $7,112 (SD $9,670) and $4,405 (Q1 $2,147, Q3 $8,461). Mean and median back pain related costs in the second year were $11,989 (SD $20,316) and $5,935 (Q1 $3,892, Q3 $10,678). Costs continued to be incurred in years 3 to 5 at a reduced rate. The cumulative mean cost for back pain over the 5 years following the initial diagnosis was $31,459 (SD $39,545). The majority of costs were from outpatient services. Almost a quarter of the high utilizers remained in the top quartile of back pain-related costs during years 3 to 5 after the initial diagnosis, and another 19% remained in the top quartile for 2 of the 3 subsequent years. For these two groups combined (42%), the 5-year cumulative mean cost for back pain was $43,818 (SD $48,270). Patient characteristics associated with a higher likelihood of remaining as high utilizers were diabetes, having a greater number of outpatient visits and pharmacologic prescriptions, and lower utilization of imaging services. CONCLUSIONThis is one of the first studies to use an administrative claims database to identify high healthcare resource utilizers among a population of United States individuals with nonsurgical, chronic low back pain and follow their utilization over time. There was a population of individuals who continued to experience high costs 5 years beyond their initial diagnosis, and the majority of individuals continued to seek outpatient services. Further longitudinal claims research that incorporates symptom severity is needed to understand the economic implications of this condition.

The Impact of Examination System Reform on English Education

With the acceleration of globalization, English education in China has become increasingly important. In recent years, Chinas education system, especially the examination system, has undergone a series of reforms. However, as these reforms take place, new issues have emerged one after another. This paper believes that as the transition of Chinas examination system to the New Gaokao system, the focus of the examination has shifted from rote learning to quality education. However, after the reform, the main problems in English education today include an excessive emphasis on exam-oriented teaching, wastage of educational resources and low resource utilization efficiency, as well as a weakening advantage for top English students. To address these problems, it is crucial and essential to transform the approach to English education development, enhance the categorization of digital resources, and improve policies related to examinations. This article believes that only by fully understanding and addressing these issues can the new examination system truly promote the healthy and sustainable development of English education.