Response Coefficients Research Articles

Accounting Reporting Complexity as a Source of Insider Information Advantage: Evidence From Insider Trades

ABSTRACTAccounting reporting complexity (ARC) represents the overall difficulty in preparing and consuming the financial statements and is of growing concern to regulators and capital market stakeholders. Although financial statements serve to mitigate information asymmetry between preparers and users, complexity can hinder their effectiveness. We contribute to the evidence on this issue by examining insider trading profits conditional on ARC. Consistent with ARC producing an information advantage to insiders, we document higher returns to executives on the trades executed in the 60 days after the report filing as ARC increases. The most financially sophisticated and informed insiders, the CEO and CFO, appear to adjust their trading activity accordingly. Furthermore, high ARC firms have a weaker correlation between current and future earnings, a weaker earnings response coefficient, and a longer return drift. Collectively, the results document a novel and material cost of ARC. ARC impairs the informativeness of financial reports to market participants, exacerbating the information gap between insiders and other stakeholders. Insiders can use this to their advantage, as they face few trading constraints during the post‐filing period.

Free Cash Flow Moderated Income Smoothing, Earning Persistence Toward Earnings Response Coefficient

The aim of this study is to investigate: (1) the effect of income smoothing, earnings persistence, and systematic risk on ERC; (2) whether free cash flow moderates the relationship between income smoothing, earnings persistence, and systematic risk on ERC; (3) the influence of income smoothing, earnings persistence, and systematic risk on free cash flow; (4) how these factors affect free cash flow; and (5) the impact of free cash flow on ERC. The research uses a quantitative approach, drawing on secondary data from energy and mineral companies listed on the Indonesia Stock Exchange, with purposive sampling criteria for the 2018-2021 period (4 years). The findings reveal that (1) income smoothing, earnings persistence, and systematic risk do not have a significant impact on ERC; (2) free cash flow does not serve as a moderating variable, as it does not amplify the effect of income smoothing, earnings persistence, and systematic risk on ERC; (3) earnings persistence affects free cash flow; and (4) free cash flow influences ERC. The originality of this study lies in its contribution to transparency literature, demonstrating that (1) the market's reaction to earnings information is not linked to financial statement manipulation, with the market responding more to the availability of funds for investors.

Data-driven based identification of closed-loop errors-in-variables systems: a cascaded case

Identification of the EIV system has attracted much interest due to the potential usage of the EIV system in the engineering sciences and elsewhere. It is a more difficult problem in comparison to the standard identification of a linear system. Based on the basic assumptions, a data-driven approach for the closed-loop identification of the cascaded EIV system is proposed. In the absence of external excitation, identifiability and consistency of model estimation of the plant and the controller are achieved. Without relying on prior knowledge, time delay and model order of the plant and the controller are consistently determined from the respective estimated impulse response coefficients that are estimated by using correlation analysis on two sets of routine operating input/output data. Moreover, the model parameters of the plant and the controller are consistently determined by means of the parameterisation algorithm. The effectiveness of the proposed algorithm is illustrated by numerical examples.

Numerical investigation on vortex-induced vibration characteristics and vortex shedding mechanism of the triple-box girder

Box girders are increasingly utilized in extensive-span bridges due to their favorable aerodynamic stability and excellent traffic capacity. Nevertheless, the presence of gaps between these girders renders them highly sensitive to vortex-induced vibrations (VIV). This study utilized numerical simulation approach to explore the induced mechanism and vibration characteristics of vertical VIV of a triple-box girder with linear webs at [Formula: see text] attack angle. By using the numerical model verified with the wind tunnel data, the aerodynamic characteristics of the triple-box girder are detailed analyzed from the aspects of fundamental flow features, vortex shedding mechanism, force coefficients, vibration response and transient pressure coefficient distribution at various reduced wind speeds and vibration stages. The results indicate that the flow states and vortex structures differ significantly across different reduced wind speeds. The vortices shed from the upstream and middle box girders impact the windward side of the middle and downstream box girders in different forms, then separate and reattach, resulting in a significant pressure difference between the upper and lower surfaces of the middle and downstream box girders, thus driving the vortex-induced vibration of the triple-box girder. In the process of vortex-induced vibration, the lift force and lift contribution of the downstream box girder increase rapidly, which exceeds the increase of the upstream and middle box girder, and is the main cause of vortex-induced vibration of the triple-box girder. Control measures should be carried out for the downstream box girder to suppress vortex-induced vibration. Moreover, during the ascent of VIV, the phase between aerodynamic lift and displacement responses is closely aligned, whereas during the descent phase, the phase difference sharply increases, ultimately stabilizing at [Formula: see text].

Auto-tuning of filtered proportional-integral-derivative controller for industrial processes under routine operating conditions.

In general, auto-tuning implementation of PID controllers relies on dual controllers, exciting/identification experiments or some prior knowledge on the process and hence the considerable cost on auto-tuning implementation occurs. To deal with such a problem, a new auto-tuning scheme of the FPID controller is developed for minimum variance tasks under routine operating conditions in which the closed-loop system is running without any external excitation other than natural disturbances. This paper reveals that the stochastic disturbance model can be uniquely determined from the first several terms of the impulse response coefficients of the closed-loop system when the precondition on the time delay and the order of the disturbance model is satisfied. Based on the closed-loop non-parametric model (in terms of impulse response coefficients) that is estimated online by utilizing the one-shot closed-loop output data, the disturbance model is estimated by solving an optimization problem and hence the plant model is obtained. Subsequently, the new parameter set of the FPID controller is updated online by solving an optimization problem with respect to the H2 norm of the resulting closed-loop transfer function. The benefit of the proposed scheme over the existing auto-tuning methods is the cost saving of the auto-tuning implementation due to the following facts: i) it does not rely on dual controllers or identification/exciting experiments; ii) it does not require prior knowledge of the process. The effectiveness of the proposed scheme is illustrated in terms of output variance index by numerical cases and industrial examples.

FIR noise reduction using GWO and Fast Kurtogram for extracting fault characteristic frequencies in rolling bearings

Abstract Fast Kurtogram (FK) is a widely recognized method for detecting characteristic fault frequency bands. Nevertheless, its sensitivity to noise often causes diagnostic errors. This research seeks to enhance FK’s identification performance by implementing an adaptive FIR filter that mitigates noise. To validate this approach, single-point fault signals were obtained from the outer race, inner race, and rolling elements of bearings operating at four distinct motor speeds. These raw signals were then subjected to statistical analysis after normalization. To design the adaptive FIR filter, the study employs an enhanced Grey Wolf algorithm along with the Parks-McClellan algorithm to determine the optimal filter order and impulse response coefficients. Subsequently, band-pass filtering is applied to the fault characteristic frequency bands identified through FK. The fault characteristic frequencies are then extracted using envelope spectrum analysis. The bandwidth and fault characteristics derived from the processed signals are compared against those obtained from the unfiltered original signals. The findings indicate that optimal normalization preprocessing is crucial before filter design. The determined optimal order and impulse response coefficients enable the adaptive design of FIR filters. As a result, the FIR-filtered signal exhibits significantly fewer noise-induced anomalous peaks, thereby improving the signal-to-noise ratio and enhancing FK’s robustness against noise interference. Implementing an adaptive FIR filter for noise reduction significantly aids in more precise analysis and diagnosis of bearing conditions. Looking ahead, this study proposes integrating machine learning algorithms to further enhance the accuracy in identifying characteristic frequency bands associated with single-point faults in rolling elements.

Carbon emission trading scheme, investors’ attention, and earnings response coefficients

This study explores how the introduction of the carbon emissions trading scheme (ETS) affects investors’ reactions to corporate earnings surprises. We propose two non-exclusive explanations, namely, the preference-based view and the uncertainty-based view, and suggest that the implementation of ETS may influence the magnitude of investor responses to corporate unexpected earnings. Consistent with the preference-based view, by utilizing China’s introduction of ETS as a quasi-natural experiment, we observe a reduction in the earnings response coefficients (ERCs) following the implementation of ETS. We validate this result by showing that the introduction of ETS prompts investors to focus on corporate carbon risk. Cross-sectional tests find that the effect of ETS on ERCs is more pronounced in firms with higher corporate carbon risk exposure, in firms whose investors exhibit greater environmental awareness, in better-developed carbon pilot markets, and in firms with greater exposure to international capital markets, while this impact is mitigated by firms’ non-financial performance. These findings highlight the importance of environmental regulation and market liberalization in influencing investors’ resource allocation.

External electric grid impact on mode parameters at a load node

Relevance. A passive experiment is one of the advanced techniques for a static load voltage characteristic identification. External electric grid impact on mode parameters at a load node – a grid response – sophisticates the statistical processing of passive experiment results significantly. For this reason, the analysis of a grid response impact and detection of its reduction are relevant problems. Aim. Quantitative evaluation of a grid response impact is produced by grid response active and reactive power coefficients; therefore the aim of this paper is to analyze the variance of grid response coefficients in relation on scheme and mode parameters. This allows finding techniques of reducing a grid response impact. Methods. Transformations of the Jacobi matrix, when scheme and mode parameters are formed as complex numbers. Results. The authors have proposed the analytic expressions, reproducing dependences of grid response coefficients on scheme and mode parameters in general variants. Conclusions. Based on the analytic and schematic dependencies the authors determined the measures for reducing a grid response impact. It is demonstrated that with increase in the impedance among power and load nodes, a grid response impact grows as well by active and reactive power. The authors proposed the practical technique for calculating a grid response impact. The perspective trends for improving accuracy in identifying a grid response impact using current mode and scheme parameters are defined.

A Cepstrum-Informed neural network for Vibration-Based structural damage assessment

Data-driven methods for vibration-based Structural Health Monitoring (SHM) have gained significant popularity for their straightforward modeling process and real-time tracking capabilities. However, developing complex models such as deep neural networks can pose challenges, including limited interpretability and substantial computational demands, due to the large number of parameters and deep layer stacking. This study introduces a novel Cepstrum-Informed Attention-Based Network (CIABN) developed to model power cepstral coefficients of structural acceleration responses, guided by cepstrum-based physical properties to facilitate efficient structural damage assessment. The CIABN integrates three key components: a unique input–output mapping based on weighted cepstral coefficients, a novel cepstral positional encoding mechanism, and a multi-head self-attention mechanism. The unique input–output mapping enables appreciable model generalization in overall structural characteristics, with the weighted cepstral coefficients serving as informative and compact data for efficient neural network modeling. The developed cepstral positional encoding scientifically guides the model to capture the coefficient indices, and the underlying trend of cepstral coefficients primarily governed by overall structural characteristics. The multi-head attention mechanism enables computationally efficient parallel analysis of interdependencies among coefficients, facilitating the development of a lightweight network. The effectiveness and superiority of the method have been validated using both simulated and experimental structural data.

Exploring Changes in Ocular Aberrations for Different Fixation and Accommodation Stimuli

Background: Given the lack of standardization in stimulus types for assessing accommodation, we aimed to evaluate accommodative response (AR) and Zernike coefficients using four different stimuli. Methods: Sixteen healthy subjects aged 22–32 years participated. Four black transilluminated stimuli (Snellen 6/12 “E”, 6/6 “e”, Maltese Cross 6/12 “X”, 6/6 “x”) were used to stimulate accommodation from 0 D to 5 D, in 1 D increments, using the irx3 aberrometer. From the results, AR was calculated with Seidel defocus and the change in Zernike coefficient value between the non-accommodative state and the fully accommodative state (5 D) was determined. Results: Larger pupils were observed with stimulus “E” (p-value < 0.05). The mean AR at the maximum accommodative level (5 D) for the different stimuli was −1.88 ± 1.00 for “E”, −2.60 ± 1.44 for “X”, −2.00 ± 1.32 for “e”, and −2.40 ± 1.27 for “x”. No statistically significant differences were found between AR and Zernike coefficients with the four different accommodative stimuli (p-value > 0.05, one-way ANOVA). Conclusions: The study evaluated accommodative stimulus design and size on AR and Zernike coefficients and found no significant differences. However, stimuli with higher spatial frequencies (“e” and “E”) provided larger ARs compared to the other stimuli.

Spatial and Temporal Characterization of Near Space Temperature and Humidity and Their Driving Influences

Near space refers to the atmospheric region 20–100 km above Earth’s surface, encompassing the stratosphere, mesosphere, and part of the thermosphere. This region is susceptible to surface and upper atmospheric disturbances, and the atmospheric temperature and humidity profiles can finely characterize its complex environment. To analyze the relationship between changes in temperature and humidity profiles and natural activities, this study utilizes 18 years of temperature and water vapor data from the TIMED/SABER and AURA/MLS instruments to investigate the variations in temperature and humidity with altitude, time, and spatial distribution. In addition, multiple linear regression analysis is used to examine the impact mechanisms of solar activity, the El Niño–Southern Oscillation (ENSO), and the Quasi-Biennial Oscillation (QBO) on temperature and humidity. The results show that in the mid- and low-latitude regions, temperature and water vapor reach their maxima at an altitude of 50 km, with values of 265 K and 8–9 × 10⁻⁶ ppmv, respectively; the variation characteristics differ across latitudes and altitudes, with a clear annual cycle; the feedback effects of solar activity and the ENSO index on temperature and humidity in the 20–40 km atmospheric layer are significantly different. Among these factors, solar activity is the most significant influence on temperature and water vapor, with response coefficients of −0.2 to −0.16 K/sfu and 0.8 to 4 × 10⁻⁶ ppmv/sfu, respectively. Secondly, in the low-latitude stratospheric region, the temperature response to ENSO is approximately −1.5 K/MEI, while in the high-latitude region, a positive response of 3 K/MEI is observed. The response of water vapor to ENSO varies between −1 × 10⁻⁷ and −4 × 10−⁷ ppmv/sfu. In the low-latitude stratospheric region, the temperature and humidity responses to the QBO index exhibit significant differences, ranging from −1.8 to −0.6 K/10 m/s. Additionally, there are substantial differences in responses between the polar regions and the low-latitude equatorial region. Finally, a three-dimensional model coefficient was constructed to illustrate the influence of solar activity, ENSO, and QBO on temperature and humidity in the near space. The findings of this study contribute to a deeper understanding of the temperature and humidity variation characteristics in near space and provide valuable data and model references for predicting three-dimensional parameters of temperature and humidity in this region.

Impact resistance characteristics of pipeline system covered with W-shape elastic-porous metallic damper

As a novel elastic-porous damping material fabricated through entangled wire mesh, W-shape elastic-porous metallic damper (W-EPMD) is considered an ideal damping element for coated pipeline system due to the micro dry friction between metal wires, which induces energy dissipation. The complex interwoven cellular formations of metallic wire mesh pose challenges in characterizing its dynamic characteristics. In this work, the dynamic properties of the pipeline system covered with W-EPMD under various impact conditions, including the acceleration response and impact isolation coefficient, were investigated by numerical simulations and experimental analysis. Constitutive models used to characterize the hysteresis behavior of W-EPMD were introduced, comprising Yeoh and Bergström-Boyce models, and parameter identification were conducted through quasi-static experiments. The reliability of the established numerical model was confirmed through drop impact experiments. The results demonstrate that there is a maximum discrepancy of 9.1 % between the simulation predictions and experimental results of the stress-strain curve. The impact isolation coefficient of the pipeline system covered with W-EPMD exhibits a fluctuating trend with the rise of the pulse peak, while the maximum compression of W-EPMD steadily increases. During the pipeline impact process, the increased density of W-EPMD reduces the impact resistance of the pipeline system, while excessively low density leads to over-compression and structural damage to W-EPMD. Furthermore, the discrepancy of the acceleration response between experimental and numerical results under various excitation signals remain within 6 %, demonstrating that the hysteresis model effectively characterizes the impact resistance characteristics of the pipeline system covered the W-EPMD.

Hyperbranched Thermosensitive Polymer-AuNP Composite Probe for Temperature Colorimetric Detection.

Temperature detection is particularly important in the medical and scientific fields. Although there are various temperature detection methods, most of them focus on broad temperature detection, and basic research in specific fields, especially the detection of subtle temperature changes (32-34 °C) during wound infection, is still insufficient. For this purpose, a novel colorimetric temperature sensing probe is designed in this paper, which can quickly and intuitively respond to small temperature changes within a specific range through color changes. In this paper, hyperbranched polyethyleneimine (HPEI) was modified by isobutyrylation to prepare hyperbranched temperature-sensitive polymer (HPEI-IBAm). And it was combined with gold nanoparticles (AuNPs) prepared by a sodium citrate reduction method to construct an HPEI-IBAm-AuNP colorimetric probe. The probe exhibits excellent stability, even at salt concentrations of up to 12 g/L, thanks to the abundant amino functional groups and the large steric hindrance effect unique to HPEI-IBAm. In particular, the temperature detection range of the probe is precisely locked within 32-34 °C, enabling it to respond quickly and accurately to small temperature changes of only 2 °C. This feature is perfectly suited to the practical needs of temperature detection in infected wounds. The linear fitting coefficient of the temperature response is as high as 0.9929, ensuring the accuracy of the test results. The detection performance of the probe remained highly consistent over 10 cycles, fully proving its excellent reusability and durability. In addition, a flexible colorimetric sensor was prepared by combining the probe with polydimethylsiloxane (PDMS) film. This sensor is capable of rapidly detecting human skin temperature in real time, achieving an accuracy of 99.07% to 100.61%. It can provide a possible solution to the challenges of delayed and difficult temperature detection caused by different body parts and uneven surfaces, among others. This demonstrates its extensive practical value and potential, and it is expected to be further applied in the monitoring of wound infections.

The effect of internal control on earnings response coefficient

The effect of internal control on earnings response coefficient

PENGARUH CORPORATE SOCIAL RESPONSIBILITY (CSR), FINANCIAL DISTRESS, CORPORATE’S GROWTH, DAN FIRM SIZE TERHADAP EARNINGS RESPONSE COEFFICIENT (ERC)

The purpose of this study is to determine the influence of corporate social responsibility (CSR), financial distress, corporate's growth and firm size on earnings response coefficient (ERC). The data analysis method used in this study is a quantitative method and uses panel data regression analysis techniques. The population in this study is manufacturing companies listed on the Indonesia Stock Exchange in 2015-2022. The results show that financial distress has an effect on earnings response coefficient (ERC), corporate social responsibility (CSR), corporate's growth, and firm size has no effect on earnings response coefficient (ERC).

Ultra-compact and high-precision differential detection method based on liquid crystal polarization grating for miniature atomic magnetometer.

Atomic magnetometers (AMs) that use alkali vapors, such as rubidium, are among the most sensitive sensors for magnetic field measurement. They commonly use polarization differential detection to mitigate common-mode noise. Nevertheless, traditional differential detection optics, including polarization beam splitters (PBS) and half-wave plates, are typically bulky and large, which restricts further reductions in sensor dimensions. In this study, a combination of liquid crystal polarization grating (LCPG) and liquid crystal quarter-wave plate is used for differential detection in AMs, with magnetic field strength determined by measuring the intensity of two diffracted beams from the LCPG. The experimental findings indicate that the fabricated LCPG exhibits a circularly polarized extinction ratio of 3,656 and achieves an average diffraction efficiency of 99 %. In addition, the differential detection method based on LCPG can achieve an angular resolution of 1.48 × 10-7 rad. Subsequently, the method is employed in an AM to achieve an average magnetic sensitivity of 13.8 fT/Hz1/2. Compared to the PBS-based differential detection method, this method enhances the magnetometer response coefficient by 13 % and achieves co-side distribution of the two diffracted beams, thereby avoiding the need for additional vertical optical paths. The effective thickness of the detection optics is reduced to the micrometer scale, allowing for future integration as thin films onto microfabricated vapor cells. This study offers a practical solution for miniaturized AMs with exceptionally high sensitivity.

Hybrid model-based predictive HVAC control through fast prediction of transient indoor temperature fields

Efforts to reduce energy demand in the building sector have prompted a focus on the operational control of HVAC systems. Despite extensive research on HVAC control based on temperature prediction models, existing approaches often rely on node-based or average temperature predictions, which lack the detailed temperature distribution data necessary for accurate control, especially in transient situations with both spatial and temporal variations. This study introduces a precise HVAC control method based on a fast temperature field prediction model. By combining the single-step prediction response coefficient (SPRC) method with Convolutional Neural Network (CNN) architecture, sub-temperature field prediction models for multiple independent heat sources were constructed and integrated to achieve fast temperature field predictions. Subsequently, utilizing the predicted temperature field, air conditioning operation parameters were optimized and controlled to minimize energy consumption. Application of the proposed method in real building scenarios demonstrated the temperature field predictions closely aligned with computational fluid dynamics (CFD) simulations, achieving a mean absolute error (MAE) of 0.27 °C and a root mean square error (RMSE) of 0.24 °C. Furthermore, this model achieved a notable 57.8 % improvement in prediction accuracy compared to models relying solely on single-step prediction responses. Additionally, the model predictive control based on the hybrid model's temperature field predictions significantly reduced the runtime of the HVAC system by 18.18 % while maintaining temperatures within the comfort range throughout the operation period. The method presents a promising avenue for optimizing HVAC operations and minimizing energy consumption in building environments, thereby contributing to sustainable building management practices.

Optimizing the Migratory Environment of Wildebeests in the Maasai Mara Reserve with a New Ecological Corridor and Customized Buffer Zone Model

Conflicts between wildlife and humans are a major ecological issue. During migration, wildlife, especially wildebeest, often encounter significant environmental pressures from human activities. However, relatively few studies have been conducted to provide a concise, quantitative description of wildebeest migration in the Maasai Mara National Reserve (MMNR). In this study, we identified changes in the location of the wildebeest population over time in the Maasai Mara National Reserve. We then used a K-means algorithm (R2 = 0.926) to fit coordinates representing the changes in the location of the wildebeests to enable a quantitative representation of their migration routes. Subsequently, we developed an environmental stress model to assess the changes in environmental stresses faced by wildebeests along their migration routes. We proposed a model of “migratory ecological corridors and customized buffer zones” and determined the response coefficient T_res. We used the response coefficients T_res = 0.06, 0.09, and 0.12 as the critical values to categorize the areas along the routes into weak, medium, and strong response regions. Then, we set the width of the buffer zones on both sides of the routes as 5 km, 7 km, and 9 km, respectively, and evaluated the buffer effect. This type of model achieved a good effect of reducing the environmental pressure by 54.06%. The “Migratory Ecological Corridor and Customized Buffer Zone” model demonstrated a high degree of economic feasibility while showing good practicality in mitigating the environmental conflicts between humans and migratory wildlife. The variability in the environmental pressures across the region indicates that the Nairobi and Nakuru districts may be undergoing a particular stage of urbanization that unleashes potential threats to the migration of wildebeests. Further research is essential to assess the feasibility of larger buffer zones.

Adaptive Variable Universe Fuzzy Droop Control Based on a Novel Multi-Strategy Harris Hawk Optimization Algorithm for a Direct Current Microgrid with Hybrid Energy Storage

In the off-grid photovoltaic DC microgrid, traditional droop control encounters challenges in effectively adjusting the droop coefficient in response to varying power fluctuation frequencies, which can be influenced by factors such as line impedance. This paper introduces a novel Multi-strategy Harris Hawk Optimization Algorithm (MHHO) that integrates variable universe fuzzy control theory with droop control to develop an adaptive variable universe fuzzy droop control strategy. The algorithm employs Fuch mapping to evenly distribute the initial population across the solution space and incorporates logarithmic spiral and improved adaptive weight strategies during both the exploration and exploitation phases, enhancing its ability to escape local optima. A comparative analysis against five classical meta-heuristic algorithms on the CEC2017 benchmarks demonstrates the superior performance of the proposed algorithm. Ultimately, the adaptive variable universe fuzzy droop control based on MHHO dynamically optimizes the droop coefficient to mitigate the negative impact of internal system factors and achieve a balanced power distribution between the battery and super-capacitor in the DC microgrid. Through MATLAB/Simulink simulations, it is demonstrated that the proposed adaptive variable universe fuzzy droop control strategy based on MHHO can limit the fluctuation range of bus voltage within ±0.75%, enhance the robustness and stability of the system, and optimize the charge and discharge performance of the energy storage unit.

The association between structural connectivity and anti-seizure medication response in patients with temporal lobe epilepsy.

This study aimed to investigate the differences in structural connectivity and glymphatic system function between patients with temporal lobe epilepsy (TLE) and hippocampal sclerosis (HS) and healthy controls. Additionally, we analyzed the association between structural connectivity, glymphatic system function, and antiseizure medication (ASM) response. We retrospectively enrolled patients with TLE and HS and healthy controls who underwent diffusion tensor imaging at our hospital. We assessed structural connectivity in patients with TLE and HS and healthy controls by calculating network measures using graph theory and evaluated glymphatic system function using the diffusion tensor image analysis along the perivascular space (DTI-ALPS) index. Patients with TLE and HS were categorized into two groups: ASM poor and good responders. We enrolled 55 patients with TLE and HS and 53 healthy controls. Of the 55 patients with TLE and HS, 39 were ASM poor responders, and 16 were ASM good responders. The assortativity coefficient in patients with TLE and HS was higher than that in healthy controls (0.004 vs. -0.007, p = 0.004), and the assortativity coefficient in ASM poor responders was lower than that in ASM good responders (-0.001 vs. -0.197, p = 0.003). The DTI-ALPS index in patients with TLE and HS was lower than that in healthy controls (1.403 vs. 1.709, p < 0.001); however, the DTI-ALPS index did not differ between ASM poor and good responders (1.411 vs. 1.385, p = 0.628). The DTI-ALPS index had a significant negative correlation with age in patients with TLE and HS (r = -0.267, p = 0.049). We confirmed increased assortativity coefficient in structural connectivity and decreased DTI-ALPS index in patients with TLE and HS compared with healthy controls. Additionally, we demonstrated an association between decreased assortativity coefficient in structural connectivity and ASM poor response in patients with TLE patients and HS. This study investigates the relationship between brain connectivity changes and glymphatic system function with antiseizure medication response in patients with temporal lobe epilepsy and hippocampal sclerosis. The research reveals that these patients show altered brain connectivity and glymphatic function compared to healthy individuals. A key finding is the strong link between a specific connectivity measure (assortativity coefficient) and antiseizure medication response, providing valuable insights that could influence epilepsy treatment and future research directions.