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Addition is a fundamental computer arithmetic operation that is widely performed in microprocessors, digital signal processors, and application-specific processors. The design of a high-speed and energy-efficient adder is thus useful and important for practical applications. In this context, this paper presents the designs of novel asynchronous carry look-ahead adders (CLAs) viz. a standard CLA (SCLA) and a block CLA (BCLA). The proposed CLAs are monotonic, dual-rail encoded, and are realized according to return-to-zero handshake (RZH) and return-to-one handshake (ROH) protocols using a 28-nm CMOS process technology. The proposed BCLA has a slight edge over the proposed SCLA, and the proposed BCLA reports the following optimizations in design metrics such as cycle time (delay), area, and power compared to a recently presented state-of-the-art asynchronous CLA for a 32-bit addition: (i) 32.6% reduction in cycle time, 29% reduction in area, 4.3% reduction in power, and 35.5% reduction in energy for RZH, and (ii) 31.4% reduction in cycle time, 28.9% reduction in area, 4.4% reduction in power, and 34.4% reduction in energy for ROH. Also, the proposed BCLA reports reductions in cycle time and power/energy compared to many other asynchronous adders.

Background Various nonpharmaceutical interventions (NPIs) against COVID-19 continue to have an impact on socioeconomic and population behaviour patterns. However, the effect of NPIs on notifiable infectious diseases remains inconclusive due to the variability of the disease spectrum, high-incidence endemic diseases and environmental factors across different geographical regions. Thus, it is of public health interest to explore the influence of NPIs on notifiable infectious diseases in Yinchuan, Northwest China. Methods Based on data on notifiable infectious diseases (NIDs), air pollutants, meteorological data, and the number of health institutional personnel in Yinchuan, we first fitted dynamic regression time series models to the incidence of NIDs from 2013 to 2019 and then estimated the incidence for 2020. Then, we compared the projected time series data with the observed incidence of NIDs in 2020. We calculated the relative reduction in NIDs at different emergency response levels in 2020 to identify the impacts of NIPs on NIDs in Yinchuan. Results A total of 15,711 cases of NIDs were reported in Yinchuan in 2020, which was 42.59% lower than the average annual number of cases from 2013 to 2019. Natural focal diseases and vector-borne infectious diseases showed an increasing trend, as the observed incidence in 2020 was 46.86% higher than the estimated cases. The observed number of cases changed in respiratory infectious diseases, intestinal infectious diseases and sexually transmitted or bloodborne diseases were 65.27%, 58.45% and 35.01% higher than the expected number, respectively. The NIDs with the highest reductions in each subgroup were hand, foot, and mouth disease (5854 cases), infectious diarrhoea (2157 cases) and scarlet fever (832 cases), respectively. In addition, it was also found that the expected relative reduction in NIDs in 2020 showed a decline across different emergency response levels, as the relative reduction dropped from 65.65% (95% CI: -65.86%, 80.84%) during the level 1 response to 52.72% (95% CI: 20.84%, 66.30%) during the level 3 response. Conclusions The widespread implementation of NPIs in 2020 may have had significant inhibitory effects on the incidence of respiratory infectious diseases, intestinal infectious diseases and sexually transmitted or bloodborne diseases. The relative reduction in NIDs during different emergency response levels in 2020 showed a declining trend as the response level changed from level 1 to level 3. These results can serve as essential guidance for policy-makers and stakeholders to take specific actions to control infectious diseases and protect vulnerable populations in the future.

Somatic symptom disorder (SSD) is a common yet frequently overlooked comorbidity in patients with gastroesophageal reflux disease (GERD), compromising quality of life (QOL) and increasing healthcare utilization. This study aimed to evaluate its impacts of SSD on symptom perception and QOL and to determine the risk factors for SSD in GERD patients. A cross-sectional study was conducted in a tertiary gastroenterology department. A total of 209 GERD patients completed validated questionnaires assessing reflux symptoms, somatic symptoms, anxiety, depression, sleep quality, and QOL. SSD was diagnosed based on DSM-5 criteria. All patients underwent upper endoscopy and a subset completed esophageal manometry and 24-h pH-impedance monitoring investigations. The proportion of SSD was 30.14% (63/209). Despite having milder objective reflux burden on 24-h pH-impedance monitoring and esophageal manometry, patients with SSD reported higher reflux symptom scores [GerdQ: 9(5) vs. 8(4),  = 0.032; RSI: 12(9) vs. 4(6),  < 0.001], more severe anxiety and depression, worse sleep quality (all  < 0.001), and lower QOL [GIQLI: 94(37) vs. 121(27.5),  < 0.001] than non-SSD patients. Female gender, laryngopharyngeal reflux symptoms, anxiety, and depression were independent risk factors for SSD. SSD partially mediated the negative impacts of reflux symptoms and poor sleep on QOL (23.52% for GerdQ, 26.30% for RSI and 14.05% for PSQI). The PHQ-15 demonstrated high accuracy (AUC = 0.851) in identifying comorbid anxiety/depression with sleep disturbances at a cutoff score of 12.5. SSD aggravates GERD symptoms and mediates impaired QOL by reflux and sleep disturbance. Screening SSD with PHQ-15 is recommended, particularly in females and those with laryngopharyngeal reflux symptoms.

Background Sleep disorders exhibit a high prevalence among chronic pain (CP) patients, yet knowledge gaps regarding sleep disorder trajectories in older CP patients may hinder healthy aging. This study aimed to identify sleep disorder trajectories and their predictive factors, and to develop tools for predicting these trajectories using an explainable machine learning (XML) approach. Methods This prospective cohort study was conducted in hospitalized older adults (aged ≥ 60 years) with chronic pain. Data on general characteristics, pain level, anxiety, depression, and perceived social support were collected at admission. Sleep disorder was assessed from admission to six months during routine chronic pain management, including pharmacologic treatment, non-pharmacologic interventions, psychosocial support, nursing care, and discharge education. Predictors were selected using Boruta algorithm and LASSO regression, and ten XML models were developed. Results Among 596 patients, the prevalence of sleep disorders in the overall cohort ranged from 42.8% to 52.2% across the four assessment time points. Four heterogeneous sleep disorder trajectories (stable-low, high-declining, moderate-increasing, and high-persistent) and seven predictors were identified. The XML model determined pain level, depression, anxiety, and perceived social support were the most important predictors. Conclusion Sleep disorders in older CP patients exhibited a high prevalence from admission to six months, manifesting in four distinct trajectories of deterioration or improvement. These findings may enhance the understanding of the heterogeneous progression of sleep disorders. Prioritizing key factors such as pain level, depression, anxiety, and perceived social support in preventive strategies and risk stratification may inform more targeted interventions to improve sleep health outcomes.

As China’s anti-corruption campaign intensifies, several of those involved in various capacities in the public sector are demonstrating slackness, a lack of moral awareness, and a weakened sense of responsibility. This study analyzes the multiple mechanisms influencing the responsible behavior of public sector personnel by combining certain key factors, such as managers’ moral leadership style, psychological security, and the notion of interactive justice. Based on social information processing theory and social exchange theory, this study constructs a dual mediation model (psychological security and interactive justice) to explore the mediating psychological mechanism of moral leadership influencing employees’ responsible behavior. The results of a two-stage paired survey and structural equation modeling analysis of civil servants from 48 departments and eight units in Shandong and Henan provinces show that moral leadership positively impacts responsible behavior. Moreover, psychological security and interactive justice play a mediating role between them. Furthermore, this study elaborates upon the mechanism of moral leadership affecting the responsible behavior of employees, which is of great value for improving the efficiency of government management and guiding civil servants to behave more responsibly.

Background Mild cognitive impairment (MCI) is prevalent in older adults with chronic pain, making early detection crucial for dementia prevention and healthy aging. This study aimed to determine MCI risk factors in older patients with chronic pain and to develop 9 machine learning models to identify MCI risk. Methods A total of 612 older patients with chronic pain were recruited between October 2023 and July 2024. Data collected included patients’ general information, cognitive function, pain level, depression, and sleep quality. The dataset was randomly divided into training set and testing set, and processed by Min-Max Normalization and SMOTETomek comprehensive sampling. SVM-RFE and LASSO regression were used for variable selection. We then developed machine learning models and interpreted them by SHAP. Results Age, education level, number of pain sites, pain duration, pain level, depression and sleep quality were risk factors of MCI in older patients with chronic pain. The Extreme Gradient Boosting (XGBoost) model performed best (AUC 0.925), with pain level, age, and depression as the most important variables. Conclusions We successfully developed 9 machine learning models to identify MCI risk. These models provide a tool for nurses to detect MCI risk early. We recommend that nurses integrate machine learning techniques into clinical nursing practice for managing MCI. However, these findings require validation with longitudinal data to confirm predictive validity for MCI progression.

Waterfront resources are an important support system for the social and economic development within the region along the Yangtze River. Container ports are an important component of the Yangtze River port system, as well as for the growth point of waterfront utilization. Based on the summary of remote sensing images and relevant data, this paper calculates the waterfront utilization of the container ports along the Yangtze River in the Yangtze River Delta (YRD), analyzes the waterfront organization pattern and change characteristics, and puts forward the enlightenment and countermeasures for the sustainable development of the port waterfronts. Extending the study of port resources from coastal areas to inland areas is an academic contribution of this paper. At the same time, it has practical significance for the high-quality development of port and shipping and the development and protection of land resources along the Yangtze River. In the YRD, the waterfront utilization of container ports has increased along the Yangtze River, showing a decrease from downstream to upwards, and it has formed dense zones attached to the central cities and major manufacturing bases. The ports with higher length of waterfront are mostly located in the shipping central cities and the Yangtze River estuary. The development direction of container ports is large-scale and specialized. The utilization of the container port waterfront is approaching the periphery of the city and areas with convenient transportation. The utilization of container port shorelines will be close to the periphery of the city and convenient transportation areas. The container port waterfronts occupy the ecological reserve, and the conflicts are expanding with the development of shipping, mainly distributed in the Yangtze River estuary. Based on the empirical analysis, this paper puts forward four enlightenments. First, the exploitation and utilization of the port waterfront has experienced multiple stages of “exploitation—conflict—mitigation”. With the transformation of productive waterfront utilization, the pattern of sustainable development along the Yangtze River has changed. Secondly, the conflict between waterfront utilization and protection is inevitable. Additionally, it is necessary to face up to the temporary rapid rise of encroachment on the reserve. Third, through the horizontal coordination of the port system along the river, the original focus on the hub cities will be transferred to the comprehensive consideration of the port cities in the whole region, and the waterfront load of different types of container ports can be balanced. Fourth, the Yangtze River Delta integration mechanism can solve the barriers between higher and lower levels or between different departments and cities.

Warehouse 4.0 adopts automation, IoT, and big data technologies to establish an intelligent warehousing system for efficient, real-time management of storage, handling, and picking. Addressing challenges like unreasonable storage allocation and inefficient order fulfillment, this paper presents an integrated framework that utilizes swarm intelligence algorithms and collaborative scheduling strategies to optimize inbound/outbound operations. First, for inbound processes, an algorithm-driven storage allocation model is proposed to solve stacker crane scheduling problems. Then, for outbound operations, a “1+N+M” mathematical model is developed, optimized through a three-stage algorithm addressing order picking and distribution scheduling. Finally, a case study of an industrial warehouse validates the proposed methods. The improved mayfly algorithm demonstrates excellent performance, achieving 64.5–74.5% faster convergence and 20.1–24.7% lower fitness values compared to traditional algorithms. The three-stage approach reduces order fulfillment time by 12% and average processing time by 1.8% versus conventional methods. These results confirm the framework’s effectiveness in enhancing warehouse operational efficiency through intelligent automation and optimized resource scheduling.

Incentive salience processes are important for the development and maintenance of addiction. Eye characteristics such as gaze fixation time, pupil diameter, and spontaneous eyeblink rate (EBR) are theorized to reflect incentive salience and may serve as useful biomarkers. However, conventional cue exposure paradigms have limitations that may impede accurate assessment of these markers. This study sought to evaluate the validity of these eye-tracking metrics as indicators of incentive salience within a virtual reality (VR) environment replicating real-world situations of nicotine and tobacco product (NTP) use. NTP users from the community were recruited and grouped by NTP use patterns: nondaily (n=33) and daily (n=75) use. Participants underwent the NTP cue VR paradigm and completed measures of nicotine craving, NTP use history, and VR-related assessments. Eye-gaze fixation time (attentional bias) and pupillometry in response to NTP versus control cues and EBR during the active and neutral VR scenes were recorded and analyzed using ANOVA and analysis of covariance models. Greater subjective craving, as measured by the Tobacco Craving Questionnaire-Short Form, following active versus neutral scenes was observed (F1,106=47.95; P<.001). Greater mean eye-gaze fixation time (F1,106=48.34; P<.001) and pupil diameter (F1,102=5.99; P=.02) in response to NTP versus control cues were also detected. Evidence of NTP use group effects was observed in fixation time and pupillometry analyses, as well as correlations between these metrics, NTP use history, and nicotine craving. No significant associations were observed with EBR. This study provides additional evidence for attentional bias, as measured via eye-gaze fixation time, and pupillometry as useful biomarkers of incentive salience, and partially supports theories suggesting that incentive salience diminishes as nicotine dependence severity increases.

Neutrophil percentage-to-albumin ratio (NPAR), dually reflecting inflammation and malnutrition, is related to poor prognosis closely in a range of diseases. However, prognostic value of NPAR in maintenance hemodialysis (MHD) patients remains to be confirmed. This study aimed to investigate the association between NPAR and all-cause mortality in MHD patients. Patients undergoing maintenance hemodialysis in the blood purification department of The First Affiliated Hospital of Guangxi Medical University from January 2021 to June 2021 were prospectively studied. NPAR was calculated by dividing neutrophils percentage by Albumin. Participants were followed for 36 months, with all-cause mortality as the primary endpoint. There were 146 male and 80 female MHD patients included in this study, with a median age of 56 years. 53 (23.5%) patients died during the follow-up period. Kaplan-Meier (K-M) analysis revealed significantly lower survival in patients with high NPAR (>16.96) compared to those with low NPAR (≤16.96) (log rank test p=0.000). Multivariate Cox regression has identified NPAR as an independent predictor of all-cause mortality (HR=1.346, 95% CI 1.192-1.521, p=0.000). Receiver operating characteristic (ROC) analysis demonstrated that the Area Under the Curve (AUC) of NPAR was 0.821 (95% CI: 0.759-0.882, p=0.000) and had a trend to be better than that of neutrophil-to-lymphocyte ratio (NLR; AUC=0.710), platelet-to-lymphocyte ratio (PLR; AUC=0.647), neutrophil count (AUC=0.606), albumin (ALB; AUC=0.752), and high-sensitivity C-reactive protein (hs-CRP; AUC=0.670). NPAR is independently associated with all-cause mortality in MHD patients and may serve as an emerging indicator for risk stratification and prognostic management for this group of patients.