Green innovation for the ecological footprints of tourism in China. Fresh evidence from ARDL approach

This study’s objective is to analyze ecological footprints that exist among China’s economic growth, energy consumption, carbon dioxide emissions, and the revenue that is generated from tourism in other countries. The years 1995 through 2020 are the focus of this particular research endeavor. The relationship between tourism and carbon emissions has been discovered by a large number of researchers; nevertheless, the findings have been inconsistent and do not give a clear picture of the situation. We can only hope that the results of the study will improve the existing body of knowledge on tourism and the quality of the surrounding environment. Throughout the whole of this investigation, the autoregressive distributed lagged (ARDL) model was used to explore both long-run and short-run estimations. A dynamic ordinary least squares (DOLS) model was used in the study to arrive at long-term estimations that could be relied upon. Even though money from tourism does not have a substantial influence on the quality of the environment in China, growth and increasing energy usage are primary donors to carbon emissions in the nation. ARDL model’s long-term projections were shown to be correct by the DOLS approach, which offered this validation. The results of the research provide fresh insights into the body of knowledge that has been accumulated on the subject of the linkage between tourism and the natural environment. Because the receipts from tourism do not have any significant negative exteriority toward the environment, energy usage is an important element of environmental degradation and policymakers should prioritize the development of the tourism sector over energy-focused manufacturing activities to maintain the growth of the nation in the upper quartiles. This is because tourismdoes not have any significant negative externalities on the environment. Sustainable tourism minimizes environmental and cultural damage while boosting profits. Developing the appropriate technology, physical infrastructure, and human capital requires money, time, and effort

Audio-visual child-adult speaker classification in dyadic interactions

Interactions involving children span a wide range of important domains from learning to clinical diagnostic and therapeutic contexts. Automated analyses of such interactions are motivated by the need to seek accurate insights and offer scale and robustness across diverse and wide-ranging conditions. Identifying the speech segments belonging to the child is a critical step in such modeling. Conventional child-adult speaker classification typically relies on audio modeling approaches, overlooking visual signals that convey speech articulation information, such as lip motion. Building on the foundation of an audio-only child-adult speaker classification pipeline, we propose incorporating visual cues through active speaker detection and visual processing models. Our framework involves video pre-processing, utterance-level child-adult speaker detection, and late fusion of modality-specific predictions. We demonstrate from extensive experiments that a visually aided classification pipeline enhances the accuracy and robustness of the classification. We show relative improvements of 2.38% and 3.97% in F1 macro score when one face and two faces are visible, respectively.Comment: In review for ICASSP 2024, 5 page

Understanding Spoken Language Development of Children with ASD Using Pre-trained Speech Embeddings

Speech processing techniques are useful for analyzing speech and language development in children with Autism Spectrum Disorder (ASD), who are often varied and delayed in acquiring these skills. Early identification and intervention are crucial, but traditional assessment methodologies such as caregiver reports are not adequate for the requisite behavioral phenotyping. Natural Language Sample (NLS) analysis has gained attention as a promising complement. Researchers have developed benchmarks for spoken language capabilities in children with ASD, obtainable through the analysis of NLS. This paper proposes applications of speech processing technologies in support of automated assessment of children's spoken language development by classification between child and adult speech and between speech and nonverbal vocalization in NLS, with respective F1 macro scores of 82.6% and 67.8%, underscoring the potential for accurate and scalable tools for ASD research and clinical use.Comment: Accepted to Interspeech 2023, 5 page

The Immediate Analgesic Effect of Acupuncture for Pain: A Systematic Review and Meta-Analysis

Although acupuncture is gaining popularity for the treatment of nonspecific pain, the immediate analgesic effect of acupuncture has never been reviewed. We conducted a systematic review and meta-analysis of randomized controlled trials (RCTs) on disease-related pain to critically evaluate the immediate effect of acupuncture for pain relief. The PubMed and Cochrane Central Register of Controlled Trials databases as well as three Chinese databases including the China National Knowledge Infrastructure (CNKI), Wanfang, and VIP platforms were searched through November 2016. The outcome was the extent of pain relief from baseline within 30 min of the first acupuncture treatment. We evaluated all RCTs comparing acupuncture with other interventions for disease-related pain. Real acupuncture showed statistically significantly greater pain relief effect compared to sham acupuncture (SMD, −0.56; 95% confidence interval [CI], −1.00 to −0.12; 9 RCTs) and analgesic injection (SMD, −1.33; 95% CI, −1.94 to −0.72; 3 RCTs). No serious adverse events were documented. Acupuncture was associated with a greater immediate pain relief effect compared to sham acupuncture and analgesic injections. Further RCTs with stricter design and methodologies are warranted to evaluate the immediate pain relief effect of acupuncture for more disease-related pain

SIoTFog: Byzantine-resilient IoT fog networking

The current boom in the Internet of Things (IoT) is changing daily life in many ways, from wearable devices to connected vehicles and smart cities. We used to regard fog computing as an extension of cloud computing, but it is now becoming an ideal solution to transmit and process large-scale geo-distributed big data. We propose a Byzantine fault-tolerant networking method and two resource allocation strategies for IoT fog computing. We aim to build a secure fog network, called “SIoTFog,” to tolerate the Byzantine faults and improve the efficiency of transmitting and processing IoT big data. We consider two cases, with a single Byzantine fault and with multiple faults, to compare the performances when facing different degrees of risk. We choose latency, number of forwarding hops in the transmission, and device use rates as the metrics. The simulation results show that our methods help achieve an efficient and reliable fog network

Averaged Behavior Model of Current-Mode Buck Converters for Transient Power Noise Analysis

Accurate Evaluation and Simulation of Power Noise is Critical in the Development of Modern Electronic Devices. However, the Widely Used Target Impedance Fails to Predict the Low-Frequency Noise Generated in a Device Due to the Existence of the Dc–dc Converter, Whose Output Impedance Can Change under Different Loading Conditions. a Physical Circuit Model is Then Desired to Replicate the Behavior of a Voltage Regulator Module, and the Average Technique is an Efficient Method to Estimate the Noise of a Pulse Width-Modulated (PWM) Converter. with the Emergence of Converters with Adaptive On-Time (AOT) Controllers, More Complex Averaging Methods Are Required, But None of Them Supports Transient Simulation. a General, Efficient, and Accurate Modeling Technique is Presented in This Article, Whose Framework Supports Both Current-Mode PWM and AOT Controllers. in Addition, a Novel Two-Step Parameter Extraction Method is Proposed, Which Can Be Used to Evaluate the Equivalent Values of Internal Feedback Parameters of an Encrypted Simulation Model or from Measurement. the Modeling Method is Validated by Both Simulation and Measurement

Methodology for Analyzing Coupling Mechanisms in RFI Problems based on PEEC

In This Article, a Method for Analyzing Coupling Mechanisms in Radio Frequency Interference (RFI) Problems is Proposed. the Partial Element Equivalent Circuit (PEEC) Method is First Used to Derive the Retarded Inductances and Capacitances between Different Mesh Cells. with the Introduction of a Novel Partitioning Algorithm, the Capacitive Coupling and Inductive Coupling between Arbitrary Layout Parts Can Be Quantified based on the Magnitude of the Displacement Current and Induced Voltage Drop. the Accuracy of the PEEC Models is Validated by Comparison with Different Commercial Tools. the Proposed Coupling Mechanism Analysis Flow Provides a Useful Prelayout Tool for RFI Risk Analysis

Preparation and characterization of \u3ba-carrageenase immobilized onto magnetic iron oxide nanoparticles

Background: Carboxyl-functionalized magnetic nanoparticles were synthesized via chemical co-precipitation method and modified with oleic acid which was oxidized by potassium permanganate, and \u3ba-carrageenase from Pseudoalteromonas sp. ASY5 was subsequently immobilized onto them. The immobilization conditions were further optimized, and the characterizations of the immobilized \u3ba-carrageenase were investigated. Results: The \u3ba-carrageenase was immobilized onto magnetic iron oxide nanoparticles, and the bonding was verified by Fourier transform infrared spectroscopy. The optimal conditions for \u3ba-carrageenase immobilization were 2.5% (w/v) glutaraldehyde, 13.9 U \u3ba-carrageenase for 20 mg of magnetic nanoparticles, a 2-h cross-linking time, and a 2-h immobilization time at 25\ub0C. Under these conditions, the activity of the immobilized enzyme and the enzyme recovery rate were 326.0 U \ub7 g-1 carriers and 46.9%, respectively. The properties of the immobilized \u3ba-carrageenase were compared with those of the free enzyme. The optimum temperatures of the free and immobilized \u3ba-carrageenase were 60 and 55\ub0C, respectively, and the optimum pH of \u3ba-carrageenase did not change before and after immobilization (pH 7.5). After immobilization, \u3ba-carrageenase exhibited lower thermal stability and improved pH stability, as well as better storage stability. The immobilized \u3ba-carrageenase maintained 43.5% of the original activity after being used 4 times. The kinetic constant value (Km) of \u3ba-carrageenase indicates that the immobilized enzyme had a lower binding affinity for the substrate. Conclusions: Under optimal conditions, the activity of the immobilized enzyme and enzyme recovery rate were 326.0 U \ub7 g-1\ub7\u3ba-carrageenase-CMNPs and 46.9%, respectively. The thermal, pH, and storage stabilities of \u3ba-carrageenase-CMNPs were relatively higher than those of free \u3ba-carrageenase

Exploring the Effects of Carpooling on Travelers’ Behavior during the COVID-19 Pandemic: A Case Study of Metropolitan City

Transportation accounts for more than a quarter of the greenhouse gas emissions that are causing climate change. Carpooling is a subset of the sharing economy, in which individuals share their vehicle with commuters to save travel expenses. In recent decades, carpooling has been promoted as a feasible alternative to car ownership with the potential to alleviate traffic congestion, parking demand, and environmental problems. Unstable economic conditions, cultural norms, and lack of infrastructure make cultural exchange activities and mobility habits different in developing nations to those in developed countries. The rapid evolution of sharing mobility has reshaped travelers’ behavior and created a dire need to determine the travel patterns of commuters living in megacities in developing countries. To obtain data, a web-based stated choice (SC) experiment was used in this study. It used mode-related variables, socioeconomic demographic variables, and a coronavirus disease 2019 (COVID-19) precautionary measure variable. Logit models, namely the mixed logit regression model (ML) and the multinomial logit regression model (MNL), were applied to analyze the available data. According to modeling and survey data, economic variables associated with modes of transport, such as trip time and trip cost, were determined to be significant. Additionally, the results revealed that commuters were more conscious of COVID-19 preventive measures, which was determined to be highly significant. The findings showed that the majority of residents in the COVID-19 pandemic continue to rely on automobiles and motorcycles. It is noteworthy that individuals with more than two members in their family and a travel distance of less than seven miles were more likely to prefer a carpooling service. This study’s findings will provide a basis for researchers to aid existing operators in the field of transportation, as well as offer guidelines for governments in developing countries to enhance the utility of transportation networks