Although the performance of perovskite solar cells(PSCs)has been dramatically increased in recent years,stability is still the main obstacle preventing the PSCs from being *** device instability can be caused by a var...
详细信息
Although the performance of perovskite solar cells(PSCs)has been dramatically increased in recent years,stability is still the main obstacle preventing the PSCs from being *** device instability can be caused by a variety of reasons,including ions diffusion,surface and grain boundary defects,*** this work,the cross-linkable tannic acid(TA)is introduced to modify perovskite film through post-treatment *** numerous organic functional groups(–OH and C=O)in TA can interact with the uncoordinated Pb^(2+)and I^(-)ions in perovskite,thus passivating defects and inhibiting ions *** addition,the formed TA network can absorb a small amount of the residual moisture inside the device to protect the perovskite ***,TA modification regulates the energy level of perovskite,and reduces interfacial charge ***,following TA treatment,the device efficiency is increased significantly from 21.31%to 23.11%,with a decreased hysteresis ***,the treated device shows excellent air,thermal,and operational *** light of this,the readily available,inexpensive TA has the potential to operate as a multipurpose interfacial modifier to increase device efficiency while also enhancing device stability.
Mode collapse poses a critical challenge in training generative adversarial networks (GANs), particularly in applications such as medical imaging, where diverse and clinically relevant outputs are essential. This syst...
详细信息
The growing demand for Domain-Specific Architecture (DSA) has driven the development of Agile Hardware Development Methodology (AHDM). Hardware Construction Language (HCL) like Chisel offers high-level abstraction fea...
详细信息
The Internet of Things (IoT) is growing more popular with applications like healthcare services, traffic monitoring, video streaming, smart homes, etc. These applications produce an enormous amount of data, so a reali...
The Internet of Things (IoT) is growing more popular with applications like healthcare services, traffic monitoring, video streaming, smart homes, etc. These applications produce an enormous amount of data, so a realistic option in this instance is to offload computational tasks to their proximity fog nodes (FNs) instead of the remote cloud. However, a negligent offloading strategy may cause anomalous computational traffic load at the FNs, causing congestion that may adversely affect the latency. However, the latency of task flows from IoT devices comprises communications latency at BS and computational latency at FNs. Therefore, designing offloading algorithms to distribute the computational load at FN evenly and efficiently utilize the FN resources is crucial. To solve this problem, we proposed LBA in a fog network with a binary offloading strategy using the matching theory-based approach. We utilize the Analytic Hierarchy Process (AHP) to generate the preference list. Furthermore, the binary offloading technique follows the deferred acceptance algorithm (DAA) to produce a stable assignment, and the complete offloading problem is modeled as a one-to-many matching game. Comprehensive simulations ensure that LBA can accomplish a better-balanced assignment for homogeneous and heterogeneous input concerning all the baseline algorithms.
The paper introduces LLM2FedLLM, a tool designed for Software Engineering (SE) researchers to simulate fine-tuning Large Language Models (LLMs) within a federated learning (FL) framework. Unlike existing FL frameworks...
详细信息
ISBN:
(数字)9798331502232
ISBN:
(纸本)9798331502249
The paper introduces LLM2FedLLM, a tool designed for Software Engineering (SE) researchers to simulate fine-tuning Large Language Models (LLMs) within a federated learning (FL) framework. Unlike existing FL frameworks that facilitate real client collaboration, our simulator provides a controlled environment for experimenting with FL scenarios on a single machine. The LLM2FedLLM Simulator addresses SE code tasks, such as code summarization, code review, and code translation, within a federated learning framework by first partitioning the selected code dataset into heterogeneous subsets for multiple clients. It then fine-tunes the chosen LLM and evaluates its performance against vanilla, centralized, and individual client models using various metrics. The tool supports several federated aggregation methods and PEFT for supervised learning, with the flexibility to easily integrate additional techniques. The evaluation of our tool on Python code summarization showed that FedLLM performs comparably to centralized models and outperforms individual clients, particularly in low-data scenarios. Our tool aims to facilitate research advances in secure collaborative training simulations within the SE community. https://***/-byKkaiBchw.
Nowadays, microservices-based applications such as E-Business, E-Healthcare, 3D-Gaming, and Augmented Reality have latterly drawn attention in the research area. The microservices enabled applications are different fr...
详细信息
This paper presents a 120 GHz receiver for Near-Field IoT sensors, without the conventional LNA and mixers building blocks. A new technique for the reduction of the low-frequency 1/f noise is also presented. The techn...
This paper presents a 120 GHz receiver for Near-Field IoT sensors, without the conventional LNA and mixers building blocks. A new technique for the reduction of the low-frequency 1/f noise is also presented. The technique is used to improve the sensitivity of the 120GHz receiver with a data rate of 100kb/s, by lowering the 1/f noise corner frequency. The technique involves the complementary switching of two MOSFET transistors in order to effectively act as a single MOSFET transistor. It also involves the isolation of the drain terminals of these identical MOSFET transistors in order to eliminate any electric field from the complementary transistors which may affect the de-trapping process. The complementary switching in the literature has only been applied to low-frequency circuits with the highest operating frequency being 120 kHz. The proposed technique leads to a 9dB reduction in the drain current noise at frequencies up to 100 kHz. Realized in a 65nm CMOS LPE technology from GlobalFoundries™, the receiver has a measured sensitivity of −46dBm, power consumption of $\mathbf{52}\ \boldsymbol{\mu} \mathbf{W}$ , and energy efficiency of 5.2pJ/bit. The occupied on-chip area is 0.56mm 2 .
Neuropeptides (NPs) are fragile proteins that serve as essential signaling molecules in the neurological system, playing a key role in modulating various physiological processes. Identifying particular neuropeptide se...
详细信息
ISBN:
(数字)9798350362480
ISBN:
(纸本)9798350362497
Neuropeptides (NPs) are fragile proteins that serve as essential signaling molecules in the neurological system, playing a key role in modulating various physiological processes. Identifying particular neuropeptide sequences relevant to specific disorders would be beneficial for accelerating the development of diagnostic tools. The study proposed another approach to detecting NPs with multi-layer perception (MLP) and a bagging classifier-based meta-learning method called NeuroBooster. This investigation initially focused on five feature extractions based on composition, such as AAC, PAAC, physicochemical properties, QSO, and transfer-learning, such as Bert, and F2V strategies. Subsequently, we used the XGB feature selection method in the Bert and F2V methods to obtain the most 100D crucial features. The predicted probabilistic outcomes of NPs from the 8 preliminary models merged and derived a two-stage dataset with 40 dimensions of features and transmitted them into three classic models and two meta-models, through rigorous criteria for evaluation. Compared with the existing predictor, our proposed model NeuroBooster achieved a higher accuracy of 91.91% in the independent test method. Consequently, we discovered important features in these five models underscoring that physicochemical properties are potential targets for identification, thereby revealing new avenues for therapies.
Wireless Charging Vehicles (WCVs) have been widely explored as a means of enabling continuous operation of sensors that are powered by batteries. However, the energy consumption of WCVs can be inefficient, leading to ...
Wireless Charging Vehicles (WCVs) have been widely explored as a means of enabling continuous operation of sensors that are powered by batteries. However, the energy consumption of WCVs can be inefficient, leading to insufficient energy supply for sensors that are located in challenging-to-access areas. Consequently, there is a need to design an effective charging and energy sharing scheme for sensors to improve the quality of service in this setup. This paper focuses on the Joint optimization of Mobile charging and Energy sharing of sensors (JOIN-ME) problem, which is known to be NP-hard. To address this challenge, we first transform JOIN-ME into a submodular maximization problem with general constraints. Subsequently, we propose the Routing planning, Mobile charging, and Energy sharing for Sensing devices (RMES) algorithm, which has an approximation ratio of 1/8(1-1/e). Finally, we conduct experiments to showcase the superior performance of RMES compared to existing baselines, under varying scales and constraints. Our work on the design of an efficient charging and energy sharing scheme for sensors can significantly improve the reliability and longevity of wireless sensor networks, enabling the deployment of these networks in critical applications such as environmental monitoring, crowd sensing, and security surveillance.
Large Language Models primarily operate through text-based inputs and outputs, yet human emotion is communicated through both verbal and non-verbal cues, including facial expressions. While Vision-Language Models anal...
详细信息
暂无评论