Energy Storage Sharing Strategy in Distribution Networks Using Bi-level Optimization Approach

In this paper, we address the energy storage management problem in distribution networks from the perspective of an independent energy storage manager (IESM) who aims to realize optimal energy storage sharing with multi-objective optimization, i.e., optimizing the system peak loads and the electricity purchase costs of the distribution company (DisCo) and its customers. To achieve the goal of the IESM, an energy storage sharing strategy is therefore proposed, which allows DisCo and customers to control the assigned energy storage. The strategy is updated day by day according to the system information change. The problem is formulated as a bi-level mathematical model where the upper level model (ULM) seeks for optimal division of energy storage among Disco and customers, and the lower level models (LLMs) represent the minimizations of the electricity purchase costs of DisCo and customers. Further, in order to enhance the computation efficiency, we transform the bi-level model into a single-level mathematical program with equilibrium constraints (MPEC) model and linearize it. Finally, we validate the effectiveness of the strategy and complement our analysis through case studies

Dynamically Mitigating Data Discrepancy with Balanced Focal Loss for Replay Attack Detection

It becomes urgent to design effective anti-spoofing algorithms for vulnerable automatic speaker verification systems due to the advancement of high-quality playback devices. Current studies mainly treat anti-spoofing as a binary classification problem between bonafide and spoofed utterances, while lack of indistinguishable samples makes it difficult to train a robust spoofing detector. In this paper, we argue that for anti-spoofing, it needs more attention for indistinguishable samples over easily-classified ones in the modeling process, to make correct discrimination a top priority. Therefore, to mitigate the data discrepancy between training and inference, we propose to leverage a balanced focal loss function as the training objective to dynamically scale the loss based on the traits of the sample itself. Besides, in the experiments, we select three kinds of features that contain both magnitude-based and phase-based information to form complementary and informative features. Experimental results on the ASVspoof2019 dataset demonstrate the superiority of the proposed methods by comparison between our systems and top-performing ones. Systems trained with the balanced focal loss perform significantly better than conventional cross-entropy loss. With complementary features, our fusion system with only three kinds of features outperforms other systems containing five or more complex single models by 22.5% for min-tDCF and 7% for EER, achieving a min-tDCF and an EER of 0.0124 and 0.55% respectively. Furthermore, we present and discuss the evaluation results on real replay data apart from the simulated ASVspoof2019 data, indicating that research for anti-spoofing still has a long way to go.Comment: This work has been accepted by the 25th International Conference on Pattern Recognition (ICPR2020

Attacking Split Manufacturing from a Deep Learning Perspective

The notion of integrated circuit split manufacturing which delegates the front-end-of-line (FEOL) and back-end-of-line (BEOL) parts to different foundries, is to prevent overproduction, piracy of the intellectual property (IP), or targeted insertion of hardware Trojans by adversaries in the FEOL facility. In this work, we challenge the security promise of split manufacturing by formulating various layout-level placement and routing hints as vector- and image-based features. We construct a sophisticated deep neural network which can infer the missing BEOL connections with high accuracy. Compared with the publicly available network-flow attack [1], for the same set of ISCAS-85 benchmarks, we achieve 1.21X accuracy when splitting on M1 and 1.12X accuracy when splitting on M3 with less than 1% running time

The loss of plant functional groups increased arthropod diversity in an alpine meadow on the Tibetan Plateau

Plant species loss, driven by global changes and human activities, can have cascading effects on other trophic levels, such as arthropods, and alter the multitrophic structure of ecosystems. While the relationship between plant diversity and arthropod communities has been well-documented, few studies have explored the effects of species composition variation or plant functional groups. In this study, we conducted a long-term plant removal experiment to investigate the impact of plant functional group loss (specifically targeting tall grasses and sedges, as well as tall or short forbs) on arthropod diversity and their functional groups. Our findings revealed that the removal of plant functional groups resulted in increased arthropod richness, abundance and the exponential of Shannon entropy, contrary to the commonly observed positive correlation between plant diversity and consumer diversity. Furthermore, the removal of different plant groups had varying impacts on arthropod trophic levels. The removal of forbs had a more pronounced impact on herbivores compared to graminoids, but this impact did not consistently cascade to higher-trophic arthropods. Notably, the removal of short forbs had a more significant impact on predators, as evidenced by the increased richness, abundance, the exponential of Shannon entropy, inverse Simpson index and inverse Berger-Parker index of carnivores and abundance of omnivores, likely attributable to distinct underlying mechanisms. Our results highlight the importance of plant species identity in shaping arthropod communities in alpine grasslands. This study emphasizes the crucial role of high plant species diversity in controlling arthropods in natural grasslands, particularly in the context of plant diversity loss caused by global changes and human activities

Plasma microRNA Profiles as a Potential Biomarker in Differentiating Adult-Onset Still's Disease From Sepsis

Adult-onset Still's disease (AOSD) is a systemic inflammatory disease characterized by cytokine storm. However, a diagnostic test for AOSD in clinical use is yet to be validated. The aim of our study was to identify non-invasive biomarkers with high specificity and sensitivity to diagnosis of AOSD. MicroRNA (miRNA) profiles in PBMC from new-onset AOSD patients without any treatment and healthy controls (HCs) were analyzed by miRNA deep sequencing. Plasma samples from 100 AOSD patients and 60 HCs were used to validated the expression levels of miRNA by qRT-PCR. The correlations between expression levels of miRNAs and clinical manifestations were analyzed using advanced statistical models. We found that plasma samples from AOSD patients showed a distinct miRNA expression profile. Five miRNAs (miR-142-5p, miR-101-3p, miR-29a-3p, miR-29c-3p, and miR-141-3p) were significantly upregulated in plasma of AOSD patients compared with HCs both in training and validation sets. We discovered a panel including 3 miRNAs (miR-142-5p, miR-101-3p, and miR-29a-3p) that can predict the probability of AOSD with an area under the receiver operating characteristic (ROC) curve of 0.8250 in training and validation sets. Moreover, the expression levels of 5 miRNAs were significantly higher in active AOSD patients compared with those in inactive patients. In addition, elevated level of miR-101-3p was found in AOSD patients with fever, sore throat and arthralgia symptoms; the miR-101-3p was also positively correlated with the levels of IL-6 and TNF-α in serum. Furthermore, five miRNAs (miR-142-5p, miR-101-3p, miR-29c-3p, miR-29a-3p, and miR-141-3p) expressed in plasma were significantly higher in AOSD patients than in sepsis patients (P < 0.05). The AUC value of 4-miRNA panel (miR-142-5p, miR-101-3p, miR-29c-3p, and miR-141-3p) for AOSD diagnosis from sepsis was 0.8448, revealing the potentially diagnostic value to distinguish AOSD patients from sepsis patients. Our results have identified a specific plasma miRNA signature that may serve as a potential non-invasive biomarker for diagnosis of AOSD and monitoring disease activity

工時有幾長？貧窮新一代的就業與貧窮報告

近年，社會出現有關年輕人「躺平」的討論，指年輕新一代不願找工作、不願勤奮加班、不規構未來。教育局局長楊潤雄亦曾在網誌撰文表示憂慮，批評「躺平這種消極的人生心態，長遠會窒礙社會的進步」。 不過，根據香港政府統計處2021年收入及工時按年統計調查報告， 2021年5月至6月，15至24歲及25至34歲受訪者每周的工作時數中位數分別為44.3及42小時，比亞洲其他國家要高；而2022年4至6月，20至24歲及25至29歲的勞動人口參與率分別為53.5%及87%，後者更是所有年齡組別中第二高（僅次於30至34歲年齡組別），反映年輕人並沒有不願意長時間工作和就業，與成年人無異。 另外，根據中大亞太研究所於本年四月進行的一項調查，超過半數受訪者認為香港社會並沒有足夠機會與年輕一代向上流動，更有六成半受訪者認為，現時香港向上流動的機會比過去10年要少。 在是次研究中，發現有不少手持大學文憑的畢業生甫踏入社會，便從事三行、保安、飲食業等藍領工種，以體力勞動換取更高薪酬。文職的工作機會雖不算難找，但對於畢業生來說，待遇多不及前者；初入職場的年輕人便要忍受長工時以換取較可觀的薪金，更因長期體力勞動及工作環境惡劣，導致工傷或患上職業病，造成不可逆轉的傷害；選擇白領工作的年輕人則花空餘時間研究創業路向，望有天可擺脫低工資、長OT的工作常態。 年輕人是社會重要的群體，促進社會不同面向的發展和進步。勞工議題，正正就是影響年輕人最深遠的問題。不過，社會一般會把青年遇到的問題視為過渡性現象，隨著他們成長而消失。坊間少有深入討論，以致一般年輕人以為工作的沉重是理所當然，對自身的勞工權益鮮有認識。 上述所提及的年輕人工時長的狀況，窒礙他們在成長階段的探索，因長工時影響反心健康、無法進修、交結朋友、和家人相處及了解自我。工時長等問題在港是老生常談，卻是待解決的重要議題。香港人多年來一直受長工時的困擾，回顧香港的工時政策，卻未見突破。由勞顧會於2018年宣布推出「11個行業工時指引」，距今已4年，仍未見進展。更何況，指引的條文亦不適用於大部份長工時的打工仔女，例如只保障月薪不超過1.1萬元作為界定工資較低的基層僱員的工資線等規定。可見，像過往一樣，政府及商界等對標準工時並不持開放態度。 誠然，參考不同地區及國家的工時政策，香港則顯得相當落後。中國、日本、台灣、韓國及新加坡均有明確法例規管工時，如超時工作的補水機制、每天工作8小時作為標準工時等。如香港政策要推行工時相關的政策，大有參考例子。本報告亦整合及分析了上述地區的工時政策，有助了解其他地區的勞工法例。 是次研究探討了現行的工時政策下香港年輕僱員的工作現況。透過深入訪談及問卷調查，發現長工時與生理、心理、社交及家庭關係的影響，以及就研究結果提倡相應的政策改革，讓社會大眾及年輕人自身了解他們的勞動現況，剖析處境並提出有效的改善方針。https://commons.ln.edu.hk/ccrd_report/1002/thumbnail.jp