Miracle-dz/UniFAR-FTUs huggingface dataset:
FPT-Former: A Flexible Parallel Transformer of Recognizing Depression by Using Audiovisual Expert-Knowledge-Based Multimodal Measures

Background and Objective. Currently, depression is a widespread global issue that imposes a significant burden and disability on individuals, families, and society. Deep learning (DL) has emerged as a valuable approach for automatically detecting depression by extracting cues from audiovisual data and making a diagnosis. PHQ-8 is considered a validated diagnostic tool for depressive disorders in clinical studies, and the objective of this experiment is to improve the accuracy of PHQ-8 prediction. Furthermore, this paper aims to demonstrate the effectiveness of expert knowledge in depression diagnosis and discuss a novel multimodal network architecture. Methods. This research paper focuses on multimodal depression analysis, proposing a flexible parallel transformer (FPT) model capable of extracting data from three distinct modalities (i.e., one video and two audio descriptors). The FPT-Former model incorporates three paths, each using expert-knowledge-based descriptors from one modality as inputs. These descriptors are represented into 32 features by the encoder part of a transformer module, and these features are fused to realize the final regression of PHQ-8 score. The extended distress analysis interview corpus (E-DAIC) is an expansion of WOZ-DAIC which comprises semiclinical interviews intended to assist in the diagnosis of psychological distress conditions. It encompasses a sample size of 275 participants, and in this study, it was utilized to test the model in a way of 10-fold cross-validation. Results. The FPT presented herein achieved comparable performance to the state-of-the-art works, with a root mean square error (RMSE) of 4.80 and a mean absolute error (MAE) of 4.58. The ablation experiments demonstrate that the three-modality-fused model outperforms other two-modality-fused and single-modality models. While using a PHQ-8 score threshold of 10, the accuracy of the depression classification is 0.79. Conclusions. Leveraging the strength of expert-knowledge-based multimodal measures and parallel transformer structure, the FPT model exhibits promising performance in depression detection. This model improved the accuracy of depression diagnosis through audio and video, and it also proved the effectiveness of using expert-knowledge in the diagnosis of depression. The traits of flexible structure, high predictive efficiency, and secure privacy protection make our model a promotable intelligent system in mental healthcare.
THE QUALITY PERFORMANCE OF INTENSIVE ENGLISH COURSE LECTURER

Intensive English Course (IC) has been considered an essential program to prepare qualified English Department students in higher education in the first semester. The lecturers teach this program for the four skills and two components; listening, reading, speaking, writing, vocabulary and grammar in an integrated manner for one semester. This course will be a success if the lecturers have the capability in content and pedagogy to teach the IC program. Furthermore, this study aimed at describing lecturers’ quality performance in teaching IC at UIN Sjech M. Djamil Djambek (SMDD) Bukittinggi. The data was collected by distributing a questionnaire to 95 first-semester English department students in the 2022/2023 academic year. The findings showed the lecturers’ average was 58,83. It means that the lecturers were qualified in teaching Intensive English Courses in all indicators; tangible, reliability, responsiveness, assurance, and empathy.
Empowering Agriculture: Mitigating Postharvest Losses in Tomatoes and Peppers among Male and Female Farmers in Ekiti State, Nigeria with Indigenous Technology

This research focused on the application of local technologies to minimize postharvest losses of tomatoes and peppers in Ekiti State, Nigeria. Utilizing a two-stage sampling method, 138 male and 138 female vegetable farmers were randomly chosen for the study. Data collection involved questionnaires, Focus Group Discussions (FGD), and In-depth Interviews (IDI), with factor analysis employed for quantitative data and transcription according to FGD and IDI reporting standards for qualitative data. The study found that sack bags (86.2% of male and 61.2% of female farmers) and woven cane baskets (97.1% of male and 100% of female farmers) were the primary technologies used. Other methods like sun-drying, drying under fire, and blanching were less popular. Factors influencing the choice of local technologies included community (26.9%), cost/economic considerations (15.9%), education (12.9%), proximity to major towns (10.0%), and access to inputs/resources (8.4%). These factors collectively accounted for about 74.35% of the variation in technology utilization. The study concluded that reinforcing community beliefs and practices could promote the use of these indigenous technologies to reduce losses, while also encouraging farmers to adopt more of these available local methods.
Verification of June 1993 IOP assimilation dataset and its use in driving a Single-Column CCM3 Model [presentation]

One goal of the Atmospheric Radiation Measurement (ARM) Program is to improve general circulation models (GCMs) by obtaining detailed meteorological information in limited areas of order 200 km square and comparing GCM parameterizations with the mean radiative and convective properties in such areas. Typical GCM grid boxes are 100 km to 200 km square, but there is in reality much structure at smaller scales that is represented by their parameterizations. Meteorological observations alone cannot represent this structure, so we use a full-physics mesoscale model forced by large-scale tendencies to give as complete a picture of the sub-200-km-scale structures as possible. This allows us to produce a full four-dimensional characterization of the atmosphere that, given sufficiently complete physics in the model and sufficiently good data, will provide a representation of the actual state of the atmosphere.
SNAKE LINE ANALYSIS FOR LAHAR FLOW WARNING SYSTEM (CASE STUDY IN PUTIH RIVER, MOUNT MERAPI)

Lahar flow in the region of Mount Merapi after an eruption of 2010 is still considered potentially to happen and threat the region along the river from the upstream. The development of warning criteria against the potential occurrence of lahar flow is a thing that should be done continuously to accommodate dynamics data availability (rainfall data and lahar flow occurrence data), although with limited data. This study aims to develop lahar warning system applying snake line as a rain phenomenon in Putih catchment area which will affect the occurrence of lahar flow and to evaluate the success rate of snake line for deciding the warning system. This study used the main reference from Guidelines for Development of Warning and Evacuation System against Sediment Disasters in Developing Countries released by Ministry of Land, Infrastructure and Transport Infrastructure Development Institute – Japan (2004). This research was conducted through several stages, i.e. secondary data collection in the form of rainfall data, lahar flow occurrence data, making correlation graph between rainfall intensity and working rainfall, determination of critical line, warning line and evacuation line. The results show that standard rainfall for warning and evacuation alert in Putih River are 22 mm, and 49 mm, respectively. The accuracy of warning criteria and the evacuation criteria against snake line for warning line is 30%, evacuation line is 61% and the critical line is 83%. The behavior of snake line that indicates lahar flow occurrence in Putih River forming an angle of 40o up to 45o.
Chronic Toxoplasma gondii Infection Modulates Hearing Ability across the Adult Life Span

While several studies have shown associations between hearing disorders and congenital toxoplasmosis, the present study investigated the impact of chronic, latent Toxoplasma gondii (T. gondii) infection on hearing loss. We used a regression analysis to explore whether latent T. gondii infection modulates changes in hearing thresholds over an age range from 20 to 70 years. We analyzed audiometric data of 162 T. gondii IgG-positive and 430 T. gondii-negative participants, collected in the Dortmund Vital Study (DVS, ClinicalTrials.gov Identifier: NCT05155397), a prospective study on healthy cognitive aging. The regression analysis indicated that latent toxoplasmosis was associated with an accelerated development in hearing loss over the observed age range. Hearing loss was less frequent in IgG-positive than in IgG-negative participants up to the age of about 40 for a low (0.125–1 kHz)-frequency range. For high (2–8 kHz) frequencies, this pattern reversed for ages above 65 years. We discuss these findings on hearing function in the context of a recently proposed model, suggesting that latent toxoplasmosis can differentially affect brain functions across a lifespan.
Satellite imagery and visualization of the Caicos Platform

The Caicos Platform has proven to be an area of continuing interest to researchers in modern carbonates, an important training venue, and a valuable modern analog for understanding facies patterns of subsurface isolated platforms. We hope to promote this interest by making readily available a set of processed satellite images and an offshore/onshore digital elevation model (DEM), along with examples of how this data can be visualized and used.
Fused-IoU Loss: Efficient Learning for Accurate Bounding Box Regression

The importance of the loss function in object detection algorithms based on deep learning has grown significantly technological progress. The accuracy of object detection is significantly affected by bounding box regression, which is a crucial factor. Since the introduction of the Intersection over Union (IoU) loss in 2016, many improvements have been proposed based on this loss function. These studies considered various geometric factors related to bounding boxes, and constructed penalty terms to address this issue. This paper summarizes these functions and introduces a new Fused IoU (FIoU) loss function that leads to superior performance. The FIoU loss function not only solves the problem of gradient vanishing during the backpropagation process of the IoU loss function but also solves the problem of some IoU-based loss functions degenerating into IoU loss functions under certain conditions. In addition, in the simulation experiments, the FIoU loss function resulted in faster convergence speed. In our ablation experiments across different datasets and algorithms, our aim was to compare the mAP metrics under different loss functions. On the test set of the Pascal VOC dataset, employing the Faster R-CNN algorithm, FIoU demonstrated improvements of 1.1% and 1.7% over GIoU and Smooth $\ell _{1}$ , respectively. With the YOLOX algorithm, FIoU outperformed GIoU and IoU by 1.0% and 0.8%. Utilizing the YOLOv7 algorithm, we evaluated seven loss functions, achieving optimal results with FIoU. On the validation set of the MS-COCO 2017 dataset, using YOLOv7 and YOLOv8, FIoU exhibited gains of 0.4%, 0.2%, 0.2% over EIoU, DIoU, GIoU, and 0.3%, 0.5%, 0.3% over EIoU, DIoU, GIoU, respectively.
MOVING AWAY FROM ERINDI-ROUKAMBE: TRANSFERABILITY OF A RURAL COMMUNITY-BASED CO-DESIGN

It has become increasingly clear that situated design and contextualized research needs to undergo a validation phase to determine transferability. Within our longitudinal research project in rural Namibia, we have reached a maturity of methods and product. Yet little do we know about their validity beyond the limited context in the absence of cross-contextual verification. In Erindi-roukambe, the site of our community-based co-design, we have learned to understand and include local perspectives and structures within the dialogic of a participatory action research approach. By engaging with the community over a long period of time local research findings, as well as mutual knowledge have fostered and enriched design decisions. Recognizing that indigenous rural communities in the regional and globally face similar challenges with inappropriate mainstream technology we are currently investigating the applicability of our findings, processes and prototype in other contexts. We have introduced our approach at three other rural sites, two in Namibia and one in East Malaysia. The communities responded well to the technology demonstrating intuitive use and engagement. However, although we have gained promising results we wish to caution pre-mature conclusions on transferability without a more profound understanding of the depth of community engagement, transformation, contextual similarities, and cross-contextual validation.
Male Reproductive Toxicity of Antifouling Chemicals: Insights into Oxidative Stress-Induced Infertility and Molecular Mechanisms of Zinc Pyrithione (ZPT)

Zinc pyrithione (ZPT), a widely utilized industrial chemical, is recognized for its versatile properties, including antimicrobial, antibacterial, antifungal, and antifouling activities. Despite its widespread use, recent research has shed light on its toxicity, particularly towards the male reproductive system. While investigations into ZPT’s impact on male reproduction have been conducted, most of the attention has been directed towards marine organisms. Notably, ZPT has been identified as a catalyst for oxidative stress, contributing to various indicators of male infertility, such as a reduced sperm count, impaired sperm motility, diminished testosterone levels, apoptosis, and degenerative changes in the testicular tissue. Furthermore, discussions surrounding ZPT’s effects on DNA and cellular structures have emerged. Despite the abundance of information regarding reproductive toxicity, the molecular mechanisms underlying ZPT’s detrimental effects on the male reproductive system remain poorly understood. This review focuses specifically on ZPT, delving into its reported toxicity on male reproduction, while also addressing the broader context by discussing other antifouling chemicals, and emphasizing the need for further exploration into its molecular mechanisms.
Sustained Extracellular Electrical Stimulation Modulates the Permeability of Gap Junctions in rd1 Mouse Retina with Photoreceptor Degeneration

Neurons build vast gap junction-coupled networks (GJ-nets) that are permeable to ions or small molecules, enabling lateral signaling. Herein, we investigate (1) the effect of blinding diseases on GJ-nets in mouse retinas and (2) the impact of electrical stimulation on GJ permeability. GJ permeability was traced in the acute retinal explants of blind retinal degeneration 1 (rd1) mice using the GJ tracer neurobiotin. The tracer was introduced via the edge cut method into the GJ-net, and its spread was visualized in histological preparations (fluorescent tagged) using microscopy. Sustained stimulation was applied to modulate GJ permeability using a single large electrode. Our findings are: (1) The blind rd1 retinas displayed extensive intercellular coupling via open GJs. Three GJ-nets were identified: horizontal, amacrine, and ganglion cell networks. (2) Sustained stimulation significantly diminished the tracer spread through the GJs in all the cell layers, as occurs with pharmaceutical inhibition with carbenoxolone. We concluded that the GJ-nets of rd1 retinas remain coupled and functional after blinding disease and that their permeability is regulatable by sustained stimulation. These findings are essential for understanding molecular signaling in diseases over coupled networks and therapeutic approaches using electrical implants, such as eliciting visual sensations or suppressing cortical seizures.
IoT Device-to-Cloud Continuous Authentication using Lightweight Key Establishment Mechanism

Cloud computing is an important component for the success of the Internet of Things (IoT). IoT devices generate massive amounts of data, and cloud computing provides the necessary infrastructure to store, process, and analyze this data. The IoT environment is heterogeneous and connects billions of devices, making it a high-value target for attackers. Impersonation attacks and denial-of-service attacks (DoS) are two common threats that can compromise the availability and security of IoT devices. Continuous authentication is a technique that can help mitigate the risk of session hijacking and unauthorized access to IoT devices. While many existing continuous authentication schemes focus on cloud-to-device authentication, it is also important to authenticate devices themselves, as a compromised device can put the entire system at risk. This research proposes a solution for a secure cloud-to-device continuous authentication protocol that relies on devices' features (such as token, battery). Continuous authentication has been introduced as a solution to the problems related to static authentication. The protocol considers the software and hardware limitations of smart IoT devices by using hash function lightweight cryptography.
A Delta-Sigma Modulated Power Converter With Inherent Zero-Voltage Switching

In this paper an advanced modulation strategy is proposed that generates the switch control signals of a power converter while inherently guaranteeing zero-voltage switching (ZVS) operation. The proposed modulator is based on a first-order <inline-formula><tex-math notation="LaTeX">$\Delta \Sigma$</tex-math></inline-formula>-modulator combined with a hold-off circuit. This hold-off circuit consists of a D-latch and maintains the converter's switching state until ZVS commutation can be achieved while an integrator ensures the correct average output. Furthermore, features are implemented to improve start-up and transient behavior. The operating principle and stability of the proposed modulator is discussed and simulation results are presented. Finally, the proposed modulator is validated on a <inline-formula><tex-math notation="LaTeX">$2 \,\mathrm{k}\mathrm{W}$</tex-math></inline-formula> GaN-based synchronous buck converter. Results show that the converter naturally operates under ZVS conditions by continuously varying its switching frequency without having to compute switching times or frequencies. The proposed method is an interesting alternative to pulse-width modulation especially when operating at high switching frequencies or when the switching loss in the power converter is significant.
An Architecture and Review of Intelligence Based Traffic Control System for Smart Cities

City traffic congestion can be reduced with the help of adaptable traffic signal control system. The technique improves the efficiency of traffic operations on urban road networks by quickly adjusting the timing of signal values to account for seasonal variations and brief turns in traffic demand. This study looks into how adaptive signal control systems have evolved over time, their technical features, the state of adaptive control research today, and Control solutions for diverse traffic flows composed of linked and autonomous vehicles. This paper finally came to the conclusion that the ability of smart cities to generate vast volumes of information, Artificial Intelligence (AI) approaches that have recently been developed are of interest because they have the power to transform unstructured data into meaningful information to support decision-making (For instance, using current traffic information to adjust traffic lights based on actual traffic circumstances). It will demand a lot of processing power and is not easy to construct these AI applications. Unique computer hardware/technologies are required since some smart city applications require quick responses. In order to achieve the greatest energy savings and QoS, it focuses on the deployment of virtual machines in software-defined data centers. Review of the accuracy vs. latency trade-off for deep learning-based service decisions regarding offloading while providing the best QoS at the edge using compression techniques. During the past, computationally demanding tasks have been handled by cloud computing infrastructures. A promising computer infrastructure is already available and thanks to the new edge computing advancement, which is capable of meeting the needs of tomorrow's smart cities.
Is intergenerational care associated with depression in older adults?

Background It has become an alarming issue that older adults in China are facing mental health issues, causing severe depression. In this context, intergenerational care, in which grandparents care for young children instead of the young parents, is gaining importance. This study aims to explore the correlation between intergenerational care and depression among older adults, seeking alternative approaches to enhancing their quality of life. This study concludes that intergenerational care is an effective strategy for promoting active aging. Methods This study used multiple linear regression, propensity score matching, and additional analysis of heterogeneity, mediation, and moderation effects, with data from the 2018 CHARLS survey. Results The results indicated a negative correlation between intergenerational care and depression among older adults. The correlation was stronger for females and younger older adults people. Additionally, social activities served as a mediator between intergenerational care and depression among older adults, and health satisfaction positively moderated this relationship. Conclusion This study posits that intergenerational care serves as an effective approach for promoting active aging. It emphasizes the necessity for supportive government policies and community-family collaborations to encourage intergenerational care and its beneficial impact on mental health among older adults.
Influence of continuously variable permeability and Basin rock properties on three dimensional heat and mass balance models of Basin & Range geothermal systems

Basin & Range (B&R) geothermal systems raise vexing heat and mass balance questions. In systems without magmatic heat sources, observed temperatures of >200 – 280°C at depths of a few km require deep circulation of meteoric fluids in regions with steep geothermal gradients, apparently along structurally localized conduits. Previous heat and mass balance modeling studies (Moulding and Brikowski, GRC 2014) matched fluid temperatures and flow rates seen at the Dixie Valley geothermal field via a steeply plunging conduit (representative of a fault stepover or intersection) within a very low uniform permeability host rock (< 2 x 10-17 m2), with regional crustal heat flow of 85 mWm-2. These results also indicate that Basin and Range geothermal systems can be spatially isolated along strike of normal faults, allowing for the possibility of numerous discrete systems in geothermally active areas. Current modeling efforts address specific rock properties that may be important factors for the development of the non-magmatic B&R geothermal systems and may help predict geothermal favorability for other sites. These properties include (1) low permeability of host rock materials which appears critical for the development of these systems (2) insulating properties of basin sediments and volcanics which may play an important role in some of the hotter systems (i.e. Dixie Valley), (3) anisotropic (layered) permeability in fluid source regions (basin sediments) which can enhance the supply of fluids for these systems, and (4) narrowing of the conduit at the surface discharge zone to limit penetration of cold surface fluids into the geothermal system, which is more consistent with observations and could explain why so many of these systems have been blind discoveries.