Search Results (67)

Importance: Multiple myeloma (MM) is the second most common hematologic malignancy in the U.S., with a higher incidence among Black patients than White patients. Chimeric antigen receptor T-cell (CAR-T) therapies show clinical promise, but their limited availability raises concerns about access. Objective: To examine associations between disease characteristics, treatment location, and patient demographics with receipt of CAR-T therapy among patients with MM. Design: Retrospective cohort study using electronic health record data from the University of California Health Data Warehouse (UCHDW) between January 2021 and January 2025. Setting: Six academic health centers and twelve affiliated hospitals within the UCHDW. Participants: A population-based cohort of 12,360 adult patients diagnosed with MM and treated at a University of California facility offering CAR-T administration. Analyses were conducted from February 2025 to March 2025. Exposures: Receipt of multiple cancer therapies following MM diagnosis. Main Outcomes and Measures: Logistic regression was used to estimate odds ratios (ORs) and 95% confidence intervals (CIs) for associations between disease characteristics, treatment locations, and patient demographics with receipt of CAR-T therapy. A zero-shot GPT-4 inference model was applied to UCSF clinical notes to assess whether CAR-T therapy was discussed, determine documented eligibility, and classify rationale for eligibility determinations. Results: Among 12,360 patients with MM (mean age, 68.5 years; 51.6% male), 320 (2.6%) received CAR-T therapy. Disease characteristics at diagnosis, measured by the International Staging System (ISS), was distributed as follows: Stage I (65.3%), Stage II (24.4%), Stage III (2.8%), and Unknown (7.5%). Patients treated at UC-1 (49.3%), and UC-2 (50.0%) were more frequently diagnosed with ISS Stage II, whereas patients treated at UC-3 (55.5%) were more frequently diagnosed with ISS Stage I. Our model showed that patients identifying as Black or African American had lower odds of receiving CAR-T therapy compared with White patients (OR, 0.33; [95% CI, 0.17–0.62]). Patients treated at UC-3 also had lower odds of receiving CAR-T therapy compared with UC-1 (OR, 0.42; [95% CI, 0.30–0.59]). Among 270 UCSF patients assessed for CAR-T eligibility using clinical notes, the proportion of patients deemed eligible without documented CAR-T discussions was highest among those identifying as Other Pacific Islander (50%), followed by Black or African American (4.2%), Asian (3.2%), and White patients (0.6%). Conclusions and Relevance: Within a large academic health system, receipt of CAR-T therapy varied by treatment location and patient-reported race. A subset of patients with documented eligibility lacked recorded discussions of CAR-T therapy, suggesting potential differences in referral, documentation, or care pathways influencing observed treatment patterns. Full article

Background: Globally, an estimated 1.4 billion people had hypertension in 2014, yet only just over 20% had controlled blood pressure, and about 580 million remained undiagnosed. Evidence indicates that salt substitutes facilitate meaningful blood-pressure reductions, yet their implementation remains restricted by social and healthcare constraints. The comparative effectiveness of alternative sodium-reduction interventions for elevated blood pressure remains unclear, limiting their introduction across diverse clinical and public health contexts. This study is registered with PROSPERO (CRD420251130153). Methods: We systematically searched PubMed, MEDLINE, and supplementary sources for randomised controlled trials (RCTs) published between 2000 and 2025. All behavioural sodium-reduction interventions among populations with elevated blood pressure, including hypertension, were included. The mean difference in systolic blood pressure (SBP) was the primary outcome, as evidence indicates that intensive control of SBP to levels below 120–130 mmHg is significantly associated with a reduced risk of major cardiovascular disease (CVD) and all-cause mortality. Network and subgroup pairwise meta-analyses were performed, with sensitivity analyses conducted to assess robustness of the findings and subgroup analyses used to explore clinical and public health factors influencing intervention effectiveness (clinical factors: blood pressure stage, trial duration, and medication status; public health factors: setting, implementation period, and country income level). Results: Of 10,404 records identified, 42 studies (46 trials, n = 46,771) were included. While the use of salt substitutes was ranked the most effective intervention in the network meta-analysis, with reductions of −6.78 mmHg (95% CI, −8.42, −5.14) compared to no intervention and −5.35 mmHg (95% CI, −7.89, −2.81) compared to conventional health education, self-monitoring devices and low-sodium diets, when combined with health education, demonstrated similar magnitudes of SBP reductions. Digital health education showed a larger point estimate for SBP reduction by −3.59 mmHg (95% CI −7.40 to 0.22) than conventional education (−1.43 mmHg; 95% CI −3.49 to 0.63), but both confidence intervals crossed zero, indicating no statistically significant difference. Subgroup analyses indicated that, except for trial duration, intervention setting, and country income level in specific intervention comparisons, clinical and public health factors did not generally account for differences in SBP reduction. No evidence of publication bias was observed, except between salt substitutes and no intervention and low-sodium diets and no intervention. Conclusions: Network meta-analysis ranked the use of salt substitutes as the most effective intervention, yet self-regulated interventions, such as low-sodium diets and self-monitoring devices, when combined with education-based sodium-reduction approaches, showed comparable point estimates for SBP reductions. Digital health education showed promise as a supportive adjunct to self-regulated interventions, although its effects were variable and require further quantification. These findings underscore the need for alternative sodium-reduction interventions supported by digital or conventional health education to improve blood pressure control. Health education on sodium reduction, including clinical counselling, should be viewed primarily as a complementary component that enhances other interventions. Full article

Accurate prognosis of the remaining useful life (RUL) for lithium-ion batteries is critical for mitigating range anxiety and ensuring the operational safety of electric vehicles. However, existing data-driven methods often struggle to maintain robustness when transferring from controlled laboratory conditions to complex, sensor-limited, real-world environments. To bridge this gap, this study presents U-H-Mamba, a novel uncertainty-aware hierarchical framework trained on a massive hybrid repository comprising over 146,000 charge–discharge cycles from both laboratory benchmarks and operational electric vehicle datasets. The proposed architecture employs a two-level design to decouple degradation dynamics, where a Multi-scale Temporal Convolutional Network functions as the base encoder to extract fine-grained electrochemical fingerprints, including derived virtual impedance proxies, from high-frequency intra-cycle measurements. Subsequently, an enhanced Pressure-Aware Multi-Head Mamba decoder models the long-range inter-cycle degradation trajectories with linear computational complexity. To guarantee reliability in safety-critical applications, a hybrid uncertainty quantification mechanism integrating Monte Carlo Dropout with Inductive Conformal Prediction is implemented to generate calibrated confidence intervals. Extensive empirical evaluations demonstrate the framework’s superior performance, achieving a RMSE of 3.2 cycles on the NASA dataset and 5.4 cycles on the highly variable NDANEV dataset, thereby outperforming state-of-the-art baselines by 20–40%. Furthermore, SHAP-based interpretability analysis confirms that the model correctly identifies physics-informed pressure dynamics as critical degradation drivers, validating its zero-shot generalization capabilities. With high accuracy and linear scalability, the U-H-Mamba model offers a viable and physically interpretable solution for cloud-based prognostics in large-scale electric vehicle fleets. Full article

This study examines the relationship between national open educational resource (OER) policies and Sustainable Development Goal 4 (SDG4) outcomes across 187 countries between 2015 and 2024, with particular attention to the moderating role of artificial intelligence (AI) readiness. Despite widespread optimism about digital technologies as catalysts for universal education, systematic evidence linking formal OER policy frameworks to measurable improvements in educational access and completion remains limited. The analysis employs fixed effects and difference-in-differences estimation strategies using an unbalanced panel dataset comprising 435 country-year observations. The research investigates how OER policies associate with primary completion rates and out-of-school rates while testing whether these relationships depend on countries’ technological and institutional capacity for advanced technology deployment. The findings reveal that AI readiness demonstrates consistent positive associations with educational outcomes, with a ten-point increase in the readiness index corresponding to approximately 0.46 percentage point improvements in primary completion rates and 0.31 percentage point reductions in out-of-school rates across fixed effects specifications. The difference-in-differences analysis indicates that OER-adopting countries experienced completion rate increases averaging 0.52 percentage points relative to non-adopting countries in the post-2020 period, though this estimate remains statistically imprecise (p equals 0.440), preventing definitive causal conclusions. Interaction effects between policies and readiness yield consistently positive coefficients across specifications, but these associations similarly fail to achieve conventional significance thresholds given sample size constraints and limited within-country variation. While the directional patterns align with theoretical expectations that policy effectiveness depends on digital capacity, the evidence should be characterized as suggestive rather than conclusive. These findings represent preliminary assessment of policies in early implementation stages. Most frameworks were adopted between 2019 and 2022, providing observation windows of two to five years before data collection ended in 2024. This timeline proves insufficient for educational system transformations to fully materialize in aggregate indicators, as primary education cycles span six to eight years and implementation processes operate gradually through sequential stages of content development, teacher training, and institutional adaptation. The analysis captures policy impacts during formation rather than at equilibrium, establishing baseline patterns that require extended longitudinal observation for definitive evaluation. High-income countries demonstrate interaction coefficients between policies and readiness that approach marginal statistical significance (p less than 0.10), while low-income subsamples show coefficients near zero with wide confidence intervals. These patterns suggest that OER frameworks function as complementary interventions whose effectiveness depends critically on enabling infrastructure including digital connectivity, governance quality, technical workforce capacity, and innovation ecosystems. The results carry important implications for how countries sequence educational technology reforms and how international development organizations design technical assistance programs. The evidence cautions against uniform policy recommendations across diverse contexts, indicating that countries at different stages of digital development require fundamentally different strategies that coordinate policy adoption with foundational capacity building. However, the modest short-term effects and statistical imprecision observed here should not be interpreted as evidence of policy ineffectiveness, but rather as confirmation that immediate transformation is unlikely given implementation complexities and temporal constraints. The study contributes systematic cross-national evidence on aggregate policy associations while highlighting the conditional nature of educational technology effectiveness and establishing the need for continued longitudinal research as policies mature beyond the early implementation phase captured in this analysis. Full article

Standing sedation in horses provides immobilization and analgesia for surgery while avoiding the high risks of general anesthesia. Ketamine at subanesthetic doses may enhance sedation and reduce xylazine requirements, but evidence in clinical settings is limited. In a randomized blinded trial, we evaluated whether adding a low-dose ketamine infusion could reduce the xylazine dose required for effective sedation during standing ventriculocordectomy and laryngoplasty. Fifty-one horses were randomly assigned to sedation with xylazine alone (SX group) or xylazine plus ketamine (KX group) in a continuous rate infusion. The ketamine group received ketamine (0.25 mg/kg intravenous (IV) bolus followed by 0.5 mg/kg/h infusion), while xylazine was administered in both groups via a titrated infusion to effect according to the Ghent Sedation Algorithm. Sedation depth, ataxia, surgical condition scores, and cardiorespiratory parameters were recorded. Data are presented as median (25th–75th percentiles) and estimated effect with 95% confidence intervals (CI). Statistical significance was set at p < 0.05 and at 95% CIs excluding zero. The addition of ketamine did not significantly reduce xylazine requirements (0.9 (0.7–1.3) vs. 0.8 (0.5–1.1) mg/kg/h for SX and KX, respectively; p = 0.139). However, horses receiving ketamine (KX) achieved deeper sedation (Estimate = 2.74; 95% CI: 0.95 to 4.63) with no differences in ataxia or surgical conditions. Cardiorespiratory variables remained stable in both groups, and no adverse events occurred. In conclusion, adding a subanesthetic ketamine infusion improved sedation depth without adverse effects but did not significantly reduce the xylazine requirement. Full article

The first aim of this study is to point out new aspects of approximation theory applied to a few classes of holomorphic functions via Vitali’s theorem. The approximation is made with the aid of the complex moments of the functions involved, which are defined similarly to the moments of a real-valued continuous function. By applying uniform approximation of continuous functions on compact intervals via Korovkin’s theorem, the hard part concerning uniform approximation on compact subsets of the complex plane follows according to Vitali’s theorem. The theorem on the set of zeros of a holomorphic function is also applied. In the end, the existence and uniqueness of the solution for a multidimensional moment problem are characterized in terms of limits of sums of quadratic expressions. This is the application appearing at the end of the title. Consequences resulting from the first part of the paper are pointed out with the aid of functional calculus for self-adjoint operators. Full article

The Turkish steel industry aims to reduce its sectoral carbon dioxide (CO₂) emissions by 55% by 2030, in line with Türkiye’s Paris Agreement commitments and the European Green Deal (EGD), and consistent with the ambition of the European Union’s economy-wide ‘Fit for 55’ emissions-reduction target. Türkiye faces significant challenges in achieving net-zero greenhouse gas (GHG) emissions, particularly as a developing country confronting the impacts of climate change and in the market situation, such as the effects of the ongoing Russia-Ukraine conflict, limited access to affordable raw materials, and rising operational costs. This study serves as a guideline for the Turkish steel sector’s roadmap towards modernization and eventual compliance with net-zero targets. The consideration and integration of new technologies planned for the Turkish steel industry, in both electric arc furnace (EAF) and blast furnace-basic oxygen furnace (BF-BOF) facilities, have been outlined in conjunction with green hydrogen and with Carbon Capture and Storage (CCS) technologies. Four different scenarios were analysed to understand the reduction in CO₂ emissions: (1) In a Business-As-Usual (BAU) scenario without any reduction, (2) 39.9% CO₂ emission reduction with the Moderate scenario, (3) 59.6% reduction with the Advanced scenario, and (4) 82.9% reduction in CO₂ emissions from the Turkish steel sector with the Net-Zero scenario. To quantify the uncertainty in these long-term projections, a Monte Carlo simulation was conducted, generating probabilistic confidence intervals that reinforce the robustness and credibility of the net-zero pathway. The official roadmap for the sector is not available as of today; however, an in-depth discussion with a policy innovation leading to it is the objective of this study. Full article

Background: Rabies is a fatal yet preventable zoonosis. In Saudi Arabia, uneven surveillance and limited public awareness may delay post-exposure prophylaxis (PEP). In Makkah, where residents regularly encounter free-roaming dogs, knowledge gaps could elevate exposure risks. Objectives: This study aims to assess public knowledge, attitudes, and post-bite practices regarding rabies, including wound washing and access to PEP among adult residents of the Makkah Region, and to examine associations with pet dog ownership. Methods: A cross-sectional survey was conducted in the Makkah Region (March–June 2025). An online validated bilingual questionnaire targeted residents ≥ 18 years via social media. Descriptive statistics, chi-square tests, 95% confidence intervals, and binomial logistic regression were applied in IBM SPSS v26; p < 0.05 was significant. Results: Of 523 respondents, 91.8% lived in Makkah city, 52.8% were female, and the age distribution was 18–24 years (44.2%), 25–34 years (35.6%), 35–44 years (12.0%), and ≥45 years (8.2%). Pet dog ownership was rare (1.9%), yet 39.4% reported stray dogs in their communities. Overall, 60.6% knew what rabies is and 63.7% knew it is vaccine-preventable, but 52.2% wrongly believed that transmission occurs only via dog bites. Hospitals (79.7%) and health centers (79.2%) were the most cited vaccination sites; social media was the dominant information source (74.6%). No significant association was found between pet ownership and rabies awareness (all p > 0.05). In multivariable regression (n = 509), adequate rabies knowledge increased the odds of an appropriate intended response (AOR 1.85, 95% CI: 1.27–2.68). Participants aged 30–40 years and those >50 years had significantly lower odds (AOR 0.45, 95% CI: 0.24–0.85 and AOR 0.23, 95% CI: 0.09–0.56, respectively). Conclusions: Despite moderate awareness, critical misconceptions and inconsistent first aid intentions persist. Priority actions include clear, locally adapted education on immediate wound washing and prompt PEP, standardized bite management pathways across facilities, reliable access to vaccines and immunoglobulin, and targeted social media micro-campaigns. By identifying public misconceptions, knowledge gaps, and preferred communication channels, this study provides baseline evidence to guide community awareness programs, intersectoral collaboration, and One Health-based surveillance essential for Saudi Arabia’s progress toward the global “Zero rabies by 2030” goal. Full article

Small and Medium-sized Enterprises (SMEs) face disproportionately high risks from Advanced Persistent Threats (APTs), which often evade traditional cybersecurity measures. Existing frameworks catalogue adversary tactics and defensive solutions but provide limited quantitative guidance for allocating limited resources under uncertainty, a challenge amplified by the growing use of AI in both offensive operations and digital forensics. This paper proposes a game-theoretic model for improving digital forensic readiness (DFR) in SMEs. The approach integrates the MITRE ATT&CK and D3FEND frameworks to map APT behaviors to defensive countermeasures and defines 32 custom DFR metrics, weighted using the Analytic Hierarchy Process (AHP), to derive utility functions for both attackers and defenders. The main analysis considers a non-zero-sum attacker–defender bimatrix game and yields a single Nash equilibrium in which the attacker concentrates on Impact-oriented tactics and the defender on Detect-focused controls. In a synthetic calibration across ten organizational profiles, the framework achieves a median readiness improvement of 18.0% (95% confidence interval: 16.3% to 19.7%) relative to pre-framework baselines, with targeted improvements in logging and forensic preservation typically reducing key attacker utility components by around 15–30%. A zero-sum variant of the game is also analyzed as a robustness check and exhibits consistent tactical themes, but all policy conclusions are drawn from the empirical non-zero-sum model. Despite relying on expert-driven AHP weights and synthetic profiles, the framework offers SMEs actionable, equilibrium-informed guidance for strengthening forensic preparedness against advanced cyber threats. Full article

Improving the efficiency of buck–boost converters has long been a major focus in power electronics. To enhance efficiency and overcome existing limitations, this paper proposes a soft-switching technique for a cascaded buck–boost converter (CBBC). The proposed approach integrates high-frequency switching of four gallium nitride (GaN) devices, improving both dynamic and steady-state performance from hardware and control perspectives. First, a soft-switching modulation scheme based on negative-current pulse width modulation (PWM) is implemented by introducing a new switching sequence in the CBBC, controlled by a modulation variable. This scheme ensures that the GaN switches operate under zero-current switching (ZCS) and zero-voltage switching (ZVS) conditions during transitions. Furthermore, the CBBC operating modes are divided into four intervals for modeling and analysis, upon which a model predictive control (MPC) strategy is developed to achieve fast closed-loop regulation of both output voltage and current. To further minimize current ripple and device losses, the MPC cost function is optimized by constraining the control parameters. Experimental results obtained from a 300-W hardware prototype verify the effectiveness and feasibility of the proposed soft-switching control method. Full article

Background/Objectives: Preoxygenation with 100% oxygen is commonly used but poses risks such as hyperoxia and atelectasis. Using 80% oxygen may reduce these effects but shortens duration of apnoea without desaturation (DAWD). This study compared preoxygenation efficiency between 80% and 100% oxygen and evaluated changes in the Oxygen Reserve Index™ (ORi™). Methods: Patients undergoing elective laparoscopic cholecystectomy were randomized to preoxygenation with 80% or 100% oxygen. Adequate preoxygenation was defined as a ≤10% difference between fraction of inspired oxygen and end-tidal oxygen (EtCO₂). The primary outcome was DAWD, the interval from apnoea onset to peripheral oxygen saturation (SpO₂) of 93%. Secondary outcomes included time to adequate preoxygenation and additional warning time from ORi™ zero to SpO₂ 97%. Results: Thirty patients were randomised to 80% (n = 15) or 100% oxygen (n = 15) oxygen groups. One patient in the 100% group was excluded due to spontaneous breathing before SpO₂ 93%, leaving 29 for DAWD analysis. DAWD was 345 ± 136 s (80%) and 430 ± 163 s (100%) with a mean difference of 86 s (p = 0.135). No significant differences were observed in tie to adequate preoxygenation or additional warning time. Conclusions: Preoxygenation with 80% oxygen resulted in a numerically shorter DAWD compared with 100% oxygen, without a significant difference in ORi™. These findings may suggest the potential feasibility of using 80% oxygen for preoxygenation. However, given the limited sample size and uncertain clinical relevance, further large-scale studies are warranted to clarify the safety and clinical implications of lower oxygen concentration during anaesthesia induction. Full article

This paper proposes an enhanced hierarchical fuzzy Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) integrated with the Analytic Network Process (ANP) for solving multi-criteria decision-making (MCDM) problems under uncertainty. Conventional fuzzy TOPSIS models often face significant challenges, such as restrictions to specific fuzzy number formats, difficulties in normalization when zero or very small values appear, and limited capacity to capture hierarchical interdependencies among criteria. To address these limitations, we develop a generalized fuzzy geometric mean approach for deriving weights from pairwise comparisons that can accommodate multiple fuzzy number types. Moreover, a novel normalization function is introduced, which ensures mathematically valid outcomes within the [0, 1] interval while avoiding division-by-zero and inconsistency issues. The proposed method is validated through both a numerical building selection problem and a practical supplier selection case study. Comparative analyses against established fuzzy MCDM models demonstrate the improved robustness, flexibility, and accuracy of the approach. Additionally, a sensitivity analysis confirms the stability of results with respect to variations in criteria weights, fuzzy number formats, and normalization techniques. These findings highlight the potential of the proposed fuzzy hierarchical TOPSIS-ANP framework as a reliable and practical decision support tool for complex real-world applications, including supply chain management and resource allocation under uncertainty. Full article

Lithium-ion batteries are essential to electric vehicles, so it is crucial to continuously monitor and control their health. However, since today’s battery packs consist of hundreds or thousands of cells, monitoring all of them is challenging. Additionally, the performance of the entire battery pack is often limited by the weakest cell. Therefore, developing effective monitoring techniques that can reliably forecast the remaining time to depletion (RTD) of lithium-ion battery cells is essential for safe and efficient battery management. However, even in robust systems, this data can be lost due to electromagnetic interference, microcontroller malfunction, failed contacts, and other issues. Gaps in voltage measurements compromise the accuracy of data-driven forecasts. This work systematically evaluates how different voltage reconstruction methods affect the performance of recurrent neural network (RNN) forecast models trained to predict RTD through quantile regression. The paper uses experimental battery pack data based on the behavior of an electric vehicle under dynamic driving conditions. Artificial gaps of 500 s were introduced at the beginning, middle, and end of each discharge phase, resulting in over 4300 reconstruction cases. Four reconstruction methods were considered: a zero-order hold (ZOH), an autoregressive integrated moving average (ARIMA) model, a gated recurrent unit (GRU) model, and a hybrid unscented Kalman filter (UKF) model. The results presented here reveal that the UKF model, followed by the GRU model, outperform alternative reconstruction methods. These models minimize signal degradation and provide forecasts similar to the original past data signal, thus achieving the highest coefficient of determination and the lowest error indicators. The reconstructed signals were fed into LSTM and GRU RNNs to estimate RTD, which produced confidence intervals and median values for decision-making purposes. Full article

This paper proposes an enhanced Model Predictive Direct Speed Control (MPDSC) framework for Permanent Magnet Synchronous Motor (PMSM) drives, integrating duty ratio optimization and load torque disturbance compensation to significantly improve both transient and steady-state performance. Traditional finite-control-set MPC strategies, which apply a single voltage vector per sampling interval, often suffer from steady-state ripples, elevated total harmonic distortion (THD), and high computational complexity due to exhaustive switching evaluations. The proposed approach addresses these limitations through a novel dual-stage cost function structure: the first cost function optimizes dynamic response via predictive control of speed error, while the second adaptively minimizes torque ripple and harmonic distortion by adjusting the active–zero voltage vector duty ratio without the need for manual weight tuning. Robustness against time-varying disturbances is further enhanced by integrating a real-time load torque observer into the control loop. The scheme is validated through both MATLAB/Simulink R2020a simulations and real-time experimental testing on a dSPACE 1202 rapid control prototyping platform across small- and large-scale PMSM configurations. Experimental results confirm that the proposed controller achieves a transient speed deviation of just 0.004%, a steady-state ripple of 0.01 rpm, and torque ripple as low as 0.0124 Nm, with THD reduced to approximately 5.5%. The duty ratio-based predictive modulation ensures faster settling time, improved current quality, and greater immunity to load torque disturbances compared to recent duty-ratio MPC implementations. These findings highlight the proposed DR-MPDSC as a computationally efficient and experimentally validated solution for next-generation PMSM drive systems in automotive and industrial domains. Full article

The widespread application of multi-agent robotic systems in domains such as agricultural collaboration and automation has accentuated the challenges faced in seeking to achieve rapid synchronization and sustain high-performance control under conditions where velocity states remain unmeasurable. To relieve these challenges, a synchronization control framework is proposed for multi-agent systems, employing non-uniform sampling communication protocols. Initially, a state-variable transformation is applied to construct a composite Lyapunov function that integrates a sampling term. An explicit relation is then derived between the communication interval and the global exponential synchronization rate, thereby establishing a theoretical foundation for the design of non-periodic sampling-based control strategies. Second, a linear-state feedback controller is introduced, which balances convergence speed with the limited frequency of information updates, ensuring asymptotic stability even under prolonged sampling intervals. Third, a velocity observer was designed based on Immersion and Invariance ($I\&I$) theory to solve the problem of unmeasurable velocity states, ensuring the exponential convergence of the estimation error. Finally, the simulation results demonstrate that, with sampling intervals of $h\in [0.03,0.08]$ s, the position errors $\parallel q_i-q_{d,i}\parallel$ of all six robots converge to below ${10}^{-2}$ within 7 s; meanwhile, the velocity estimation errors decay to nearly zero within 7 s, confirming the effectiveness of the proposed method. The main contributions of this work can be summarized as follows: (1) a new $I\&I$ velocity observer is tailored for discrete-time communication; (2) rigorous proof of global exponential convergence is provided via a composite Lyapunov energy function; (3) a reproducible MATLAB simulation framework is presented that enhances both the verifiability and applicability of the proposed approach. Full article